Organisms grow in wave pattern, similar to ocean circulation – Big Think

When an egg cell of almost any sexually reproducing species is fertilized, it sets off a series of waves that ripple across the egg's surface.

These waves are produced by billions of activated proteins that surge through the egg's membrane like streams of tiny burrowing sentinels, signaling the egg to start dividing, folding, and dividing again, to form the first cellular seeds of an organism.

Now MIT scientists have taken a detailed look at the pattern of these waves, produced on the surface of starfish eggs. These eggs are large and therefore easy to observe, and scientists consider starfish eggs to be representative of the eggs of many other animal species.

In each egg, the team introduced a protein to mimic the onset of fertilization, and recorded the pattern of waves that rippled across their surfaces in response. They observed that each wave emerged in a spiral pattern, and that multiple spirals whirled across an egg's surface at a time. Some spirals spontaneously appeared and swirled away in opposite directions, while others collided head-on and immediately disappeared.

The behavior of these swirling waves, the researchers realized, is similar to the waves generated in other, seemingly unrelated systems, such as the vortices in quantum fluids, the circulations in the atmosphere and oceans, and the electrical signals that propagate through the heart and brain.

"Not much was known about the dynamics of these surface waves in eggs, and after we started analyzing and modeling these waves, we found these same patterns show up in all these other systems," says physicist Nikta Fakhri, the Thomas D. and Virginia W. Cabot Assistant Professor at MIT. "It's a manifestation of this very universal wave pattern."

"It opens a completely new perspective," adds Jrn Dunkel, associate professor of mathematics at MIT. "You can borrow a lot of techniques people have developed to study similar patterns in other systems, to learn something about biology."

Fakhri and Dunkel have published their results today in the journal Nature Physics. Their co-authors are Tzer Han Tan, Jinghui Liu, Pearson Miller, and Melis Tekant of MIT.

Previous studies have shown that the fertilization of an egg immediately activates Rho-GTP, a protein within the egg which normally floats around in the cell's cytoplasm in an inactive state. Once activated, billions of the protein rise up out of the cytoplasm's morass to attach to the egg's membrane, snaking along the wall in waves.

"Imagine if you have a very dirty aquarium, and once a fish swims close to the glass, you can see it," Dunkel explains. "In a similar way, the proteins are somewhere inside the cell, and when they become activated, they attach to the membrane, and you start to see them move."

Fakhri says the waves of proteins moving across the egg's membrane serve, in part, to organize cell division around the cell's core.

"The egg is a huge cell, and these proteins have to work together to find its center, so that the cell knows where to divide and fold, many times over, to form an organism," Fakhri says. "Without these proteins making waves, there would be no cell division."

MIT researchers observe ripples across a newly fertilized egg that are similar to other systems, from ocean and atmospheric circulations to quantum fluids. Courtesy of the researchers.

In their study, the team focused on the active form of Rho-GTP and the pattern of waves produced on an egg's surface when they altered the protein's concentration.

For their experiments, they obtained about 10 eggs from the ovaries of starfish through a minimally invasive surgical procedure. They introduced a hormone to stimulate maturation, and also injected fluorescent markers to attach to any active forms of Rho-GTP that rose up in response. They then observed each egg through a confocal microscope and watched as billions of the proteins activated and rippled across the egg's surface in response to varying concentrations of the artificial hormonal protein.

"In this way, we created a kaleidoscope of different patterns and looked at their resulting dynamics," Fakhri says.

The researchers first assembled black-and-white videos of each egg, showing the bright waves that traveled over its surface. The brighter a region in a wave, the higher the concentration of Rho-GTP in that particular region. For each video, they compared the brightness, or concentration of protein from pixel to pixel, and used these comparisons to generate an animation of the same wave patterns.

From their videos, the team observed that waves seemed to oscillate outward as tiny, hurricane-like spirals. The researchers traced the origin of each wave to the core of each spiral, which they refer to as a "topological defect." Out of curiosity, they tracked the movement of these defects themselves. They did some statistical analysis to determine how fast certain defects moved across an egg's surface, and how often, and in what configurations the spirals popped up, collided, and disappeared.

In a surprising twist, they found that their statistical results, and the behavior of waves in an egg's surface, were the same as the behavior of waves in other larger and seemingly unrelated systems.

"When you look at the statistics of these defects, it's essentially the same as vortices in a fluid, or waves in the brain, or systems on a larger scale," Dunkel says. "It's the same universal phenomenon, just scaled down to the level of a cell."

The researchers are particularly interested in the waves' similarity to ideas in quantum computing. Just as the pattern of waves in an egg convey specific signals, in this case of cell division, quantum computing is a field that aims to manipulate atoms in a fluid, in precise patterns, in order to translate information and perform calculations.

"Perhaps now we can borrow ideas from quantum fluids, to build minicomputers from biological cells," Fakhri says. "We expect some differences, but we will try to explore [biological signaling waves] further as a tool for computation."

This research was supported, in part, by the James S. McDonnell Foundation, the Alfred P. Sloan Foundation, and the National Science Foundation.

Reprinted with permission of MIT News. Read the original article.

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Organisms grow in wave pattern, similar to ocean circulation - Big Think

Join Team Hackaday To Crunch COVID-19 Through Folding@Home – Hackaday

Donate your extra computer cycles to combat COVID-19. The Folding@Home project uses computers from all over the world connected through the Internet to simulate protein folding. The point is to generate the data necessary to discover treatments that can have an impact on how this virus affects humanity. The software models protein folding in a search for pharmaceutical treatments that will weaken the virus ability to attack the human immune system. Think of this like mining for bitcoin but instead were mining for a treatment to Coronavirus.

Initially developed at Standford University and released in the year 2000, this isnt the first time Hackaday has advocated for Folding@Home. The Team Hackaday folding group was started by readers back in 2005 and that team number is still active, so lets pile on and work our way up the rankings. At the time of writing, were ranked 267 in the world, can we get back up to number 30 like we were in 2008? To use the comparison to bitcoin once again, this is like a mining pool except what we end up with is a show of goodwill, something I think we can all use right about now.

You can get set up in five minutes. The software package is just a few megabytes and configuration is minimal:

Thats about it, just open FAHControl and the software will connect to the Folding at Home servers and request a Work Unit (WU) part of the protein folding math puzzle currently being solved. Once it has a WU the software will solve that unit and upload the result. Rinse and repeat and youre a worker bee in a super-computer thats distributed throughout the world.

The F@H project is seeing a surge of new computers on the network. Because of this you may run into a situation where no new WUs are getting downloaded. I experienced this on Wednesday morning and believe its simply caused by the buffer of work running out and needing to be replenished. The nice thing is you dont need to do anything, so just let your instance run and itll get to work when more is available.

The software does allow you to use your GPU for much more efficient calculations, but that setup may be non-trivial and beyond the scope of this article. I suggest you just get the client up and running and then look to configure GPU as a later step.

Are you making a difference? Yes! But of course metrics tell this message the best. You can see the team summary above. This statistics page includes a user summary showing 21 active users right now, including the hackaday_wrencher instance I added when working on this article which is just beginning to score points.

This group has over 1600 members right now but most are inactive. Can we reactivate those? Can we double that number? Grab those gaming rigs and let the electrons flow. Folding@Home has made a huge impact on research over the last twenty years and now more than ever we can build on that groundwork by joining in to fight this global pandemic.

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Join Team Hackaday To Crunch COVID-19 Through Folding@Home - Hackaday

How Ethereum Mining Rigs Can Help Battle the Coronavirus – Live Bitcoin News

Several cryptocurrency mining projects particularly those devoted to extracting new Ethereum tokens have been pulled away from their mining duties and been made to turn their attention towards coronavirus research.

The coronavirus was recently declared a global pandemic by the World Health Organization (WHO). As many as 245,000 people have been infected with the virus at the time of writing, while more than 11,000 deaths across the globe have been recorded.

Recently, world leaders such as President Donald Trump in the United States have declared a national emergency, while the governors of both California and New York have issued stay at home orders, asking that residents stay within their domiciles and limit their outdoor activities with others to stop the virus spread.

At this time, it seems like people need all the help they can get, and research regarding how to combat the virus is at an all-time high, but how, exactly, can crypto mining rigs help to get this done?

Its not so much that they help with the research aspect, but what they do have is high computational power enough so that the computers and devices conducting or holding present research can stay operational and functional during these stressing times, and its here where the mining rigs can serve great purpose.

Among the major companies working to better understand the problems and symptoms associated with the growing respiratory virus is Stanford Universitys Folding @home, which helps to develop therapeutic drugs. As recently as last month, the company was devoting much of its time, energy and resources towards establishing drugs and products designed to combat HIV, but now, it has shifted focus to work on coronavirus research.

One of the main things that Folding @home does is sort through protein structures of products approved by the Food and Drug Administration (FDA). Proteins, depending on how theyre built, can lessen a disease or even fully treat it, and the venture is looking to see which proteins are available that could potentially bring the virus to its knees.

In a statement, the company explains:

Proteins have lots of moving parts, so we really want to see the protein in action. The structures we cant see experimentally may be the key to discovering a new therapeutic.

Right now, Folding @home and several other drug-related companies are getting their power from sources such as Core Weave, which is one of the largest Ethereum mining projects in the rural United States. At press time, Core Weave is dedicating mountains of computational power to these companies to assist in their time spend performing appropriate research.

The mining venture stated:

Core Weave is proud to support this effort with over 6,000 of our high-end GPUs.

As many as 20 separate companies are presently working on a coronavirus vaccine.

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How Ethereum Mining Rigs Can Help Battle the Coronavirus - Live Bitcoin News

Thousands of These Computers Were Mining Cryptocurrency. Now They’re Working on Coronavirus Research – CoinDesk – Coindesk

CoreWeave, the largest U.S. miner on the Ethereum blockchain, is redirecting the processing power of 6,000 specialized computer chips toward research to find a therapy for the coronavirus.

These graphics processing units (GPUs) will be pointed toward Stanford University's Folding@home, a long-standing research effort that unveiled a project on Feb. 27 specifically to boost coronavirus research by way of a unique approach to developing pharmaceutical drugs: connecting thousands of computers from around the world to form a distributed supercomputer for disease research.

CoreWeave co-founder and Chief Technology Officer (CTO) Brian Venturo said the project has at least a shot at finding a drug for the virus. As such, CoreWeave has responded by doubling the power of the entire network with its GPUs, which are designed to handle repetitive calculations.

According to Venturo, those 6,000 GPUs made up about 0.2 percent of Ethereum's total hashrate, earning roughly 28 ETH per day, worth about $3,600 at press time.

There is no cure for the coronavirus just yet (though various groups are working on vaccines and research to combat the disease, including IBM's supercomputer). Venturo noted that Folding@home has been used to contribute to breakthroughs in the creation of other important drugs.

"Their research had profound impacts on the development of front-line HIV defense drugs, and we are hoping our [computing power] will aid in the fight against coronavirus," Venturo said.

The coronavirus is taking a toll across the world. Italy and Spain are on lockdown. Conferences, stores and restaurants are closing to stem the spread of the disease; by stoking fears, it's slamming the financial markets in the process.

World computer

When the idea of using GPUs for coronavirus research was mentioned to CoreWeave, the team didn't think twice.

They had a test system up and running "within minutes," Venturo said. Since then, the project quickly snowballed. CoreWeave has been contributing over half of the overall computing power going into the coronavirus wing of Folding@home.

"The idea of 'should we do this?' was never really brought up, it kind of just happened. We were all enthusiastic that we might be able to help," Venturo added.

Folding@home is a decentralized project in the same vein as Bitcoin. Instead of one research firm alone using a massive computer to do research, Folding@home uses the computing power of anyone who wants to participate from around the world even if it's just a single laptop with a little unused computing power to spare.

In this case, the computing power is used to find helpful information relating to the coronavirus. Much like in bitcoin mining, one user might detect a "solution" to the problem at hand, distributing this information to the rest of the group.

"Their protein simulations attempt to find potential 'pockets' where existing [U.S. federal agency Food and Drug Administration (FDA)] approved drugs or other known compounds could help inhibit or treat the virus," Venturo said.

Viruses have proteins "that they use to suppress our immune systems and reproduce themselves. To help tackle coronavirus, we want to understand how these viral proteins work and how we can design therapeutics to stop them," a Folding@home blog post explains.

Simulating these proteins and then looking at them from different angles helps scientists to understand them better, with the potential of finding an antidote. Computers accelerate this process by shuffling through the variations very quickly.

"Our specialty is in using computer simulations to understand proteins moving parts. Watching how the atoms in a protein move relative to one another is important because it captures valuable information that is inaccessible by any other means," the post reads.

Long shot

Folding@home could use even more power. Venturo urges other GPU miners to join the cause.

Even without these calls for participation, though, miners of other cryptocurrencies are already independently taking action. Tulip.tools founder Johann Tanzer put out a call to action to Tezos bakers (that blockchains equivalent of miners) last week, promising to send the leading contributor to Folding@home a modest 15 XTZ, worth roughly $20 at press time.

The initiative blew up, to Tanzer's surprise. Though they might not be contributing as much power as CoreWeave, 20 groups of Tezos miners are now contributing a slice of their hashing power to the cause. Tanzer's pot has swelled to roughly $600 as Tezos users caught wind of the effort and donated.

But that's not to say all miners can participate. While GPUs are flexible, application-specific integrated circuits (ASICs), a type of chip designed specifically for mining, aren't, according to Venturo. Though ASICs are more powerful than GPUs, they're really only made for one thing: To mine cryptocurrency. This is one advantage Venturo thinks Ethereum has over Bitcoin, since GPU mining still works on the former, whereas the latter is now dominated by ASICs.

"This is one of the great things about the Ethereum mining ecosystem, it's basically the largest GPU compute resource on the planet. We were able to redeploy our hardware to help fight a global pandemic in minutes," Venturo said. (However, it's worth noting that Ethereum has seen ASICs enter the fray. Not to mention, ether miners might soon go extinct when a pivotal upgrade makes its way into the network.)

ASICs are useless for the Folding@Home effort, but if bitcoin miners have old GPUs lying around from the early days that they could contribute, too.

Even if other miners join up, though, it's still a long shot that the effort will lead to a helpful drug.

"After discussing with some industry experts [...] we believe the chance of success in utilizing the work done on Folding@Home to deliver a drug to market to be in the 2-5% range," Venturo said.

The leader in blockchain news, CoinDesk is a media outlet that strives for the highest journalistic standards and abides by a strict set of editorial policies. CoinDesk is an independent operating subsidiary of Digital Currency Group, which invests in cryptocurrencies and blockchain startups.

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Thousands of These Computers Were Mining Cryptocurrency. Now They're Working on Coronavirus Research - CoinDesk - Coindesk

PC gamers and researchers asked to donate GPU and CPU time to help fight coronavirus – www.computing.co.uk

PC gamers and researchers asked to donate GPU and CPU time to help fight Coronavirus

A distributed computing project is urging researchers and PC gamers worldwide to donate some of their CPU and GPU computing power to help in the fight against coronavirus, also known as COVID-19.

Folding@home (FAH) is an international project based in the Pande Lab at Stanford University. Led by Dr Greg Bowman, the project utilises the idle computing power of hundreds of thousands of PCs owned by volunteers across the world to simulate the molecular dynamics of protein folding and misfolding in various diseases.

According to FAH, those simulations will help scientists in discovering new drug opportunities against diseases.

Please be patient with us! There is a lot of valuable science to be done

The FAH team is currently aiming to investigate how specific proteins in the novel coronavirus (COVID-19) operate and how those proteins can be destroyed to prevent the virus from multiplying within the human body.

Last month, the FAH team announced that it was taking up the fight again coronavirus, with the aim to help develop a therapeutic antibody, similar to that previously developed for SARS-Cov in 2003.

"To help tackle coronavirus, we want to understand how these viral proteins work and how we can design therapeutics to stop them," FAH said.

The deadly coronavirus has already killed more over 6,300 people worldwide, while hundreds of thousands still remain infected with the virus. Governments across the world are responding to coronavirus outbreak by curbing the movements of citizens and tightening borders.

FAH has so far added 23 coronavirus projects to use donated GPU or CPU power to study the coronavirus.

Contributing to the FAH project is easy, as users just need to download and install the client for their operating system from the FAH website. Once installed, the client will be configured to 'lightly' use system's GPU and CPU processing power to perform protein simulations.

Users can also use 'Medium' or 'Full' options to increase the amount of CPU and GPU utilisation.

"Usually, your computer will never be idle, but we've had such an enthusiastic response to our COVID-19 work that you will see some intermittent downtime as we sprint to setup more simulations," FIH said.

"Please be patient with us! There is a lot of valuable science to be done, and we're getting it running as quickly as we can."

Continued here:
PC gamers and researchers asked to donate GPU and CPU time to help fight coronavirus - http://www.computing.co.uk

Thousands of These Computers Were Mining Cryptocurrency. Now Theyre Working on Coronavirus Research – Yahoo Money

CoreWeave, the largest U.S. miner on the Ethereum blockchain, is redirecting the processing power of 6,000 specialized computer chips toward research to find a therapy for the coronavirus.

These graphics processing units (GPUs) will be pointed toward Stanford Universitys Folding@home, a long-standing research effort that unveiled a project on Feb. 27 specifically to boost coronavirus research by way of a unique approach to developing pharmaceutical drugs: connecting thousands of computers from around the world to form a distributed supercomputer for disease research.

CoreWeave co-founder and Chief Technology Officer (CTO) Brian Venturo said the project has at least a shot at finding a drug for the virus. As such, CoreWeave has responded by doubling the power of the entire network with its GPUs, which are designed to handle repetitive calculations.

Related: State Power After Coronavirus, Feat. Peter McCormack

See also: Bitcoiners Are Biohacking a DIY Coronavirus Vaccine

According to Venturo, those 6,000 GPUs made up about 0.2 percent of Ethereums total hashrate, earning roughly 28 ETH per day, worth about $3,600 at press time.

There is no cure for the coronavirus just yet (though various groups are working on vaccines and research to combat the disease, including IBMs supercomputer). Venturo noted that Folding@home has been used to contribute to breakthroughs in the creation of other important drugs.

Their research had profound impacts on the development of front-line HIV defense drugs, and we are hoping our [computing power] will aid in the fight against coronavirus, Venturo said.

Related: SkyWeaver Didnt Plan for a Captive Audience of Millions but It Sure Helps

The coronavirus is taking a toll across the world. Italy and Spain are on lockdown. Conferences, stores and restaurants are closing to stem the spread of the disease; by stoking fears, its slamming the financial markets in the process.

When the idea of using GPUs for coronavirus research was mentioned to CoreWeave, the team didnt think twice.

They had a test system up and running within minutes, Venturo said. Since then, the project quickly snowballed. CoreWeave has been contributing over half of the overall computing power going into the coronavirus wing of Folding@home.

The idea of should we do this? was never really brought up, it kind of just happened. We were all enthusiastic that we might be able to help, Venturo added.

Folding@home is a decentralized project in the same vein as Bitcoin. Instead of one research firm alone using a massive computer to do research, Folding@home uses the computing power of anyone who wants to participate from around the world even if its just a single laptop with a little unused computing power to spare.

See also: Bitcoiners in Europe Reflect on Economic Shocks as Coronavirus Spreads

In this case, the computing power is used to find helpful information relating to the coronavirus. Much like in bitcoin mining, one user might detect a solution to the problem at hand, distributing this information to the rest of the group.

Their protein simulations attempt to find potential pockets where existing [U.S. federal agency Food and Drug Administration (FDA)] approved drugs or other known compounds could help inhibit or treat the virus, Venturo said.

Viruses have proteins that they use to suppress our immune systems and reproduce themselves. To help tackle coronavirus, we want to understand how these viral proteins work and how we can design therapeutics to stop them, a Folding@home blog post explains.

Simulating these proteins and then looking at them from different angles helps scientists to understand them better, with the potential of finding an antidote. Computers accelerate this process by shuffling through the variations very quickly.

Our specialty is in using computer simulations to understand proteins moving parts. Watching how the atoms in a protein move relative to one another is important because it captures valuable information that is inaccessible by any other means, the post reads.

Folding@home could use even more power. Venturo urges other GPU miners to join the cause.

Even without these calls for participation, though, miners of other cryptocurrencies are already independently taking action. Tulip.tools founder Johann Tanzer put out a call to action to Tezos bakers (that blockchains equivalent of miners) last week, promising to send the leading contributor to Folding@home a modest 15 XTZ, worth roughly $20 at press time.

Story continues

The initiative blew up, to Tanzers surprise. Though they might not be contributing as much power as CoreWeave, 20 groups of Tezos miners are now contributing a slice of their hashing power to the cause. Tanzers pot has swelled to roughly $600 as Tezos users caught wind of the effort and donated.

But thats not to say all miners can participate. While GPUs are flexible, application-specific integrated circuits (ASICs), a type of chip designed specifically for mining, arent, according to Venturo. Though ASICs are more powerful than GPUs, theyre really only made for one thing: To mine cryptocurrency. This is one advantage Venturo thinks Ethereum has over Bitcoin, since GPU mining still works on the former, whereas the latter is now dominated by ASICs.

See also: Israeli Bitcoiners See Surveillance as Unavoidable During Coronavirus Crisis

This is one of the great things about the Ethereum mining ecosystem, its basically the largest GPU compute resource on the planet. We were able to redeploy our hardware to help fight a global pandemic in minutes, Venturo said. (However, its worth noting that Ethereum has seen ASICs enter the fray. Not to mention, ether miners might soon go extinct when a pivotal upgrade makes its way into the network.)

ASICs are useless for the Folding@Home effort, but if bitcoin miners have old GPUs lying around from the early days that they could contribute, too.

Even if other miners join up, though, its still a long shot that the effort will lead to a helpful drug.

After discussing with some industry experts [] we believe the chance of success in utilizing the work done on Folding@Home to deliver a drug to market to be in the 2-5% range, Venturo said.

Continued here:
Thousands of These Computers Were Mining Cryptocurrency. Now Theyre Working on Coronavirus Research - Yahoo Money

How Every PC Gamer In The World Can Join The Fight Against Coronavirus – Forbes

Graphics cards are obviously a useful tool for gaming, video editing and even cryptocurrency mining. But did you know you can donate your AMD Radeon or Nvidia GPUs spare compute cycles to researching and potentially fighting against the ongoing coronavirus pandemic?

The Folding@Home "Web Control" page shows what projects your PC is dedicating resources to and gives ... [+] you control over when it's active.

Folding@Home is a distributed computing project that was founded in October 2000 at Stanford University, and it was specifically designed for disease research. It has historically been targeted at researching cancer, ALS, Parkinsons, Huntingtons and more.

The software uses the spare CPU and GPU cycles of thousands of computers globally to simulate protein folding and computational drug design.

Put another way, Folding@Home uses computer simulations to understand a proteins moving parts. Once scientists have a firm grasp on how the atoms move and interact within a protein, they can venture closer to discovering therapeutics to treat it.

But these simulations require massive computational power. And the Folding@Home team now wants to direct as much global compute power as possible at fighting SARS-CoV-2.

Viruses also have proteins that they use to suppress our immune systems and reproduce themselves. To help tackle coronavirus, we want to understand how these viral proteins work and how we can design therapeutics to stop them.

The video above demonstrates part of a simulation facilitated through Folding@Home of a protein where the atoms (shown as spheres) move aside, exposing a site where a drug can bind. The Folding@Home team has used similar simulations to expose a druggable site in the lethal Ebola virus.

The science behind what the team is doing is simultaneously fascinating and confusing (at least for the layman), and beyond the scope of this article. But I do encourage you to digest it on your own time via Greg Bowmans detailed writeup here.

The TL;DR

If you own a Windows, macOS or Linux PC that has a graphics card, you can join thousands of others around the world by donating your spare GPU cycles. This helps power the advanced simulations that could unlock a key to more deeply understanding the novel coronavirus (SARS-CoV-2) and its resulting disease COVID-19.

A list of Folding@Home installers

To get started, all you have to do is download the Folding@Home client for the OS youre currently using. The linked page should automatically detect your OS and present the right installer.

After that, simply install Folding@Home. Should you need help, heres a link to detailed installation guides for Windows, macOS and Linux.

Tips For Folding@Home

While this is meaningful software for a terrific cause, it isnt the most elegant. So Ill include a few tips for getting it up and running without grinding your PC to a halt.

1) Once the software is installed and launched, it should automatically open a web page that acts as a simple controller and monitor. If it doesnt, point your browser at https://client.foldingathome.org.

If you use Folding@Home with a GPU, choose "Any disease" will tell the software to direct its ... [+] computational power to various coronavirus-related research

2) If you have a GPU and you want to dedicate those compute cycles to various coranavirus research, choose Any disease from the dropdown box labeled I support research fighting...

3) You may need to manually add your GPU to the FAHControl app, but thats pretty straightforward. Just open FAHControl, select the Configure button on the top left, navigate to the Slots tab, and check the box designated as GPU (image below). Leave all the other options alone, as the software can handle that for you.

Configure a GPU in Folding@Home

4) When Folding@Home is active, it will put a heavy load on your system. This is normal. If youd rather the software run only when youre not using your PC, select only when idle. Ive also found that a Medium power setting strikes a good balance between the apps performance and system usability.

5) If it looks like your PC isnt getting any work, thats because theres been an unusually heavy influx of new users for the software, due in part to Nvidias call to action. You may experience some downtime, but the Folding@Home team says its working diligently to add new simulations to meet the increased demand.

No GPU? Your CPU Is Still Useful

If you dont have a dedicated graphics card, you can still make a difference. CPU-only workloads contribute to researching Alzheimers, Parkinsons, Huntingtons and cancer. The Folding@Home team is also working on adding COVID-19 simulations to CPU workloads as well, but no timeline was given for that.

View Forbes complete coronavirus coverage.

Read more:
How Every PC Gamer In The World Can Join The Fight Against Coronavirus - Forbes

Microsoft, Zuckerberg and Allen team up to use AI in the fight against coronavirus and are challenging other – Business Insider India

The initiative includes Microsoft Research, the Allen Institute for AI founded by Microsoft co-founder Paul Allen and the Chan Zuckerberg Initiative, set up by Facebook founder Mark Zuckerberg and his wife Priscilla Chan.

The entire database will be updated on a weekly basis, adding new research from peer-reviewed journals and other archival services. In order to motivate researchers to take the challenge head-on, Kaggle is hosting the Covid-19 Open Research Dataset Challenge (CORD-19).

All hands on deckThe actual papers are provided by the National Institute of Healths National Library of Medicine. Its also linked to the World Health Organisations (WHO) database of publications on coronavirus. The project is being coordinated by Georgetown Universitys Center for Security and Emerging Technology (CSET).

Even though the database was requested by the White Houses Office of Science and Technology Policy anyone from around the world harness the information to make their own deductions.

With this step, weve made available full-text, machine-readable resources to help speed response to this global crisis, said Dewey Murdick, CSETs director of data science.

Another tech company called Fold@Home is distributing a computing project online that helps users and contributors conduct research on Covid-19 by simulating molecular dynamics. Researchers can simulate processes like protein folding and drug design to understand how the coronavirus would react.

As of today, there are over 165,000 confirmed cases of the coronavirus worldwide across 146 countries with 126 people infected in India.

See also:Coronavirus recovery rate at 54% as doctors race to find a cure

Coronavirus pandemic: Bill Gates warned us that this day would come five years ago

See original here:
Microsoft, Zuckerberg and Allen team up to use AI in the fight against coronavirus and are challenging other - Business Insider India

This online puzzle game may find a coronavirus treatment – Fast Company

Coronavirus, which the World Health Organization has now officially labeled a pandemic, is taking a toll on communities around the world. Theres currently no cure for COVID-19, but scientists are working on drugs that could help slow its spread. Fortunately, citizens can get involved in the process.

Foldit is an online video game that challenges players to fold various proteins into shapes where they are stable. Generally, folding proteins allows scientists (and citizens) to design new proteins from scratch, but in the case of coronavirus, Foldit players are trying to design the drugs to combat it. Coronavirus has a spike protein that it uses to recognize human cells, says Brian Koepnick, a biochemist and researcher with the University of Washingtons Institute for Protein Design who has been using Foldit for protein research for six years. Foldit players are designing new protein drugs that can bind to the COVID spike and block this recognition, [which could] potentially stop the virus from infecting more cells in an individual who has already been exposed to the virus.

First released in 2008, Foldit grew out of an experimental research project developed by the University of Washingtons Center for Game Science along with the Department of Biochemistry. Foldits coronavirus puzzle is the games 1,808th ever. Playerswho can work alone or in teamsare using the games puzzle system to develop new protein structures that can be tested by biochemists in the lab for use in antiviral drugs.

In Foldit, you change the shape of a protein model to optimize your score. This score is actually a sophisticated calculation of the folds potential energy, says Koepnick, adding that professional researchers use an identical score function in their work. The coronavirus puzzles are set up such that high-scoring models have a better chance of actually binding to the target spike protein. Ultimately, high-scoring solutions are analyzed by researchers and considered for real-world use.

Since its inception, over half a million people have created accounts and played Foldit, and over 2,500 players have worked on the games coronavirus puzzles so far.

[Image: courtesy Fold.it]Seth Cooper, the games lead designer and an assistant professor at Northeasterns Khoury College of Computer Sciences, says Foldit was created because the design team figured that people could come up with better solutions than the computer could, and that itd be helpful for people to interact with the 3-D compositions of protein structures to truly understand how they function.

Though these online puzzles werent designed to necessarily address a steadily growing virus such as COVID-19, its become an efficient way to conduct research on the disease safely, at home. I think its really exciting to be able to potentially help out with something like this. . . . Its the kind of thing I think we would have hoped to be able to do [when we started out], Cooper says.

In the past, Foldit players have puzzled together successful synthetic and natural protein structuressuch as ones that helped solve the Mason-Pfizer monkey virus in 2011. Some of the players who are very good at Foldit dont have backgrounds in biochemistry, but the beauty of the games design is that it makes science accessible to laypeople, and it ultimately ends up teaching nonprofessionals a lot. (A handful of Foldit players were credited as authors in a paper Cooper and his colleagues published recently.)

According to Cooper, this solution-based crowdsourcing project is a way to put video games toward a good purpose. When people are playing games, theyre solving problems anyway, so its nice to apply that brainpower to solving problems in the real world.

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This online puzzle game may find a coronavirus treatment - Fast Company

Play this game to help create a cure for the coronavirus! Here are the more details !! – Gizmo Posts 24

Citizen scientists have a chance to help fight coronavirus by playing a game about folding proteins. Its name is Foldit, it is free-to-play, and it has a puzzle dedicated to COVID-19.

The developers of the game released the Coronavirus Spike Protein Binder Design. It allows users to try and create an antiviral protein that can counter the viruss spike protein. The puzzles description states: Coronaviruses display a spike protein on their surface, which binds tightly to a receptor protein found on the surface of human cells.

Once the coronavirus spike binds to the human receptor, the virus can infect the human cell and replicate it If we can design a protein that binds to this coronavirus spike protein, it could be used to block the interaction with human cells and halt the infection.

Researchers in the University of Washington created Foldit with the intention of using the tireless compulsions in the game to solve problems that would aid innovation. In the game, the player folds protein structures and creates new ones.

This helps in further understanding of protein chains. The FAQ reads: The more we know about how certain proteins fold, the better new proteins we can design to combat the disease-related proteins and cure the diseases.

According to the Universitys Center for Game Science, the developers originally designed the game to work on curing cancer, AIDS, and a host of diseases.

The puzzle has two difficulty levels. In the easier option, players can fold an already existing coronavirus binding protein. In the harder option, they can design the protein from scratch.

The designs that are the most promising will go through testing at UWS Institute for Protein Design. Itll be the beginnings of the cure, if not the cure itself. The UW says that Foldit has more than 200,00 players. They are keeping hopes high for discoveries due to the sheer number of players.

[emailprotected] is also a facility that allows citizens to help with researching protein structures. Those with unused computational resources on their Mac can donate them so that researchers can generate more data.

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Play this game to help create a cure for the coronavirus! Here are the more details !! - Gizmo Posts 24

The mechanism of Hsp90-induced oligomerizaton of Tau – Science Advances

Tau is an intrinsically disordered protein (IDP) known to bind to and stabilize microtubules (MTs) and regulate axonal transport in its physiological function (13). In pathology, filamentous aggregates of Tau constitute a hallmark of neurodegenerative diseases, among them Alzheimers disease (AD) (4). Both MT binding and self-aggregation of Tau are mediated by the Tau repeat domain (Tau-RD) consisting of four imperfect repeats in the longest Tau isoform (Fig. 1A) (5, 6).

While Tau in solution is generally disordered and highly dynamic, long-range interactions mediate folding back of both termini onto Tau-RD, resulting in an overall paper-clip arrangement of monomeric Tau, which has been well established by a variety of experimental techniques such as nuclear magnetic resonance (NMR) and fluorescence resonance energy transfer (FRET) (7, 8). In filamentous aggregates of Tau, Tau-RD forms the ordered filamental core, while N- and C-terminal regions remain a disordered fuzzy coat (9, 10). Tau filaments exist in different morphologies with notable differences in the fold of the filamental cores, which are probably disease specific (11, 12). Although fibrils have long been considered the neurotoxic species, neuronal death appears rather to be caused by prefibrillar soluble aggregates and oligomers of Tau (13, 14), which are also considered responsible for spreading Tau pathogenicity from cell to cell in a prion-like concept (15, 16).

The molecular chaperone heat shock protein 90 (Hsp90) (17, 18) initiates proteasomal degradation (1921) and induces oligomerization of Tau (2224). Tau-RD is part of the Hsp90/Tau interaction interface (25). While insight into the molecular mechanism of the emergence of toxic Tau oligomers is highly relevant in the context of neuropathology, its structural principle is elusive.

The lack of a defined three-dimensional fold of IDPs like Tau makes their structural characterization challenging. Electron paramagnetic resonance (EPR) spectroscopy in combination with site-directed spin labeling has proven powerful in the investigation of IDPs and their aggregation behavior also in the presence of diverse interaction partners (2631). EPR spectroscopy (i) provides information about the side-chain dynamics of a single residue (32). Dipolar spectroscopy, i.e., DEER (double electron-electron resonance) spectroscopy, (ii) gives access to distance information in the nanometer range between two spin labels by measuring their magnetic dipolar interaction frequency dd (3335). Here, we exploit the combination of these approaches to investigating the molecular mechanism of Hsp90-induced Tau oligomerization.

We genetically engineered Tau derivatives containing one or two cysteines at specific sites and performed thiol-specific spin labeling (Fig. 1B). A range of biochemical and biophysical assays was used to monitor the success of the labeling reaction and the structural integrity of the protein (figs. S1 to S3 and table S1).

Next, we set out to characterize the structural properties of Tau by obtaining long-range intramolecular distance information with DEER on doubly spin-labeled Tau. Typical experimental DEER form factors for Tau are shown in Fig. 1C (full data in fig. S4) in comparison to simulated data for a hypothetical, well-defined distance. In contrast to the latter, the experimental traces for Tau showed no distinct modulations, indicating a broad distribution of spin-spin distances and thus implying a vast conformational ensemble of Tau in solution.

For these experimental DEER traces, the standard method for DEER data analysis fails, and the extraction of precise distance distributions is precluded (36, 37). First, we tested whether the experimental DEER data are in agreement with a simple random coil (RC) model (fig. S5). We chose RC model parameters as published by Rhoades and co-workers (38, 39) for assessing the results of FRET experiments on Tau. For certain spin-labeled stretches of Tau, e.g., Tau-17*-103*, the RC model agreed well with the experimental results (Fig. 1D and fig. S5), indicating an RC-like structural ensemble in the corresponding Tau segments. However, the RC model cannot describe the whole DEER dataset even taking variation of RC parameters depending on solvent quality into account [see fig. S6; (40, 41)]: For Tau-17*-291* and Tau-17*-433*, the deviation between the experiment and the RC model indicates a considerable contribution from Tau conformations more compact than RC (Fig. 1E and fig. S5). This is in good agreement with the well-established finding that Tau does not adopt RC conformation in solution but rather a paper clip (7, 8).

Hinderberger and co-workers (36, 37) proposed a data analysis procedure, which we adapted for analyzing the broad conformational ensemble of a large IDP like Tau. We evaluated the DEER data using the effective modulation depth eff, which is the signal decay of the DEER time trace at time t = 3 s (Fig. 1C). While a DEER trace in the absence of Hsp90 delivers a reference eff value for each Tau sample, the change eff upon addition of Hsp90 characterizes transitions in the conformational equilibrium: Negative eff values indicate an increase in spin-spin separation, while positive eff values are consistent with the spins coming into closer proximity of each other (see details of modulation depthbased approach in fig. S7). This allows extracting distance information from DEER traces not analyzable in the conventional way.

The systematic analysis of the experimental eff values supports the paper-clip model proposed on the basis of FRET and NMR experiments for Tau in solution, where N and C termini are in proximity to each other, and Tau-RD is in an overall more compact fold than RC (7, 8). On the one hand, these results demonstrate the capacity of the eff approach for obtaining structural information from DEER traces reflecting vast protein ensembles, while on the other hand, they define the paper clip as a reference structural ensemble of Tau in solution, which is in agreement with the results obtained for the Tau structural ensemble in previous studies, suggesting a paper clip or S shape in solution (7, 8, 39, 42).

It has been shown that Hsp90 induces oligomerization of Tau fragments (22). Here, we analyzed the oligomerization behavior of full-length Tau by density gradient centrifugation (Fig. 2). Pure Tau was mainly found in its monomeric form, while heparin induced the formation of mature fibrils. In the presence of Hsp90, the amount of small oligomeric Tau species increased. Notably, the formation of highmolecular weight Tau aggregates and fibrils was prevented in the presence of Hsp90. Electron micrographs of K18 Tau fragments in the presence of Hsp90 also show the formation of small protein conglomerates, while fibril formation is prevented (43).

Dot blot summarizing the results of density gradient centrifugation and quantification (the color code represents Tau preparations as reported on top of the right graph): Pure Tau is mostly monomeric. Heparin induces formation of highmolecular weight fibrils. Hsp90 leads to an increase in small Tau oligomeric species, while formation of fibrils is prohibited. A.U., arbitrary units.

To identify the oligomerization domain in Tau relevant for Hsp90-induced oligomerization, we performed intermolecular DEER measurements using singly spin-labeled Tau: Upon oligomerization, eff would increase locally where inter-Tau contacts are established. We observed very small eff values for all Tau derivatives in the absence of Hsp90 (Fig. 3), indicating only minor subpopulations of oligomeric Tau species. Addition of Hsp90 leads to a considerable increase in eff for Tau-322* and Tau-354*, depicted as difference values eff. This suggests that the oligomerization interface is located in Tau-RD and specifically in R3/R4. Notably, Tau oligomerization initiates in the same Tau region responsible for AD fibril formation and Hsp90 binding (11, 25). This is remarkable, as it suggests that the same stretch of Tau mediating fibril formation (25) is addressed by Hsp90 to promote the formation of oligomers.

Information about intermolecular Tau/Tau interactions obtained with DEER of singly spin-labeled Tau in the absence (dark gray) and presence (light gray) of Hsp90. Nonzero eff values represent small amounts of nonmonomeric Tau in the absence of Hsp90. Hsp90 increased eff values for Tau-322* and Tau-354* (light green bars) in accordance with an increase in Tau oligomers mediated by R3/R4 of Tau-RD. Positions probed in the experiment are also indicated on a schematic representation of the Tau sequence, with indicated Hsp90-binding site (25) and the core of the AD fibril (11), yellow and green stars indicating spin labeling positions without and with changes in eff upon addition of Hsp90, respectively.

The dynamic properties of Tau in solution and with Hsp90 are reported by EPR spectra of spin-labeled Tau side chains. In general, we observed rather fast rotational dynamics with rotational correlation times corr around 1 ns (Fig. 4A). This is in accordance with Tau presiding in a largely unstructured state with a broad conformational ensemble and a high degree of dynamical disorder (26). Addition of Hsp90 induced only subtle changes in the spectra (fig. S9), indicating that dynamic disorder in Tau persists also when bound. The generally still fast dynamics in the Tau spectra hints toward a transient nature of the Tau/Hsp90 complex, as only a small portion of spin-labeled Tau might be motionally restricted by intermolecular contacts, while other Tau molecules retain unrestricted rotational diffusion. We determined the half lifetime of the Tau/Hsp90 complex by quartz crystal microbalance (QCM) affinity measurements at ~10 s, which is typical for transient protein-protein interactions (fig. S10 and table S2) (44). The Tau/Hsp90 complex appears to be characterized by transient interactions between individual residues, involving a structural multiplicity of Tau.

(A) Local side-chain dynamics accessed by cw EPR of 28 M singly spin-labeled Tau derivatives: Rotational correlation times corr determined in the absence (dark gray) and presence (light gray) of 56 M Hsp90 and respective changes corr (purple/pink) are shown. Arrows indicate a decrease (pink) or increase (purple) in side-chain mobility at the respective site. (B) Intramolecular distance information obtained by DEER spectroscopy with doubly spin-labeled Tau derivatives: Effective modulation depths eff determined in the absence (dark gray) and presence (light gray) of Hsp90 and respective changes eff (light/dark green) are shown. Arrows indicate a decrease (light green) or increase (dark green) in spin label separation.

We observed local restrictions of the reorientational mobility for spin-labeled side chains Tau-291* and Tau-322* in the presence of Hsp90. Both residues are located in Tau-RD, which has been identified as the Hsp90-binding region before (25). Thus, the altered dynamics are attributed to direct Tau/Hsp90 interaction, while also oligomer formation might restrict side chain dynamics of Tau-322*.

Spin label mobilities increased in Tau-17* and Tau-103* upon addition of Hsp90, indicating that these side chains gain a larger conformational space. Thus, one might speculate that the N terminus detaches from Tau-RD upon binding of Hsp90, opening up the paper-clip fold.

To elucidate the structural influence of Hsp90 on the Tau conformational ensemble, we performed DEER spectroscopy of doubly spin-labeled Tau. DEER traces remained modulation free upon addition of Hsp90 (fig. S4). Thus, dynamic disorder prevails in Tau also when interacting with the chaperone (Fig. 1C). Addition of Hsp90 changed eff values, indicating a shift in the conformational equilibrium of Tau (Fig. 4B): A pronounced increase in the average spin-spin separation occurred for Tau-17*-291* and Tau-17*-433*. This indicates that the N terminus detaches from both Tau-RD and the C terminus and folds outward, opening up the paper clip (Fig. 5). eff values suggested a slight stretching of N-terminal Tau between Tau-17*-103* and of Tau-RD in the region between Tau-291*-322* in R2/R3, while the overall dimension of Tau-RD between Tau-244*-354* remained unchanged. While individual repeat sequences, e.g., R2/R3 expanded while accommodating Hsp90, there seems to be considerable flexibility in the remaining Tau-RD for preserving its overall dimension. A similar structural reorganization of Tau toward an open conformation was reported upon binding to tubulin, where stretches between individual repeats expanded, while the overall dimension of Tau-RD remained unaffected (38). Our results report the conformational basis of Tau oligomerization in the presence of Hsp90 and suggest that binding to Hsp90 opens the compact Tau solution structure, exposing Tau-RD residues and presenting them to other Tau molecules. As the Tau/Hsp90 complex is of a transient nature, oligomerization of Tau molecules may then occur via exposed Tau-RD.

A structural model of Tau in the absence (left) and presence of Hsp90 (right) can be derived from EPR data of the Tau conformational ensemble. In the absence of Hsp90, Tau adopts a paper-clip shape with both termini folded back onto Tau-RD. In the presence of Hsp90 the N terminus folds outward, thereby uncovering Tau-RD.

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The mechanism of Hsp90-induced oligomerizaton of Tau - Science Advances

Blurring the Line Between Natural and Artificial – Discovery Institute

In 2010, Craig Venters lab embedded text and images into the DNA of a bacterium. Would a future investigator be able to tell? It would take special tools to see the insertion, but the difference should be detectable. What if bioengineers invent new genes that use the cells translation machinery to build non-natural proteins? This is already coming to pass with CRISPR/Cas9 methods. If the insertion were made in an embryo, all the adult cells would inherit the change. The line between natural and artificial is getting more blurry.

In a sense, the new bioengineering developments are similar in principle to longstanding cases of artificial interference in nature, as in agriculture, camouflage, or construction of simple dwellings with available materials like grass or fallen branches. The Design Filter takes into account what chance and natural law can do. There will always be difficult cases; ID errs on the side of non-intelligent causes when the degree of specified complexity is borderline. But now, specified complexity exists in both natural DNA and DNA altered by human intelligence. There should be ways to distinguish between human intelligent causes and non-human intelligent causes, whether those be space aliens, spirit beings, or a transcendent Creator.

In their epilogue to the book The Mystery of Lifes Origin (newly updated and expanded by Discovery Institute Press), Charles Thaxton, Walter Bradley, and Roger Olsen considered five sources for a more satisfactory theory of origins. These included: new natural laws, panspermia, directed panspermia, special creation by a creator within the cosmos, and special creation by a creator outside the cosmos. The last four involve intentional, mind-directed activity; only #5 necessarily involves the supernatural. To the investigator, though, the output of the Design Filter would be the same. It boils down to natural versus artificial: unguided, or mind-directed. But what happens when the mind-directed interference of bioengineers gets so good, it looks natural? It becomes a case of the perfect crime, leaving the investigator baffled. Todays Mars rovers are easily distinguished from the rocky, dusty environment of Mars. But what if future designers made them look like rocks, functioning when they roll over in the wind?

This is a growing challenge for ID as bioengineering progresses. News from ETH Zurich says:

Every living creature on earth has parents, grandparents, great-grandparents and so on representing an unbroken line of ancestry all the way back to the very first organisms that lived here billions of years ago. Soon we will have life forms that have no such direct lineage. The first of these organisms will be bacteria. Bioengineers will use computers to develop such bacteria and specifically tailor them for applications in medicine, industry or agriculture. With the help of DNA synthesisers, they will build these bacterias genomes from the ground up to produce artificial life forms. [Emphasis added.]

This implies that an investigator will have to search the ancestry of an organism to make a design inference.

I dont mean organisms in which only individual genes have been altered a technique that has been applied in biotechnology and crop breeding for decades, and that todays CRISPR gene scissors have made very simple. No, I mean organisms for which bioengineers have literally developed the genome from scratch so that they can synthesise it in the lab.

The author, Dr. Beat Christen of ETH, says this is not science fiction. The tools to do this are already in place. I am convinced that they will soon be a reality, he says. It may not require designing every molecular machine de novo.

Digital databases store over 200,000 genome sequences from a broad range of organisms providing us access to a wealth of molecular building plans. By cleverly combining or modifying known genetic functions, bioengineers can develop microorganisms with new and useful characteristics.

How would an investigator in such cases be able to differentiate a synthetic organism from known examples of mosaic organisms or natural organisms containing orphan genes? On ID the Future recently, Paul Nelson acknowledged from his trip to the Galpagos Islands that Darwin got something right: organisms have a history. There can be some natural modification in a lineage over time, as in the case of flightless cormorants, he said, and ID advocates need to build that into their theory of design. With bioengineering entering the mix, they will also have to distinguish natural history from artificial history in the codes of life.

This is an extension of what they must do in distinguishing the artificial history of cultivated crops and animal breeds. The dachshund looks very different from the wolf from which domestic dogs descended. The ears of corn we buy in supermarkets differ substantially from the maize or teosinte from which farmers selectively bred them. But now that bioengineers can selectively edit the genes, they will have to discern the history in the genotype as well as the phenotype. The ability to do this could become very important.

Another challenge will arise as human history progresses. Right now, we have more clues to trace genetic editing to particular labs. But as the number of gene editing labs grows over time, and editing becomes routine maybe even to individuals it may become impossible to trace the edits to their source. This happens with artificial breeding as well; unless particular breeders documented their work, historians and archaeologists can only gain indirect clues to the time and place of origin for a particular breed. It could have started in ancient Babylon, Egypt, or Rome. Its not IDs job to identify the agent, the books explain (e.g., The Design Revolution, Chapter 26); the investigator should be able to detect design from its effects alone. Genetic tinkering will make that inference more difficult, if genetic engineers continue to blur the line between natural genetic information and edited genetic information. Moreover, not all gene editors publish their work. As in the case of bioweapons, the source may intentionally try to conceal its designs.

In Nature, three scientists wrote a review titled, The coming of age of de novo protein design. The opening sentence of the article by Huang, Boyken, and Baker makes a point that Douglas Axe and Ann Gauger would agree with: functional space is dwarfed by sequence space.

There are 20200 possible amino-acid sequences for a 200-residue protein, of which the natural evolutionary process has sampled only an infinitesimal subset. De novo protein design explores the full sequence space, guided by the physical principles that underlie protein folding. Computational methodology has advanced to the point that a wide range of structures can be designed from scratch with atomic-level accuracy. Almost all protein engineering so far has involved the modification of naturally occurring proteins; it should now be possible to design new functional proteins from the ground up to tackle current challenges in biomedicine and nanotechnology.

The summary on Phys.org has the title, Scientists can now design new proteins from scratch with specific functions. One of the techniques of de novo protein design involves evolutionary algorithms, in which the intelligent agent provides the selective pressure to find the fittest protein for the chosen goal. If engineers succeed in taking an amino acid sequence that folds in silico and then can reverse engineer the genetic code for it so that it can be translated by a natural bacteriums cellular machinery, does it become indistinguishable from an orphan gene? In both instances, the Design Filter would register a positive, but should ID advocates be able to tell the difference? Does it matter?

Another blurring of lines between the natural and the artificial occurs in cases of guiding organisms to do unnatural things. At the Israel Institute of Technology (Technion), biotechnicians have turned a bacterial cell into a biological computer.

In recent decades, the barriers between engineering and life sciences have been falling, and from the encounter between the two different disciplines, a new science synthetic biology was born. Synthetic biology introduces engineering into biology, makes it possible to design and build biological systems that dont exist in nature, and supplies an innovative toolbox for reprogramming the genetic code in living creatures, including humans.

We built a kind of biological computer in the living cells. In this computer, as in regular computers, circuits carry out complicated calculations, said Barger. Only here, these circuits are genetic, not electronic, and information are [sic] carried by proteins and not electrons.

Once again, telling the difference will require a robust design inference. This type of tinkering might be compared to animal training. Shown two wolves, one trained to respond to human words and one in its wild state, could the investigator tell them apart by their behavior alone? Probably, but discriminating biological computers from wild bacteria could be a lot tougher, tractable only to molecular biologists.

These examples in the news present both challenges and opportunities. As lines blur between the natural and the synthetic in the 21st century, the design inference must be tightened accordingly. The specified-complexity criterion is robust against false positives (This is designed when its not), but not against false negatives (This isnt designed when it is; see William Dembski, No Free Lunch, pp. 22-28). To avoid a growing number of false negatives, the investigator must now become aware of the history of the genotype as well as the phenotype.

Its well and good to lump all instances of complex specified information into the designed category, whether a gene was edited by humans or designed by a transcendent entity. But these rapidly growing capabilities for bioengineering raise additional challenges for the ID community. Fortunately, with the challenges come opportunities. The very act of genetic engineering must surely be raising awareness in the scientific community of the degree of specified complexity in natural organisms, and the extremely limited tolerances for success. Nature confesses:

It is useful to begin by considering the fraction of protein sequence space that is occupied by naturally occurring proteins [1012 out of 20200]Evidently, evolution has explored only a tiny region of the sequence space that is accessible to proteins.

The design inference is not changing in principle; it only needs clarification to fit more challenging cases. It also affords opportunities to communicate design principles to those still clinging to the hope that blind, unguided processes are capable of navigating endless fields of haystacks for a tiny number of needles.

Photo: Topiary animals, by Doko Jozef Kotuli / CC BY.

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Blurring the Line Between Natural and Artificial - Discovery Institute

How Home-Baked Bread Is Defying the Industrial Food System – YES! Magazine

As more home bakers rediscover how to capture wild yeast and turn it into nourishing loaves of bread, they are part of a growing kitchen movement standing up to the industrial food system.

Step 1: Capture wild yeast and make your own sourdough starter

Sourdough bread begins with the starter, made by capturing wild yeast from the environment and using it to ferment flour and water. Because yeast cultures vary depending on where you are, every sourdough starter tastes a little different. Heres how to make your own sourdough starter using an ancient grain.

Youll need:

Day 1: Mix 60g of flour with 60g of water, and let it sit for 24 hours at room temperature. (Do not sterilize your jar. Your starter uses naturally occurring and varied yeasts and lactobacilli bacteria from your environment.)

Day 2-5: Repeat the flour-water feeding. You should notice bubbles around day 2 or 3 (excitingyour starter is alive!). Once this happens, store your starter in the refrigerator to slow fermentation.

Day 6: At this point you should have an active starter. Give it a name! Throw out half of your starter and give it a hearty meal of 100g flour and 100g water.

To keep your starter alive, youll need to feed it equal parts water and flour every third day or so. I usually recommend 60g of water and 60g flour. If youre not planning to bake bread at the feeding time, throw out half the starter before feeding to keep your starter at a manageable size. It can take two to three bread-baking cycles before your starter is strong and yields predictable results. Professional bakeries have had their starters for generations.

Flour:

I chose einkorn wheat for this recipe. Einkorn was domesticated around 9000 B.C. Little about the grain has changed because it nearly became extinct and was never hybridized for industrial markets. Einkorn is noted for higher protein and nutrient content as well as gluten that is more digestible than that in industrial wheat. Emmer and spelt are also ancient grains that work for bread baking. You can experiment with different types and ratios of flour as long as the total added flour equals 500g. For example, if youre looking for a denser bread try a higher ratio of whole wheat flour.

Tools:

You might want to consider adding a couple of tools to your toolbox. Only the scale is absolutely necessary, but all will make bread-baking considerably easier.

Time:

2.5 hours plus a 12-hour cold bulk fermentation.

Ingredients:

Instructions:

1. Add the starter, water, and flour in a bowl. Mix well so that no dry flour remains. The mixture will be quite stickydo not worry. Let sit for 1020 minutes.

2. Add the salt and water mixture to the bread dough. Incorporate, mixing only as much as necessary. Let sit 1020 minutes.

3. Folding: In the bowl, grab the bottom of the north side of the dough. Stretch until just before it rips and then fold the dough towards you three-quarters of the way. Take the south side of the dough, stretch, and fold all the way over. Repeat this process with the east and west sides. Let the bread rest for 20 minutes. Then repeat this folding and resting step three more times.

4. Shaping: Lightly flour your work surface. Take the dough out of the bowl, and shape it so that its rectangular, arranging it so that the short side faces you. Take the south side of the dough, stretch it, and fold it up three-quarters of the way. Take the east side of the bread, stretch it, and fold it up and to the left. Repeat this with the west side. Then take the north flap and fold it all the way over the bread. Roll the dough so that the folding seams are underneath, touching your counter.

Use your hands to push the dough away from your body and then tuck it back toward you, creating surface tension along the outside of the dough. Rotate the dough with each push and tuck and continue this motion until the boule is a uniform shape with strong surface tension. This is not kneading, but shaping. Most sourdough, including this one, is actually a no-knead bread. We want the natural yeast to do as much of the work as possible.

5. Place your bread seam-side up in a lightly floured banneton. Cover with a tea towel and place it in the refrigerator for 12 hours.

6. Next day, preheat your oven (with the Dutch oven in it) to 500 degrees. Take out the Dutch oven, and gently roll your bread into it directly from the refrigerator. Sprinkle some flour on the top and score the dough by slashing the top. Put the lid on the Dutch oven and bake the loaf for 25 minutes. Then take the lid off, and bake for another 1520 minutes.

7. Remove the bread from the Dutch oven, and allow it to cool for about 10 minutes before slicing, sharing, and enjoying!

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How Home-Baked Bread Is Defying the Industrial Food System - YES! Magazine

Black Friday Is Absolutely Massive. Here Are a Bunch of Deals We Couldn’t Call Out Individually – Gear Patrol

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Black Friday Is Absolutely Massive. Here Are a Bunch of Deals We Couldn't Call Out Individually - Gear Patrol

Argonne Researchers to Share Scientific Computing Insights at SC19 – HPCwire

Nov. 15, 2019 The Supercomputing 2019 (SC19) conference, scheduled for November 1722 in Denver, will bring together the global high-performance computing (HPC) community, including researchers from the U.S. Department of Energys (DOE) Argonne National Laboratory, to share scientific computing advances and insights with an eye toward the upcoming exascale era.

Continuing the laboratorys long history of participation in the SC conference series, more than 90 Argonne researchers will contribute to conference activities and studies on topics ranging from exascale computing and big data analysis to artificial intelligence (AI) and quantum computing.

SC is a tremendous venue for Argonne to showcase its innovative uses of high-performance and data-intensive computing to advance science and engineering, said Salman Habib, director of Argonnes Computational Science division. We look forward to sharing our research and connecting with and learning from our peers, who are also working to push the boundaries of extreme-scale computing in new directions.

As the future home to one of the worlds first exascale supercomputers Aurora, an Intel-Cray machine scheduled to arrive in 2021 Argonne continues to drive the development of technologies, tools and techniques that enable scientific breakthroughs on current and future HPC systems. To fully realize exascales potential, the laboratory is creating an environment that supports the convergence of AI, machine learning and data science methods alongside traditional modeling and simulation-based research.

We are seeing rapid advances in the application of deep learning and other forms of AI to complex science problems at Argonne and across the broader research community, said Ian Foster, director of Argonnes Data Science and Learning division, Argonne Distinguished Fellow and also the Arthur Holly Compton Distinguished Service Professor of Computer Science at the University of Chicago. SC provides a forum for the community to get together and share how these methods are being used to accelerate research for a diverse set of applications.

The laboratorys conference activities will include technical paper presentations, talks, workshops, birds of a feather sessions, panel discussions and tutorials. In addition, Argonne will partner with other DOE national laboratories to deliver talks and demos at the DOEs conference booth (#925). Some notable Argonne activities are highlighted below. For the full schedule of the laboratorys participation in the conference, visitArgonnes SC19 webpage.

DOE Booth Talk: Scientific Domain-Informed Machine Learning

Argonne computer scientist Prasanna Balaprakash will delivera talk at the DOE boothon the laboratorys pivotal research with machine learning. His featured talk will cover Argonnes efforts to develop and apply machine learning approaches that enable data-driven discoveries in a wide variety of scientific domains, including cosmology, cancer research and climate modeling. Balaprakash will highlight successful use cases across the laboratory, as well as some exciting avenues for future research.

In Situ Analysis for Extreme-Scale Cosmological Simulations

Argonne physicist and computational scientist Katrin Heitmann will deliver thekeynote talkat the In Situ Infrastructures for Enabling Extreme-scale Analysis and Visualization (ISAV 2019) workshop. Her talk will cover the development of in situ analysis capabilities (i.e., data analysis while a simulation is in progress) for the Hardware/Hybrid Accelerated Cosmology Code, which has been used to carry out several extreme-scale simulations on DOE supercomputers. Heitmann will discuss the current limitations of her teams on the fly analysis tool suite and how they are developing solutions to prepare for the arrival of DOEs forthcoming exascale systems.

Full-State Quantum Circuit Simulation by Using Data Compression

Researchers from Argonne and the University of Chicago will present atechnical paperon their work to develop a new quantum circuit simulation technique that leverages data compression, trading computation time and fidelity to reduce the memory requirements of full-state quantum circuit simulations. Demonstrated on Argonnes Theta supercomputer, the teams novel approach provides researchers and developers with a platform for quantum software debugging and hardware validation for modern quantum devices that have more than 50 qubits.

Deep Learning on Supercomputers

Argonne scientists will have a strong presence at the Deep Learning on Supercomputers workshop. Co-chaired by Foster, the workshop provides a forum for researchers working at the intersection of deep learning and HPC. Argonne researchers are part of a multi-institutional team that will present DeepDriveMD: Deep-Learning-Driven Adaptive Molecular Simulations for Protein Folding. The study provides a quantitative basis by which to understand how coupling deep learning approaches to molecular dynamics simulations can lead to effective performance gains and reduced times-to-solution on supercomputing resources.

A team of researchers from Argonne and the University of Chicago will present Scaling Distributed Training of Flood-Filling Networks on HPC Infrastructure for Brain Mapping at the Deep Learning on Supercomputers workshop. The teams paper details an approach to improve the performance of flood-filling networks, an automated method for segmenting brain data from electron microscopy experiments. Using Argonnes Theta supercomputer, the researchers implemented a new synchronous and data-parallel distributed training scheme that reduced the amount of time required to train the flood-filling network.

Priority Research Directions for In Situ Data Management: Enabling Scientific Discovery from Diverse Data Sources

At the 14th Workshop on Workflows in Support of Large-Scale Science (WORKS19), Argonne computer scientist Tom Peterkaskeynote talkwill cover six priority research directions that highlight the components and capabilities needed for in situ data management to be successful for a wide variety of applications. In situ analysis tools can enable discoveries from a broad range of data sources HPC simulations, experiments, scientific instruments and sensor networks by helping researchers minimize data movement, save storage space and boost resource efficiency, often while simultaneously increasing scientific precision.

The Many Faces of Instrumentation: Debugging and Better Performance using LLVM in HPC

Argonne computational scientist Hal Finkel will deliver a keynote talk on the open-source LLVM compiler infrastructure at theWorkshop on Programming and Performance Visualization Tools (ProTools 19). LLVM, winner of the 2012 ACM Software System Award, has become an integral part of the software-development ecosystem for optimizing compilers, dynamic-language execution engines, source-code analysis and transformation tools, debuggers and linkers, and a host of other programming language- and toolchain-related components. Finkel will discuss various LLVM technologies, HPC tooling use cases, challenges in using these technologies in HPC environments, and interesting opportunities for the future.

About Argonne National Laboratory

Argonne National Laboratoryseeks solutions to pressing national problems in science and technology. The nations first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance Americas scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed byUChicago Argonne, LLCfor theU.S. Department of Energys Office of Science.

About the U.S. Department of Energys Office of Science

The U.S. Department of Energys Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit https://energy.gov/science

Source: Jim Collins, Argonne National Laboratory

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Argonne Researchers to Share Scientific Computing Insights at SC19 - HPCwire

How to Make the Most of Your Old Tech – New York Magazine

Photo: Billy H.C. Kwok/Bloomberg via Getty Images

If youre the kind of tech person who likes to stay on the cutting edge, the kind who upgrades their phone every year or rotates laptops with a significant frequency, then it can be tough to know what you should do with your old stuff. I mean, yeah, you could throw it out or try to sell it on eBay, but you can also put it to work in other useful ways. Here are some ideas.

Remote control

A lot of people, myself included, use their phones to control their TV and stereo. You can cast stuff video to your TV or music to a smart speaker via functions like Airplay or Chromecast. If you want to unplug and put your smartphone away when youre at home, having a separate device for a remote control is extremely helpful.

Gaming

Yeah, smartphones are good for mobile games, but theres some cool stuff on the horizon as well. Companies like Google and Microsoft are working on cloud gaming, letting you (theoretically) play console-quality games on your phone by streaming video from a remote server. Newer Android phones and iPhones with iOS 13 are compatible with Xbox and PlayStation controllers, so its worth keeping an old smartphone around if youre interested in checking it out.

Webcam

If youre worried about security but not worried enough to buy a dedicated camera, you can use an old Android phone instead. Most guides recommend an app called IP Webcam to get it working. Once its set up, you can check in on things while youre out.

Spare GPS device

Even if you dont have an internet connection, your old smartphones GPS system should still work. Popular apps like Google Maps let you cache navigational data and save it offline, so theres nothing stopping you from keep an old phone in your car just in case.

PC media server

Instead of junking your old PC, set it up as a media server (heres a tutorial) so you can access movies, music, and family photos from any device on your network. It makes it easier to share stuff with your household without manually sending files around.

Participate in a science project

Folding@home is a distributed computing project that simulates protein folding, computational drug design, and other types of molecular dynamics. Its a program that runs in the background on computers and aids medical research. Is your old PC going to cure cancer? Probably not. But itll help in a small way.

Strip it for parts

This is a long shot but theres a healthy aftermarket for old PC parts, in part because you cant really buy individual components directly from manufacturers. You can also do it just to see if you can. iFixIt sells plenty of ready-made kits for any assembly/disassembly project you might pursue.

Give it to your parents

This ones pretty obvious but you can save yourself some time and headaches by taking an old computer or phone, setting it up yourself, and then giving it to your parents. Theyre not going to replace that old Gateway on their own!

Paperweight

When the electrical grid eventually fails and we return to using paper for everything, youre gonna need something to hold all of that paper down.

Daily news about the politics, business, and technology shaping our world.

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How to Make the Most of Your Old Tech - New York Magazine

Study Reveals Hepatitis A Originated in Insects – Advanced Science News

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Hepatitis viruses are major threats to human health, resulting in the death of approximately 1.34 million people in 2015chronic infection from the viruses can severely damage the liver, leading to cirrhosis and hepatocellular cancer.

Despite sharing some common characteristics, such as a tropism for liver cells, human hepatitis viruses are extremely diverse and belong to different families. In recent years, the availability of high-throughput technologies has revealed that relatives of human hepatitis viruses can be found in a wide variety of animals, such as monkeys, woodchucks, rabbits, fish, and camels. By comparing infection patterns among viruses in human and animal hosts, scientists hope to gain a clearer understanding of the evolution and viral properties that led to human infection.

Now, researchers from the University of Arkansas for Medical Sciences and Molecular Microbiology and Genomics Consultants in Zotzenheim, Germany have reported a similarity between hepatitis A virus and the Triatoma virus that infects blood-sucking kissing bugs.

According to the studys lead author, Dr. Trudy Wassenaar, the codon use of hepatitis A virus (its preference for codons used to produce proteins from genes) is highly sub-optimal for propagation in its human host.

One would expect hepatitis A to have similarities to other hepatitis viruses, such as hepatitis C and E, as they all have similar genetic materials. Navely one could therefore think they would share genetic characteristics, but that is not the case, their genes are completely different, says Wassenaar. Nevertheless, the comparison of hepatitis C and E assisted in building a hypothesis and asking the right questions.

Mainly, why are hepatitis A infections self-limiting, while hepatitis C and E often cause chronic infections, and why does hepatitis A not propagate as quickly as the other two? The rules of evolution would not easily allow a virus to end up in such a bad situation [referring to its limited propagation], she says. We considered the possibility that there once existed an alternative host in which the hepatitis A virus had propagated and for which its codon use had been more optimized.

Two theories currently exist that try to explain this strange, sub-optimal performance. One proposes that hepatitis A uses uncommon codons to compete with the translational machinery of the host cell, while the other proffers that slower production of one particular protein is beneficial for its proper folding.

An evolutionary drive cant result in slower growth of a virus from an existing situation that allowed faster growth, says Wassenaar. That is simply impossible, so we dismissed [the first] theory. If [proper folding] had been the driving force to produce the observed codon use, one would expect the sequences coding for that [specific] protein to [be the only ones to] use uncommon codons, but that was not what we observed. Therefore, an alternative theory was needed.

The team therefore started looking for viruses that shared the same codons as hepatitis A virus, and surprisingly they found that some insect viruses were adequate matches.

To the best of our knowledge, this is the first example of a mammalian virus originating from an insect virus, but it most likely will not be the last. Further, it shows that a virus can maintain itself in a host even when it is not optimally equipped for that host. It illustrates just how versatile viruses can be, she adds.

To compare the genetic materials of the viruses, the team used specialized software that quickly calculates the frequency of each codon that a virus uses and compared the preference for codons of hepatitis A virus to the codons that human cells prefer.

Others had done this before us, but they had always corrected for particular differences and we deliberately omitted those corrections, says Wassenaar. That resulted in such an incredible difference: every single codon that human cells hate was preferred by the hepatitis A virus, and vice versa.

Then the team stumbled upon an insect virus that did exactly the same. That we found this was a bit of serendipity, as I was actually searching for something else. It was my lucky day! Suddenly all pieces of the puzzle matched. We then had to perform further analyses to convince ourselves, as it was a bit of a wild idea that an insect virus can actually propagate in mammals. But now we are convinced this is what happened, and we have started to look for other examples.

This study provides valuable insight into the evolution of human hepatitis viruses, challenging the idea that they all evolved from mammalian viruses.

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Study Reveals Hepatitis A Originated in Insects - Advanced Science News

Coronavirus pushes Folding@Homes crowdsourced molecular science to exaflop levels – TechCrunch

The long-running Folding@Home program to crowdsource the enormously complex task of solving molecular interactions has hit a major milestone as thousands of new users sign up to put their computers to work. The network now comprises an exaflop of computing power: 1,000,000,000,000,000,000 operations per second.

Folding@Home started some 20 years ago as a way then novel, and pioneered by the now-hibernating SETI@Home to break up computation-heavy problems and parcel them out to individuals for execution. It amounts to a crude supercomputer distributed over the globe, and while its not as effective as a real supercomputer in blasting through calculations, it can make short work of complex problems.

The problem in question being addressed by this tool (administrated by a group at Washington University in St. Louis) is that of protein folding. Proteins are one of the many chemical structures that make our biology work, and they range from small, relatively well-understood molecules to truly enormous ones.

The thing about proteins is that they change their shape depending on the conditions temperature, pH, the presence or absence of other molecules. This change in shape is often what makes them useful for example, a kinesin protein changes shape like a pair of legs taking steps to carry a payload across a cell. Another protein like an ion channel will open to let charged atoms through only if another protein is present, which fits into it like a key in a lock.

Image Credits: Voelz et al.

Some such changes, or convolutions, are well-documented, but most by far are totally unknown. But through robust simulation of the molecules and their surroundings we can discover new information about proteins that may lead to important discoveries. For example, what if you could show that once that ion channel is open, another protein could lock it that way for longer than usual, or close it quickly? Finding those kinds of opportunities is what this sort of molecular science is all about.

Unfortunately its also extremely computation-expensive. These inter- and intra-molecular interactions are the kind of thing supercomputers can grind away at endlessly to cover every possibility. Twenty years ago supercomputers were a lot rarer than they are today, so Folding@Home started as a way to do this sort of heavy computing load without buying a $500 million Cray setup.

The program has been chugging along the whole time, and likely got a boost when SETI@Home recommended it as an alternative to its many users. But the coronavirus crisis has made the idea of contributing ones resources to a greater cause highly attractive, and as such there has been a huge increase in users so much so that the servers are struggling to get problems out to everyones computers to solve.

Examples of COVID-19-related proteins as visualized by Folding@Home.

The milestone its celebrating is the achievement of an exaflop of processing power, which is I believe a sextillion (a billion billion) operations per second. An operation is a logical operation, like AND or NOR, and several of them together form mathematical expressions, which eventually add up to useful stuff like saying at temperatures above 38 degrees Celsius this protein deforms to allow a drug to bind at this site and disable it.

Exascale computing is the next goal of supercomputers; Intel and Cray are building exascale computers for the National Laboratories that are expected to come online in the next couple of years but the fastest supercomputers available today operate at a scale of hundreds of petaflops, or about half to a third the speed as an exaflop.

Naturally these two things are not directly comparable Folding@Home is marshaling an exaflops worth of computing power, but it is not operating as a single unit working on a single problem, as the exascale systems are built to. The exa- label is there to give a sense of scale.

Will this kind of analysis lead to coronavirus treatments? Perhaps later, but almost certainly not in the immediate future. Proteomics is basic research in that it is at heart about better understanding the world around (and within) us period.

COVID-19 (like Parkinsons, Alzheimers, ALS and others) isnt a single problem, but a large, poorly bounded set of unknowns; its proteome and related interactions are part of that set. The point isnt to stumble onto a magic bullet but to lay a foundation for understanding so that when we are evaluating potential solutions, we can pick the right one even 1% faster because we know that this molecule in that situation acts like so.

As the project noted in a blog post announcing the release of coronavirus-related work:

This initial wave of projects focuses on better understanding how these coronaviruses interact with the human ACE2 receptor required for viral entry into human host cells, and how researchers might be able to interfere with them through the design of new therapeutic antibodies or small molecules that might disrupt their interaction.

If you want to help, you can download the Folding@Home client and donate your spare CPU and GPU cycles to the cause.

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Coronavirus pushes Folding@Homes crowdsourced molecular science to exaflop levels - TechCrunch

AMD COVID-19 HPC Fund to Deliver Supercomputing Clusters to Researchers Combatting COVID-19 – HPCwire

SAN CLARA, Calif., Jun 1, 2020 AMD and technology partnerPenguin Computing Inc., a division ofSMART Global Holdings, Inc., announced that New York University (NYU), Massachusetts Institute of Technology (MIT) and Rice University are the first universities named to receive complete AMD-powered, high-performance computing systems from the AMD HPC Fund for COVID-19 research. AMD also announced it will contribute a cloud-based system powered by AMD EPYC and AMD Radeon Instinct processors located on-site at Penguin Computing, providing remote supercomputing capabilities for selected researchers around the world. Combined, the donated systems will collectively provide researchers with more than seven petaflops of compute power that can be applied to fight COVID-19.

High performance computing technology plays a critical role in modern viral research, deepening our understanding of how specific viruses work and ultimately accelerating the development of potential therapeutics and vaccines, said Lisa Su, president and CEO, AMD. AMD and our technology partners are proud to provide researchers around the world with these new systems that will increase the computing capability available to fight COVID-19 and support future medical research.

The recipient universities are expected to utilize the new compute capacity across a range of pandemic-related workloads including genomics, vaccine development, transmission science and modeling. Additionally, scientists from around the world conducting COVID-19 research can request access to the remote AMD-powered cloud HPC cluster at Penguin Computing by submitting proposals to[emailprotected].

University Engagement

The receiving universities are preparing their research plans and infrastructure now to receive the systems, including defining specific research projects that can have both immediate and long-term impact.

NYU

The COVID-19 pandemic has had a profound impact on higher education research, both in terms of its direction and the need for immediate results, so the timing of this donation is particularly fortuitous, and were tremendously grateful to AMD, said Russel Caflisch, director of the NYU Courant Institute of Mathematical Sciences. The computing resources donated by AMD will be put to use by NYU researchers from a wide range of disciplines in projects to address the many important facets of the COVID-19 crisis, including: discovery of drugs that may be therapeutic for COVID-19 and future SARS virus mutations, retrieval of relevant research results from the vast biomedical literature, analysis of medical imaging for screening of patients, and analyzing political attitudesand voting behavior in response to financial hardships.

MIT

Across MIT we are engaged in work to address the global COVID-19 pandemic, from that with immediate impact such as modeling, testing, and treatment, to that with medium and longer term impact such as discovery of new therapeutics and vaccines. Nearly all of this work involves computing, and much of it requires the kind of high performance computing that AMD is so generously providing with this gift of a Petaflop machine, says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing.

Rice

At the Center for Theoretical Biological Physics,Rice researcher Jos Onuchic is using his previous studies on influenza A as a guide to explore how the coronaviruss surface proteins facilitate entrance to human cells, the critical first step of infection. Another scientist, Peter Wolynes, is using principles from his foundational theories of protein folding to screen thousands of drug molecules and identify the best candidates for clinical tests based upon how well they bind to the viruss surface proteins.

The AMD gift will be truly transformational for Rices computational attack on COVID-19, said Peter Rossky, dean of Rices Wiess School of Natural Sciences. We have the methods to progress, but studies of large, complex systems are at the cutting-edge of computational feasibility. The AMD contribution of dedicated, state-of-the-art computational power will be a game changer in accelerating progress toward defeating this virus.

AMD Ecosystem Partners

AMD has joined with well-known HPC and AI solutions firm Penguin Computing to define, build, and deliver the on-premises systems and Penguins Penguin on Demand (POD) cluster, powered by AMD. Penguin Computings POD support will be collocated in data center space donated by DataBank. Contributions from Penguin Computing, NVIDIA, Gigabyte, and others are helping the AMD HPC Fund advance COVID-19 research.

Penguin Computing is looking forward to supporting and contributing to the COVID-19 research efforts through this AMD collaboration. We are committed to providing our applications and technology expertise in high performance computing, artificial intelligence and data analytics to both the University on-premises and our remote POD cloud environments, said Sid Mair, President, Penguin Computing Inc.

Ultra-fast data speeds and smart data-processing are key to delivering insights that science demands, particularly in these challenging times, said Gilad Shainer, senior vice-president of marketing for Mellanox networking at NVIDIA. NVIDIA Mellanox HDR 200 gigabit InfiniBand solutions provide high data throughput, extremely low latency, and application offload engines that accelerate bio-science simulations and further the development of treatments against the coronavirus.

Gigabyte is supplying its G290-Z21 compute nodes for the Penguin clusters, built around a single, 48-core AMD EPYC 7642 processor paired with eight Radeon Instinct MI50 GPU accelerators. The system R182-291 management nodes, also from Gigabyte, each utilize two 16-core, AMD EPYC 7302 processors.

AMD Commitment to COVID-19 Research

The AMDCOVID-19 HPC fund was established to provide research institutions with computing resources to accelerate medical research on COVID-19 and other diseases. In addition to the initial donations of $15 million of high-performance computing systems, AMD has contributed technology and technical resources to nearly double the peak system of the Corona system at Lawrence Livermore National Laboratory which is being used to provide additional computing power for molecular modeling in support of COVID-19 research.

About AMD

For more than 50 years AMD has driven innovation in high-performance computing, graphics and visualization technologies the building blocks for gaming, immersive platforms and the datacenter. Hundreds of millions of consumers, leading Fortune 500 businesses and cutting-edge scientific research facilities around the world rely on AMD technology daily to improve how they live, work and play. AMD employees around the world are focused on building great products that push the boundaries of what is possible. For more information about how AMD is enabling today and inspiring tomorrow, visit the AMD (NASDAQ: AMD)website,blog,FacebookandTwitterpages.

Source: AMD

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AMD COVID-19 HPC Fund to Deliver Supercomputing Clusters to Researchers Combatting COVID-19 - HPCwire

Club Log Allocates 100% of its Computing Resources to COVID-19 Protein Research – ARRL

03/24/2020

Michael Wells, G7VJR, has announced that Club Log is contributing 120 CPU cores (most running at 3.4 GHz) to the Folding@Home Project thats simulating the dynamics of COVID-19 proteins to hunt for new therapeutic opportunities. Wells said hes assigned a higher priority to the Folding@Home work, so radio amateurs may experience slightly longer upload times.

You can help, too, by contributing your own computer to the project, Wells said. If you have a recent home computer with a good graphics card, and if a lot of people make a contribution, it will make a significant difference to the research, potentially reducing decades of work to a far shorter time frame that will make a practical difference this year. He cautions that computers involved in the project will be operating at 100% CPU, when not otherwise in use.

Club Logs Folding@Home team number is 246763.

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Club Log Allocates 100% of its Computing Resources to COVID-19 Protein Research - ARRL