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We have the technology to potentially add a 47th chromosome, to compound as it were, a new human entity. The implications are enormously consequential.

C.S. Lewis warned about our final mastery over nature, and the inevitable drift into a future world where knowledge about the old world completely vanishes, where what once was, irretrievably transforms into something else:

… We do not look at trees either as Dryads or as beautiful objects while we cut them into beams: The first man who did so may have felt the price keenly, and the bleeding trees in Virgil and Spenser may be far-off echoes of that primeval sense of impiety … The great minds know very well that the object, so treated, is an artificial abstraction, that something of its reality has been lost.[1]

Continue reading “The 47th State Panacea or Perversion” | >

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Continue reading “Make Aging History! /Bioviva Ceo Elizabeth Parrish 207. Debt Nation” | >

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Precision oncology relies on accurate discovery and interpretation of genomic variants, enabling individualized diagnosis, prognosis and therapy selection. We found that six prominent somatic cancer variant knowledgebases were highly disparate in content, structure and supporting primary literature, impeding consensus when evaluating variants and their relevance in a clinical setting. We developed a framework for harmonizing variant interpretations to produce a meta-knowledgebase of 12,856 aggregate interpretations. We demonstrated large gains in overlap between resources across variants, diseases and drugs as a result of this harmonization. We subsequently demonstrated improved matching between a patient cohort and harmonized interpretations of potential clinical significance, observing an increase from an average of 33% per individual knowledgebase to 57% in aggregate. Our analyses illuminate the need for open, interoperable sharing of variant interpretation data. We also provide a freely available web interface (search.cancervariants.org) for exploring the harmonized interpretations from these six knowledgebases.

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Mumbai: Prime Minister Narendra Modi’s call for a nine-minute blackout at 9 pm on April 5 has raised concerns for power grid managers as they are gearing up for ensuring grid stability during the period.

State-run Power System Operation Corporation (POSOCO), which is responsible for integrated operation of the grid, is working towards ensuring there is no pressure on the grid due to the possible grid collapse and resultant blackout throughout the country.

The Central Electricity Regulatory Authority (CERA) necessitates permissible range of the frequency band of 49.95−50.05 Hz for normal running of grid and if there is any discrepancy in the same with sudden increase or decrease in power flow, it might result into grid collapse.

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The Royal Society is to create a network of disease modelling groups amid academic concern about the nation’s reliance on a single group of epidemiologists at Imperial College London whose predictions have dominated government policy, including the current lockdown.

It is to bring in modelling experts from fields as diverse as banking, astrophysics and the Met Office to build new mathematical representations of how the coronavirus epidemic is likely to spread across the UK — and how the lockdown can be ended.

The first public signs of academic tensions over Imperial’s domination of the debate came when Sunetra Gupta, professor of theoretical epidemiology at Oxford University, published a paper suggesting that some of Imperial’s key assumptions could be wrong.

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Despite the huge contributions of deep learning to the field of artificial intelligence, there’s something very wrong with it: It requires huge amounts of data. This is one thing that both the pioneers and critics of deep learning agree on. In fact, deep learning didn’t emerge as the leading AI technique until a few years ago because of the limited availability of useful data and the shortage of computing power to process that data.

Reducing the data-dependency of deep learning is currently among the top priorities of AI researchers.

In his keynote speech at the AAAI conference, computer scientist Yann LeCun discussed the limits of current deep learning techniques and presented the blueprint for “self-supervised learning,” his roadmap to solve deep learning’s data problem. LeCun is one of the godfathers of deep learning and the inventor of convolutional neural networks (CNN), one of the key elements that have spurred a revolution in artificial intelligence in the past decade.

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