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July 16, 2021 Archives
Friday, July 16, 2021
CONTEXT: “Journalist” Learns a New Phrase
What in the world pic.twitter.com/tR9APKPJCo
— Casey Toner (@ctoner) July 16, 2021
Is this scammy SEO work, laziness, someone with a concussion? pic.twitter.com/FR8Y2L4UAg
— Casey Toner (@ctoner) July 16, 2021
How precious—it's like a toddler who's just learned a new word.
THE HAPPENING WORLD: SpaceX: Super Heavy Booster 3 Static Fire as Soon as Monday 2021-07-19
Probably Monday
— Elon Musk (@elonmusk) July 16, 2021
CONTINUITY: A Simple Luxury—Ice through the Ages
Those who consider asteroid mining implausible may be unaware that in the nineteenth century ice, harvested in the winter, was exported year-round from Boston to India, a distance of 24,000 km and voyage of 130 days, in wooden sailing ships, to save the customers who eagerly purchased it from the horrors of tepid gin and tonics. By 1870, 17,000 tonnes of ice were shipped halfway around the globe every year. For details, see Gavin Weightman's superb book, The Frozen Water Trade.
TRACKING WITH CLOSEUPS: A Non-Euclidean Rendering Engine
CONTEXT: Federal Aviation Administration vs. SpaceX—Crab Mentality and the Road to the Stars
FAA warns SpaceX it has not approved new Texas launch site tower https://t.co/31TqIb07jI
— CNBC (@CNBC) July 14, 2021
What is crab mentality?
In any sane world, the only thing an aviation regulator should have to say about a tower fixed to the ground is whether it has the prescribed anti-collision warning lights and is properly marked on aeronautical charts. But ours is not a sane world, or at least not in these crazy years we're enduring. As I've been saying for some time, “Elon, time to emigrate—again.”
Here are some headlines from the Crazy Years, as described in Robert A. Heinlein's novel Methuselah's Children:
BABY BILL BREAKS BANK 2-year toddler youngest winner $1,000,000 TV jackpot White House phones congrats
COURT ORDERS STATEHOUSE SOLD Colorado Supreme Bench Rules State Old Age Pension Has First Lien All State Property
N.Y. YOUTH MEET DEMANDS UPPER LIMIT ON FRANCHISE
“U.S. BIRTH RATE ‘TOP SECRET!' ”—DEFENSE SEC
CAROLINA CONGRESSMAN COPS BEAUTY CROWN “Available for draft for President” she announces while starting tour to show her qualifications
IOWA RAISES VOTING AGE TO FORTY-ONE Rioting on Des Moines Campus
EARTH-EATING FAD MOVES WEST: CHICAGO PARSON EATS CLAY SANDWICH IN PULPIT “Back to simple things,” he advises flock.
LOS ANGELES HI-SCHOOL MOB DEFIES SCHOOL BOARD “Higher Pay, Shorter hours, no Homework—We Demand Our Right to Elect Teachers, Coaches.”
SUICIDE RATE UP NINTH SUCCESSIVE YEAR AEC Denies Fall-Out to Blame
THE HAPPENING WORLD: AlphaFold Methods Published, Source Code Released
Last year we presented #AlphaFold v2 which predicts 3D structures of proteins down to atomic accuracy. Today we’re proud to share the methods in @Nature w/open source code. Excited to see the research this enables. More very soon!https://t.co/6uiV51Xly5https://t.co/CLo7EKubBT pic.twitter.com/5dAgg9mOMN
— Demis Hassabis (@demishassabis) July 15, 2021
Abstract
Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort, the structures of around 100,000 unique proteins have been determined, but this represents a small fraction of the billions of known protein sequences. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the 3-D structure that a protein will adopt based solely on its amino acid sequence, the structure prediction component of the ‘protein folding problem’, has been an important open research problem for more than 50 years. Despite recent progress, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even where no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14), demonstrating accuracy competitive with experiment in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.