tag:blogger.com,1999:blog-19251284317675184412024-02-21T15:58:43.208+02:00Greece: Politics Economics and other..."Ό,τι η ψυχή επιθυμεί, αυτό και πιστεύει." Δημοσθένης (Whatever the soul wishes, thats what it believes, Demosthenes)
CChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.comBlogger2875125tag:blogger.com,1999:blog-1925128431767518441.post-36977769896589689372023-03-21T11:30:00.000+02:002023-03-21T11:30:39.489+02:00Για το δυστύχημα στα Τέμπη 28-3-2023.<p> </p><p>Η γνώμη μου για το θέμα και την δημόσια συζήτηση που γίνεται όλες αυτές τις ημέρες.</p><p>Για το δυστύχημα υπάρχει <b>ατομική ευθύνη</b> του σταθμάρχη, κυρίως, αλλά και των υπόλοιπων που εμπλέκονται χωρίς να είναι στον ίδιο βαθμό.</p><p>Ότι προδιέγραψε και οδήγησε στο δυστύχημα, μπορούσε ο σταθμάρχης να το δει και να το ελέγξει, από τον τοπικό πίνακα ελέγχου στην Λάρισσα. Δεν θα άλλαζε τίποτε αν μπορούσε να δει και το Πλατύ και την Θεσσαλονίκη και την Αθήνα. Αν από την άλλη ο υπεύθυνος στη Θεσσαλονίκη και την Αθήνα μπορούσε να δει το λάθος στην Λάρισσα, θα μπορούσε να το διορθώσει, αλλά τότε, ξαναερχόμαστε στο (ουσιαστικό και μείζων) πρόβλημα της ατομικής ευθύνης και επάρκειας που μόλις προσπεράσαμε για τον σταθμάρχη της Λάρισσας: οι υπάλληλοι που θα το έβλεπαν θα έπρεπε να είναι επαρκείς και υπεύθυνοι ώστε να αντιληφθούν και να διορθώσουν το λάθος.</p><p>Δηλαδή η τηλεδιοίκηση απλώς μεταθέτει το πρόβλημα. </p><p>Το πρόβλημα είναι ότι χρειαζόμαστε σωστούς και υπεύθυνους υπαλλήλους σε αυτές τις θέσεις είτε έχουμε τηλεδιοίκηση είτε όχι, γιατί αν βάλουμε ανθρώπους σαν τον σταθμάρχη-κατηγορούμενο, τα συστήματα ασφάλειας δεν μας προστατεύουν.</p><p>Από την στιγμή που το πετύχουμε αυτό, δηλαδή την τοποθέτηση ικανών ανθρώπων στις θέσεις κλειδιά, τότε, αρχίζουμε να μιλάμε και για την τηλεδιοίκηση και για την ασφάλεια των επιβατών και των εργαζομένων. Αυτό το τελευταίο είναι η <b>πολιτική ευθύνη</b>.</p>CChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com0tag:blogger.com,1999:blog-1925128431767518441.post-10530496331225240522022-03-20T16:05:00.017+02:002022-03-20T16:07:30.199+02:00For the U.S., a Tenuous Balance in Confronting Russia<p> www.nytimes.com /2022/03/19/us/politics/us-ukraine-russia-escalation.html</p><p><br /></p><p>Mark Mazzetti, Helene Cooper, Julian E. Barnes, David E. Sanger13-16 minutes 19/3/2022</p><p>Navigating between aiding Ukraine and avoiding an escalation with Moscow has led to a tangle of decisions and sometimes tortured distinctions over weapons and other elements of policy.</p><p>Credit...Ivor Prickett for The New York Times</p><p>March 19, 2022</p><p>WASHINGTON — In the first weeks of the first major European land war of the 21st century, the United States has sent tank-killing weapons to Ukrainian forces, but not fighter jets. It is equipping embattled Ukrainian troops with lightweight “kamikaze” attack drones, but not, at least in an obvious way, conducting an aggressive cyberwar to degrade Russia’s technological advantage.</p><span><a name='more'></a></span><p><br /></p><p>The White House will commit no American or NATO planes to the skies above Ukraine, a move American officials fear could risk turning a regional war into a global conflagration, but it is providing Ukraine with missiles that could accomplish the same task of destroying Russian aircraft.</p><p>Such is the tenuous balance the Biden administration has tried to maintain as it seeks to help Ukraine lock Russia in a quagmire without inciting a broader conflict with a nuclear-armed adversary or cutting off potential paths to de-escalation.</p><p>Navigating this path has led to a tangle of decisions, and sometimes tortured distinctions, when it comes to what kinds of assistance Washington should provide, even as the situation on the ground evolves, pictures of dead civilians circulate around the globe and President Volodymyr Zelensky of Ukraine pleads with Congress and President Biden to do more to help.</p><p>The balancing act informs every aspect of American policy about the war, including the scope of the punishing sanctions imposed on the Russian economy, the granularity of the battlefield intelligence provided to Ukrainian troops, the killing power of the weapons systems coming over the border and whether, as Mr. Biden did this past week, to label President Vladimir V. Putin of Russia as a war criminal.</p><p><br /></p><p>C.I.A. officers are helping to ensure that crates of weapons are delivered into the hands of vetted Ukrainian military units, according to American officials. But as of now, Mr. Biden and his staff do not see the utility of an expansive covert effort to use the spy agency to ferry in arms as the United States did in Afghanistan against the Soviet Union during the 1980s. They have judged that such a campaign would be an unnecessary provocation, in part because NATO supply lines remain open and there is a functioning government in Kyiv.</p><p><br /></p><p>The new war has forced a recalculation on other fronts. In one example, American officials have floated the idea of Turkey’s government providing Ukraine with the sophisticated S-400 antiaircraft system. It is the very system, made by Russia, that American officials punished Turkey — a NATO ally — for buying from Moscow several years ago. Now American diplomats see a way to pull Turkey away from its dance with Russia — and give the Ukrainians one of the most powerful, long-range antiaircraft systems in existence.</p><p>In the White House and the Pentagon, there have been active debates over which lethal weapons delivered to Ukraine meet the nuanced interpretations of what international law allows. American officials acknowledge that the judgments of government lawyers are valuable only up to a point, and that all that really matters is the judgment of one person: Mr. Putin.</p><p>The Russian president has his own complex calculus about when the military support to Ukraine from the United States and its NATO allies crosses the line. He has his own reasons not to escalate, given the combined power of the NATO members and his own military’s evident difficulties against Ukrainian forces. But he is also unlikely to accept defeat or a stalemate in Ukraine without further testing American resolve, despite explicit public warnings from Washington against the use of chemical, biological or even nuclear weapons.</p><p>American intelligence about Mr. Putin’s decision making is maddeningly imprecise, and the West does not have a strong track record predicting what he might consider an aggression that cannot be tolerated. William J. Burns, the C.I.A. director, told Congress this month that Mr. Putin’s views had “hardened over the years.”</p><p>The Russian threshold could also be changing by the day, or even the hour. On Friday, Russia’s foreign minister, Sergey V. Lavrov, suggested that his country was prepared to raise the costs for any nation helping the Ukrainians in their struggle, declaring that all vehicles shuttling weapons into Ukraine would be considered legitimate military targets.</p><p>A Clash Over Fighter Planes</p><p>On Wednesday, the task of articulating the intricacies of America’s military policy toward Ukraine fell to a group of generals under a barrage of questions from top members of the House and Senate Armed Services Committees.</p><p>Mr. Biden has made clear that he will not accede to Mr. Zelensky’s insistent pleas that NATO impose a no-fly zone over Ukraine. Clearing hostile aircraft from the skies would put the United States and its allies in combat with Russian forces. And a required step for a no-fly zone — suppressing enemy antiaircraft weaponry — would mean attacking Russian air defense installations inside Russian territory.</p><p>But during the closed-door session, lawmakers pressed members of the Pentagon’s Joint Staff about another flashpoint: the administration’s decision not to help supply Ukraine with the MIG-29 fighter jets that Poland has offered and that Mr. Zelensky has said his forces desperately need.</p><p>Administration officials have said the move would be “escalatory,” and, according to people briefed on the exchanges, the lawmakers asked the generals if there was any hard intelligence that the jets might push Mr. Putin toward intensifying the conflict by treating the United States as a “cobelligerent” in the war.</p><p>Intelligence officials have, in fact, told the administration that the MIGs could trigger a Russian move against NATO. During the congressional briefing, the generals said that the main issue was the capability of a MIG-29 to threaten Russian soil.</p><p>In contrast to a Javelin antitank missile that has only limited range on the battlefield, a MIG-29 could fly from Kyiv to Moscow in a matter of minutes, the generals said, a capability that the Kremlin might see as a direct threat.</p><p>The same day, the White House put forth another consideration: that to be delivered to Ukraine, the MIGs would have to take off from an air base in a NATO country, possibly inviting retaliation on NATO territory by the Russians.</p><p>Some American officials assert that as a matter of international law, the provision of weaponry and intelligence to the Ukrainian Army has made the United States a cobelligerent, an argument that some legal experts dispute. But while Mr. Putin has made threats about launching attacks to impede the military assistance, he has not yet acted to stop it by attacking bases in neighboring countries — NATO allies — where the equipment originates.</p><p>That could change, U.S. officials said, especially if Mr. Putin thinks he is cornered or in danger of losing.</p><p>“It is a fine line the administration is still walking in every dimension of its support for Ukraine,” said Andrea Kendall-Taylor, a former senior intelligence official who specialized in Russia and is now at the Center for a New American Security. “They are trying to figure out how do you get right up to the line without crossing over in a way that would risk direct confrontation with Russia.”</p><p>Planes with pilots might be off the table, but armed drones are not. This past week, Mr. Biden announced that the United States would ship small Switchblade drones to Ukraine that could be used to blow up Russian armored vehicles. The single-use kamikaze drones have blade-like wings, do not require either a long runway or a complex satellite uplink, and can be controlled to divebomb tanks or troops, self-destructing when they explode.</p><p>Unlike the large Predator and Reaper drones used for decades in Iraq, Afghanistan, Pakistan and other countries, the portable drones pose no threat to Russian soil. Still, the White House authorized an initial shipment of only 100 of them to Ukraine — a small batch that could be intended to see how Mr. Putin reacts to their deployment on the Ukrainian front lines. Depending on the response, hundreds or thousands more could be on the way.</p><p>The proposal for Turkey to supply Ukraine with Russian-made S-400 antiaircraft systems would also test what Mr. Putin is willing to accept from NATO — and how far a NATO ally that in recent years often appeared to be building bridges to Moscow is willing to go in reiterating its commitment to the alliance and backing Ukraine.</p><p>The idea came up when Wendy R. Sherman, the deputy secretary of state, visited Turkey two weeks ago. Ms. Sherman declined to talk about her discussions.</p><p>A different senior American official said the United States knew the proposal would anger Mr. Putin. Ukraine already uses Turkish-made drones, but Turkey is worried that providing the antiaircraft systems could make the country a target of Russia’s wrath.</p><p>At the same time, the upside for Turkey could be substantial: It was suspended by the Trump administration from the F-35 fighter program — in which it was both a buyer and a manufacturer of parts for the advanced aircraft — after its purchase of the Russian S-400s. A deal to send the antiaircraft systems to Ukraine could open the door to re-entry into the F-35 program.</p><p>The State Department declined to comment. Officials at Turkey’s embassy in Washington did not respond to messages seeking comment.</p><p>The Conflict of the Future, Delayed</p><p>The Ukraine war features tank columns and trenches, all features of Europe’s bloody conflicts of the past century. Thus far, there is little evidence that the United States — or Russia — is eager to escalate the conflict in the 21st century battleground of cyberspace.</p><p>Days before the war began, there was a flurry of cyberattacks on Ukrainian financial institutions and government ministries, including one that was found and partly neutralized by Microsoft. A European satellite system sometimes used by the Ukrainian military was also hit, knocking out service, though it is still unclear whether the Russians carried out the attack.</p><p>What has been missing thus far is a large-scale Russian cyberattack that disables the power grid or communications systems inside Ukraine, which for the most part continue to operate despite the withering Russian barrage of artillery and airstrikes.</p><p>Until the invasion began, United States Cyber Command had a unit based in Kyiv that was helping the government fend off attacks. It is now operating from a nearby NATO country. There is fragmentary evidence that the United States and its allies worked to counter some of the attacks and to prevent others from being launched. But action seems to have been limited.</p><p>Inside the Biden administration, there is a view that Mr. Putin could be choosing his moment to launch a cyberattack against the American financial system in retaliation for the devastating financial sanctions imposed on his country by the United States and its allies. Unless and until that happens, the administration appears resolved not to launch a significant first strike and invite retaliation — especially given the risks to the U.S. economy and financial system if Russia were to target them.</p><p>Keeping the Temperature Down</p><p>“The water in Afghanistan must boil at the right temperature,” Pakistan’s president, Gen. Mohammad Zia ul-Haq, told his intelligence chief as Pakistan began supplying the mujahedeen in their grueling battle against Soviet troops in neighboring Afghanistan.</p><p>In other words, hot enough to convince the Russians that the country was not worth the struggle, but not so hot to provoke a broader war in the region.</p><p>The weapons that helped turn the tide of that conflict, shoulder-fired Stinger surface-to-air missiles, are now being unloaded from cargo planes in NATO countries and delivered to Ukrainian troops on the front lines to help keep Russia from controlling the skies.</p><p>American officials are divided on how much the lessons from Cold War proxy wars, like the Soviet Union’s war in Afghanistan, can be applied to the ongoing war in Ukraine. Some officials say those conflicts established that the great powers could ship small arms and missiles to proxy forces without triggering a wider war.</p><p>At the same time, Ukraine is far more important to Mr. Putin than Afghanistan was to Soviet leaders.</p><p>Given Russia’s bloody history with Stinger missiles, American officials have been wary of advertising their use in Ukraine. This month, when Gen. Mark A. Milley, the chairman of the Joint Chiefs of Staff, visited an airfield near Ukraine’s border where Stingers were being unloaded, reporters traveling with him were barred from disclosing the whereabouts of the base.</p><p>Even after two senior American officials told the House Armed Services Committee during a public hearing that Stinger missiles were among the munitions being sent to Ukraine, spokespeople avoided using the S-word from the lecterns at the White House and Pentagon.</p><p>Until this past week. On Wednesday, the White House released a detailed list of the weapons it was providing as part of an $800 million package of arms to Ukraine.</p><p>Eric Schmitt, Adam Goldman and Michael Crowley contributed reporting.</p><p><br /></p>CChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com0tag:blogger.com,1999:blog-1925128431767518441.post-19781839502925438372022-01-31T11:37:00.003+02:002022-01-31T11:40:08.643+02:00Άρθρο από την (διαδικτυακή) εφημερίδα ΤΟ ΒΗΜΑ για το κλείσιμο της Αττικής Οδού<p> <i>Δεν ξέρω αν και σε ποιο βαθμό είναι αληθή όσα ακολουθούν. Δεν θα με εξέπλητε το αληθές της ιστορίας. Αλλά δεν είναι σοφία να πούμε ότι μικρές κρίσιμες λεπτομέρειες μπορούν να κρίνουν την έκβαση.</i></p><p><i>Για ένα καρφί χάθηκε το πέταλο</i></p><p><i>Για ένα πέταλο χάθηκε το άλογο</i></p><p><i>Για ένα άλογο χάθηκε ο καβαλάρης</i></p><p><i>Για έναν καβαλάρη χάθηκε η μάχη</i></p><p><i>Για μια μάχη χάθηκε το βασίλειο. </i></p><p><i>(από τον ιστότοπο https://www.lecturesbureau.gr/1/for-a-nai-a-kingdom-was-lost-2061/)</i></p><p><i>Λοιπόν σύμφωνα με το άρθρο το καρφί ήταν το πετρέλαιο που δεν είχαν τα εκχιονιστικά. Και επειδή δεν είχαν πετρέλαιο και δεν μπορούσαν να βάλουν από το πρατήριο που τους είπε η Αττική Οδός, δεν βγήκαν να καθαρίσουν όταν μπορούσαν και όταν βάλανε πετρέλαιο δεν μπορούσαν να βγουν γιατί είχε πέσει πολύ χιόνι και δεν είχαν αλυσίδες και όταν ήρθαν τα εκχιονιστικά και την άνοιξαν για να βγουν να κάνουν την δουλειά τους είχε κλείσει η Αττική Οδός από τα οχήματα που είχαν εγκλωβιστεί. Και έτσι για λίγο πετρέλαιο χάθηκε ο πόλεμος.</i></p><p><span></span></p><a name='more'></a><i><br /></i><p></p><p>Νέα αποκάλυψη: Η Αττική Οδός έκλεισε για €100! Πού «κόλλησαν» τα εκχιονιστικά | Ειδήσεις - νέα - Το Βήμα Online</p><p>www.tovima.gr /2022/01/31/society/nea-apokalypsi-i-attiki-odos-ekleise-gia-100-eyro-giati-kollisan-ta-ekxionistika-stin-katexaki/</p><p>Λαμπρόπουλος Βασίλης 31.01.2022, 08:454-5 minutes 31/1/2022</p><p>Οι οδηγοί τους δεν έβαζαν καύσιμα γιατί είχαν εντολή από την «Αττική Οδό» να τροφοδοτούνται μόνο από πρατήριο του αυτοκινητόδρομου που ήταν μέσα στα χιόνια -Το παρασκήνιο μίας ακόμη τραγελαφικής ιστορίας στο «φιάσκο του χιονιού»</p><p>Νέα αποκάλυψη: Η Αττική Οδός έκλεισε για… 100 ευρώ! – Γιατί «κόλλησαν» τα εκχιονιστικά στην Κατεχάκη | tovima.gr</p><p><br /></p><p>Η «Αττική Οδός» έκλεισε για …100 ευρω! Το συμπέρασμα αυτό προκύπτει από την ανάλυση των ενεργειών της ΕΛ.ΑΣ και της Πυροσβεστικής για την αποφυγή του εγκλωβισμού χιλιάδων πολιτών στη συγκεκριμένη οδική αρτηρία και τις οποίες παρουσιάζει «Το Βήμα».</p><p><br /></p><p>Όπως έχει ήδη αναφερθεί όταν επιχειρήθηκε να αποφευχθεί η διακοπή της κυκλοφορίας στον συγκεκριμένο οδικό άξονα, προσπάθησαν να ενεργοποιήσουν τρία εκχιονιστικά μηχανήματα της Αττικής Οδού κοντά στα διόδια της λεωφόρου Κατεχάκη αλλά διαπιστώθηκε ότι αυτά δεν είχαν καύσιμα. Τη σχετική διαπίστωση επιβεβαίωσε και στην διάρκεια της ομιλίας του στη Βουλή ο υπουργός Κλιματικής Κρίσης κ. Χρήστος Στυλιανίδης. Όπως ανέφερε «ήμουν παρών όταν ο Υφυπουργός Προστασίας του Πολίτη, κ. Λευτέρης Οικονόμου, στο Κέντρο Διαχείρισης Κρίσεων, έψαξε και βρήκε 200 λίτρα πετρέλαιο για να βάλει στα εκχιονιστικά της εταιρείας το απόγευμα της Δευτέρας. Κι αυτό δείχνει κάτι».</p><p><br /></p><p>Ομως το εντυπωσιακό είναι η ανάλυση αυτής της υπόθεσης με τα άδεια ρεζερβουάρ και τι συνέβη πριν και μετά.</p><p>Το σχέδιο, λοιπόν, των επιτελών για να υποβοηθηθεί η κυκλοφορία των οχημάτων στην Αττική οδό το βράδυ της Δευτέρας ήταν ν’ ανοίξει από τα χιόνια η περιφερειακή οδός του Υμηττού. Ετσι ώστε να βρουν από εκεί διέξοδο τα αυτοκίνητα που άρχισαν να «κολλούν» στον κύριο οδικό άξονα Μαρκόπουλου-Ελευσίνας επειδή είχαν «διπλώσει» νταλίκες στο ύψος της Ανθούσας. Πλήρωμα πυροσβεστικού οχήματος έφθασε λοιπόν στον σταθμό της Αττικής Οδού στην Κατεχάκη όπου βάσει της σχετικής χαρτογράφησης κι ενημέρωσης της Πολιτικής Προστασίας υπήρχαν τα τρία εκχιονιστικά. Για αρκετή ώρα φαίνεται αρχικά οι πυροσβέστες να αναζητούσαν τους οδηγούς που βρίσκονταν σε γειτονικό κλειστό χώρο αλλά εκείνοι δεν τους άκουγαν. Όταν τους εντόπισαν και τους ζήτησαν να κινητοποιήσουν τα εκχιονιστικά, εκείνοι τους είπαν ότι τα οχήματα δεν είχαν καύσιμα.</p><p><br /></p><p>Όμως έκπληκτοι οι υπεύθυνοι της Πολιτικής Προστασίας διαπίστωσαν γιατί είχε συμβεί αυτό. Οι οδηγοί των εκχιονιστικών είχαν αναλώσει τα προηγούμενα καύσιμά τους σε προσπάθεια καθαρισμού του δρόμο. Αξίζει να σημειωθεί, βέβαια, ότι η Κατεχάκη, στο κέντρο της Αθήνας είχε κλείσει από νωρίς το μεσημέρι! Κι αυτό δείχνει και τις μεγάλες τρύπες στο «σχέδιο» να κρατηθούν οι κεντρικοί άξονες τις πρωτεύουσας ανοιχτοί, που δεν μπόρεσε να υλοποιήσει τόσο το υπουργείο Κλιματικής Κρίσης όσο και η Περιφέρεια, η Πυροσβεστική και οι δήμοι, αφού ο συντονισμός ήταν ανύπαρκτος.</p><p><br /></p><p>Όταν, λοιπόν, ζητήθηκε να βάλουν εκ νέου καύσιμα, εκείνοι ανέφεραν ότι είχαν εντολή αυτό να γίνεται μόνο από πρατήριο καυσίμων που βρισκόταν στην Αττική Οδό, στο ύψος της Κάντζας όπου όμως υπήρχε ήδη χιονόπτωση και ήταν δύσκολη η πρόσβαση. Δεν προχώρησαν σε καμιά κίνηση να πάρουν καύσιμα από άλλα πρατήρια -με κόστος περίπου 100 ευρώ- γιατί δεν ήθελαν να διαθέσουν άλλα χρήματα κι από την «τσέπη» τους αφού η Αττική Οδός τους είχε επιβάλλει την συμφωνία μόνο με το συγκεκριμένο δυσπρόσιτο πρατήριο στην Κάντζα.</p><p>Ετσι οργανώθηκε η μεταφορά των καυσίμων από γειτονικό πρατήριο στον σταθμό Κατεχάκη, προκειμένου να κινηθούν τα τρία αυτά εκχιονιστικά. Όμως ήταν πλέον αργά κι όταν έφθασαν τα καύσιμα η χιονόπτωση είχε εγκλωβίσει τα οχήματα εκχιονισμού που δεν είχαν κι αλυσίδες για να κινηθούν. Κι έτσι ζητήθηκε βοήθεια από τα μηχανήματα αποχιονισμού από τον σταθμό της Νέας Εθνικής Οδού (σσ. που δεν είχε κλείσει) από τον σταθμό της Μαλακάσας που όμως έφθασαν με σημαντική καθυστέρηση.</p><p><br /></p><p>Η πρωτοφανής ταλαιπωρία των πολιτών στην Αττική Οδό με κύρια ευθύνη των υπευθύνων λειτουργείας της δεν «σβήνει στο χιόνι» τα ερωτήματα γιατί οι κεντρικοί άξονες της πρωτεύουσας (Κατεχάκη, Μεσογείων, Πεντέλης) έκλεισαν νωρίς με αποτελέσματα με απίστευτες εικόνες ταλαιπωρίας για οδηγούς και επιβαίνοντες και γιατί οι αρμόδιοι φορείς δεν μπόρεσαν, για μία ακόμη φορά, να συντονιστούν μεταξύ τους!</p><p><br /></p><p>Ακολουθήστε στο Google News και μάθετε πρώτοι όλες τις ειδήσεις</p><p><br /></p><p>Δείτε όλες τις τελευταίες Ειδήσεις από την Ελλάδα και τον Κόσμο, από </p>CChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com0tag:blogger.com,1999:blog-1925128431767518441.post-59520557115165431992021-10-29T14:52:00.005+03:002021-10-29T14:52:43.698+03:00Greek military goes shopping<p> mondediplo.com /2021/11/01edito</p><p>3-4 minutes 1/11/2021</p><p>Christmas has come early for Greece’s armed forces: this year the government is giving them 24 Rafale fighter jets and three cutting-edge frigates; later, they’ll be getting Lockheed Martin F-35s, Sikorsky helicopters, drones, torpedoes and missiles. The Greek military won’t be the only ones celebrating, though: French arms manufacturers, Dassault in particular, are among their biggest suppliers.</p><p><br /></p><p>Back in 2015, Greece was ruined, gasping, reduced to a protectorate of the ‘troika’ — the European Commission, European Central Bank and International Monetary Fund — which scrutinised every last item of its spending to force it to repay a debt even the IMF acknowledged was ‘unsustainable’. On Germany’s insistence, the troika was particularly tough on social welfare spending. There were huge increases in tax and health insurance contributions, and reductions in unemployment benefit and the minimum wage (cut by 32% for under 25s); retirement age rose to 67 (while pensions shrank 14 times in a row); and over-crowded hospitals ran short ofresources and drugs.</p><p><br /></p><p>But military spending seems to have escaped these strict controls, rising from 2.46% of GDP in 2015 to 2.79% in 2020, the highest in the EU. Clearly, if you want peace, prepare for war. Greece does indeed feel threatened by Turkey, which is acting more and more provocatively in the eastern Mediterranean, and has illegally occupied part of Cyprus for nearly 50 years. But that hasn’t stopped Greece and Turkey from both being members of NATO, nor Germany from being one of Turkey’s main arms suppliers.</p><p><br /></p><p>In 2015, when the European banks crushed the ‘Greek Spring’, Le Figaro was particularly savage, saying that Greece, even bled dry, was like ‘a patient who slaps his doctor in the face’ when it should be paying its creditors on the nail. Otherwise, Le Figaro claimed (as did almost all French media), ‘every French citizen will have to pay €735 to write off Greece’s debt’ (1). In 2015 that debt was 177% of GDP; as of December 2020 it had topped 205%. Yet Le Figaro has stopped worrying about European lenders. Why? No one dares suggest it is because Greece is buying armaments from the Dassault group, which owns Le Figaro (2).</p><p><br /></p><p>There won’t be a happy ending until Turkish submarines purchased from Germany sink Greek frigates built in France. Then Greece may finally decide to buy back the port it sold to China (Piraeus) (3). And the ‘Franco-German partnership’ having demonstrated its flexibility, ‘Europe’s strategic autonomy’ will be well on its way...</p><p><br /></p><p>(1) Le Figaro, 8 January 2015. France’s two largest TV channels, TF1 and France 2, echoed this the same evening, a few hours after the Greek left won the general election.</p><p><br /></p><p>(3) A Chinese state-owned company has a 67% stake in the Piraeus port authority. See Niels Kadritzke, ‘Greece is sold off and sold out’, Le Monde diplomatique, English edition, July 2016.</p><p><br /></p><p>(1) Le Figaro, 8 January 2015. France’s two largest TV channels, TF1 and France 2, echoed this the same evening, a few hours after the Greek left won the general election.</p><p><br /></p><p>(3) A Chinese state-owned company has a 67% stake in the Piraeus port authority. See Niels Kadritzke, ‘Greece is sold off and sold out’, Le Monde diplomatique, English edition, July 2016.</p>CChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com0tag:blogger.com,1999:blog-1925128431767518441.post-49839498275245091182021-03-15T22:26:00.002+02:002021-03-15T22:27:49.334+02:00America Saw a Historic Rise in Murders in 2020. Why?<p> </p><p>Jesse Singal</p><p>11-14 minutes</p><p><br /></p><p>Photo: Andrew Lichtenstein/Corbis via Getty Images</p><p><br /></p><p>For understandable reasons, “2020” and “coronavirus” are going to be synonymous in the American mind for generations to come. But there was another, quieter public-health menace that killed an alarming number of Americans last year: gun violence. As Devlin Barrett put it in the Washington Post, “The United States has experienced the largest single one-year increase in homicides since the country started keeping such records in the 20th century, according to crime data and criminologists.” We only have data for the first nine months of 2020, but according to the FBI there was a 20.9 percent increase in murders compared to the same period in 2019, a genuinely shocking increase. And exploring the reasons behind that increase can help illuminate just how insidious a problem violent crime is and how difficult it is to stem these cycles of violence once they accelerate.</p><span><a name='more'></a></span><p><br /></p><p>At the most basic level of analysis, experts view the surge as the result of a worst-case confluence of forces — the stresses of a pandemic and the intensity of the protests that followed the killing of George Floyd — that pushed already-frayed neighborhoods into spirals of violence. That can partly explain why the bloodshed wasn’t evenly distributed. Some places remained as peaceful as ever. In others, the rise in murders was even more dramatic than it was nationally. Chicago saw a 37 percent year-over-year increase between the first halves of 2019 and 2020. And in New York City, by December 20, 2020, there had been a 40 percent increase over the 2019 numbers.</p><p><br /></p><p>Violent crime is one of many areas of behavioral science where there tends to be a stark divide between how laypeople, including many journalists, discuss the subject and how experts do. Mainstream observers often posit simple, unicausal explanations for rises or dips in the crime rate. Experts tend to view things as much more complicated. The social mechanisms driving changes in the violent-crime rate are complex and intertwined and not fully understood — and it isn’t always clear how shocks to one element of the system affect others or the whole. At the moment, the FBI has released violent-crime data for just the first nine months of 2020. So researchers are only beginning to unravel exactly what happened and consider ways to stem the tide.</p><p><br /></p><p>It’s normal for the violent-crime rate to vary a bit year-to-year, of course, but 2020 marked a genuinely historical increase. “If you wanted to think of this as potentially erasing several decades worth of progress, that wouldn’t be an overstatement,” said Max Kapustin, an assistant professor at the Department of Policy Analysis and Management at Cornell University, as well as an affiliate at the University of Chicago Crime Lab and Education Lab. In Chicago, for instance, Kapustin said that the murder rate is back to where it was in the mid- to late 1990s.</p><p><br /></p><p>But broader context is important here, too: At a national level, even after this terrible year, the rate of violent crime in America is nowhere near where it was at its peak in the second half of the 20th century. “We’re still, thankfully, far away from that,” said Kapustin. To take just one example, the final per-100,000 homicide rate for 2020 will likely be significantly higher than 2019’s, which was 5.8, but it will still be well below the equivalent figures for 1970 or 1980 (8.8 and 10.4, respectively). As long as it is a short-term rise, rather than a sustained one, we will remain under those historical levels.</p><p><br /></p><p>In addition to the sheer magnitude of the increase, part of what made 2020 so noteworthy was a split between violent crime and other types, such as property, that researchers weren’t expecting — just about every sort of crime other than gun homicides went down. “Seeing the sort of divergence in patterns between gun violence specifically going up and lots of other crimes, including property crimes and others, going down is very unusual,” said Roseanna Ander, founding executive director of the UChicago Crime Lab and Education Lab. Given that most of the year took place under lockdowns of varying severity, one might have thought the murder rate would have fallen alongside everything else, said Kapustin. “You might think, ‘Oh, maybe there will be fewer opportunities for gun violence to happen.’” Instead, the opposite happened. The pandemic “disrupted a whole host of institutions that act as a first line against gun violence,” he said.</p><p><br /></p><p>A major reason for this might simply be that gun violence is a bit more complicated than various other crimes. Vandalism and theft are often more random and more likely to occur simply as a result of people being out and about. That’s not to say there aren’t random shootings, because of course there are, but in many ways gun violence is different from less serious types of crimes and requires unique countermeasures — countermeasures that were directly disrupted by the pandemic. “Everything that we do as a society to reduce gun violence has just been completely disrupted due to COVID,” said Kapustin. As Ander put it, these effects manifested themselves the most in “neighborhoods that, even on a good day before the pandemic, were under tremendous stress. You just had enormous fuel for that fire, and fewer resources, and fewer outlets, and fewer supports.”</p><p><br /></p><p>The impact of the coronavirus on police behavior alone might explain part of the rise in violent crime. In many cities around the country, the pandemic has both thinned the ranks of on-duty police and forced those who remain healthy to take an approach that entails fewer of the friendly (or at least neutral) interactions with citizens that mark good community policing. “Even the ones who aren’t out sick, they’re keeping their distance and pulling back in a variety of ways,” said Kapustin.</p><p><br /></p><p>And on the social-services side, “everything from schools, libraries, mentors, social workers — all that’s been disrupted.” That, of course, includes groups whose entire purpose is to help disrupt shootings and cycles of retaliation in response to them. Think about the myriad subtle and not-so-subtle ways after-school programs or sports leagues or dedicated programs help prevent violence among young men — the most at-risk group — in poor neighborhoods. Then think about what happens when, in one horrible poof, all those programs shutter.</p><p><br /></p><p>So while it’s understandable that political leaders have focused most intently on the well-being of the over-65 population during the COVID pandemic, young people have been enduring less readily apparent health threats from the crisis — including some neighborhoods becoming much, much more dangerous. “[Among] young people who are at much higher risk for gun violence, for example, there’s been a huge economic and mental-health toll throughout the pandemic,” said Kapustin. He said there were segments of the 18-to-24-year-old population, for example, who are experiencing Depression-era levels of unemployment, as well as off-the-charts levels of suicidal ideation and other mental-health problems. “These are horrific numbers.”</p><p><br /></p><p>Skyrocketing tension between communities and law enforcement following the killing of George Floyd sparked a second crisis atop the first. “You have this once-in-a-century pandemic and then all of these other strains to the system that are because of COVID or because of the pandemic,” said Ander, “and then you have the increased cynicism or distrust of police and the criminal-justice system at the same time. You’d be hard-pressed to imagine a more challenging set of circumstances for cities to face.”</p><p><br /></p><p>Ander explained that while the pandemic forced beat cops into a more hands-off approach to their work, the protests raised the question of whether, and to what extent, communities want police heavily involved in day-to-day life in high-crime neighborhoods in the first place. The skepticism comes from an understandable place, given the extent to which “engaged” policing has come to be seen as synonymous with “abusive” policing. “Unfortunately, the measures we have for proactive policing are things like street stops,” she said. “But there are other forms of proactive policing that I think are important but not well measured, like police engaging with residents, getting out of their cars, talking to business owners, and really being engaged. And I think what we’ve seen is a reduction in all proactive policing, both the kind we can measure and the kind that is not well measured, and that is probably having an impact on what we’re seeing in terms of community trust in police, but also police effectiveness.”</p><p><br /></p><p>While Ander said she was skeptical of the most straightforward accounts of police disengagement sometimes offered by (primarily) conservatives — some version of police saying, Well, if they don’t want us in their neighborhoods, I guess we’ll just stop trying to clear cases — she was open to the idea of a more complicated back-and-forth where police and civilian behavior reinforce one another in negative ways. For example, imagine a neighborhood where police disengage a bit owing to protests and COVID. The residents see fewer cops out and about — now they’re just faceless authority figures cocooned in their cruisers, in turn making the community less forthcoming with law enforcement. Less information from the community makes police even less engaged, further fueling distrust, and so on. “You get into this vicious cycle or this really negative equilibrium,” said Ander. All of which could easily lead to more violent crime.</p><p><br /></p><p>Ander counseled subtlety when it comes to the discussion over police defunding. “In the neighborhoods facing the greatest burdens of both gun violence and the harms of the criminal-justice system, the residents that I have spoken to over the years have a far more nuanced view of things,” she said. “Certainly not a monolithic view. They want their problems addressed. They want their government to work for them. What I hear more often than anything is they want their fair share of resources — including police resources — but they want the police to treat them with the dignity, fairness, and respect they deserve. But even that is anecdotal. I hope policy-makers and opinion leaders can create more avenues for the voices of the residents for whom these decisions will be most consequential to be heard — and acted on.”</p><p><br /></p><p>None of these observations point to easy answers. And both Ander and Kapustin expressed worry that policy-makers and others are going to conclude, understandably but wrongly, that as soon as the pandemic ends, America’s recent violent-crime concerns will abate, too. “You can kind of see the light at the end of the tunnel for the pandemic with the rollout of the vaccines,” said Ander. “It’s not clear what’s going to reverse that increase in gun violence.”</p><p><br /></p><p>America Saw a Historic Rise in Murders in 2020. Why?</p>CChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com0tag:blogger.com,1999:blog-1925128431767518441.post-40604720373393887712020-10-29T20:35:00.003+02:002020-10-29T20:35:16.446+02:00Muslims have 'right to punish' French, says Malaysia's Mahathir<p> Muslims have 'right to punish' French, says Malaysia's Mahathir</p><p>Reuters Staff</p><p>2-3 minutes</p><p>OCTOBER 29, 20205:32 PMUPDATED 27 MINUTES AGO</p><p>In a blog post Mahathir, 95, a respected leader in the Muslim world, said he believed in freedom of expression but that it should not be used to insult others.</p><p>“Muslims have a right to be angry and to kill millions of French people for the massacres of the past. But by and large the Muslims have not applied the ‘eye for an eye’ law. Muslims don’t. The French shouldn’t,” Mahathir said in a blog post, which he also posted on Twitter.</p><p>“Since you have blamed all Muslims and the Muslims’ religion for what was done by one angry person, the Muslims have a right to punish the French,” he said.</p><p>Twitter said the message violated its rules and it had removed the tweet.</p><p>Several Muslim-majority countries have denounced remarks by French officials, including President Emmanuel Macron, defending the use of cartoons of the Prophet Mohammad in a French school classroom. The caricatures are seen as blasphemous by Muslims.</p><p>The dispute flared after a French teacher who showed his pupils satirical cartoons of the Prophet during a civics lesson was later beheaded in the street by an attacker of Chechen origin.</p><p>French officials said the killing was an attack on the core French value of freedom of expression and defended the right to publish the cartoons. Macron has also said he would redouble efforts to stop conservative Islamic beliefs subverting French values.</p><p><br /></p><p>Reporting by A. Ananthalakshmi; Editing by Jon Boyle and Toby Chopra</p>CChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com0tag:blogger.com,1999:blog-1925128431767518441.post-62240441139943636002020-10-25T13:32:00.002+02:002020-10-25T13:32:19.874+02:00Man Beheads Teacher on the Street in France and Is Killed by Police<p> www.nytimes.com /2020/10/16/world/europe/france-decapitate-beheading.html</p><p>By Adam Nossiter</p><p>7-9 minutes</p><p>The victim was immediately depicted as a martyr to freedom of expression. France’s antiterrorism prosecutors are investigating the attack, which took place in a suburb north of Paris.</p><p>Police officers near the scene north of Paris where a man decapitated a schoolteacher.</p><p>Credit...Kiran Ridley/Getty Images</p><p>Published Oct. 16, 2020Updated Oct. 20, 2020</p><p>PARIS — A knife-wielding man decapitated a teacher near a school in a suburb north of Paris on Friday afternoon and was later shot dead by the police, officials said, abruptly hitting France with a national trauma that revived memories of recent terrorist attacks.</p><p>A police officer and parents with knowledge of the attack confirmed French media reports that the victim was a history teacher at the school who had shown caricatures of the Prophet Muhammad in a class on freedom of expression, which had incited anger among some Muslim families.</p><p>The teacher, still unidentified by Friday evening, was immediately depicted as a martyr to freedom of expression across the political spectrum. Representatives in France’s Parliament rose to their feet to “honor the victim’s memory,” as the president of the session, parliamentary deputy Hugues Renson, declared. And President Emmanuel Macron hurried to the scene of the attack Friday night.</p><p>“This was an attempt to strike down the republic,” Mr. Macron said.</p><p>Seizing on the symbolic nature of an attack against a schoolteacher, and reprising anti-Islamist themes he has lately emphasized, Mr. Macron said the teacher had been “the victim of a terrorist, Islamist attack.”</p><p>The teacher was struck down “because he taught, because he taught the liberty of expression, the liberty to believe and not believe,” the French president said in a brief televised address.</p><p>France’s antiterrorism prosecutors immediately took over the investigation of the attack, which happened at the junction of two adjoining Paris suburbs, Eragny and Conflans-Sainte-Honorine.</p><p>Much remained obscure Friday night in the absence of an official police narrative. But the underlying themes of what was known conjured up France’s recent history of terrorist attacks: an assailant carefully choosing a victim thought to symbolize an offense against Islam.</p><p>President Emmanuel Macron of France visited the scene and spoke with the press on Friday.</p><p>Turmoil at the school over the teacher’s method of instruction — Mr. Macron alluded to this in his remarks — had evidently preceded the killing. “We have seen the principal, who in these last weeks has withstood with remarkable courage a great deal of pressure,” Mr. Macron said.</p><p>In a video that widely circulated on YouTube before the attack, a Muslim parent at the teacher’s school, College du Bois-d’Aulne, expresses anger that an unidentified teacher had asked Muslims in the class of 13-year-olds to leave because “he was going to show a photo that would shock them.”</p><p>The parent asks on the video: “Why this hatred? Why does a history teacher act like this in front of 13-year-olds?”</p><p>The assailant is not known to have a connection to the school. French media reported that he was 18 and of Russian origin.</p><p>A police union official told the French television station BFM that witnesses had seen the assailant cutting the victim’s throat. The national police were called, officials said, and after having discovered the decapitated victim, confronted the assailant nearby, close to the school. Brandishing a large knife, he threatened the officers, and after refusing to surrender, was shot 10 times, they said.</p><p>French media, quoting witnesses, said the assailant was heard to yell “Allahu akbar” at the moment of the knife attack. A photograph of a corpse lying in the middle of a leafy suburban street appeared on French television not long afterward.</p><p>The attack came three weeks after a knife-wielding assailant wounded two people in Paris near the site of the former Charlie Hebdo office — the scene of a 2015 terrorist attack targeting the satirical newspaper for its caricatures of the Prophet Muhammad.</p><p>French officials of all political stripes rushed to denounce the teacher’s killing. The interior minister, in charge of the police, cut short an official trip to Morocco and flew home to Paris.</p><p>“The assassination of a history teacher is an attack on freedom of expression and the values of the republic,” the president of the National Assembly, Richard Ferrand, said on Twitter. “To attack a teacher is to attack all French citizens and freedom.”</p><p>“Frightful. Huge emotion and anger in the face of this terrorist barbarism,” a leading Socialist parliamentarian, Boris Vallaud, wrote on Twitter.</p><p>“So this week, he allowed himself to tell them, the Muslims, Muslim students raise your hands,” the parents says. “So they raised their hands, and he said, ‘right, leave the class.’ So my daughter refused to leave and asked him, ‘why?’ And he said he was going to show a photo that would shock them. And then he showed them a naked man, telling them it was the prophet.”</p><p>Another parent, Carine Mendes, 41, whose child had attended the class, offered a more nuanced view of what happened. She called the teacher “a very sweet person, in his words, in his expressions.”</p><p>Ms. Mendes said the teacher had suggested to Muslim students who did not want to see the cartoon that they leave the classroom temporarily, and had asked those who remained not to tell their Muslim classmates about the cartoon in order not to offend their faith.</p><p>“He really tried to do things with respect, he didn’t want to hurt anyone,” she said.</p><p>But in a second class where the teacher gave the course, a shocked student refused to leave the room and told her father about what happened. He was the father who later complained in the video posted online.</p><p>The next day, the teacher apologized to his students and the principal sent an email message to parents to try to clear up the situation. The teacher’s suggestion to leave the classroom, the principal said, had been insensitive.</p><p>“Without wanting to offend anyone, it turned out that by offering this possibility to the students, he still offended the student,” the principal’s email read.</p><p>Ms. Mendes said that what happened “was awful.”</p><p>“He was just giving a course on freedom of expression,” she said.</p><p>“A teacher was killed just for doing his job,” Sophie Venetitay, a teachers’ union official, told BFM.</p><p>Constant Méheut and Antonella Francini contributed reporting.</p><p><br /></p><p>Adam Nossiter is the Paris bureau chief. Previously, he was a Paris correspondent, the West Africa bureau chief, and led the team that won the 2015 Pulitzer Prize for International Reporting for coverage of the Ebola epidemic.</p><p>A version of this article appears in print on Oct. 17, 2020, Section A, Page 8 of the New York edition with the headline: Man Kills Teacher In Paris Suburb, Decapitating Him. Order Reprints | Today’s </p>CChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com0tag:blogger.com,1999:blog-1925128431767518441.post-37896630273617588412020-09-08T22:01:00.001+03:002020-09-08T22:01:54.245+03:00Byzantium and the Agents of the Renaissance<iframe allowfullscreen="" frameborder="0" height="270" src="https://www.youtube.com/embed/mzyRBNeoYhM" width="480"></iframe>CChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com0tag:blogger.com,1999:blog-1925128431767518441.post-14957430622412844362020-05-31T00:02:00.001+03:002020-08-13T17:40:18.878+03:00Misinformation Has Created a New World Disorderwww.scientificamerican.com /article/misinformation-has-created-a-new-world-disorder/<br />
Claire Wardle<br />
18-23 minutes<br />
As someone who studies the impact of misinformation on society, I often wish the young entrepreneurs of Silicon Valley who enabled communication at speed had been forced to run a 9/11 scenario with their technologies before they deployed them commercially.<br />
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One of the most iconic images from that day shows a large clustering of New Yorkers staring upward. The power of the photograph is that we know the horror they're witnessing. It is easy to imagine that, today, almost everyone in that scene would be holding a smartphone. Some would be filming their observations and posting them to Twitter and Facebook. Powered by social media, rumors and misinformation would be rampant. Hate-filled posts aimed at the Muslim community would proliferate, the speculation and outrage boosted by algorithms responding to unprecedented levels of shares, comments and likes. Foreign agents of disinformation would amplify the division, driving wedges between communities and sowing chaos. Meanwhile those stranded on the tops of the towers would be livestreaming their final moments.<br />
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Stress testing technology in the context of the worst moments in history might have illuminated what social scientists and propagandists have long known: that humans are wired to respond to emotional triggers and share misinformation if it reinforces existing beliefs and prejudices. Instead designers of the social platforms fervently believed that connection would drive tolerance and counteract hate. They failed to see how technology would not change who we are fundamentally—it could only map onto existing human characteristics.<br />
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Online misinformation has been around since the mid-1990s. But in 2016 several events made it broadly clear that darker forces had emerged: automation, microtargeting and coordination were fueling information campaigns designed to manipulate public opinion at scale. Journalists in the Philippines started raising flags as Rodrigo Duterte rose to power, buoyed by intensive Facebook activity. This was followed by unexpected results in the Brexit referendum in June and then the U.S. presidential election in November—all of which sparked researchers to systematically investigate the ways in which information was being used as a weapon.<br />
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During the past three years the discussion around the causes of our polluted information ecosystem has focused almost entirely on actions taken (or not taken) by the technology companies. But this fixation is too simplistic. A complex web of societal shifts is making people more susceptible to misinformation and conspiracy. Trust in institutions is falling because of political and economic upheaval, most notably through ever widening income inequality. The effects of climate change are becoming more pronounced. Global migration trends spark concern that communities will change irrevocably. The rise of automation makes people fear for their jobs and their privacy.<br />
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Bad actors who want to deepen existing tensions understand these societal trends, designing content that they hope will so anger or excite targeted users that the audience will become the messenger. The goal is that users will use their own social capital to reinforce and give credibility to that original message.<br />
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" /></div>
Most of this content is designed not to persuade people in any particular direction but to cause confusion, to overwhelm and to undermine trust in democratic institutions from the electoral system to journalism. And although much is being made about preparing the U.S. electorate for the 2020 election, misleading and conspiratorial content did not begin with the 2016 presidential race, and it will not end after this one. As tools designed to manipulate and amplify content become cheaper and more accessible, it will be even easier to weaponize users as unwitting agents of disinformation.<br />
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Generally, the language used to discuss the misinformation problem is too simplistic. Effective research and interventions require clear definitions, yet many people use the problematic phrase “fake news.” Used by politicians around the world to attack a free press, the term is dangerous. Recent research shows that audiences increasingly connect it with the mainstream media. It is often used as a catchall to describe things that are not the same, including lies, rumors, hoaxes, misinformation, conspiracies and propaganda, but it also papers over nuance and complexity. Much of this content does not even masquerade as news—it appears as memes, videos and social posts on Facebook and Instagram.<br />
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In February 2017 I created seven types of “information disorder” in an attempt to emphasize the spectrum of content being used to pollute the information ecosystem. They included, among others, satire, which is not intended to cause harm but still has the potential to fool; fabricated content, which is 100 percent false and designed to deceive and do harm; and false context, which is when genuine content is shared with false contextual information. Later that year technology journalist Hossein Derakhshan and I published a report that mapped out the differentiations among disinformation, misinformation and malinformation.<br />
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Purveyors of disinformation—content that is intentionally false and designed to cause harm—are motivated by three distinct goals: to make money; to have political influence, either foreign or domestic; and to cause trouble for the sake of it.<br />
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Those who spread misinformation—false content shared by a person who does not realize it is false or misleading—are driven by sociopsychological factors. People are performing their identities on social platforms to feel connected to others, whether the “others” are a political party, parents who do not vaccinate their children, activists who are concerned about climate change, or those who belong to a certain religion, race or ethnic group. Crucially, disinformation can turn into misinformation when people share disinformation without realizing it is false.<br />
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We added the term “malinformation” to describe genuine information that is shared with an intent to cause harm. An example of this is when Russian agents hacked into e-mails from the Democratic National Committee and the Hillary Clinton campaign and leaked certain details to the public to damage reputations.<br />
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Having monitored misinformation in eight elections around the world since 2016, I have observed a shift in tactics and techniques. The most effective disinformation has always been that which has a kernel of truth to it, and indeed most of the content being disseminated now is not fake—it is misleading. Instead of wholly fabricated stories, influence agents are reframing genuine content and using hyperbolic headlines. The strategy involves connecting genuine content with polarizing topics or people. Because bad actors are always one step (or many steps) ahead of platform moderation, they are relabeling emotive disinformation as satire so that it will not get picked up by fact-checking processes. In these efforts, context, rather than content, is being weaponized. The result is intentional chaos.<br />
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Take, for example, the edited video of House Speaker Nancy Pelosi that circulated this past May. It was a genuine video, but an agent of disinformation slowed down the video and then posted that clip to make it seem that Pelosi was slurring her words. Just as intended, some viewers immediately began speculating that Pelosi was drunk, and the video spread on social media. Then the mainstream media picked it up, which undoubtedly made many more people aware of the video than would have originally encountered it.<br />
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Research has found that traditionally reporting on misleading content can potentially cause more harm. Our brains are wired to rely on heuristics, or mental shortcuts, to help us judge credibility. As a result, repetition and familiarity are two of the most effective mechanisms for ingraining misleading narratives, even when viewers have received contextual information explaining why they should know a narrative is not true.<br />
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Bad actors know this: In 2018 media scholar Whitney Phillips published a report for the Data & Society Research Institute that explores how those attempting to push false and misleading narratives use techniques to encourage reporters to cover their narratives. Yet another recent report from the Institute for the Future found that only 15 percent of U.S. journalists had been trained in how to report on misinformation more responsibly. A central challenge now for reporters and fact checkers—and anyone with substantial reach, such as politicians and influencers—is how to untangle and debunk falsehoods such as the Pelosi video without giving the initial piece of content more oxygen.<br />
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Memes: A misinformation powerhouse<br />
In January 2017 the NPR radio show This American Life interviewed a handful of Trump supporters at one of his inaugural events called the DeploraBall. These people had been heavily involved in using social media to advocate for the president. Of Trump's surprising ascendance, one of the interviewees explained: “We memed him into power.... We directed the culture.”<br />
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The word “meme” was first used by theorist Richard Dawkins in his 1976 book, The Selfish Gene, to describe “a unit of cultural transmission or a unit of imitation,” an idea, behavior or style that spreads quickly throughout a culture. During the past several decades the word has been appropriated to describe a type of online content that is usually visual and takes on a particular aesthetic design, combining colorful, striking images with block text. It often refers to other cultural and media events, sometimes explicitly but mostly implicitly.<br />
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This characteristic of implicit logic—a nod and wink to shared knowledge about an event or person—is what makes memes impactful. Enthymemes are rhetorical devices where the argument is made through the absence of the premise or conclusion. Often key references (a recent news event, a statement by a political figure, an advertising campaign or a wider cultural trend) are not spelled out, forcing the viewer to connect the dots. This extra work required of the viewer is a persuasive technique because it pulls an individual into the feeling of being connected to others. If the meme is poking fun or invoking outrage at the expense of another group, those associations are reinforced even further.<br />
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The seemingly playful nature of these visual formats means that memes have not been acknowledged by much of the research and policy community as influential vehicles for disinformation, conspiracy or hate. Yet the most effective misinformation is that which will be shared, and memes tend to be much more shareable than text. The entire narrative is visible in your feed; there is no need to click on a link. A 2019 book by An Xiao Mina, Memes to Movements, outlines how memes are changing social protests and power dynamics, but this type of serious examination is relatively rare.<br />
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Indeed, of the Russian-created posts and ads on Facebook related to the 2016 election, many were memes. They focused on polarizing candidates such as Bernie Sanders, Hillary Clinton or Donald Trump and on polarizing policies such as gun rights and immigration. Russian efforts often targeted groups based on race or religion, such as Black Lives Matter or Evangelical Christians. When the Facebook archive of Russian-generated memes was released, some of the commentary at the time centered on the lack of sophistication of the memes and their impact. But research has shown that when people are fearful, oversimplified narratives, conspiratorial explanation, and messages that demonize others become far more effective. These memes did just enough to drive people to click the share button.<br />
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Technology platforms such as Facebook, Instagram, Twitter and Pinterest play a significant role in encouraging this human behavior because they are designed to be performative in nature. Slowing down to check whether content is true before sharing it is far less compelling than reinforcing to your “audience” on these platforms that you love or hate a certain policy. The business model for so many of these platforms is attached to this identity performance because it encourages you to spend more time on their sites.<br />
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Researchers are now building monitoring technologies to track memes across different social platforms. But they can investigate only what they can access, and the data from visual posts on many social platforms are not made available to researchers. Additionally, techniques for studying text such as natural-language processing are far more advanced than techniques for studying images or videos. That means the research behind solutions being rolled out is disproportionately skewed toward text-based tweets, Web sites or articles published via URLs and fact-checking of claims by politicians in speeches.<br />
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Although plenty of blame has been placed on the technology companies—and for legitimate reasons— they are also products of the commercial context in which they operate. No algorithmic tweak, update to the platforms' content-moderation guidelines or regulatory fine will alone improve our information ecosystem at the level required.<br />
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Participating in the solution<br />
In a healthy information commons, people would still be free to express what they want—but information that is designed to mislead, incite hatred, reinforce tribalism or cause physical harm would not be amplified by algorithms. That means it would not be allowed to trend on Twitter or in the YouTube content recommender. Nor would it be chosen to appear in Facebook feeds, Reddit searches or top Google results.<br />
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Until this amplification problem is resolved, it is precisely our willingness to share without thinking that agents of disinformation will use as a weapon. Hence, a disordered information environment requires that every person recognize how he or she, too, can become a vector in the information wars and develop a set of skills to navigate communication online as well as offline.<br />
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Currently conversations about public awareness are often focused on media literacy and often with a paternalistic framing that the public simply needs to be taught how to be smarter consumers of information. Instead online users would be better taught to develop cognitive “muscles” in emotional skepticism and trained to withstand the onslaught of content designed to trigger base fears and prejudices.<br />
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Anyone who uses Web sites that facilitate social interaction would do well to learn how they work—and especially how algorithms determine what users see by “prioritiz[ing] posts that spark conversations and meaningful interactions between people,” in the case of a January 2018 Facebook update about its rankings. I would also recommend that everyone try to buy an advertisement on Facebook at least once. The process of setting up a campaign helps to drive understanding of the granularity of information available. You can choose to target a subcategory of people as specific as women, aged between 32 and 42, who live in the Raleigh-Durham area of North Carolina, have preschoolers, have a graduate degree, are Jewish and like Kamala Harris. The company even permits you to test these ads in environments that allow you to fail privately. These “dark ads” let organizations target posts at certain people, but they do not sit on that organization's main page. This makes it difficult for researchers or journalists to track what posts are being targeted at different groups of people, which is particularly concerning during elections.<br />
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Facebook events are another conduit for manipulation. One of the most alarming examples of foreign interference in a U.S. election was a protest that took place in Houston, Tex., yet was entirely orchestrated by trolls based in Russia. They had set up two Facebook pages that looked authentically American. One was named “Heart of Texas” and supported secession; it created an “event” for May 21, 2016, labeled “Stop Islamification of Texas.” The other page, “United Muslims of America,” advertised its own protest, entitled “Save Islamic Knowledge,” for the exact same time and location. The result was that two groups of people came out to protest each other, while the real creators of the protest celebrated the success at amplifying existing tensions in Houston.<br />
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Another popular tactic of disinformation agents is dubbed “astroturfing.” The term was initially connected to people who wrote fake reviews for products online or tried to make it appear that a fan community was larger than it really was. Now automated campaigns use bots or the sophisticated coordination of passionate supporters and paid trolls, or a combination of both, to make it appear that a person or policy has considerable grassroots support. By making certain hashtags trend on Twitter, they hope that particular messaging will get picked up by the professional media and direct the amplification to bully specific people or organizations into silence.<br />
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Understanding how each one of us is subject to such campaigns—and might unwittingly participate in them—is a crucial first step to fighting back against those who seek to upend a sense of shared reality. Perhaps most important, though, accepting how vulnerable our society is to manufactured amplification needs to be done sensibly and calmly. Fearmongering will only fuel more conspiracy and continue to drive down trust in quality-information sources and institutions of democracy. There are no permanent solutions to weaponized narratives. Instead we need to adapt to this new normal. Just as putting on sunscreen was a habit that society developed over time and then adjusted as additional scientific research became available, building resiliency against a disordered information environment needs to be thought about in the same vein.<br />
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This article was originally published with the title "A New World Disorder" in Scientific American 321, 3, 88-93 (September 2019)<br />
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doi:10.1038/scientificamerican0919-88CChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com0tag:blogger.com,1999:blog-1925128431767518441.post-40748405820895283302020-04-30T12:35:00.002+03:002020-04-30T12:35:46.739+03:00Hopes rise on coronavirus drug remdesivirwww.nature.com /articles/d41586-020-01295-8<br />
Heidi Ledford<br />
7-8 minutes<br />
A patient at the intensive care unit receives treatment from two hospital workers in Hefei, China<br />
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Coronavirus causes severe respiratory illness in some people.Credit: Zhang Yazi/China News Service via Getty<br />
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An experimental drug — and one of the world’s best hopes against COVID-19 — could shorten the time to recovery from coronavirus infection, according to the largest and most rigorous clinical trial of the compound.<br />
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The experimental drug, called remdesivir, interferes with replication of some viruses, including the SARS-CoV-2 virus responsible for the current pandemic. On 29 April, Anthony Fauci, director of the US National Institute of Allergy and Infectious Disease (NIAID), announced that a clinical trial of more than a thousand people showed that people taking remdesivir recovered in 11 days on average, compared to 15 days for those on a placebo.<br />
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“Although a 31% improvement doesn’t seem like a knockout 100%, it is a very important proof of concept,” Fauci said. “What it has proven is that a drug can block this virus.”<br />
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Deaths were also lower in trial participants who received the drug, he said, but that trend was not statistically significant. The shortened recovery time, however, was significant, and was enough of a benefit that investigators decided to stop the trial early for ethical reasons, he said, to ensure that those participants who were receiving placebo could now access the drug. Fauci added that remdesivir would become a standard treatment for COVID-19.<br />
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The news comes after weeks of data leaks and on a day of mixed results from clinical trials of the drug. In a trial run by the drug’s maker, Gilead Sciences of Foster City, California, more than half of 400 participants with severe COVID-19 recovered from their illness within two weeks of receiving treatment. But the study lacked a placebo controlled arm, making the results difficult to interpret. Another smaller trial run in China found no benefits from remdesivir when compared with a placebo. But the trial was stopped early due to the difficulty in enroling participants as the outbreak subsided in China. Nevertheless, onlookers are hopeful that the large NIAID trial provides the first glimmer of hope in a race to find a drug that works against the coronavirus, which has infected more than 3 million people worldwide.<br />
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“There is a lot of focus on remdesivir because it’s potentially the best shot we have,” says virologist Stephen Griffin at the University of Leeds in the UK.<br />
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Small trials<br />
The fast-flowing, conflicting information on remdesivir has left people reeling over the past weeks. In the rush to find therapies to combat COVID-19, small, clinical trials without control groups have been common. “I’m just very annoyed by all of these non-controlled studies,” says Geoffrey Porges, an analyst for the investment bank SVB Leerink in New York City. “It’s reassuring that 50–60% of patients are discharged from the hospital, but this is a disease that mostly gets better anyway.”<br />
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With so much uncertainty, the remdesivir-watchers were waiting anxiously for final results from the NIAID trial, which were not expected until the end of May. In lieu of a vaccine, which could still be more than a year away, effective therapies are critical to reducing deaths and limiting economic damage from the pandemic. Yet, despite the flood of small clinical trials, no therapy has been convincingly shown to boost survival in people with COVID-19.<br />
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The NIAID results put a new sheen on remdesivir. “It may not be the wonder drug that everyone’s looking for, but if you can stop some patients from becoming critically ill, that’s good enough,” says Griffin.<br />
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Fauci said the finding reminded him of the discovery in the 1980s that the drug AZT helped to combat HIV infection. The first randomized, controlled clinical only showed a modest improvement, he said, but researchers continued to build on that success, eventually developing highly effective therapies. For now, he said, remdesivir would become a standard treatment for COVID-19.<br />
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Remdesivir works by gumming up an enzyme that some viruses, including SARS-CoV-2, use to replicate. In February, researchers showed that the drug reduces viral infection in human cells grown in a laboratory.<br />
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Gilead began to ramp up production of remdesivir well before the NIAID results. By the end of March, the company had produced enough to treat 30,000 patients. By streamlining its manufacturing process and finding new sources of raw materials, Gilead announced that it hoped to produce enough remdesivir to treat more than a million people by the end of the year.<br />
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That calculation was based on the assumption that people would take the drug for 10 days, but the results announced from Gilead’s trial today suggest that a 5-day course of treatment could work just as well. If so, that would effectively double the number of people who could be treated, says Porges.<br />
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Many drugs needed<br />
In the long term, clinicians will likely want a bevy of anti-viral drugs — with different ways of disabling the virus — in their arsenal, says Timothy Sheahan, a virologist at the University of North Carolina in Chapel Hill, who has teamed up with Gilead researchers to study remdesivir. “There is always the potential for antiviral resistance,” he says. “And to hedge against that potential, it’s good to have not only a first-line, but also a second-, third-, fourth-, fifth-line antiviral.”<br />
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Researchers are furiously testing a wide range of therapies, but early results, while not yet definitive, have not been encouraging. The malaria drugs chloroquine and hydroxychloroquine, both of which also have anti-inflammatory effects, drew so much attention from physicians and the public that some countries have depleted their supplies of the drugs. Yet studies in humans have failed to show a consistent benefit, and some have highlighted the risks posed by side effects of the drugs on the heart.<br />
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Early interest in a mix of two HIV drugs called lopinavir and ritonavir flagged when a clinical trial in nearly 200 people did not find any benefit of the mix for those with severe COVID-19. Another promising therapeutic hypothesis — that inhibiting the action of an immune system regulator called IL-6 could reduce the severe inflammation seen in some people with severe COVID-19 — has met with mixed results thus far.<br />
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Still, a host of other therapies are being tested in people, and many researchers are hunting for new drugs at the bench. Sheahan and his colleagues have found a compound that is active against SARS-CoV-2 and other coronaviruses, including a remdesivir-resistant variant of a coronavirus, when tested in laboratory-grown human cells.<br />
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But much more testing would need to be done before the compound could be tried in people. “What we’re doing now will hopefully have an impact on the current pandemic,” he says. “But maybe more importantly, it could position us to better respond more quickly in the future.”CChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com1tag:blogger.com,1999:blog-1925128431767518441.post-43435840308103660562020-04-28T22:35:00.000+03:002020-04-28T22:35:20.142+03:00A Nobel Laureate Said the New Coronavirus Was Made in a Lab. He's Wrong. - The Wire Science<br />
22/04/2020<br />
5-6 minutes<br />
Luc Montagnier during the TV interview. Photo: YouTube.<br />
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<b>...It is surprising to have a scientist of Montagnier’s stature utter such questionable statements – although Montagnier himself is a controversial figure. Among other causes, he has supported anti-vaxxers, homeopathy and a silly claim that DNA emits “electromagnetic waves”....</b><br />
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The 2008 Nobel Laureate for physiology or medicine from France, Luc Antoine Montagnier, caught the media’s attention when he recently endorsed a COVID-19 conspiracy theory – that the virus is human-made. His proclamation was subsequently magnified by various news outlets, including many in India (e.g., The Week, The Hindu Businessline, and Times of India).<br />
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Montagnier argued during a TV interview with a French TV channel that elements of the HIV-1 retrovirus, which he co-discovered in 1983, can be found in the genome of the new coronavirus. He also said elements of the “malaria germ” – the parasite Plasmodium falciparum – can also be seen in the virus’s genome.<br />
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His full quote: “We were not the first since a group of Indian researchers tried to publish a study which showed that the complete genome of this coronavirus [has] sequences of another virus, which is HIV.”<br />
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In a separate podcast episode with a different outlet, Montagnier further said the virus had escaped in an “industrial accident” from the Wuhan city laboratory when Chinese scientists were attempting to develop a vaccine against HIV.<br />
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The new coronavirus is an RNA virus, like HIV. Scientists already know that many viruses incorporate pieces of other genomes into their own in the natural course of evolution, both of plants and animals. Indeed, fully 43% of the human genome is composed of mobile genetic element sequences, which are the leftovers of viral infections that our ancestors experienced over the last 300,000 years.<br />
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The new virus also has an exceptionally large genome, of about 30,000 nucleobases. Mobile genetic elements have been discovered in many viruses with large genomes, including coronaviruses. The Indian study Montagnier referred to had been authored by a team from IIT Delhi, among others. They had uploaded their manuscript to the bioRxiv preprint repository only to quickly take it down after commentators pointed out numerous errors in their analysis.<br />
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An article published more recently in the journal Nature Medicine analysing the new virus’s genome concluded thus: “Our analyses clearly show that SARS-CoV-2 is not a laboratory construct or a purposefully manipulated virus.”<br />
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What Montagnier called the “elements” of HIV were short cis-acting elements that scientists had discovered in the genome of coronaviruses in 2005. They are required for genome replication and are shared by many coronaviruses. So if what Montagnier said is true, the whole family of coronaviruses – which originated over 10,000 years ago – would have to be lab-made, and this is obviously nonsensical.<br />
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Many experts have already pointed out this obvious flaw in Montagnier’s argument. As Étienne Simon-Lorière, a professor at the Institut Pasteur in Paris, said, “If we take a word from a book and it looks like another word, can we say that one has copied from the other? This is absurd!”<br />
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It is surprising to have a scientist of Montagnier’s stature utter such questionable statements – although Montagnier himself is a controversial figure. Among other causes, he has supported anti-vaxxers, homeopathy and a silly claim that DNA emits “electromagnetic waves”.<br />
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As he lost credibility among his peers, scientific agencies around Europe began to reject his grant applications, and eventually he was left with no money to pursue his ideas. In a 2010 interview, Montagnier said he was leaving Europe to “escape the intellectual terror.” He added, “I’m no longer allowed to work at a public institute (in France). I have applied for funding from other sources, but I have been turned down.”<br />
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Pandemics have historically been breeding grounds for fake news and conspiracy theories. For example, in the 14th century, the bubonic plague epidemic in Europe fuelled a misbelief among Christians that the Jews were deliberately poisoning wells and rivers with infectious “miasma”, leading to the mass persecution of Jews. Even when Montagnier helped discover the HIV virus (alongside Françoise Barré-Sinoussi) in the early 1980s, a prominent conspiracy theory in the US was that HIV is a human-made virus that the government had created to wipe out black people.<br />
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And because pandemics are so fraught with misinformation, they also make for an important time to communicate good science, double-check suspicious comments, refuse to accept claims without good reason, and not amplify pseudoscience without suitable qualification.<br />
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Felix Bast is a science writer and an associate professor at the Central University of Punjab. This article was originally published on Medium and has been republished here with permission.CChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com1tag:blogger.com,1999:blog-1925128431767518441.post-44403157463264167162020-04-24T21:41:00.000+03:002020-04-24T21:42:08.879+03:00The Science behind How Coronavirus Tests Work<br />
Jeffery DelViscio<br />
8-10 minutes<br />
<a href="https://www.scientificamerican.com/video/the-science-behind-how-coronavirus-tests-work/?utm_source=newsletter&utm_medium=email&utm_campaign=week-in-science&utm_content=link&utm_term=2020-04-24_top-stories&spMailingID=64605428&spUserID=NTM5ODI2NjQwMQS2&spJobID=1862943477&spReportId=MTg2Mjk0MzQ3NwS2">From Scientific American</a><br />
So you think you may have COVID-19, and you want to get tested.<br />
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Your first problem might be finding a test, depending on where you live and how sick you currently are.<br />
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A recent survey conducted with administrators from 323 hospitals across the United States found...<br />
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Quote: “Hospitals reported that severe shortages of testing supplies and extended waits for test results limited hospitals’ ability to monitor the health of patients and staff....”<br />
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Adding: “... they were unable to keep up with testing demands because they lacked complete kits and/or the individual components ... used to detect the virus.”<br />
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But let’s say that you can actually get a test for COVID-19.<br />
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When testing first began in the U.S. in January, there was only one type of assay that could confirm COVID-19. It relied on a technique called RT-PCR, or reverse transcriptase polymerase chain reaction.<br />
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This process isolates and amplifies the viral code of SARS-CoV-2, which is responsible for the illness we call COVID-19.<br />
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Many of the tests completed since that day have used RT-PCR, which typically takes over an hour to produce a result.<br />
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Now there are several tests out there, using different methods and taking varying amounts of time to return results.<br />
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So why are there different approaches? And how do they all work? And how might testing help us fight the global pandemic?<br />
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Let’s start with how the virus is collected from your body.<br />
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Many tests for active COVID-19 infection start with taking a sample from your upper respiratory tract, where the virus is known to reside.<br />
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This means pushing a collection swab deep into your nose, throat or nasopharynx, the space that connects the two.<br />
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It’s time to analyze your sample. Let’s start with the PCR part, or the “polymerase chain reaction.”<br />
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It requires that samples be processed by trained technicians on specialized machines in testing laboratories.<br />
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That means patients and the machines that test their samples are potentially far apart.<br />
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That transit time can add hours or days to getting results back, especially if the testing facility isn’t close ...<br />
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… or if there is a backlog in testing.<br />
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But when analysis begins, the aim is to amplify the viral genetic code. That code is a single strand of RNA.<br />
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But the viral RNA must first be isolated and extracted from your own cells.<br />
Once isolated, the small amounts of viral RNA must be amplified to detectable levels.<br />
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That’s where the “RT” or “Reverse Transcriptase” comes in. It takes the single-stranded viral RNA and uses it as a template to produce double-stranded DNA.<br />
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That DNA is then copied over and over using the PCR, or “polymerase chain reaction.”<br />
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PCR does this by using cycles of heating and cooling.<br />
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It breaks apart the double strand.<br />
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Added chemicals called “primers” seek out specific genes, or portions of that now separated DNA.<br />
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RT-PCR tests can target different coronavirus genes that do different things to help the virus replicate.<br />
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Some of those genes help to create the virus’s outer protein envelope. Some make its spiky surface proteins.<br />
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The point of this whole process is to exponentially increase the viral DNA with every heating-cooling step. After many cycles of PCR, one section of viral DNA in the sample would turn into millions or billions.<br />
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Large analysis machines can also run multiple samples at once. Even if they take a few hours to process, many results are returned at one time.<br />
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But the results of the test must still be collected and communicated back to you.<br />
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This causes another delay in getting your COVID-19 diagnosis.<br />
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But while the bulk of continued COVID-19 testing will probably happen this way, new kinds of tests are starting to show up.<br />
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On February 29th, 2020, the FDA issued an immediately in effect guidance that allows laboratories to submit rapid SARS-CoV-2 diagnostic tests to be approved for use under an emergency authorization.<br />
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This includes several so-called point-of-care diagnostic tests.<br />
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Some of these are just smaller versions of the RT-PCR machines but with prepackaged primers so that any health care worker can run the sample as long as that person’s facility owns the company’s sample analysis machine.<br />
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One of these rapid diagnostics uses a new method, called isothermal amplification.<br />
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Recently, President Donald Trump mentioned one company developing this new kind of test.<br />
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TRUMP: “On Friday, the FDA authorized a new test developed by Abbott Labs that delivers lightning-fast results in as little as five minutes. That’s a whole new ballgame....”<br />
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The testing machine is small, which means it can be located in hospitals and doctor’s offices. And “isothermal” means it detects the virus without having to go through the time-consuming heating and cooling cycles that PCR uses. Here’s how it works.<br />
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Under the hood of this test is something called a “nicking enzyme amplification reaction,” or NEAR.<br />
It uses primers to target pieces of the SARS-CoV-2 viral code to make more copies with a kind of genetic copier-printer template.<br />
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Added enzymes find the copied pieces of viral code and “nick,” or very selectively cut, the replicated viral code out of that template like a paper cutter at the end of a printer.<br />
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And this printer prints from both ends, so it spits out two new viral sequences with every new print operation.<br />
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Each piece of viral code in your sample can be harnessed by these double-ended genetic copier-printers. This means both the printers and the copies from them rapidly increase if they detect coronavirus RNA.<br />
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Each new viral sequence copy also gets a fluorescent beacon—it’s like it leaves the printer with a streak of glowing highlighter on it.<br />
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This rapid copy print of viral sequences is what allows for a positive ID of COVID-19 in just a few minutes.<br />
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As of mid April, USA Today reported that the company had “shipped about 500,000 of the rapid tests, which run on instruments that sell for $4,500 apiece, to all 50 states, Washington D.C., Puerto Rico and the Pacific Islands.”<br />
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The last type of test is a blood test. And it is critical for finding out how many of us have been infected, and, perhaps, who might be able to return to work, and when.<br />
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It’s called an antibody test, immunoassay, or serologic test.<br />
It works by identifying some of your body’s own defenses against the virus, called antibodies.<br />
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These proteins, called immunoglobulins, only exist inside you if you’ve mounted an immune response to the coronavirus. These tests often use a portion of the viral code to search for these antibodies in your blood.<br />
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It’s important to note that these tests are not replacements for the swab-based tests for active infections.<br />
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The U.S. FDA says as much, citing the delay between onset of infection and antibody build up.<br />
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These tests do excel at finding out who has already been infected.<br />
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Knowing how many of us have been infected and fought off the virus, sometimes with mild or no symptoms, is critical for long-term disease surveillance.<br />
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And if you’ve already had COVID-19 and lived through it, your body may know how to fight off reinfection in the future—at least for some period of time.<br />
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Exactly how long your immunity might last is an open question, though it opens up the possibility of those who have recovered returning to normal work and contact with others.<br />
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Also, if your immune response was strong enough at beating back the virus, you may carry effective antibodies in your blood.<br />
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Those would be useful to others with weaker immune responses to the virus.<br />
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In fact, the FDA recently designated so-called “convalescent plasma” as an investigational product for critically ill coronavirus patients.<br />
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This plasma could also help in the development of effective vaccines.<br />
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Regardless of what tests you take for COVID-19 in the coming months, what remains clear is that all of these methods will be necessary to beat the coronavirus and end the pandemic.CChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com1tag:blogger.com,1999:blog-1925128431767518441.post-87810499019754639452020-04-18T22:48:00.000+03:002020-04-18T22:48:29.439+03:00Will antibody tests for the coronavirus really change everything?<br />
<br />
Smriti Mallapaty<br />
10-13 minutes<br />
COVID-19 coronavirus antibody test kits.<br />
More at www.nature.com /articles/d41586-020-01115-z<br />
Antibody tests might be used to help stem the COVID-19 pandemic — but first must overcome several hurdles.Credit: Greg Baker/AFP/Getty<br />
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British Prime Minister Boris Johnson called them a ‘game changer’. Antibody tests have captured the world’s attention for their potential to help life return to normal by revealing who has been exposed, and might now be immune, to the new coronavirus.<br />
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Dozens of biotech companies and research laboratories have rushed to produce the blood tests. And governments around the world have bought millions of kits, in the hope that they could guide decisions on when to relax social-distancing measures and get people back to work. Some have even suggested that the tests could be used as an ‘immunity passport’, giving the owner clearance to interact with others again.<br />
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Many scientists share this enthusiasm. The immediate goal is a test that can tell healthcare and other essential workers whether they are still at risk of infection, says David Smith, a clinical virologist at the University of Western Australia in Perth. In the future, they could also assess whether vaccine candidates give people immunity.<br />
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But as with most new technologies, there are signs that the promises of COVID-19 antibody tests have been oversold, and their challenges underestimated. Kits have flooded the market, but most aren’t accurate enough to confirm whether an individual has been exposed to the virus.<br />
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And even if tests are reliable, they can't indicate whether someone is immune to reinfection, say scientists. It will be a while before kits are as useful as hoped, says Smith. “Countries are still gathering the evidence.”<br />
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The UK government learnt about this the hard way after it ordered 3.5 million tests from several companies in late March, only to later discover that none of these tests performed well enough.<br />
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“No test is better than a bad test,” says Michael Busch, director of the Vitalant Research Institute in San Francisco.<br />
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Antibody tests are also being used by researchers globally to estimate the extent of coronavirus infections at a population level, which is extremely valuable given that many places aren’t doing enough standard testing, and people with mild or no symptoms will probably be missed in official case counts. These surveys test a portion of the population and use that to estimate infections among the broader community. More than a dozen groups worldwide are doing such studies.<br />
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Flood of tests<br />
When a virus invades the body, the immune system produces antibodies to fight it. Kits detect the presence of antibodies using components from the virus, known as antigens. Tests generally fall into one of two categories: lab tests that need to be processed by trained technicians and take about a day, and point-of-care tests that give rapid, on-the-spot results within 15 minutes to half an hour. Several companies, including Premier Biotech in the United States and China-based Autobio Diagnostics, offer point-of-care kits, which are designed to be used by health professionals to check if an individual has had the virus — but some companies market them for people to use at home.<br />
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The tests don’t detect the virus itself, so have limited use in diagnosing active infections, say health agencies. But in some countries, such as the United States and Australia, tests are being used in some cases to diagnose people who have suspected COVID-19, but who test negative on a standard PCR test, says Smith. (A study1 by researchers at Shenzhen Third People’s Hospital in China found that PCR tests did not always diagnose patients infected with the virus.)<br />
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Early studies in people who have recovered from COVID-19 have detected three kinds of SARS-CoV-2-specific antibody, and manufacturers and research institutes have developed tests that target these antibodies. For instance, the German biopharmaceutical company EUROIMMUN has developed a lab test that detects SARS-CoV-2-specific immunoglobulin G and immunoglobulin A.<br />
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Because of the ongoing emergency, the US Food and Drug Administration (FDA) has relaxed the rules that govern the use of such tests. It has authorized their use in laboratories and by health-care workers to diagnose active COVID-19 infection, with the disclaimer that they have not been reviewed by the FDA and that results should not be used as the sole basis for confirming that someone has the disease. Australia has also introduced similar emergency authorizations.<br />
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These measures are appropriate given the pandemic situation, says Smith. Antibody tests in people who might be actively infected can be an important part of managing patients at hospitals, and contact tracing, although the results need to be interpreted cautiously, he says.<br />
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Test the tests<br />
One problem, however, is that most kits have not undergone rigorous testing to ensure they’re reliable, says Busch. During a meeting at the UK Parliament’s House of Commons Science and Technology Select Committee on 8 April, Kathy Hall, the director of the testing strategy for COVID-19, said that no country appeared to have a validated antibody test that can accurately determine whether an individual has had COVID-19.<br />
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Kits need to be trialled on large groups of people to verify their accuracy: hundreds of people who have had COVID-19, and hundreds of people who haven't, says Peter Collignon, a physician and laboratory microbiologist at Australian National University in Canberra. But so far, most test assessments have involved only some tens of individuals because they have been developed quickly.<br />
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It seems that many tests available now are not accurate enough at identifying people who have had the disease, a property called test sensitivity, and those who haven’t been infected, known as test specificity. A high-quality test should achieve 99% or more sensitivity and specificity, adds Collignon. That means that testing should turn up only about 1 false positive and 1 false negative for every 100 true positive and true negative results.<br />
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But some commercial antibody tests have recorded specificities as low as 40% early in the infection. In an analysis2 of 9 commercial tests available in Denmark, 3 lab-based tests had sensitivities ranging 67–93% and specificities of 93–100%. In the same study, five out of six point-of-care tests had sensitivities ranging 80–93%, and 80-100% specificity, but some kits were tested on fewer than 30 people. Testing was suspended for one kit. Overall, the sensitivity of all the tests improved over time, with the highest sensitivity recorded two weeks after symptoms first appeared. Some of these tests are also being used to test individuals in other countries, including Germany and Australia.<br />
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Point-of-care tests are even less reliable than tests being used in labs, adds Smith. This is because they use a smaller sample of blood — typically from a finger prick — and are conducted in a less controlled environment than a lab, which can affect their performance. They should be used with caution, he says. The WHO recommends that point-of-care tests only be used for research.<br />
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Without reliable tests, “we may end up doing more harm than good,” says Collignon.<br />
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Timing is critical<br />
One unknown that affects both kinds of test is the interplay between timing and accuracy. If a test is done too soon after a person is infected and the body hasn’t had time to develop the antibodies the test is designed to detect, it could miss an infection. But scientists don’t yet know enough about the timing of the body’s immune responses to SARS-CoV-2 to say exactly when specific antibodies develop.<br />
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By contrast, false positives crop up if a test uses an antigen that doesn't only target antibodies produced to fight SARS-CoV-2, and instead picks up antibodies for another pathogen as well, says Smith. An analysis3 of EUROIMMUN’s antibody test found that although it detected SARS-CoV-2 antibodies in three people with COVID-19, it returned a positive result for two people with another coronavirus.<br />
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Ironing out all these issues takes time and involves trial and error, says Collignon. It took several years to develop antibody tests for HIV with more than 99% specificity, he says.<br />
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Infection doesn't equal immunity<br />
Another big question surrounding antibody tests is the extent to which being infected with a pathogen confers immunity to reinfection. To have protective immunity, the body needs to produce a certain type of antibody, called a neutralizing antibody, which prevents the virus from entering cells.<br />
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But it’s not clear whether all people who have had COVID-19 develop these antibodies. An unpublished analysis4 of 175 people in China who had recovered from COVID-19 and had mild symptoms reported that 10 individuals produced no detectable neutralizing antibodies — even though some had high levels of binding antibodies. These people had been infected, but it’s unclear whether they have protective immunity, says Wu Fan, a microbiologist at Fudan University in Shanghai, China, who led the study. “The situation for patients is very complicated,” says Fan.<br />
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So far, researchers say they have not seen any evidence that people can get reinfected with the virus. Rhesus macaques infected with SARS-CoV-2 could not be reinfected at just under one month following their initial infection, according to an unreviewed study5 by researchers at Peking Union Medical College in Beijing. “We should presume that once you have been infected, your chance of getting a second infection two to three months later is low,” says Collignon. But how long that protective immunity will last is not known.<br />
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Even if it becomes clear that most people do develop neutralizing antibodies, most tests currently don’t detect them. And tests that do are more complex to develop and not widely available.<br />
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The fact that most antibody tests can't detect neutralizing antibodies is also relevant because some politicians are pushing the idea that these tests be used to clear those with past COVID-19 infections to interact with others again, a so-called immunity passport. Researchers are trying to determine whether the antibodies detected by current kits can act as a proxy for protective immunity, says Smith.<br />
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Another complicating factor for immunity passports is that antibody tests can’t rule out that a person is no longer infectious, says Smith. A study6 published in Nature this month found that viral RNA declines slowly after antibodies are detected in the blood. The presence of viral RNA could mean that the person is still shedding infectious virus.<br />
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Despite the challenges, once reliable antibody tests are available, they could be important to understanding which groups of people have been infected how to stop further spread, says Collignon. They could even be used to diagnose active infections when PCR tests fail, adds Smith.<br />
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Additional reporting by Elizabeth Gibney.CChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com1tag:blogger.com,1999:blog-1925128431767518441.post-34909132249806743632020-04-18T18:22:00.000+03:002020-04-18T18:36:59.744+03:00More Encouraging Signs for Remdesivir as COVID-19 Treatment<br />
Alice Park<br />
5-7 minutes<br />
<i>Comment from the owner: What a pitty TIME...</i><br />
Researchers at University of Chicago reported promising results from a small study of remdesivir in treating people with COVID-19.<br />
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<b>The findings were not published in a peer-reviewed journal</b>, but revealed in an internal video discussion of the drug trial among University of Chicago faculty that was obtained by STAT.<br />
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The study included 125 people with COVID-19, all of whom were treated with the remdesivir, which is <b>not currently approved in the U.S. for treating any disease</b>. Of the 125 patients in the Chicago study, 113 had severe disease, meaning they had difficulty breathing. In the video discussion, Kathleen Mullane, a professor of medicine at the university who is overseeing the trial, said most of the patients taking the drug had improved enough to be discharged from the hospital, and only two died. Mullane was not available to discuss the results, but in a statement, a university spokesperson said “Partial data from an ongoing clinical trial is by definition incomplete and should never be used to draw conclusions about the safety or efficacy of a potential treatment that is under investigation. In this case, information from an internal forum for research colleagues concerning work in progress was released without authorization. Drawing any conclusions at this point is premature and scientifically unsound.”<br />
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Remdesivir works by mimicking one of the genetic elements that the COVID-19 virus, known as SARS-CoV-2, uses to make more copies of itself. The substitute blocks the virus from copying its genome, jamming up the viral copy machine.<br />
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The drug was initially developed by Gilead as a possible treatment for Ebola, but when early trials against that disease were disappointing, the company put further development on hold. However, previous lab studies showed that remdesivir actually had stronger antiviral activity against coronaviruses like SARS and MERS than against Ebola, so when COVID-19, also caused by a coronavirus, emerged, scientists aware of the lab data began using the drug to treat the new disease. Because the drug is not approved for treating any disease, doctors cannot use it off label to treat COVID-19 patients. However, they can ask the manufacturer to provide the drug on a “compassionate use” basis to treat the sickest patients who have no other treatment options.<br />
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The University of Chicago results follow other encouraging findings from a study published April 10 in the New England Journal of Medicine that followed 53 patients who were treated with remdesivir in the U.S., Europe, Canada and Japan on a compassionate use basis. In that study, 68% improved and 57% of those needing ventilators no longer needed mechanical breathing support after taking the drug for 10 days.<br />
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Gilead itself is also conducting two studies of remdesivir on COVID-19 patients, one in those with milder breathing problems, and another in people with more severe respiratory disease. Those results are expected in several months. Other studies of the drug in the U.S. and Europe, including the one at University of Chicago, are also ongoing, although the earliest ones, in China, had to be recently suspended because the researchers could not find enough patients with severe disease to enroll.<br />
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While encouraging, the results from the University of Chicago and the compassionate use trial don’t mean that sick patients with COVID-19 should rely on remdesivir to alleviate their symptoms. Nether study compared the drug to a placebo, which is the gold standard for evaluating new drugs on safety and effectiveness. It will be several months more before those results are available.<br />
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The Coronavirus Brief. Everything you need to know about the globaCChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com1tag:blogger.com,1999:blog-1925128431767518441.post-57699911291610052562020-04-13T11:51:00.002+03:002020-04-13T11:51:27.295+03:00COVID-19: What we must do to prevent a global depression<br />
Klaus Schwab Founder and Executive Chairman, World Economic Forum<br />
12-15 minutes<br />
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No diagrams & photos, original in the <a href="https://www.weforum.org/agenda/2020/04/covid-19-how-to-prevent-a-global-depression/">link</a>.<br />
Without a vaccine or effective COVID-19 treatment, we could face continued infections and death until at least the end of 2020.<br />
To prevent further spread of coronavirus, we must monitor what fraction of the population has been in contact with the virus and is potentially immune.<br />
To prevent an economic collapse, governments will need to take on large and unprecedented roles in securing business continuity and jobs.<br />
A few months in, it is still hard to grasp the scale and scope of COVID-19’s global impact. A third of the world population is under some sort of “lockdown.” Over 200 countries are affected, and the number of new cases and deaths in many places are still growing exponentially. All the while, a second crisis, in the form of an economic recession, is underway.<br />
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We all want to leave this crisis behind as soon as possible. But as eager as we are to restart social and economic life, to do so, we must give prime focus on public health. That comes with an enormous cost, but it is better than the alternative. Government and business collaborating, based on the latest scientific evidence are our best chance at preventing a hopefully short-term recession from becoming a global depression.<br />
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While governments and companies who have “bent the curve” can cautiously start initiatives to gets parts of social and economic life going again, always monitored by public health officials, companies should leave their competitive interests temporarily behind, and work together to ensures that the most effective vaccine can be determined as fast as possible, and the necessary production can start on a large scale as fast as possible. It is the only true way out of this crisis.<br />
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What is the World Economic Forum doing about the coronavirus outbreak?<br />
A new strain of Coronavirus, COVID 19, is spreading around the world, causing deaths and major disruption to the global economy.<br />
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Responding to this crisis requires global cooperation among governments, international organizations and the business community, which is at the centre of the World Economic Forum’s mission as the International Organization for Public-Private Cooperation.<br />
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The Forum has created the COVID Action Platform, a global platform to convene the business community for collective action, protect people’s livelihoods and facilitate business continuity, and mobilize support for the COVID-19 response. The platform is created with the support of the World Health Organization and is open to all businesses and industry groups, as well as other stakeholders, aiming to integrate and inform joint action.<br />
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As an organization, the Forum has a track record of supporting efforts to contain epidemics. In 2017, at our Annual Meeting, the Coalition for Epidemic Preparedness Innovations (CEPI) was launched – bringing together experts from government, business, health, academia and civil society to accelerate the development of vaccines. CEPI is currently supporting the race to develop a vaccine against this strand of the coronavirus.<br />
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Let us start by acknowledging what is known medically today. While we don’t know the full facts on COVID-19 yet, it is clear will pose an exceptional risk to global public health for at least another year and possibly much longer, because of three crucial reasons. First, this novel coronavirus is extremely infectious. Second, the COVID-19 disease it causes is very severe. And third – and this is crucial – we have no “background” immunity in the population and don’t have a vaccine yet.<br />
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Consider first its infectiousness. Anyone infected with this new virus infects on average between two and three other people and nobody has immunity against this brand-new virus. Within a few weeks, this virus has infected millions (the official case count is still under 2 million, but the unofficial one is likely a least five times as high and counting). And in the next few months, COVID-19 imperils most of the global population.<br />
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That means we are facing a full-scale pandemic, and there is no shortcut out of it. This virus in the past 100 years is only comparable with the Spanish flu of 1918, that over two years killed approximately 50 million people and was followed by a 1920-1921 economic depression. That flu was a very virulent and infectious virus as well, to which the world population had no immunity at all. If that disaster repeats itself, we’re facing the prospect of many millions of deaths and a prolonged depression.<br />
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Spread of COVID-19 globally.<br />
Spread of COVID-19 globally.<br />
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Image: World Economic Forum<br />
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Second, we now know that the death rate of confirmed COVID-19 cases is in the order of 5 percent. The mortality rate in all cases (including the as yet unrecognized) is still unknown, but it is most likely at least 1 percent. Such percentages are very serious: they are at least 10 to 50 times higher than for the seasonal Influenza, and comparable to the infamous “Spanish Flu.” All epidemiological parameters (infectiousness, virulence and lack of immunity) are similar between the Spanish flu and COVID-19.<br />
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As a consequence, hospitals around the world are now flooded with patients. From New York to Tokyo, and from Barcelona to Tehran, thousands are already dying every day, in what can only be described as a very exceptional global health emergency: all “frontline” health care workers around the globe testify that they have never seen this.<br />
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And third, and this is the most problematic fact: there is no vaccine, and not even a very effective treatment. A flu vaccine is useless against COVID, as are most other existing drugs that have been tested. Reports on (partial) efficacy of the chloroquine or anti-HIV drugs have to be confirmed. That leaves us, with the outlook of continued infections and death until at least the end of 2020.<br />
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If this wasn’t enough of a dramatic outlook, consider that the impact of the virus has so far been felt mostly in the developed world, the “Global North.” The final death toll and much of its socio-economic impacts, however, will be determined by its spread and mortality rate in the low- and middle-income countries, in the “Global South”, which count several billions of people who are as of now still only at the start of the pandemic.<br />
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A hopeful sign is there that the population is much younger and less affected with the cardiovascular and other co-morbidities (which increase the risk on severe disease in the North). But, people in the South have a much higher burden of “background” infections and less access to enough quality food and medical services. What happens in the poor “inner city” areas of New York City is probably partly predictive of what will happen soon in the Global South, and that picture looks rather bleak.<br />
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Knowing all this, which social, economic and public health scenarios should we reasonably plan for, and what is the solution?<br />
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In the absence of a vaccine, this pandemic will only stop when a large part of the population acquires immunity after infection. It is the famous concept of “herd immunity.”<br />
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You could argue (as the U.K. did for a while) that we should let that happen, the sooner the better. The problem with that strategy is what we witness today in New York City: before this herd immunity is installed, you have a very rapid exponential “natural spread.” So many people get sick simultaneously, desperately in need for medical care, that the health system crashes and many (hundreds of) thousands of people will simply suffocate, exacerbating social and political tensions.<br />
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We are better off then, maintaining to various degrees the present measures of containment: not to kill the virus or to end the epidemic very soon, because that is impossible without a vaccine. But to slow the epidemic down sufficiently to give our health systems the chance to cope: support patients that have serious breathing problems with oxygen and artificial ventilation to give their immune system the chance to overcome the infection. We may get to herd immunity, but only very gradually.<br />
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Looking forward, the big question we are left to answer is this: how long should the lockdown be maintained and when and how do we release it gradually?<br />
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In the end, we must enable the economy to restart, and prevent a second epidemic of mental health or social problems, while avoiding a new surge in the epidemic. China, Korea, Germany and Austria are already cautiously re-starting or planning to re-start parts of societal and economic life. But they must to do so in an extremely cautious manner, led by public health experts. Failing to do so could trigger financial and mental health problems comparable in impact to a public health crisis.<br />
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The impact could be worse in hard-hit regions such as North Africa and the Middle East.<br />
The impact could be worse in hard-hit regions such as North Africa and the Middle East.<br />
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Image: Statista<br />
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Two complementary strategies to prevent further epidemic growth can be rolled out, which enable various governments to take decisions on how to gradually restart social life:<br />
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The first is serological testing, i.e. looking for COVID-specific antibodies in the general population. By doing this, you can monitor what fraction of the population has been in contact with the virus and is potentially immune.<br />
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The second is to develop a reliable “rapid antigen tests” to quickly diagnose those who carry the virus (without or with minimal symptoms) and install “contact tracing” by app-technology to rapidly identify contacts of the infected persons that could be quarantined to prevent further spreading.<br />
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For governments and businesses, combining both strategies may be their best chance of getting the economy going again. Which aspects they start first, between opening schools, workplaces, shops and restaurants, should be a country-by-country choice. But once best practices become clear, countries should be willing to learn and coordinate with each other. We will only be able to get out of this crisis together. If we fail to help each other, we risk a serious relapse, making the recipe for a crisis turned depression.<br />
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Ultimately it should be clear: the only long-term strategy to eradicate this virus is a COVID drug and/or vaccine. This type of development supposes that one has at least a few dozens of candidates that work very well in vitro and in animal models. And then it usually takes several years to bring one or two to the market. Given this knowledge, we shouldn’t plan for an economic and social recovery in a year, simply out of hope.<br />
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Of course, we could be lucky that an existing, already approved drug could also act against this virus, or that an efficacious COVID drug could prevent mortality and promote recovery of infected subjects. On the former, mass screening is happening today, so we will know soon. Also new schemes for rapid testing of candidate vaccines are set up and a suitable candidate could emerge within months. But even then, it will take time to produce and deliver it on a worldwide scale. Nevertheless, all global stakeholders should provide all their support, financially as well as through bureaucratic means, to get to this solution as fast as possible. This is a time for collaboration, not competition.<br />
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In the absence of a widely available vaccine, and knowing this will likely take more than a year, and possibly multiple years, not a couple of months, we must make fundamental changes to our economic system. To prevent an economic collapse, governments will need to take on large and unprecedented roles in securing business continuity and jobs. The public debt that will go hand in hand with, will need to be carried by the strongest shoulders – the companies and individuals most able to take it on. The crucial principle, that everyone will need to subscribe to, is that we’re all in this together, for the long haul, and we must all come out of it together.<br />
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We have faced grave crises before. But if we want to come out of this unscathed in the long run, we must plan for unprecedented impact and collaboration in the short run. We’ll overcome this crisis, but only if we work together and dig in.<br />
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This article originally appeared in TIME.CChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com0tag:blogger.com,1999:blog-1925128431767518441.post-56385448630948234762020-04-12T23:01:00.002+03:002020-04-12T23:01:49.265+03:00The COVID-19 vaccine development landscapeStephen Mayhew<br />
10-13 minutes<br />
(Figures and Tables ommited, for full article go <a href="https://www.nature.com/articles/d41573-020-00073-5">here</a>)<br />
The genetic sequence of SARS-CoV-2, the coronavirus that causes COVID-19, was published on 11 January 2020, triggering intense global R&D activity to develop a vaccine against the disease. The scale of the humanitarian and economic impact of the COVID-19 pandemic is driving evaluation of next-generation vaccine technology platforms through novel paradigms to accelerate development, and the first COVID-19 vaccine candidate entered human clinical testing with unprecedented rapidity on 16 March 2020.<br />
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The Coalition for Epidemic Preparedness Innovations (CEPI) is working with global health authorities and vaccine developers to support the development of vaccines against COVID-19. To facilitate this effort, we have developed and are continuously maintaining an overview of the global landscape of COVID-19 vaccine development activity. Our landscape database includes vaccine development programmes reported through the WHO’s authoritative and continually updated list, along with other projects identified from publicly available and proprietary sources (see Supplementary Box 1). The landscape provides insights into key characteristics of COVID-19 vaccine R&D and serves as a resource for ongoing portfolio management at CEPI. We have also shared our landscape information with others in the global health ecosystem to help improve coordination in the COVID-19 outbreak response and enable global resources and capabilities to be directed towards the most promising vaccine candidates.<br />
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COVID-19 vaccine R&D landscape<br />
As of 8 April 2020, the global COVID-19 vaccine R&D landscape includes 115 vaccine candidates (Fig. 1), of which 78 are confirmed as active and 37 are unconfirmed (development status cannot be determined from publicly available or proprietary information sources). Of the 78 confirmed active projects, 73 are currently at exploratory or preclinical stages. The most advanced candidates have recently moved into clinical development, including mRNA-1273 from Moderna, Ad5-nCoV from CanSino Biologicals, INO-4800 from Inovio, LV-SMENP-DC and pathogen-specific aAPC from Shenzhen Geno-Immune Medical Institute (Table 1). Numerous other vaccine developers have indicated plans to initiate human testing in 2020.<br />
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Fig. 1 | Pipeline of COVID-19 vaccine candidates by technology platform. Exploratory projects (split into confirmed and unconfirmed) are in the early planning stage with no in-vivo testing, and preclinical projects are at the stage of in-vivo testing and/or manufacturing clinical trials material.<br />
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Table 1 | Clinical-phase vaccine candidates for COVID-19<br />
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Diversity of technology platforms. A striking feature of the vaccine development landscape for COVID-19 is the range of technology platforms being evaluated, including nucleic acid (DNA and RNA), virus-like particle, peptide, viral vector (replicating and non-replicating), recombinant protein, live attenuated virus and inactivated virus approaches (Fig. 1). Many of these platforms are not currently the basis for licensed vaccines, but experience in fields such as oncology is encouraging developers to exploit the opportunities that next-generation approaches offer for increased speed of development and manufacture. It is conceivable that some vaccine platforms may be better suited to specific population subtypes (such as the elderly, children, pregnant women or immunocompromised patients).<br />
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Considering the candidates in Table 1, the novel platforms based on DNA or mRNA offer great flexibility in terms of antigen manipulation and potential for speed. Indeed, Moderna started clinical testing of its mRNA-based vaccine mRNA-1273 just 2 months after sequence identification. Vaccines based on viral vectors offer a high level of protein expression and long-term stability, and induce strong immune responses. Finally, there are already licensed vaccines based on recombinant proteins for other diseases, and so such candidates could take advantage of existing large-scale production capacity.<br />
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For some platforms, adjuvants could enhance immunogenicity and make lower doses viable, thereby enabling vaccination of more people without compromising protection. So far, at least 10 developers have indicated plans to develop adjuvanted vaccines against COVID-19, and vaccine developers including GlaxoSmithKine, Seqirus and Dynavax have committed to making licensed adjuvants (AS03, MF59 and CpG 1018, respectively) available for use with novel COVID-19 vaccines developed by others.<br />
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Public information on the specific SARS-CoV-2 antigen(s) used in vaccine development is limited. Most candidates for which information is available aim to induce neutralizing antibodies against the viral spike (S) protein, preventing uptake via the human ACE2 receptor. However, it is unclear how different forms and/or variants of the S protein used in different candidates relate to each other, or to the genomic epidemiology of the disease. Experience with SARS vaccine development indicates the potential for immune enhancement effects of different antigens, which is a topic of debate and could be relevant to vaccine advancement.<br />
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Profile of vaccine developers. Of the confirmed active vaccine candidates, 56 (72%) are being developed by private/industry developers, with the remaining 22 (28%) of projects being led by academic, public sector and other non-profit organizations (Fig. 2). Although a number of large multinational vaccine developers (such as Janssen, Sanofi, Pfizer and GlaxoSmithKine) have engaged in COVID-19 vaccine development, many of the lead developers are small and/or inexperienced in large-scale vaccine manufacture. So, it will be important to ensure coordination of vaccine manufacturing and supply capability and capacity to meet demand.<br />
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Fig. 2 | Profile of COVID-19 vaccine developers by type and geographic location. For partnerships, the location is that of the lead developer. *Excluding China.<br />
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Most COVID-19 vaccine development activity is in North America, with 36 (46%) developers of the confirmed active vaccine candidates compared with 14 (18%) in China, 14 (18%) in Asia (excluding China) and Australia, and 14 (18%) in Europe (Fig. 2). Additional vaccine development efforts have been reported for China, and CEPI is in dialogue with the Chinese Ministry of Science and Technology to confirm their status.<br />
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Lead developers of active COVID-19 vaccine candidates are distributed across 19 countries, which collectively account for over three-quarters of the global population. However, there is currently no public information on vaccine development activity in Africa or Latin America, although vaccine manufacturing capacity and regulatory frameworks exist in these regions. The epidemiology of COVID-19 might differ by geography, and it is likely that effective control of the pandemic will require greater coordination and involvement of the southern hemisphere in vaccine R&D efforts.<br />
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Outlook<br />
The global vaccine R&D effort in response to the COVID-19 pandemic is unprecedented in terms of scale and speed. Given the imperative for speed, there is an indication that vaccine could be available under emergency use or similar protocols by early 2021. This would represent a fundamental step change from the traditional vaccine development pathway, which takes on average over 10 years, even compared with the accelerated 5-year timescale for development of the first Ebola vaccine, and will necessitate novel vaccine development paradigms involving parallel and adaptive development phases, innovative regulatory processes and scaling manufacturing capacity.<br />
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Industry benchmarks for traditional vaccine development paradigms cite attrition rates for licensed vaccines of more than 90%. The approaches being applied for COVID-19 development — which involve a new virus target and often novel vaccine technology platforms and novel development paradigms as well — are likely to increase the risks associated with delivering a licensed vaccine, and will require careful evaluation of effectiveness and safety at each step. In order to assess vaccine efficacy, COVID-19 specific animal models are being developed, including ACE2-transgenic mice, hamsters, ferrets and non-human primates. Biosafety-level 3 containment measures are needed for animal studies involving live-virus challenges, and the demand for these capabilities is likely to require international coordination to ensure that sufficient laboratory capacity is available.<br />
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Finally, strong international coordination and cooperation between vaccine developers, regulators, policymakers, funders, public health bodies and governments will be needed to ensure that promising late-stage vaccine candidates can be manufactured in sufficient quantities and equitably supplied to all affected areas, particularly low-resource regions. CEPI has recently issued a call for funding to support global COVID-19 vaccine development efforts guided by three imperatives: speed, manufacture and deployment at scale, and global access. We maintain a dynamic portfolio management approach, and will make our enabling science resources available globally. We urge the global vaccine community to collectively mobilize the technical and financial support needed to successfully address the COVID-19 pandemic through a global vaccination programme, and provide a strong base to tackle future pandemics.<br />
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Acknowledgements<br />
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The authors thank F. Kristensen, N. Lurie, and M. Christodoulou from CEPI and D. Vaughn from BMGF for guidance and inputs. We thank the WHO for making the COVID-19 landscape data available.<br />
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Competing Financial Interests<br />
CEPI is a funder of some of the vaccine projects highlighted in this article. M.S. owns stock in a COVID-19 vaccine developer that is not funded by CEPI.CChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com0tag:blogger.com,1999:blog-1925128431767518441.post-11324354415374161612020-04-12T16:01:00.000+03:002020-04-12T16:02:25.412+03:00Η θορυβώδης αποκήρυξη των καλών τρόπων Βασίλης Καραποστόλης<br />
5-6 minutes<br />
<a href="https://antifono.gr/%ce%b7-%ce%b8%ce%bf%cf%81%cf%85%ce%b2%cf%8e%ce%b4%ce%b7%cf%82-%ce%b1%cf%80%ce%bf%ce%ba%ce%ae%cf%81%cf%85%ce%be%ce%b7-%cf%84%cf%89%ce%bd-%ce%ba%ce%b1%ce%bb%cf%8e%ce%bd-%cf%84%cf%81%cf%8c%cf%80%cf%89/">Αντίφωνο</a><br />
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Δεν θα έπρεπε να μας εκπλήσσει το γεγονός ότι σήμερα η αχαλίνωτη συμπεριφορά γίνεται συνήθεια ή και νόμος. Προστυχιές, χλευασμοί και βρισιές σε όλη την κλίμακα της χυδαιότητας ανταλλάσσονται με μια ευκολία που θα άφηνε άναυδο έναν άνθρωπο περασμένης εποχής, μιας εποχής από εκείνες όπου δεν είχε ακόμη διασυρθεί τόσο πολύ η λέξη «φραγμός».<br />
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Οι ρίζες του προβλήματος είναι βαθιές. Και μην πείτε ότι φταίει η κρίση, οι δυσκολίες που ενέσκηψαν και τα σμπαραλιασμένα νεύρα ολονών. Πάει πολύ πιο πίσω η υπόθεση και αν για μια στιγμή οι σκέψεις μας κατόρθωναν να βγουν έξω από τον καθημερινό κυκεώνα, θα επέτρεπαν να δούμε ότι δεν άνοιξαν από τη μια μέρα στην άλλη οι οχετοί. Είχε ήδη συντελεστεί μέσα στην οικογένεια, στα σχολεία και σ’ ολόκληρη την κοινωνία, μια θορυβώδης αποκήρυξη των «καλών τρόπων». Τους αποκάλεσαν «καθωσπρεπισμό» και νόμισαν ότι ξεμπέρδεψαν. Και δεν κατάλαβαν ότι ο τύπος ενός κανόνα όταν συγχέεται με το περιεχόμενό του παράγει εκτρώματα.<br />
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Ήταν, πράγματι, μια κάποια απελευθέρωση η απόρριψη της συμβατικότητας, της άκρας επιτήδευσης και της υποκρισίας πίσω από τις πολλές τσιριμόνιες. Μαζί μ’ αυτά όμως πήραν τα σκάγια και την έννοια της υποχρέωσης που έχουν οι άνθρωποι να μην εκσφενδονίζουν ο ένας προς τον άλλο τις επιθυμίες τους σαν να ήταν δικαιώματα που θα ’πρεπε να ικανοποιηθούν απλώς και μόνο επειδή φανερώθηκαν. Η πράξη της φανέρωσης έγινε καθεαυτή αξία. Δεν ενδιέφερε πια το τι διεκδικούσε κανείς από τους άλλους, ενδιέφερε μόνο το αν διέθετε την απροσποίητη εκείνη ορμή, την φυλακισμένη επί αιώνες, ώστε να εκδηλώσει ανοικτά το ποιος ήταν ή μάλλον, το ποιος θα ήθελε να είναι.<br />
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Η θρυαλλίδα της έκρηξης βρισκόταν εδώ. Γιατί και οι άλλοι προς την ίδια αυτο-φανέρωση θα προχωρούσαν. Οπότε, όλοι δεν θα εκόμιζαν στις σχέσεις τους παρά τις επιθυμίες τους, που, επειδή θα ήταν μεταξύ τους όμοιες και ισόβαθμες, θα ήταν αδύνατον να ικανοποιηθούν. Όταν δύο άνθρωποι δείξουν ο ένας στον άλλο ότι θέλουν αμέσως, χωρίς καμμιά αναβολή, να γίνουν σεβαστές οι απαιτήσεις τους, το πρώτο που θα συμβεί είναι να γεννηθεί αμοιβαίως η αποστροφή γι’ αυτό που ο καθένας εννοεί ως «σεβασμό».<br />
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Είναι η απέχθεια που νιώθουμε όταν η επιθυμία του άλλου εμφανίζεται γυμνή. Αυτή η γύμνια που μας ζυγώνει φέρνει μαζί της όλη την ανατριχίλα και τη φρίκη ενός πανάρχαιου παρελθόντος. Τότε που από το στόμα των προϊστορικών προγόνων μας έβγαιναν αφροί από θυμούς και πόθους, καθώς και άναρθροι ήχοι. Δεν είχε έρθει ακόμη η ώρα της γλώσσας, δεν είχαν αρχίσει τα αναγκαία παζαρέματα ανάμεσα σε εγωισμούς.<br />
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Από την Αθήνα στη σεμνότυφη βικτωριανή Αγγλία<br />
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Από τότε βέβαια έως σήμερα επινοήθηκαν πάμπολλα μέσα ώστε τα πάθη να μην αφηνιάζουν και να κρατιούνται, όσο γίνεται, υπό έλεγχο. Το τίμημα ήταν η συμπίεση της αυθορμησίας, το αίσθημα ότι οι ηδονές μας ακρωτηριάζονται. Το όφελος ήταν η εξοικονόμηση δυνάμεων που θα μπορούσαν να διατεθούν σε έργα, από τα οποία θα ερχόταν μια απόλαυση πιο λεπτή, λιγότερο έντονη, αλλά και πιο σταθερή από εκείνη που θα έδινε η αρπαγή της λείας, η κατατρόπωση των αντιπάλων, ο πόλεμος ενάντια σε ό,τι μας αντιστέκεται.<br />
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Άλλες λιγότερο και άλλες περισσότερο, οι ιστορικές εποχές χαρακτηρίστηκαν από το πώς αντιμετώπισαν αυτή την αντίθεση και πώς την έλυσαν. Πρωτόκολλα ευπρεπείας δεν υπήρχαν στην αρχαία Αθήνα. Υπήρχε, όμως, φροντίδα ώστε είτε στην Αγορά βρισκόταν ο πολίτης είτε στο συμπόσιο να φέρεται έτσι όπως όριζαν το «έθος» και οι παραδεδεγμένες αρχές της κοσμιότητας. Αργότερα, κατά την Αναγέννηση, στις αυλές της Φλωρεντίας, του Ουρμπίνο, της Φερράρα, της Μόνταινα, οι νέοι διδάσκονταν το πώς «ένας κύριος δεν πρέπει να προσβάλλει κανέναν, ούτε να τον κάνει να αισθάνεται κατώτερος με την επίδειξη της δικής του υπεροχής», σύμφωνα με το εγχειρίδιο του Καστιλιόνε «ο Αυλικός» που διαβαζόταν σαν ευαγγέλιο.<br />
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Ύστερα, η κυρίαρχη στάση άλλαξε, επεκράτησε ο θεατρινίστικος φορμαλισμός του περιβάλλοντος του Λουδοβίκου 14ου. Τέλος, η εξέλιξη έφερε τις ακαμψίες και τη σεμνοτυφία της βικτωριανής αντίληψης. Ήταν αυτή, κυρίως, η αντίληψη ενάντια στην οποία ξεσηκώθηκαν οι νεώτερες γενιές, γαλουχημένες με τις φιλολαϊκές ιδέες της φυσικότητας και της απλότητας.<br />
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Σε κάθε περίοδο πάντως, υπήρχε μεν καταπίεση, αλλά και η κερδισμένη σε αντάλλαγμα ικανότητα να πηγαίνει κανείς αντίθετα σε ορισμένες ροπές του – λογιζόταν ως επίτευγμα αυτό. Μόνο στη δική μας εποχή θεωρήθηκε κατόρθωμα η περιφρόνηση των περιορισμών εν γένει. Ας μη διαμαρτυρόμαστε, λοιπόν, που σπάζουν κλειδαριές και ανοίγουν με πάταγο κάποιες θύρες γερά ασφαλισμένες άλλοτε. Οι νέοι Κένταυροι είναι εδώ. Ελεύθεροι να λοιδορούν, να χειροδικούν, να τα κάνουν γυαλιά-καρφιά, άμα τους καπνίσει, και να σαρκάζουν με το σοκ που προκαλούν.<br />
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Οι μανιασμένοι τους πρόγονοι, σύμφωνα με τον μύθο, αναχαιτίσθηκαν με την βοήθεια του Θησέα, σ’ εκείνο το γαμήλιο τραπέζι των Λαπιθών όπου είχαν προσκληθεί. Σήμερα, ελλείψει τέτοιων ευγενών αρωγών, οι φιλήσυχοι Λάπιθες πρέπει μόνοι τους να τα βγάλουν πέρα. Και μόνοι τους, επίσης, να εντοπίσουν στις τάξεις τους αυτούς που θαυμάζουν κρυφά την πυγμή των Κενταύρων.<br />
<br />CChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com0tag:blogger.com,1999:blog-1925128431767518441.post-50513254668391438852020-03-01T12:46:00.000+02:002020-03-01T12:46:09.491+02:00Powerful antibiotics discovered using AIJo Marchant<br />
5-6 minutes<br />
<a href="https://www.nature.com/articles/d41586-020-00018-3">https://www.nature.com/articles/d41586-020-00018-3</a><br />
Coloured scanning electron micrograph (SEM) of Escherichia coli bacteria (green) taken from the small intestine of a child.<br />
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Escherichia coli bacteria, coloured green, in a scanning electron micrograph.Credit: Stephanie Schuller/SPL<br />
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A pioneering machine-learning approach has identified powerful new types of antibiotic from a pool of more than 100 million molecules — including one that works against a wide range of bacteria, including tuberculosis and strains considered untreatable.<br />
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The researchers say the antibiotic, called halicin, is the first discovered with artificial intelligence (AI). Although AI has been used to aid parts of the antibiotic-discovery process before, they say that this is the first time it has identified completely new kinds of antibiotic from scratch, without using any previous human assumptions. The work, led by synthetic biologist Jim Collins at the Massachusetts Institute of Technology in Cambridge, is published in Cell1.<br />
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The study is remarkable, says Jacob Durrant, a computational biologist at the University of Pittsburgh, Pennsylvania. The team didn’t just identify candidates, but also validated promising molecules in animal tests, he says. What’s more, the approach could also be applied to other types of drug, such as those used to treat cancer or neurodegenerative diseases, says Durrant.<br />
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Bacterial resistance to antibiotics is rising dramatically worldwide, and researchers predict that unless new drugs are developed urgently, resistant infections could kill ten million people per year by 2050. But over the past few decades, the discovery and regulatory approval of new antibiotics has slowed. “People keep finding the same molecules over and over,” says Collins. “We need novel chemistries with novel mechanisms of action.”<br />
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Forget your assumptions<br />
Collins and his team developed a neural network — an AI algorithm inspired by the brain’s architecture — that learns the properties of molecules atom by atom.<br />
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The researchers trained its neural network to spot molecules that inhibit the growth of the bacterium Escherichia coli, using a collection of 2,335 molecules for which the antibacterial activity was known. This includes a library of about 300 approved antibiotics, as well as 800 natural products from plant, animal and microbial sources.<br />
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The algorithm learns to predict molecular function without any assumptions about how drugs work and without chemical groups being labelled, says Regina Barzilay, an AI researcher at MIT and a co-author of the study. “As a result, the model can learn new patterns unknown to human experts.”<br />
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Once the model was trained, the researchers used it to screen a library called the Drug Repurposing Hub, which contains around 6,000 molecules under investigation for human diseases. They asked it to predict which would be effective against E. coli, and to show them only molecules that look different from conventional antibiotics.<br />
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From the resulting hits, the researchers selected about 100 candidates for physical testing. One of these — a molecule being investigated as a diabetes treatment — turned out to be a potent antibiotic, which they called halicin after HAL, the intelligent computer in the film 2001: A Space Odyssey. In tests in mice, this molecule was active against a wide spectrum of pathogens, including a strain of Clostridioides difficile and one of Acinetobacter baumannii that is ‘pan-resistant’ and against which new antibiotics are urgently required.<br />
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Proton block<br />
Antibiotics work through a range of mechanisms, such as blocking the enzymes involved in cell-wall biosynthesis, DNA repair or protein synthesis. But halicin’s mechanism is unconventional: it disrupts the flow of protons across a cell membrane. In initial animal tests, it also seemed to have low toxicity and be robust against resistance. In experiments, resistance to other antibiotic compounds typically arises within a day or two, says Collins. “But even after 30 days of such testing we didn’t see any resistance against halicin.”<br />
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The team then screened more than 107 million molecular structures in a database called ZINC15. From a shortlist of 23, physical tests identified 8 with antibacterial activity. Two of these had potent activity against a broad range of pathogens, and could overcome even antibiotic-resistant strains of E. coli.<br />
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The study is “a great example of the growing body of work using computational methods to discover and predict properties of potential drugs”, says Bob Murphy, a computational biologist at Carnegie Mellon University in Pittsburgh. He notes that AI methods have previously been developed to mine huge databases of genes and metabolites to identify molecule types that could include new antibiotics2,3.<br />
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But Collins and his team say that their approach is different — rather than search for specific structures or molecular classes, they’re training their network to look for molecules with a particular activity. The team is now hoping to partner with an outside group or company to get halicin into clinical trials. It also wants to broaden the approach to find more new antibiotics, and design molecules from scratch. Barzilay says their latest work is a proof of concept. “This study puts it all together and demonstrates what it can do.”CChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com0tag:blogger.com,1999:blog-1925128431767518441.post-91665957146994936612019-12-18T09:33:00.001+02:002019-12-18T09:33:41.352+02:00Who are the biggest bullshitters?www.economist.com /graphic-detail/2019/04/30/who-are-the-biggest-bullshitters<br />
Apr 30th 2019<br />
3-3 minutes<br />
North Americans seem most prone to shameless bluffing<br />
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EVERYBODY TELLS the occasional fib. But whereas liars consciously conceal the truth, reckons Harry Frankfurt, a philosopher, bullshitters are shameless: they say what they want to, without even considering the truth. Bluffers seem to be everywhere: the share of Americans who believe that most people can be trusted has fallen from 48% in 1984 to just 31% today.<br />
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A new study of the phenomenon has found that North America is especially prone to speaking bull. John Jerrim, Phil Parker and Nikki Shure, three academics, have used an educational survey of 40,000 teenage students in nine English-speaking countries to find out who is most likely to spout nonsense. They inserted a section into the questionnaire which asked students how well they understood a collection of 16 mathematical concepts. Some were familiar, such as “polygon” and “probability”, but three were fake: “proper number”, “subjunctive scaling” and “declarative fraction”.<br />
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The results show substantial differences between countries. Canadian and American teenagers were especially likely to profess knowledge of these bogus topics, whereas the Scots and Irish were perfectly happy to admit their ignorance. In news that will shock nobody, in every country men claimed to be experts more often than women. The rich were more boastful than the poor. More surprising was the finding that immigrants were generally more likely to bluff about maths than native students were.<br />
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What explains these differences? The academics doubt that the bullshitters were simply trying to impress the questionnaire’s markers. The students who bluffed about maths were just as likely as the non-bluffers to admit that they had skipped school recently, for example. A more likely answer is that the blaggers over-estimated their own knowledge. They also tended to rate themselves highly when it came to gauging their own popularity, perseverance on academic tasks and problem-solving ability. The data suggest that they might not be consciously lying, but instead be weaving their own fantasies.<br />
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<br />CChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com0tag:blogger.com,1999:blog-1925128431767518441.post-26001852515608475462019-12-13T11:35:00.000+02:002019-12-13T11:35:33.409+02:00Victory for Boris Johnson’s all-new Torieswww.economist.com /leaders/2019/12/13/victory-for-boris-johnsons-all-new-tories<br />
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print-edition iconPrint edition | LeadersDec 13th 2019<br />
7-8 minutes<br />
BRITAIN’S ELECTION on December 12th was the most unpredictable in years—yet in the end the result was crushingly one-sided. As we went to press the next morning, Boris Johnson’s Conservative Party was heading for a majority of well over 70, the largest Tory margin since the days of Margaret Thatcher. Labour, meanwhile, was expecting its worst result since the 1930s. Mr Johnson, who diced with the possibility of being one of Britain’s shortest-serving prime ministers, is now all-powerful.<br />
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The immediate consequence is that, for the first time since the referendum of 2016, it is clear that Britain will leave the European Union. By the end of January it will be out—though Brexit will still be far from “done”, as Mr Johnson promises. But the Tories’ triumph also shows something else: that a profound realignment in British politics has taken place. Mr Johnson’s victory saw the Conservatives taking territory that Labour had held for nearly a century. The party of the rich buried Labour under the votes of working-class northerners and Midlanders.<br />
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After a decade of governments struggling with weak or non-existent majorities, Britain now has a prime minister with immense personal authority and a free rein in Parliament. Like Thatcher and Tony Blair, who also enjoyed large majorities, Mr Johnson has the chance to set Britain on a new course—but only if his government can also grapple with some truly daunting tasks.<br />
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A cold coming they had of it<br />
On a rainswept night the Conservatives marched into constituencies long seen as Labour strongholds (see Britain section). Blyth Valley, an ex-mining community in the north-east where Tories have for generations been the enemy, fell before midnight. Wrexham, Labour turf for more than 80 years, declared for the Conservatives at 2am. Great Grimsby, a struggling northern port held by Labour since the second world war, was taken soon after. By dawn it was clear that the “red wall” of Labour constituencies, which stretched unbroken from north Wales to Yorkshire, had been demolished.<br />
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Mr Johnson was lucky in his opponent. Jeremy Corbyn, Labour’s leader, was shunned by voters, who doubted his promises on the economy, rejected his embrace of dictators and terrorists and were unconvinced by his claims to reject anti-Semitism.<br />
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But the result also vindicates Mr Johnson’s high-risk strategy of targeting working-class Brexit voters. Some of them switched to the Tories, others to the Brexit Party, but the effect was the same: to deprive Labour of its majority in dozens of seats.<br />
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Five years ago, under David Cameron, the Conservative Party was a broadly liberal outfit, preaching free markets as it embraced gay marriage and environmentalism. Mr Johnson has yanked it to the left on economics, promising public spending and state aid for struggling industries, and to the right on culture, calling for longer prison sentences and complaining that European migrants “treat the UK as though it’s basically part of their own country.” Some liberal Tories hate the Trumpification of their party (the Conservative vote went down in some wealthy southern seats). But the election showed that they were far outnumbered by blue-collar defections from Labour farther north.<br />
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This realignment may well last. The Tories’ new prospectus is calculated to take advantage of a long-term shift in voters’ behaviour which predates the Brexit referendum. Over several decades, economic attitudes have been replaced by cultural ones as the main predictor of party affiliation. Even at the last election, in 2017, working-class voters were almost as likely as professional ones to back the Tories. Mr Johnson rode a wave that was already washing over Britain. Donald Trump has shown how conservative positions on cultural matters can hold together a coalition of rich and poor voters. And Mr Johnson has an extra advantage in that his is unlikely to face strong opposition soon. Labour looks certain to be in the doldrums for a long time (see Bagehot). The Liberal Democrats had a dreadful night in which their leader, Jo Swinson, lost her seat.<br />
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Yet the Tories’ mighty new coalition is sure to come under strain. With its mix of blue collars and red trousers, the new party is ideologically incoherent. The northern votes are merely on loan. To keep them Mr Johnson will have to give people what they want—which means infrastructure, spending on health and welfare, and a tight immigration policy. By contrast, the Tories’ old supporters in the south believe that leaving the EU will unshackle Britain and usher in an era of freewheeling globalism. Mr Johnson will doubtless try to paper over the differences. However, whereas Mr Trump’s new coalition in America has been helped along by a roaring economy, post-Brexit Britain is likely to stall.<br />
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Any vulnerabilities in the Tories’ new coalition will be ruthlessly found out by the trials ahead. Brexit will formally happen next month, to much fanfare. Yet the difficult bit, negotiating the future relationship with Europe, lies ahead. The hardest arguments, about whether to forgo market access for the ability to deregulate, have not begun. Mr Johnson will either have to face down his own Brexit ultras or hammer the economy with a minimal EU deal.<br />
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As he negotiates the exit from one union he will face a crisis in another. The Scottish National Party won a landslide this week, taking seats from the Tories, and expects to do well in Scottish elections in 2021. After Brexit, which Scots voted strongly against, the case for an independence referendum will be powerful. Yet Mr Johnson says he will not allow one. Likewise in Northern Ireland, neither unionists nor republicans can abide the prime minister’s Brexit plans. All this will add fuel to a fight over whether powers returning from Brussels reside in Westminster or Belfast, Cardiff and Edinburgh. The judiciary is likely to have to step in—and face a hostile prime minister whose manifesto promises that the courts will not be used “to conduct politics by another means or to create needless delays”.<br />
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Led all that way for birth or death?<br />
There is no doubting the strength of Mr Johnson’s position. He has established his personal authority by running a campaign that beat most expectations. His party has been purged of rebels, and their places taken by a new intake that owes its loyalty to him personally. Having lost control of Parliament for years, Downing Street is once more in charge.<br />
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Mr Johnson will be jubilant about the scale of his victory, and understandably so. But he should remember that the Labour Party’s red wall has only lent him its vote. The political realignment he has pulled off is still far from secure.<br />
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Dig deeper:<br />
Our latest coverage of Britain’s electionCChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com0tag:blogger.com,1999:blog-1925128431767518441.post-92019614870515844242019-10-18T14:26:00.000+03:002019-10-18T14:26:17.297+03:00Terror Attacks in France: A Culture of Denialwww.gatestoneinstitute.org /15019/france-terrorism-denial<br />
Alain Destexhe<br />
7-9 minutes<br />
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Pictured: Police block a bridge near Paris Police headquarters, after a terrorist murdered four police employees on October 3, 2019 in Paris, France. (Photo by Marc Piasecki/Getty Images)<br />
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On October 3, 2019, a knife-wielding Muslim employee of the Paris Police Department Intelligence Directorate stabbed to death four other employees at police headquarters in the center of Paris, before a trainee police officer shot and killed him. While it was not the deadliest terror attack France has experienced in recent years, the fatal stabbings that took place at the Paris police headquarters were perhaps the most worrisome. Its author (a French public servant employed by the police), its highly sensitive target, and the catastrophic handling of the aftermath of the attack reveal the failure of the French institutions.<br />
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As it was the case for all recent terror attacks, French media and authorities first tried to downplay what happened. The attacker was initially described through potentially mitigating factors, such as his handicap (the killer is partly deaf and mute). It took 24 hours before it was eventually revealed that he was an Islamist militant who had carefully planned his attack.<br />
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That a radicalized militant had been able to remain undetected in a critical security institution for years sent shockwaves throughout the country. Members of the parliamentary opposition asked for the resignation of Home Affairs Minister Christophe Castaner, who at first had said that the attacker "had never shown any warning signs or behavioral difficulties."<br />
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For the record, this "very normal behavior" included cutting down to a bare minimum communication with women (he had for months being avoiding all women but his wife), attending a notoriously radical mosque, and having a phone full of Islamist contacts. His colleagues reported that already in January 2015, he had cheered the murderous Islamist terror attack on the satirical magazine Charlie Hebdo in front of other police employees. In many countries, a mistake of this scale would be enough for a government minister to resign, but not in France.<br />
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The whole picture of the attack, which is still not clear, demonstrates an incredible failure of internal control inside the French police. The French Parliament is now asking how the murderer managed to fly under the radar when everything in his behavior clearly signaled an increasing radicalization.<br />
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Notably, this is the first time that the French state and its institutions were directly targeted. Also for the first time, the victims were not journalists (as was the case for the Charlie Hebdo attacks in January 2015), Jews (who have been targeted several times in recent years) or civilians (such as the massive coordinated attacks in Paris on November 2015 that caused more than 131 deaths and 413 wounded).<br />
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This latest attack also demonstrates how inadequately prepared France is to tackle the problem. The killer was not just any civil servant: his security clearance allowed him to have access to sensitive files such as the personal details of police officers and individuals monitored by the department, including several individuals suspected of terrorism.<br />
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After Charlie Hebdo, the Bataclan in Paris, the truck-ramming massacre in Nice, and countless other attacks, French institutions have failed repeatedly. However, instead of recognizing this failure and assuming a share of responsibility, instead of hitting the problem of religious radicalization head-on, French President Emmanuel Macron regularly describes it as a "societal problem," that "institutions alone will not be able to solve." It is necessary first to recognize and name a problem in order to tackle it. In the current state of affairs, French political institutions are a long way from success in the fight against terrorism.<br />
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Beyond the political sphere, there is also a culture a denial of the Islamist threat in the French media. Journalists, academics and politicians, with a few exceptions, have consistently played down not only the risk of terrorist attacks but also the threat of growing Salafist radicalization in the country. A growing number of Muslims, while not advocating the use of violence, desire to live under Sharia law, separate from the rest of French society.<br />
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According to a study by the Montaigne Institute, 29% of Muslims in France believe that Sharia law is more important than French law. This means that almost one-third of French Muslims live according to values that are fundamentally incompatible with French or Western standards.<br />
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Although France is the European country most targeted by Islamists (263 killed since 2012), politicians are paralyzed by the fear of being accused by the mainstream media of discrimination against Muslims, of creating an amalgam between terrorists and Muslims or of "fueling tensions." Senior figures acknowledge a major problem only when they are no longer in charge. In a book published after he stepped down, the socialist former president François Hollande wrote:<br />
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"Islam? Yes, there is indeed a problem with Islam. Nobody doubts this. The Islamic veil is a form of enslavement. We cannot continue welcoming migrants without any form of control in the context of increased terror attacks."<br />
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Hollande never would have said such a thing when he was president. Like others, he sheepishly ignored the problem.<br />
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The same happened with Christophe Castaner's predecessor, Gerard Collomb, after he resigned as Interior Minister. He warned against no less than the risk of civil war in France.<br />
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"In some suburbs (...) it is the rule of the strongest, of drug dealers and radical Islamists that prevails instead of the laws of the Republic... Today we live side by side, next to each other, but tomorrow, I fear that we might end up facing each other."<br />
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It is important to note that theses quotes are not from right-wing thinkers or activists. Both François Hollande and Gerard Collomb were long-time eminent figures of the Socialist Party.<br />
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These are typical examples of what some call "la démission des élites" (the abdication of the elites): refusing to act on a situation of which they are perfectly aware but afraid to mention because of the dominant ideology of political correctness.<br />
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In the meantime, France's police officers are increasingly unmotivated and demoralized. Since the start of the year, more than 50 police officers have committed suicide. They face increasingly difficult working conditions, in particular, rioters in the suburbs of cities like Paris, Marseille, Lille or Lyon -- suburbs that are progressively escaping the control of the French authorities.<br />
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Attack after attack, the ritual is the same. There are flowers, tributes and words for the victims, political leaders affirm their resolve to act to protect the people. But after a few days, the news cycle ends and things go back to normal -- until the next terrorist attack.<br />
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Alain Destexhe, an honorary Senator in Belgium, was awarded the Prize of Liberty by Nova Civitas in 2006.<br />
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© 2019 Gatestone Institute. All rights reserved. The articles printed here do not necessarily reflect the views of the Editors or of Gatestone Institute. No part of the Gatestone website or any of its contents may be reproduced, copied or modified, without the prior written consent of Gatestone Institute.CChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com0tag:blogger.com,1999:blog-1925128431767518441.post-17394608405696618532019-10-18T13:58:00.002+03:002019-10-18T13:58:50.107+03:00Donald Trump’s betrayal of the Kurds is a blow to America’s credibilitywww.economist.com /leaders/2019/10/17/donald-trumps-betrayal-of-the-kurds-is-a-blow-to-americas-credibility<br />
print-edition iconPrint edition | LeadersOct 17th 2019<br />
6-8 minutes<br />
THE PITHIEST summary of Donald Trump’s foreign policy comes from the president himself. Referring to the mayhem he has uncorked in Syria, he tweeted: “I hope they all do great, we are 7,000 miles away!” Mr Trump imagines he can abandon an ally in a dangerous region without serious consequences for the United States. He is wrong. The betrayal of the Kurds will lead friends and foes to doubt Mr Trump’s America. That is something both Americans and the world should lament.<br />
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His decision to pull out 1,000 American troops has rapidly destroyed the fragile truce in northern Syria (see article). The withdrawal created space for a Turkish assault on the Kurds that has so far cost hundreds of lives; at least 160,000 people have fled their homes. Hordes of Islamic State (IS) backers, once guarded by the Kurds, have escaped from internment camps. With nowhere else to turn, the Kurds have sought help from Bashar al-Assad, Syria’s blood-drenched despot, an enemy of America.<br />
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Mr Trump campaigned on bringing troops home. He has argued that America must rid itself of “endless wars”. When he says Russia, Iran and Turkey can deal with the mess in Syria, many of his voters will agree. After almost two decades at war, they have tired of America acting as the world’s policeman. Some Democrats would like to pull troops out of the Middle East, too, including Elizabeth Warren, a leading contender to replace Mr Trump.<br />
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However understandable the frustration, the thoughtless abandonment of the region would be self-defeating. It undermines America’s credibility around the world, which means that the United States will have to work harder and spend more to get its way on issues that are vital to its people’s prosperity and their way of life.<br />
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Mr Trump’s exit from Syria fails the trust test on many levels. One is seriousness. The president seemingly neglected the briefing papers warning of the dire consequences of a power vacuum created by withdrawing the 1,000-strong tripwire force. The abruptness of the decision took nearly everyone by surprise, including his own officials. The Kurds were startled and appalled. British troops woke up to discover that their American brothers-in-arms were packing up. No one had time to prepare.<br />
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The policy also fails on loyalty. Kurdish troops in Syria fought beside American special forces and air power to crush IS’s “caliphate”. Some 11,000 Kurdish fighters lost their lives; five Americans also perished. The superpower had fused its matchless intelligence-gathering with a local ally to drive out the world’s worst terrorists at a relatively modest cost in blood and treasure.<br />
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Worst of all, the policy fails on strategy. Not just because of the potential revival of IS and the fillip to Mr Assad. But also because Iran, a bitter foe of America and ally of Mr Assad, will benefit from America’s withdrawal. Russians, too, are taking gleeful selfies in abandoned American bases. Vladimir Putin, Mr Assad’s backer, is claiming America’s mantle as the guarantor of order in the Middle East, a role the Soviet Union lost in the 1970s. In order to extract from Syria a small force that was sustaining few casualties, America has needlessly unleashed a new cross-border conflict, empowered its enemies and betrayed its friends.<br />
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Alas, shallowness and impulsiveness have become the hallmarks of Mr Trump’s foreign policy. After Iran attacked an American drone, he blocked retaliation at the last minute; after Iran or its proxies attacked Saudi oil facilities last month, he stood back. As if superpower diplomacy was an extension of domestic politics, governed by the same hyperbole and showmanship, he has ditched painstakingly negotiated treaties, noisily launched trade wars and, in places such as Venezuela and North Korea, promised transformations that never seem to bear fruit. Mr Trump takes momentous decisions on a whim, without pondering the likely fallout or devising a coherent strategy to contain it.<br />
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Mr Trump seems to think that he can use America’s titanic commercial clout as a substitute for hard power. Economic sanctions have become his answer to every problem—including that of Turkey’s invasion. Yet when vital interests are at stake, states rarely seem to give ground. Just as Russia still occupies Crimea, Nicolás Maduro runs Venezuela and Kim Jong Un has his nukes, so Turkey has vowed to fight on in Syria. As China’s economy develops, sanctions may also be a wasting asset. Even today, pressed by America to cut ties with Huawei, a Chinese telecoms giant, many countries are reluctant to comply.<br />
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The Syrian debacle shows how all this could harm America. In Europe even before the assault, Turkey was at loggerheads with NATO over its purchase of Russian air-defence missiles. Because the invasion has led to sanctions and arms embargoes against Turkey, the cracks in NATO will only deepen. Mr Putin may be tempted to test America’s commitment to defending the Baltic states, tiny NATO allies on Russia’s border. In Asia the Taliban will redouble their efforts, reasoning that if Mr Trump can dump the Kurds, he can dump Afghanistan, too. China will take note, bide its time and steadily press its territorial claims against its neighbours. Taiwan, an admirable democracy, has just got a little less secure. Around the world, America’s allies—of which it still has more than any nation in history—will have more reason to arm themselves, possibly fuelling regional arms races. Will South Korea or Saudi Arabia, fearful of being abandoned, be tempted to acquire nuclear weapons to guard themselves from North Korea or Iran?<br />
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Taken together, these concerns represent the unravelling of the order that America worked hard to build and sustain in the decades since the second world war, and from which it benefits in countless ways. If it pulled back it would still have to invest in arms and soldiers to protect its people and firms—and without so much support from allies. More important, distrust, once earned, could not be confined to military affairs. Other countries would be less keen to strike long-term trade deals with America. They would hesitate to join in countering Chinese industrial espionage or rule-breaking that harms the United States. Most important, America would undermine its own values. Human rights, democracy, dependability and fair dealing, however patchily honoured, are America’s most powerful weapon. If China and Russia had their way, might would be right. For the West, that would be a profoundly hostile world. ■CChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com0tag:blogger.com,1999:blog-1925128431767518441.post-16931014764861961772019-10-18T13:54:00.000+03:002019-11-27T16:09:34.893+02:00Η απόσυρση των ΗΠΑ από την Συρία.<br />
Ας δούμε την Συρία σαν το σύμβολο της παρουσίας των ΗΠΑ στην Μέση Ανατολή. Η στενή στρατιωτική σχέση τους με τους Κούρδους, οι κοινοί τους αγώνες, η εύκολη νίκης τους επί του χαλιφάτου, χάρις στους περισσότερους των 11.000 νεκρών Κούρδων, με μόλις επτά αμερικάνους νεκρούς, τι λένε στους υπόλοιπους συμμάχους των ΗΠΑ; Σε όσους δεν έχουν τέτοιες θυσίες πρόσφατα; Μήπως ότι πολέμησαν στην Νορμανδία; Και γιατί η θυσία στην Νορμανδία είναι πιο σημαντική από έναν πόλεμο σε κάποια πόλη της Β Συρίας;<br />
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<b>Η Μέση Ανατολή δεν έχει την σημασία που είχε.</b></div>
Ούτε είναι ούτε δεν είναι σημαντική. Κατά την γνώμη μου η απάντηση δεν είναι στην αριθμητική των θυσιών ή των μαχών. Είναι στην μεταβολή των συσχετισμών δύναμης σε όλο τον κόσμο, που κάνει την παρουσία των ΗΠΑ να φαίνεται υπέρβαρη σε κάποια μέρη που πριν ήταν αναγκαία. Η Μέση Ανατολή είναι πολύ σημαντικός προμηθευτής πετρελαίου για όλο τον κόσμο αλλά όχι πια για τις ΗΠΑ. Οι ΗΠΑ είναι καθαρός εξαγωγέας και δεν έχει ανάγκη από την παραγωγή του ΟΠΕΚ. Μπορεί εύκολα να ελέγξει την παραγωγή στις ακτές της Καραϊβικής και με τα κοιτάσματα φυσικού αερίου και πετρελαίου που ανακαλύπτονται και σύντομα θα εξορύσσονται, η βαρύτητα αυτού του πόρου είναι φθίνουσα. Μπορώ να προσθέσω την πολιτική για τις ανανεώσιμες πηγές ενέργειας αλλά απλώς ενισχύει την εικόνα που περιγράφω. Έτσι οι αντικειμενικές συνθήκες έχουν μειώσει την ισχύ που δικαιολογείται για τον έλεγχο αυτής της περιοχής. Η παρουσία στην Σαουδική Αραβία είναι αρκετή και το Ισραήλ είναι πάντα εκεί με τα πυρηνικά του και την συμβατική του δύναμη να ρίχνει την σκιά της. Το Ιράν θα βρει απέναντί του την Ρωσία και έτσι ούτε η μία χώρα ούτε η άλλη δεν θα κυριαρχήσει. Η δύναμη της μιας θα περιορίζει την εγγύτητα της άλλης. Που εντάσσεται σε όλο αυτό το σκηνικό η Τουρκία; Κανείς δεν θέλει την Τουρκία. Όποτε η Τουρκία θα προσπαθεί να διεκδικήσει, οι υπόλοιπες δυνάμεις με τον ένα ή με τον άλλο τρόπο θα αναδιατάσσονται για να την αποτρέψουν.<br />
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<b>Η Μέση Ανατολή ούτως ή άλλως θα έχανε την σημασία που είχε.</b></div>
Η σημασία των ενεργειακών πόρων εξαρτάται από την διάταξη των οικονομικών δυνάμεων. Η διάταξη αυτή έχει αλλάξει. Η Κίνα είναι πια η δεύτερη οικονομία στον κόσμο, σε ΑΕΠ, με ρυθμούς ανάπτυξης μεγαλύτερους από οποιαδήποτε χώρα της Δύσης. Αυτή η ανάπτυξη είναι οικονομική αλλά και στρατιωτική. Οι ανάγκες την Κίνας είναι ευρύτερες των εθνικών της ορίων, τα συμφέροντά της επεκτείνονται προς τα έξω εκεί που μέχρι σήμερα οι ΗΠΑ ήταν η μοναδική και αδιαμφισβήτητη δύναμη. Αυτή η αμφισβήτηση πρέπει να απαντηθεί από τις ΗΠΑ και το μέγεθος της Κίνας εξισορροπείται με πόρους των ΗΠΑ που δεν είναι ανεξάντλητοι. Στρατιώτες, βάσεις, πολεμοφόδια, πληροφορίες, τεχνολογία, αναδιατάσσονται για να αντιμετωπίσουν την ανερχόμενη δύναμη. Όσο νωρίτερα τόσο καλύτερα. Οι Κούρδοι σε αυτή την προσπάθεια είναι χαμηλά στην λίστα.<br />
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<b>Η απόσυρση των ΗΠΑ θα είναι ένα μήνυμα για τους υπόλοιπους συμμάχους της.</b></div>
Η απόσυρση των ΗΠΑ είναι κατά μια άποψη ένας πολύ κακός οιωνός για τους συμμάχους των ΗΠΑ. Για την Λιθουανία, την Εσθονία, την Ουκρανία, την Ταϊβάν, το Αφγανιστάν κ.ο.κ. Μπορεί να τους εγκαταλείψει και αυτούς. Μπορεί; Φυσικά και μπορεί. Πάντα μπορούσε. Έμενε συνεπής με όσους ήταν μέρος των τακτικών και στρατηγικών της, και αποχωρούσε από όπου η παρουσία της δεν δικαιολογούσε τα ωφέλη. Ότι έκαναν πάντα οι μεγάλες δυνάμεις και οι μικρές δυνάμεις το ίδιο. Ο μεγάλος αντίπαλος σήμερα είναι η Κίνα. Όσοι είναι απέναντι στην Κίνα ξέρουν ότι η μετατόπιση του βάρους των ΗΠΑ προς την Ασία μόνο αυτούς ευννοεί. Οι υπόλοιποι ξέρουν ότι έχουν ξεμείνει να φυλάνε ξεχασμένες σκοπιές. Σύντομα η δυναμική που έχει δημιουργήσει η άνοδος της Κίνας θα διαμορφώσει νέα δεδομένα. Είναι έξω από την θέληση του προέδρου των ΗΠΑ να τα αλλάξει. Είναι στην εξουσία του να τα εντάξει σε όφελος των συμφερόντων της χώρας του.<br />
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CChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com0tag:blogger.com,1999:blog-1925128431767518441.post-69772853376226640552019-10-11T13:51:00.001+03:002019-10-11T13:51:52.190+03:00ΛΑΡΚΟ: Μπορεί το κοβάλτιο να σώσει το νικέλιο;<i>Παρατήρηση: Μια εταιρεία που χρωστάει κοντά στο μισό δις, δεν μπορεί να πληρώσει το ηλεκτρικό, είναι δυνατόν να δίνει 45 εκ για 1100 εργαζόμενους; ~41 χιλιάδες τον χρόνο ή 3.400 Ευρώ τον μήνα, για κάθε εργαζόμενο κατά μέσο όρο; Με το περιβαλλοντικό κόστος που έχει, την ανύπαρκτη φροντίδα για την προστασία του περιβάλλοντος και τα πρόστιμα που επιβάλλονται εξ αυτού; Ενδεικτική για την κατάσταση που μας οδήγησε στην χρεοκοπία. </i><br />
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<b><i>...</i>Η ΛΑΡΚΟ επιβιώνει λόγω ΔΕΗ, αφού το απλήρωτο ρεύμα έχει φτάσει στα 310 εκατ. ευρώ και συνεχίζει να αυξάνεται με ρυθμό 20-30 εκατ. ανά έτος. Δεν γνωρίζουμε πόσες προβλέψεις έχει εγγράψει η ΔΕΗ, αλλά η βιωσιμότητα της ΔΕΗ περνά και από τη ΛΑΡΚΟ. Η εταιρεία επίσης χρωστά 20 εκατ. περίπου στον Οργανισμό Ανασυγκρότησης Επιχειρήσεων (ουσιαστικά δηλαδή στο Δημόσιο), 30 εκατ. στην Τράπεζα Πειραιώς και 70 εκατ. στην παλαιά ΛΑΡΚΟ, για τα οποία έχει γίνει και η κατάσχεση...</b><br />
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<b>...Τα βασικά κόστη της ΛΑΡΚΟ είναι το ηλεκτρικό ρεύμα και το εργασιακό, που είναι επίσης μη ανταγωνιστικό (ετήσια δαπάνη 45 εκατ. ευρώ για 1.100 εργαζομένους)...</b><br />
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www.kathimerini.gr /1045887/article/oikonomia/epixeirhseis/larko-mporei-to-kovaltio-na-swsei-to-nikelio<br />
ΑΝΔΡΕΑΣ ΚΟΥΤΡΑΣ* | Kathimerini<br />
7-9 minutes<br />
Το κύριο προϊόν της ΛΑΡΚΟ είναι το σιδηρονικέλιο. Ομως μέσα στο κράμα σιδηρονικελίου υπάρχει και ποσότητα κοβαλτίου που πάει χαμένη. Η ΛΑΡΚΟ δεν έχει τη δυνατότητα να ξεχωρίσει το κοβάλτιο από το νικέλιο. Αυτό σημαίνει ότι με τις σημερινές τιμές χάνει 70-100 εκατ. δολάρια ετησίως από τη μη εξαγωγή του κοβαλτίου.<br />
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Η ΛΑΡΚΟ κλείνει φέτος 56 χρόνια πολυτάραχης ζωής. Ομως η κατάσταση στην οποία έχει περιέλθει η εταιρεία δεν αφήνει πολλά περιθώρια για εορτασμούς. Ο κίνδυνος να κλείσει το εργοστάσιο που για δεκαετίες πρόσφερε εργασία σε χιλιάδες εργαζομένους και ήταν η μεγάλη πηγή σκληρού συναλλάγματος για την Ελλάδα είναι άμεσος. Τα προβλήματα της εταιρείας είναι μεγάλα και σύνθετα – αποτέλεσμα έλλειψης οράματος, και σχεδιασμού, αλλά και κακοδιαχείρισης.<br />
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Ενα βασικό πρόβλημα είναι η πολύπλοκη ιδιοκτησιακή δομή της εταιρείας. Η νέα ΛΑΡΚΟ προήλθε από τη χρεοκοπία και αναδιάρθρωση το 1989 της παλαιάς ΛΑΡΚΟ και ανήκει κατά 55,19% στο Δημόσιο (ΤΑΙΠΕΔ), 33,36% στην Εθνική Τράπεζα και 11,45% στη ΔΕΗ. Ομως οι μέτοχοι της παλαιάς ΛΑΡΚΟ έχουν ακόμα απαιτήσεις πάνω στη νέα ΛΑΡΚΟ: έχουν εντολή κατάσχεσης του εργοστασίου και έχουν προσημειώσει όλα τα ακίνητα, γήπεδα και οικόπεδα. Αυτό σημαίνει πως αν το υπερταμείο επιχειρήσει να την πουλήσει, ουσιαστικά θα πουλήσει στοιχεία ενεργητικού που είναι υπό ιδιοκτησιακή αμφισβήτηση.<br />
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Επιπλέον, η ΛΑΡΚΟ επιβαρύνει σοβαρά το περιβάλλον με εκπομπές αιωρουμένων σωματιδίων από τα περιστροφικά καμίνια, ενώ τα ηλεκτροκάμινα παράγουν δύο εκατομμύρια τόνους σκουριά τον χρόνο τα οποία ρίχνονται στον Ευβοϊκό κόλπο. Η σκουριά αυτή αποτελείται από κυρίως αδρανή υλικά, αλλά η νομοθεσία δεν προβλέπει τη ρίψη στη θάλασσα. Από το 2014 υπάρχει ένα είδος ανακωχής με την Ε.Ε. σχετικά με τις περιβαλλοντικές παρανομίες, αφού το σχέδιο που τότε είχε υποβληθεί προέβλεπε τα βάρη αυτά να τα επωμιστεί ο τυχερός αγοραστής της ΛΑΡΚΟ. Να σημειώσουμε πως η νομοθεσία καθιστά υπευθύνους για περιβαλλοντικές παρανομίες όχι μόνο το διοικητικό συμβούλιο της εταιρείας αλλά και τους μετόχους. Για τον λόγο αυτό είναι εξαιρετικά δύσκολο να βρεθεί αγοραστής για το εργοστάσιο της Λάρυμνας.<br />
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Χρέη και πρόστιμα<br />
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Η ΛΑΡΚΟ επιβιώνει λόγω ΔΕΗ, αφού το απλήρωτο ρεύμα έχει φτάσει στα 310 εκατ. ευρώ και συνεχίζει να αυξάνεται με ρυθμό 20-30 εκατ. ανά έτος. Δεν γνωρίζουμε πόσες προβλέψεις έχει εγγράψει η ΔΕΗ, αλλά η βιωσιμότητα της ΔΕΗ περνά και από τη ΛΑΡΚΟ. Η εταιρεία επίσης χρωστά 20 εκατ. περίπου στον Οργανισμό Ανασυγκρότησης Επιχειρήσεων (ουσιαστικά δηλαδή στο Δημόσιο), 30 εκατ. στην Τράπεζα Πειραιώς και 70 εκατ. στην παλαιά ΛΑΡΚΟ, για τα οποία έχει γίνει και η κατάσχεση.<br />
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Η Ε.Ε. έχει επιβάλει πρόστιμο ύψους 136 εκατ. ευρώ για παράνομες κρατικές ενισχύσεις και η Ελλάδα έχει καταδικαστεί από το Ευρωπαϊκό Δικαστήριο γιατί δεν τις έχει ανακτήσει ακόμα. Το ενδιαφέρον είναι πως το Δημόσιο όταν τις έδινε, βεβαίωνε πως δεν παραβιάζουν την κοινοτική νομοθεσία περί κρατικών ενισχύσεων. Παρ’ όλα αυτά τιμωρήθηκε η ΛΑΡΚΟ που τα πήρε και όχι το Δημόσιο που διαβεβαίωνε γραπτώς πως δεν είναι παράνομες κρατικές ενισχύσεις.<br />
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Τα βασικά κόστη της ΛΑΡΚΟ είναι το ηλεκτρικό ρεύμα και το εργασιακό, που είναι επίσης μη ανταγωνιστικό (ετήσια δαπάνη 45 εκατ. ευρώ για 1.100 εργαζομένους). Για να ανταποκριθεί στις τρέχουσες υποχρεώσεις πρέπει, σύμφωνα με σχετικούς υπολογισμούς, το νικέλιο να έχει τιμή άνω των 18.000 δολαρίων/τόνο και να αυξήσει την παραγωγή από 12.000 τόνους περίπου στις 20.000 τόνους τον χρόνο.<br />
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Η εταιρεία παρουσιάζει επίσης τεράστιες ελλείψεις σε πολλούς οργανωτικούς τομείς που για άλλες εταιρείες αυτού το μεγέθους είναι αυτονόητες (διοικητική δομή, υπηρεσίες ανθρωπίνου δυναμικού, διαδικασίες εσωτερικού ελέγχου κ.λπ.). Το πρόσφατο θανατηφόρο ατύχημα δείχνει πως ο τομέας ασφάλειας και υγείας των εργαζομένων χρειάζεται τουλάχιστον επανεξέταση και αυστηροποίηση.<br />
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Ολα αυτά τα προβλήματα, και πλήθος άλλων που προκύπτουν καθημερινά, οδηγούν στο κλείσιμο της εταιρείας. Αυτό στην ουσία είναι το σχέδιο που εκπονήθηκε το 2014 και που σύμφωνα με δημοσιεύματα θα εφαρμοστεί από τη νέα διοίκηση της εταιρείας.<br />
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Το σχέδιο αυτό προβλέπει την ξεχωριστή πώληση των μεταλλείων και τη χρεοκοπία της ΛΑΡΚΟ και τη μετέπειτα πώληση του εργοστασίου μέσω δημοπρασιών. Επίσης υπάρχει όρος για τον υπερθεματιστή του εργοστασίου να κάνει αντιπροσφορά για τα μεταλλεία και αντιστρόφως (ρήτρα «shoot out»).<br />
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Το πλεονέκτημα του σχεδίου αυτού είναι πως υπάρχουν μεγάλες πιθανότητες να αρθεί το πρόστιμο της Ε.Ε. προς τη ΛΑΡΚΟ αν δεν υπάρχει οικονομική συνέχεια μεταξύ της ΛΑΡΚΟ και του νέου αγοραστή. Το μεγάλο μειονέκτημα είναι πως οι δανειστές της (ΔΕΗ, Τρ. Πειραιώς κ.λπ.), οι συνεργαζόμενοι προμηθευτές, εγχώριοι και αλλοδαποί, και οι εργολάβοι που απασχολούνται στα εργοτάξια της εταιρείας θα χάσουν τα χρήματά τους και θα χαθούν θέσεις εργασίας, αφού ακόμα και αν βρεθεί αγοραστής, δεν θα έχει καμία υποχρέωση να προσλάβει τους παλαιούς εργαζομένους. Ακόμα και αν υποθέσουμε πως τα ιδιοκτησιακά προβλήματα με τους μετόχους της παλαιάς ΛΑΡΚΟ θα λυθούν, ο νέος μέτοχος θα βρεθεί αντιμέτωπος με όλα τα περιβαλλοντικά προβλήματα, που απαιτούν επένδυση και χρόνο. Η λογική λέει πως ουδείς σώφρων επενδυτής θα αγόραζε ένα ρυπογόνο και μη ανταγωνιστικό εργοστάσιο.<br />
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Ομως τα μεταλλεία έχουν αξία και θα ήταν εύκολο να πουληθούν σε κάποιον που έχει ήδη εργοστάσιο, ίσως και εκτός ελληνικών συνόρων. Υπάρχουν εργοστάσια στη Βόρεια Μακεδονία αλλά και στην Τουρκία που θα ήθελαν το μετάλλευμα αλλά όχι φυσικά το εργοστάσιο της Λάρυμνας. Το πιθανότερο λοιπόν είναι να πουληθούν τα μεταλλεία και να κλείσει το εργοστάσιο.<br />
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Μία κρίσιμη πρώτη ύλη<br />
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Αν και όλα τα παραπάνω δεν αφήνουν πολλά περιθώρια αισιοδοξίας για το μέλλον της ΛΑΡΚΟ, υπάρχει ένα σχέδιο –δύσκολο μεν στην εφαρμογή– που θα μπορούσε όχι μόνο να κάνει βιώσιμη και ανταγωνιστική τη ΛΑΡΚΟ, αλλά και να εγγυηθεί τα επόμενα 100 χρόνια (τόσο περίπου αναμένεται να διαρκέσουν τα βεβαιωμένα αποθέματα νικελίου, που ανέρχονται σε 2 εκατ. τόνους). <br />
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Το κύριο προϊόν της ΛΑΡΚΟ είναι το σιδηρονικέλιο. Ομως μέσα στο κράμα σιδηρονικελίου υπάρχει και ποσότητα κοβαλτίου που πάει χαμένη. Η ΛΑΡΚΟ δεν έχει τη δυνατότητα να ξεχωρίσει το κοβάλτιο από το νικέλιο. Αυτό σημαίνει ότι με τις σημερινές τιμές χάνει 70-100 εκατ. δολάρια ετησίως από τη μη εξαγωγή του κοβαλτίου. Το κοβάλτιο έχει χαρακτηριστεί από την Ε.Ε. στρατηγική πρώτη ύλη (Critical Raw Material), αφού είναι απολύτως αναγκαίο για την παραγωγή μπαταριών.<br />
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Οι ανάγκες της Ε.Ε. σε κοβάλτιο εκτιμώνται ότι το 2025 θα είναι 53.000 τόνοι, αλλά μόνο 2.300 τόνοι παράγονται εντός της Ε.Ε. (Φινλανδία). Το υπόλοιπο το προμηθεύεται κυρίως από τη Ρωσία και το Κονγκό. Εάν η ΛΑΡΚΟ επενδύσει στην υδρομεταλλουργία θα είναι σε θέση να παράγει 2.000-3.000 τόνους κοβαλτίου τον χρόνο, καλύπτοντας συνολικά το 10% των αναγκών της Ε.Ε. μαζί με τη Φινλανδία.<br />
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Ενα πιθανό σενάριο διάσωσης της ΛΑΡΚΟ θα είχε τρία σκέλη: Πρώτον, να χαρακτηριστεί η ΛΑΡΚΟ στρατηγικής σημασίας βιομηχανία για την Ε.Ε. λόγω κοβαλτίου και να αρθεί το πρόστιμο· δεύτερον, να χρηματοδοτηθεί σε πρώτη φάση η αντιμετώπιση των περιβαλλοντικών προβλημάτων και η επένδυση στην υδρομεταλλουργία από την Ευρωπαϊκή Τράπεζα Επενδύσεων μέσω του προγράμματος Γιούνκερ· και τρίτον, να αντιμετωπιστεί το ενεργειακό κόστος με ανανεώσιμες πηγές (ανεμογεννήτριες ή ηλιακή).<br />
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Για να γίνουν φυσικά όλα αυτά χρειάζονται όραμα, ικανή διοίκηση με ενθουσιασμό και όρεξη για το εγχείρημα, σκληρή δουλειά και συντονισμός από πολλούς ενδιαφερόμενους φορείς.<br />
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Ομως, τα οφέλη αν επιτύχει θα είναι πολλά και για τη χώρα και για την Ε.Ε., αλλά και για τις τοπικές κοινωνίες και τους εργαζομένους που τόσα έχουν δώσει τον τελευταίο μισό και πλέον αιώνα.<br />
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* Ο κ. Ανδρέας Κούτρας είναι τραπεζικό στέλεχοςCChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com0tag:blogger.com,1999:blog-1925128431767518441.post-22121417936036012912019-10-08T15:57:00.000+03:002019-10-08T15:57:41.033+03:00Map of the present situation in Syria. In light of the eminent Turkish invasion.<img alt="Map" src="https://ichef.bbci.co.uk/news/624/cpsprodpb/1867C/production/_109146999_syria_control_07_10_camps_map-nc.png" /><br />
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<br />CChathttp://www.blogger.com/profile/08436307260148653055noreply@blogger.com0