NVIDIA (NASDAQ:NVDA) has been upgraded by the analysts at Needham to a Buy from the previous Hold recommendation. The stocks price target has also been increased to $270, marking a 10.36 percent upside potential from current levels.

According to the investment note penned by Needham analyst Rajvindra Gill today, investors should head for companies with "superior balance sheets and robust free cash flow" during the current environment characterized by acute macro uncertainty.

COVID-19 Pandemic Accelerating Re-Shoring

Mr. Gill saw the increasing need for GPUs in distributed computing medical applications amid the ongoing coronavirus (COVID-19) pandemic as a key bullish factor behind the upgrade.

As a refresher, projects such as Folding@home utilize distributed computing power to simulate and analyze the process of protein folding along with the diseases and complications that arise from protein misfolding and aggregation.

Before discussing implications for NVIDIA, additional context may be beneficial to our readers. Proteins are essentially complex chains of amino acids that perform a variety of functions in an organisms body from acting as building blocks of bones and muscles to stimulants for biochemical reactions (enzymes). While scientists have sequenced the human genome, it is not very helpful in trying to analyze the precise manner in which a particular protein functions. This is where a folding analysis comes handy. Folding is simply the manner in which a protein folds, adopting a particular shape in the process. This shape or fold then determines the function that a specific protein performs (of course a proteins constituents are also considered in this analysis).

These findings, in turn, assist medical professionals in developing new drugs to counter a myriad of diseases, including Alzheimers disease, Huntingtons disease, cystic fibrosis, BSE (Mad Cow disease), etc. With the advent of the coronavirus pandemic, these distributed computing projects have received an added impetus as thousands of users from around the world have banded together to loan their computing power to medical experts who are trying to counter this pandemic, indirectly driving a short-term boost in GPU demand to NVIDIAs benefit. In fact, Wccftech has been an enthusiastic partner in this endeavor.

It should be noted though that these distributed computing projects run calculations on millions of PCs in order to simulate protein folding. For this reason, the success of Folding@home and other similar projects is quite hard to match as data centers take time to build and are generally not scalable. Scalability is an essential requirement for these tasks as the computing power is only required for a maximum of 2 to 3 months for a specific project. Therefore, any boost that NVIDIA receives will likely only be transitory.

Facebook (NASDAQ: FB) Stock Price Target Slashed by Cowen INC. (NASDAQ: COWN) as the Social Media Giant Reveals a Slump in Ad Revenue Amid the Economic Slowdown

Of course, this is not the only upgrade that NVIDIA has received in recent days. In an investment note published on the 12th of March, Morgan Stanley (NYSE:MS) named the company one of its top ideas in the semiconductor space. According to the Wall Street behemoth, the recent selloff has made NVIDIAs current valuation attractive.

The analyst Joseph Moore wrote:

For larger cap growth with the best chance of powering through tough conditions, we favor Nvidia.

Moreover, a recovery in cloud spending and the expected launch of NVIDIAs 7nm Ampere in the second half of 2020 provided additional impetus for Morgan Stanleys upgrade.

Interestingly, Moore pointed to the growing need for remote computing amid the coronavirus pandemic as a bullish factor for NVIDIA.

NVIDIAs stock has declined by 9.61 percent year to date (based on Mondays closing price). For comparison, the NASDAQ Composite has declined by 18.87 percent in the same timeframe. The stock is currently trading at $244.65, up by 15.33 percent (as of 10:48 a.m. ET).

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NVIDIA (NASDAQ: NVDA) Upgraded Over the Growing Deployment of Distributed Computing for Medical Applications to Counter the Coronavirus (COVID-19)...

IBM collaborates with the US government to help fight coronavirus using supercomputers

IBM has teamed up with the White House Office of Science and Technology and the US Department of Energy to form a new public/private consortium that will use supercomputers to help fight coronavirus pandemic.

The initiative, named COVID-19 High Performance Computing Consortium, aims to deliver an unprecedented amount of computing power to help researchers better understand COVID-19 and its potential cures.

It will pool supercomputing capacity of various organisations, including IBM, Oak Ridge National Laboratory, Lawrence Livermore National Lab, Sandia National Laboratory, Argonne National Lab, the National Science Foundation, Los Alamos National Laboratory, NASA, and Rensselaer Polytechnic Institute.

In total, 16 supercomputers will be used, offering 775,000 CPU cores, 34,000 GPUs, and more than 330 petaflops of computing power to researchers.

Google, Amazon, and Microsoft are among the members of the consortium.

"These high-performance computing systems allow researchers to run very large numbers of calculations in epidemiology, bioinformatics, and molecular modelling," said Dario Gil, director of IBM Research.

"These experiments would take years to complete if worked by hand, or months if handled on slower, traditional computing platforms."

In addition to offering assistance to American researchers, the consortium will also invite proposals from researchers around the world. The projects that are likely to have an immediate impact will be provided access to supercomputing resources.

According to IBM, the medical researchers at the University of Tennessee and Oak Ridge National Laboratory are already using its IBM Summit supercomputer to identify compounds that can impact the infection process by binding the main spike protein of coronavirus.

So far, the researchers have screened nearly 8,000 compounds in past few days and have identified 77 promising compounds which could potentially impair COVID-19's ability to infect human cells.

At the time of writing, there have been more than 294,110 confirmed cases of COVID-19 worldwide, as per the WHO data, with nearly 12,900 fatalities due to the infection.

Last week, distributed computing project Folding@home (FAH) urged researchers and PC gamers worldwide to donate some of their CPU and GPU computing power to help in the fight against coronavirus.

FAH, which is based in the Stanford University, 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.

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.

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

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IBM collaborates with the US government to help fight coronavirus using supercomputers - http://www.computing.co.uk

Ocean Currents. Credit: NASA/Goddard Space Flight Center Scientific Visualization Studio

Study shows ripples across a newly fertilized egg are similar to other systems, from ocean and atmospheric circulations to quantum fluids.

When an egg cell of almost any sexually reproducing species is fertilized, it sets off a series of waves that ripple across the eggs surface. These waves are produced by billions of activated proteins that surge through the eggs 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.

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

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 eggs 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. Its 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 cells cytoplasm in an inactive state. Once activated, billions of the protein rise up out of the cytoplasms morass to attach to the eggs 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 eggs membrane serve, in part, to organize cell division around the cells 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.

In their study, the team focused on the active form of Rho-GTP and the pattern of waves produced on an eggs surface when they altered the proteins 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 eggs 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 eggs 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 eggs 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, its essentially the same as vortices in a fluid, or waves in the brain, or systems on a larger scale, Dunkel says. Its 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.

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Pattern of Waves Found in Growing Organisms Similar to Ocean Circulations and Quantum Fluids - SciTechDaily

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Folding@home targets COVID-19, number of volunteer folders skyrockets

Shown is the first look at the Folding@home project's simulations of the COVID-19 spike protein. The three colors represent components of the spike protein; this is the protein that the novel coronavirus uses to infect cells. The site where the protein binds to human cells, to infect them, is on the top of the protein. Using Folding@home, the researchers are aiming to develop an accurate picture of what happens during infection. Understanding these details could help reveal ways to block the virus from infecting cells.

People around the world are isolating themselves to help slow the spread of COVID-19. But there is another way those confined to their homes but connected online can join the fight against the novel coronavirus. Among the research programs racing to develop therapies and vaccines for this new pandemic virus is one of the largest crowdsourced supercomputing projects in the world.

Led by computational biophysicist Greg Bowman, PhD, an associate professor of biochemistry and molecular biophysics at Washington University School of Medicine in St. Louis, the project is called Folding@home. It relies on the collective power of volunteers home computers to perform the complex calculations required to simulate protein dynamics.

Volunteers from all over the world can install a software program that runs those calculations when a computer otherwise would sit idle. Often motivated by personal experience with various diseases, the participants get to select an area of contribution, such as boosting cancer research, preventing Alzheimers disease or now fighting the novel coronavirus.

For example, Bowman and his team are trying to understand the structure of COVID-19s spike protein, which is what the virus uses to infect cells. Such research could reveal ways to block the protein and, consequently, infection. Since announcing in late February the projects new focus on coronavirus, the number of Folding@home volunteers has skyrocketed, with some 400,000 new folders joining the effort, Bowman said.

The response so far has been overwhelming and wonderful, but there is always more useful science to be done, he said. Understanding all the various shapes that the spike protein can take on as its molecules bounce and shift can lead to the development of new drugs that can block it, stopping the virus from infecting more cells. We are continuing to scale up our research as fast as we can.

Bowman has shared what could be thought of as a first glimpse of the moving COVID-19 spike protein. It consists of three different proteins that fit together like a 3D puzzle. The simulation reveals a pocket that helps the virus bind to human cells and infect them.

In the spirit of open science and the rapid sharing of new knowledge about COVID-19, Bowman said the research team will publish findings on free and open-access preprint sites, such as bioRxiv.

To join the effort and put your computer to work against coronavirus, visit https://foldingathome.org.

Washington University School of Medicines 1,500 faculty physicians also are the medical staff of Barnes-Jewish and St. Louis Childrens hospitals. The School of Medicine is a leader in medical research, teaching and patient care, ranking among the top 10 medical schools in the nation by U.S. News & World Report. Through its affiliations with Barnes-Jewish and St. Louis Childrens hospitals, the School of Medicine is linked to BJC HealthCare.

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Crowdsourced supercomputing project sets sights on coronavirus - Washington University School of Medicine in St. Louis

On Wednesday morning we asked the Hackaday community to donate their extra computer cycles for Coronavirus research. On Thursday morning the number of people contributing to Team Hackaday had doubled, and on Friday it had doubled again. Thank you for putting those computers to work in pursuit of drug therapies for COVID-19.

Im writing today for two reasons, we want to keep up this trend, and also answer some of the most common questions out there. Folding@Home (FAH) is an initiative that simulates proteins associated with several diseases, searching for indicators that will help medical researchers identify treatments. These are complex problems and your efforts right now are incredibly important to finding treatments faster. FAH loads the research pipeline, generating a data set that researchers can then follow in every step of the process, from identifying which chemical compounds may be effective and how to deliver them, to testing they hypothesis and moving toward human trials.

First, heres the rundown on how easy it is to set your computer up to help with Folding@Homes Coronavirus effort:

Okay, lets answer some questions! First up, does this actually make a difference?

The Folding@Home project started back in 2000. Much has been accomplished over the course of the past 20 years and I encourage you to go and read the lengthy Examples of application in biomedical research section of the Wikipedia page which takes an in-depth look at the impacts.

The effort has identified drug therapies for Alzheimers and Huntingtons diseases, its been used in drug design for combating HIV and influenza (both are viral), and is used to study how cancer mutates. Now we have the chance to apply that to the COVID-19 virus. On an explain-it-like-Im-five level, scientists are trying to simulate every possible combination of protein folding patterns, looking for locations that would let medicine grab hold and do some good.

Its a huge challenge, similar to trying every combination on a padlock, but this lock takes a mind-bogglingly large number of combinations. Research scientists highlight where the most likely solutions lie, then use the mind-bogglingly huge power of the Folding@Home network and sets to work running the simulations. How powerful is the FAH network? Wikipedia lists it at 470 petaFLOPS as of early March 2020 which means 416 quadrillion floating point operations per second. Thats 416 million billion math problems solved every second!

But heres the best part of all of this, the project is non-profit and makes the data freely available to other researchers upon request.

No, but you dont need to since the group is already prioritizing the coronavirus effort. Although the software does offer the option to work on a specific area of research, COVID-19 is not specifically listed. That is likely because this pandemic is fast moving and its not worth trying to push a new version of the software just to add this setting. For now, leave this on the default of Any and your computer will work on COVID-19 whenever there are Work Units (WU) available.

You can use the built-in web interface found at http://localhost:7396/ to see what problem your computer is currently working on. Here you can see the Learn more screen from currently running instance. This week I have only seen one time that my computer was working on a different project.

The FAH servers dish out those WUs as fast as they can, but right now the network is growing as more people add their computers to the network. When all of the staged WUs run out, your computer will be idle until more become available. This has nothing to do with you, project maintainers are working to keep this buffer full.

Im not an expert but I believe the answer is that this research seeks to identify pharmaceutical treatments and a better understanding of how the protiens in the virus work. This is not necessarily in pursuit of a vaccine.

This is still incredibly important, it means that researchers are looking for drugs that can be used to treat patients who have the virus. Right now, COVID-19 is really good at evading our bodys natural defenses our immune system. If drug therapies are discovered that weaken the virus, it may lead to our immune system having a foothold to fight the infection.

We need both a vaccine and drug therapies consider the example of the seasonal flu where we have vaccines to protect people from infection and antiviral drugs to treat at-risk populations who have been infected. Research into both should be, and is, running in parallel.

This effort is gamified, so join your fellow hackers on Team Hackaday by using team #44851 when you configure your Folding@Home software. When we first published, we had 21 active team members, by Friday afternoon there were 737. Can we make that 7000 by the end of the week?

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Coronavirus And Folding@Home; More On How Your Computer Helps Medical Research - Hackaday

A couple of weeks ago we learned about a new game called Foldit, developed by researchers at the University of Washington, that could help with the development of a treatment for the Covid-19 coronavirus. Essentially, players solve puzzles by folding protein chains into new shapes that change the function of the protein, with points awarded based on the effectiveness of the solution. Researchers can then experiment on those folded proteins in order to determine their usefulness in the real world.

If that game doesn't appeal to you, but you happen to be sitting beside an expensive, powerful PC that's not really doing anything, why not let it handle the task for you?

In Foldit you have to work for a spot on that leaderboard, but through Folding@home, you can put your PC to work. Folding@home is a distributed computing project founded by Stanford University in 2000 that uses idle PCs around the world for medical research, including the coronavirus pandemic.

The way it works, essentially, is that protein data is broken up into work units, which are then downloaded automatically by the Folding software. Your PC crunches away on it until the work unit is complete, at which point the result is uploaded to the server. A new work unit is downloaded, and the process starts again. As a weak but thematically appropriate analogy, it's a bit like Team Fortress 2, except extraordinarily slow, it's nothing but bots, and the whole world is playing in the same match.

You can fold by yourself (and bravo for doing your bit) but these things are always more fun when you're part of a teamsuch as the PC Gamer Folding@home Team. The setup instructions might look a little intimidating but it's actually quite simple, and once you're rolling it's entirely automated, although you can tweak various settings, like how much processing power to dedicate and whether or not you want it to work while you're using your PC.

If you do run into problems or have any questions with Folding@home, or just feel like chatting, the PC Gamer forum thread linked above can help out. The Folding website is struggling a little bit right now, but once it's squared away you'll be able to following along with the team's progress here.

We're maintaining a roundup of esports competitions and other gaming events that have been impacted by the coronavirus outbreak that you can keep up with here.For more information on the Covid-19 coronavirus, visit the Centers for Disease Control for updates in North America, the European Centre for Disease Prevention and Control, or the World Health Organization.

This is what it looks like when proteins fold.

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Join PC Gamer's Folding@home team and help research a cure for Covid-19 - PC Gamer

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PC gamers are being asked to donate their unused computational power to help researchers better understand the novel coronavirus (COVID-19).

Folding@home is a distributed computing project for disease research which uses idle resources to simulate protein folding.

By downloading Folding@home, PC gamers can help researchers develop treatments for COVID-19, which has so far killed over 8,000 people and caused global disruption.

"The data you help us generate will be quickly and openly disseminated as part of an open science collaboration of multiple laboratories around the world, giving researchers new tools that may unlock new opportunities for developing lifesaving drugs," said Folding@home director Greg Bowman in a post detailing the project.

Early projects are geared around understanding how the virus interacts with human host cells, and developing new antibodies to disrupt it.

Folding@home intends to make the data readily available to researchers and the public.

"While we will rapidly release the simulation data sets for others to use or analyse, we aim to look for alternative conformations and hidden pockets within the most promising drug targets, which can only be seen in simulation and not in static X-ray structures," said Folding@home computational chemist John Chodera.

"We hope that these structures -- once validated by emerging compound screening data -- could help direct the virtual screening campaigns or the targeting of new pockets for which atomistic structures were not yet available."

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Researchers turn to PC gamers for help with COVID-19 - GamesIndustry.biz

[emailprotected] is a computing project that stimulates protein folding, computational drug design, and various other molecular dynamics for disease research. It tackles diseases like cancer, Alzheimers, Huntingtons, and Parkinsons. It has announced that now it will deal with COVID-19 as well.

The project is bringing together researchers worldwide who aim to understand the coronavirus better. This will speed up the effort going to developing therapies and cures that will save lives.

You can download [emailprotected] on your Mac. By doing this, you can donate the computational resources that you havent used to the [emailprotected] Consortium. Here, researchers are attempting to examine the structures of drugs that could potentially help in the new therapies for COVID-19.

Prior to installing [emailprotected], you need to know that it can use up a lot of CPU cycles. Thus, it is advisable that you run it only when your Mac is sitting idle.

To see the visual representation of the whichever molecules your Mac at that time, you can click the Viewer button.

Since this process takes up a lot of power, if you switch your laptop to battery power, the [emailprotected] client will automatically pause. You can change this setting as well by going to Configure Advanced Power.

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How you can help in fighting coronavirus using your Mac: Read to find out. - Gizmo Posts 24

If not the most deadly, the novel coronavirus (COVID-19) is one of the most contagious diseases to have hit our green planet in the past decades. In little over three months since the virus was first spotted in mainland China, it has spread to more than 90 countries, infected more than 185,000 people, and taken more than 3,500 lives.

As governments and health organizations scramble to contain the spread of coronavirus, they need all the help they can get, including from artificial intelligence. Though current AI technologies arefar from replicating human intelligence, they are proving to be very helpful in tracking the outbreak, diagnosing patients, disinfecting areas, and speeding up the process of finding a cure for COVID-19.

Data science and machine learning might be two of the most effective weapons we have in the fight against the coronavirus outbreak.

Just before the turn of the year, BlueDot, an artificial intelligence platform that tracks infectious diseases around the world, flagged a cluster of unusual pneumonia cases happening around a market in Wuhan, China. Nine days later, the World Health Organization (WHO)released a statementdeclaring the discovery of a novel coronavirus in a hospitalized person with pneumonia in Wuhan.

BlueDot usesnatural language processingandmachine learning algorithmsto peruse information from hundreds of sources for early signs of infectious epidemics. The AI looks at statements from health organizations, commercial flights, livestock health reports, climate data from satellites, and news reports. With so much data being generated on coronavirus every day, the AI algorithms can help home in on the bits that can provide pertinent information on the spread of the virus. It can also find important correlations between data points, such as the movement patterns of the people who are living in the areas most affected by the virus.

The company also employs dozens of experts who specialize in a range of disciplines including geographic information systems, spatial analytics, data visualization, computer sciences, as well as medical experts in clinical infectious diseases, travel and tropical medicine, and public health. The experts review the information that has been flagged by the AI and send out reports on their findings.

Combined with the assistance of human experts, BlueDots AI can not only predict the start of an epidemic, but also forecast how it will spread. In the case of COVID-19, the AI successfully identified the cities where the virus would be transferred to after it surfaced in Wuhan. Machine learning algorithms studying travel patterns were able to predict where the people who had contracted coronavirus were likely to travel.

Coronavirus (COVID-19) (Image source:NIAID)

You have probably seen the COVID-19 screenings at border crossings and airports. Health officers use thermometer guns and visually check travelers for signs of fever, coughing, and breathing difficulties.

Now,computer vision algorithmscan perform the same at large scale. An AI system developed by Chinese tech giant Baidu uses cameras equipped with computer vision and infrared sensors to predict peoples temperatures in public areas. The system can screen up to 200 people per minute and detect their temperature within a range of 0.5 degrees Celsius. The AI flags anyone who has a temperature above 37.3 degrees. The technology is now in use in Beijings Qinghe Railway Station.

Alibaba, another Chinese tech giant, has developed an AI system that candetect coronavirus in chest CT scans. According to the researchers who developed the system, the AI has a 96-percent accuracy. The AI was trained on data from 5,000 coronavirus cases and can perform the test in 20 seconds as opposed to the 15 minutes it takes a human expert to diagnose patients. It can also tell the difference between coronavirus and ordinary viral pneumonia. The algorithm can give a boost to the medical centers that are already under a lot of pressure to screen patients for COVID-19 infection. The system is reportedly being adopted in 100 hospitals in China.

A separate AI developed by researchers from Renmin Hospital of Wuhan University, Wuhan EndoAngel Medical Technology Company, and the China University of Geosciences purportedly shows 95-percent accuracy on detecting COVID-19 in chest CT scans. The system is adeep learning algorithmtrained on 45,000 anonymized CT scans. According to a preprint paperpublished on medRxiv, the AIs performance is comparable to expert radiologists.

One of the main ways to prevent the spread of the novel coronavirus is to reduce contact between infected patients and people who have not contracted the virus. To this end, several companies and organizations have engaged in efforts to automate some of the procedures that previously required health workers and medical staff to interact with patients.

Chinese firms are using drones and robots to perform contactless delivery and to spray disinfectants in public areas to minimize the risk of cross-infection. Other robots are checking people for fever and other COVID-19 symptoms and dispensing free hand sanitizer foam and gel.

Inside hospitals, robots are delivering food and medicine to patients and disinfecting their rooms to obviate the need for the presence of nurses. Other robots are busy cooking rice without human supervision, reducing the number of staff required to run the facility.

In Seattle, doctors used a robot to communicate with and treat patients remotely to minimize exposure of medical staff to infected people.

At the end of the day, the war on the novel coronavirus is not over until we develop a vaccine that can immunize everyone against the virus. But developing new drugs and medicine is a very lengthy and costly process. It can cost more than a billion dollars and take up to 12 years. Thats the kind of timeframe we dont have as the virus continues to spread at an accelerating pace.

Fortunately, AI can help speed up the process. DeepMind, the AI research lab acquired by Google in 2014, recently declared that it has used deep learning to find new information about the structure of proteins associated with COVID-19. This is a process that could have taken many more months.

Understanding protein structures can provide important clues to the coronavirus vaccine formula. DeepMind is one of several organizations who are engaged in the race to unlock the coronavirus vaccine. It has leveraged the result of decades of machine learning progress as well as research on protein folding.

Its important to note that our structure prediction system is still in development and we cant be certain of the accuracy of the structures we are providing, although we are confident that the system is more accurate than our earlier CASP13 system, DeepMinds researchers wroteon the AI labs website. We confirmed that our system provided an accurate prediction for the experimentally determined SARS-CoV-2 spike protein structure shared in the Protein Data Bank, and this gave us confidence that our model predictions on other proteins may be useful.

Although its too early to tell whether were headed in the right direction, the efforts are commendable. Every day saved in finding the coronavirus vaccine can save hundredsor thousandsof lives.

This story is republished fromTechTalks, the blog that explores how technology is solving problems and creating new ones. Like them onFacebookhere and follow them down here:

Published March 21, 2020 17:00 UTC

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Why AI might be the most effective weapon we have to fight COVID-19 - The Next Web

Im a geneticist. I study the connection between information and biology essentially what makes a fly a fly and a human a human. Interestingly, were not that different. Ive been a professional geneticist since the early 1990s. Im reasonably good at this, and my research group has done some really good work over the years.

But one of the challenges of the job is coming to grips with the idea that much of what we think we know is in fact wrong. Sometimes, were just off a little and we try to get a little closer to the answer. At some point, though, its likely that were just flat out wrong in some aspect.

We cant know when were wrong, but its important to remain open-minded and adaptable so we can learn from our mistakes. Especially because sometimes the stakes can be incredibly high with lives on the line (more on this later).

In the late 1980s, cattle started wasting away. In the late stages of what was slowly recognized as a disease, cattle began acting in such bizarre manner that their condition bovine spongiform encephalitis became known as mad cow disease. Strikingly, the brains of the cattle were full of holes (hence spongiform) that were caked with plaques of proteins clumped together; these were proteins that were found in the brains of healthy cattle, but now they had an unnatural shape.

Proteins are long chains, but they fold into specific complex shapes. But the proteins in the cattles brains were misfolded. Some time after, people started dying from the same symptoms, and a connection was made between eating infected cattle and contracting the disease. Researchers determined that the culprit was consumption of brain and spinal tissue, the only tissue that showed the physical effects of infection.

One of the challenges to explaining mad cow disease was the length of time from infection to disease to death. Diseases, we knew, were transmitted by viruses and bacteria, but no scientist could isolate one that would explain this disease. Further, no one knew of other viruses or bacteria whose infection would take this long to lead to death. Science leaned toward assuming a viral cause, and careers and reputations were built on finding the slow virus.

In the late 1980s, a pair of British researchers suggested that perhaps the misfolded proteins in the plaques was key. This proposal was soon championed by Stanley Prusiner, a young American researcher early in his career. The idea was simple: the misfolded protein was both the result and cause of the infection.

The misfolded protein plaques killed brain tissue and caused correctly folded versions of the proteins to misfold. Prusiners hypothesis was straightforward, but it didnt fit the way scientists understood diseases to work. Diseases are transmitted as DNA (and in rare cases, RNA) by viruses or bacteria. But they are not transmitted in protein folding.

For holding this protein-based view of infection, Prusiner was literally and metaphorically shouted out of the room. Then he showed, experimentally and elegantly, that the misfolded proteins, which he called prions, were the cause of these diseases. For this accomplishment, he was awarded the 1997 Nobel Prize in medicine.

We now know that prions are responsible for a series of diseases in humans and other animals, including chronic wasting disease, a disease whose spread poses a serious threat to deer and elk in Ontario. And, circling back, the over-cooked burger phenomenon is because of these proteins. If you heat the prions sufficiently, they lose their unnatural shape all shape actually and they cant transmit the disease.

So in this case, the information necessary for disease transmission is the shape of the protein, not in the genetic code of an infecting virus or bacteria. This fact is why this case specifically speaks to me as a geneticist. All my career, Ive been trained to look for answers in DNA sequences. Prions remind me that sometimes really interesting answers are not were we expect them to be.

Where does this leave us? To me, the take-home message is that we need to remain skeptical but curious. Examine the world around us with open eyes and be ready to challenge and question our assumptions. Also, we shouldnt ignore what is in front of us simply because it doesnt fit our understanding of the world around us.

Climate change, for example, is real. Its another example of why its important to be open to being wrong and the need to try to get it right. Medical science only started controlling mad cow disease after we understood the role of prions, and the years of denial cost an untold number of lives.

Similarly, our global refusal to accept the massive climate change around us, and our obvious role in it, is leading us into one weather-based disaster after another, and all the loss of life associated with these disasters.

Ive spent a lot of time in my career putting together models of how the biological world works, but I know that pieces of these models are wrong. I can almost guarantee you that I have something as fundamentally wrong as those prion-deniers, I just dont know what it is. Yet.

But the important thing isnt to be right. Instead, it is to be open to seeing when you are wrong.

[ Youre smart and curious about the world. So are The Conversations authors and editors. You can read us daily by subscribing to our newsletter. ]

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In science, its better to be curious than correct - The Conversation CA

The big picture: If youve got some spare GPU horsepower now is the time to put it to good use; by simulating molecular dynamics you can contribute to a dataset that could help researchers find a cure to the coronavirus.

The Pande Lab at Stanford University has been running distributed compute network Folding@home for nearly twenty years for disease research using the idle processing resources of home personal computers. Today the lab has put it its network to work to better understand coronavirus.

Folding@home combines the power of thousands of individual home systems and treats each of them as a node in a supercomputer. Each node simulates molecular dynamics like protein folding and computational drug design. In the end, all the results are combined into a dataset of interactions that are made available to researchers. According to Wikipedia, Folding@home is one of the world's fastest computing systems, with a speed of approximately 98.7petaFLOPS as of March 2020.

I personally signed up to fold this morning and my system just finished computing one work unit. Despite a detailed explanation, I understood nothing about what my computer was just doing apart from that the simulation involved exactly 62,227 atoms. Folding@home project's reputation precedes it, so while I may not know how this is beneficial in the fight against coronavirus, listed on their website is a list of academic publications using data from the networks research into cancer, Parkinsons, Alzheimers, and other conditions, and a list of very grateful researchers.

Do note, folding is not for every system. Its pretty intensive. There are three performance tiers, though as far as I can tell medium and full do the same thing: both pin my hexacore processor at 100% utilization and my RTX graphics card at about 40%. The light setting reduces the processors load to about 40% and seems to alternate between using the processor and graphics card. Memory and storage utilization are setting agnostic and very low, and the process is offline once a small initial file is downloaded.

There are no issues with system slowdown, however. The program can be set to turn on automatically when the system is idle and pause upon user activity. Although, even at full power my system hasnt slowed down at all while writing this article; folding is designed to be an adaptable workload that scales down utilization as other applications require it. And of course, you can just turn it off through a simple browser interface.

Im going to continue to run the software in the hope that it makes a difference. If youd like to do so as well, you can download it here.

Excerpt from:
Got a graphics card? Put it to work fighting the coronavirus - TechSpot

Clare Stevenson runs the Biophysical Analysis facility at the John Innes Centre.

Recently, she explained why she is passionate about increasing the use of these Biophysical techniques and making the science more accessible to a wider range of users.

The Biophysical Analysis (BA) facility has state of the art instrumentation for looking at structures of molecules and enables scientists to observe and measure the strength of the interactions between biomolecules.

Biomolecular interactions are central to biochemistry and an understanding of these interactions can aid our knowledge across a broad range of science, so the biomolecules we look at may be proteins, DNA, RNA, small molecules or drugs.

We recently added a third technique to our biophysical service; a Circular Dichroism Spectroscopy (CD), maintained and run by Julia Mundy.

The CD spectrophotometer uses a Xenon light source to collect CD data on biological macromolecules in solution at near and far UV wavelengths. This technique is commonly used to look at the folding of proteins, provide information on the secondary structure of proteins and can detect changes in structure during protein-protein or protein-ligand binding events. Julia runs samples for scientists as a service or will train users how to use the equipment themselves.

In addition to the CD, the BA facility has two complementary techniques for studying biomolecular interactions; Surface Plasmon Resonance (SPR) and Isothermal Titration Calorimetry (ITC).

In Surface Plasmon Resonance (SPR), one of the biomolecules you are investigating is attached to a surface and the other biomolecule is flowed over the surface. If there is an interaction a response is observed. That interaction can then be observed, on the computer, in real time and you see the biomolecules binding and unbinding. Once you know that an interaction occurs you can do some further measurements to determine how strong or weak the interaction is.

The SPR is highly automated and once an experiment is optimised it can even be left testing multiple interactions while the scientist is at home sleeping. Recently I have been working with Dr Tung Le, looking at protein-DNA interactions. Once the experiment was planned, it can be run quickly and within 20 minutes we can see if binding is happening. Tungs group can easily screen many different protein samples and DNA sequences quickly in an automated manner getting the results the next day.

Weve also recently been successful in obtaining some funding which will allow us to purchase a new instrument that will enhance our SPR capability even further.

Isothermal Titration Calorimetry (ITC) is a complimentary technique to SPR but rather than requiring one biomolecule to be immobilised, it has one in a cell and one in a syringe. The molecule in the syringe is injected into the one in the cell and if binding occurs the heat given out (exothermic) or taken in (endothermic) can be measured.

Changes in heat occur when molecules bind, and the ITC can measure these tiny changes in heat with high sensitivity. This information can then tell us whether the biomolecules interact and, if they do, how strong the interactions is. Plus, knowing the heat changes in the interaction can also give us a clue to how the molecules are binding.

Both techniques are complimentary but have different advantages and disadvantages. As a facility we speak to the scientists and recommend the best technique for the desired experiment and spend time training, troubleshooting projects and analysing results.

We also run regular training courses where people interested in all three techniques can come and learn what is involved and what they need to understand before putting on their lab coat.

Our BA facility is available for anyone to book and use.

Predominantly we work with John Innes Centre scientists, but we have also worked with The Sainsbury Laboratory, University of East Anglia and external companies like Leaf Expression Systems. If you want to measure any interactions, we can help, wherever you are.

Our facility can run 24-hours-a-day, 365-days-a-year, thanks to being able to automate much of the process, which in turn means we have the instrument capacity to take on more than we are currently doing.

I enjoy my job and my career has had lots of highlights.

I am particularly proud of a method that I developed to study protein DNA interactions by SPR. This is our Reusable DNA Capture Technique (ReDCaT) and enables DNA to be attached to a surface and easily removed. This means that the SPR instrument can be used to measure the interaction of multiple proteins with many DNA sequences in a high throughput and automated manner.

I was always good at science and maths at school, but I think my parents expected me to become a doctor, rather than a scientist. I come from a medical family, full of doctors and nurses, so it was sort of assumed I would follow the family trade.

However, I was bit of a rebellious teenager, so I found myself applying to do Biochemistry at Liverpool Polytechnic, now Liverpool John Moores University, and realised that biochemistry was a fascinating subject and I wanted to learn more.

My degree included a year in industry and I really enjoyed working in the lab. After graduation I found a job with a rival company and I had a lot of fun and learnt a lot working in the area of drug metabolism.

After a new challenge in 1996 I applied for a job as a Research Technician for Professor David Lawson. His passion for solving the 3-dimensional structures of proteins using crystals was inspiring and it encouraged me to take a step sideways. I must admit, I am hugely grateful to David, because he took a bit of a chance on me, as when I first came, I had only done a little bit of protein work and knew very little about protein crystallography. However, I learnt quickly and had excellent technical skills so was able to become competent at growing protein crystals and solving structures.

I realised that in academia having a PhD is important (although believe it is your competency and skills rather than the qualification that is what is really the important thing) but I was lucky to have the support from the John Innes Centre to complete my PhD in 2007. It took over seven years studying part-time, while I was working full time and I also had a baby.

I solved several structures as part of my PhD but also had to study a protein-DNA interactions and had to learn how to use SPR. The more I did it the more I enjoyed it and over time I became the most experienced person at the John Innes Centre for using this technique and ended up training and advising others.

Over time SPR became the first technique in the facility and then it expanded to include ITC and CD. I now manage the facility but also continue working for David on the Protein Crystallography Facility.

There was, and I think remains, a feeling that biophysics is quite pedantic and difficult, but the machines are actually relatively easy to use with the right training and support. I love working with students and post docs showing them the techniques and designing their experiments with them.

Running a facility means every day is different and sometime hectic but feel lucky to work with so many great scientists and make their experiments happen.

Read more from the original source:

What is Biophysical Analysis? - The John Innes Centre

Developing a stable therapeutic protein formulation requires an intimate knowledge of the protein and its physical and chemical properties. In this article, Bernardo Perez-Ramirez and Robert Simler discuss the thermodynamic consequences that low temperature can have on the aggregation tendencies of a protein.

Proteins are dynamic entities, constantly adopting different partially-folded states as a function of temperature and other solution variables. These variables dictate the standard free energy between the native, unfolded and partially folded aggregation prone states leading to oligomerisation. As a result, not all oligomerisation events in proteins are similar. Altering these variables, such as temperature, pH, salt and ligands, could induce a protein to aggregate as a consequence of unnatural folding to balance the thermodynamically unfavourable interactions between solvent and exposed hydrophobic residues in proteins. In the same way, these variables may induce a protein to self-associate, in mostly the native state, to counteract the unfavourable interactions with the solvent. Thus, cold instability without inducing cold denaturation could destabilise the native state of a protein, making it more prone to aggregation events.

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Thermodynamic probes of instability: application to therapeutic proteins - European Pharmaceutical Review

By JeanMarie Brownson, Chicago Tribune

Homemade pie fillings prove easy. Crust not so much. Practice makes perfect. With every pie, our skills improve. Its an acquired art to turn out flaky, beautiful crust. My mother regularly reminds us of her early crust adventures many of which ended in the garbage can. No worries, she says, the crust ingredients cost far less than the filling.

So, when time allows, we practice making pie crust hearing her voice remind us to use a gentle hand when gathering the moist dough into a ball and later when rolling it out. Mom always uses a floured rolling cloth on the board and on the rolling pin. These days, I prefer to roll between two sheets of floured wax paper. We factor in plenty of time to refrigerate the dough so its at the perfect stage for easy rolling. The chilly rest also helps prevent shrinkage in the oven.

Ive been using the same pie dough recipe for years now. I like the flakiness I get from vegetable shortening and the flavor of butter, so I use some of each fat. A bit of salt in the crust helps balance sweet fillings. The dough can be made in a few days in advance. Soften it at room temperature until pliable enough to roll, but not so soft that it sticks to your work surface.

Of course, when pressed for time, I substitute store-bought frozen crusts. Any freshly baked pie with or without a homemade crust, is better than most store-bought versions.

I read labels to avoid ingredients I dont want to eat or serve my family. Im a fan of Trader Joes ready-to-roll pie crusts sold in freezer cases both for their clean ingredient line and the baked flavor. The 22-ounce box contains two generous crusts (or one bottom crust and one top or lattice). Other brands, such as Simple Truth Organics, taste fine, but at 15 ounces for two crusts, are best suited for smaller pies. Wewalka brand sells one 9-ounce crust thats relatively easy to work with. Always thaw according to package directions and use a rolling pin or your hands to repair any rips that may occur when unwrapping.

Double-crust fruit pies challenge us to get the thickener amount just right so the pie is not soupy when cut. Im a huge fan of instant tapioca in most fruit pies because it thickens the juices without adding flavor or a cloudy appearance. In general, I use one tablespoon instant tapioca for every two cups cut-up raw fruit.

Pretty, lattice-topped pies have the added benefit of allowing more fruit juice evaporation while the pie bakes. Precooking the fruit for any pie helps ensure that the thickener is cooked through; I especially employ this technique when working with cornstarch or flour-thickened pie fillings. This also allows the cook to work in advance a bonus around the busy holiday season.

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We are loving the combination of juicy, sweet Bartlett pears with tart cranberries for a gorgeous pie with hues of pink; a few crisp apples and chewy dried cranberries contribute contrasting textures. Feel free to skip the lattice work and simply add a top crust; pierce the top crust in several places with a fork to allow steam to escape. For added flavor and texture, I brush the top crust with cream and sprinkle it generously with coarse sugar before baking.

The nut-free ginger praline recipe is a riff on a longtime favorite pumpkin pie from Jane Salzfass Freiman, a former Chicago Tribune recipe columnist. She taught us to gussy up the edge of pumpkin pie with nuts, brown sugar and butter. We are employing store-bought ginger snap cookies and crystallized ginger in place of pecans for a spicy, candied edge to contrast the creamy pie interior. Think of this pie as all your favorite coffee shop flavors in one pumpkin pie spice and gingerbread, topped with whipped cream.

Happy pie days, indeed.

PEAR, DOUBLE CRANBERRY AND APPLE LATTICE PIE

Prep: 1 hour

Chill: 1 hour

Cook: 1 hour

Makes: 8 to 10 servings

1 recipe double crust pie dough, see recipe

2 1/2 pounds ripe, but still a bit firm, Bartlett pears, about 6

1 1/2 pounds Honeycrisp or Golden Delicious apples, about 4

2 cups fresh cranberries, about 8 ounces

3 tablespoons unsalted butter

3/4 cup sugar

3 tablespoons cornstarch

1 cup (4 ounces) dried cranberries

1/2 teaspoon grated fresh orange zest

1/8 teaspoon salt

Cream or milk, coarse sugar (or turbinado sugar)

Make pie dough and refrigerate it as directed. Working between two sheets of floured wax paper, roll out one disk into a 12-inch circle. Remove the top sheet of wax paper and use the bottom sheet to flip the crust into a 10-inch pie pan. Gently smooth the crust into the pan, without stretching it. Roll the edge of the dough under so it sits neatly on the edge of the pie dish. Refrigerate.

Roll the second disk of pie dough between the sheets of floured wax paper into an 11-inch circle. Slide onto a cookie sheet and refrigerate while you make the filling.

Peel and core the pears. Slice into 1/4-inch wide wedges; put into a bowl. You should have 6 generous cups. Peel and core the apples. Cut into 3/4-inch chunks; you should have about 3 1/2 cups. Add to the pears. Stir in fresh cranberries.

Heat butter in large deep skillet over medium-high until melted; add pears, apples and fresh cranberries. Cook, stirring, until nicely coated with butter, about 2 minutes. Cover and cook to soften the fruit, 3 minutes. Add sugar and cornstarch; cook and stir until glazed and tender, about 5 minutes. Remove from heat; stir in dried cranberries, orange zest and salt. Spread on a rimmed baking sheet; cool to room temperature. While the fruit mixture cools, heat oven to 425 degrees.

Pile the cooled fruit into the prepared bottom crust. Use a very sharp knife to cut the rolled top crust into 18 strips, each about 1/2 inch wide. Place 9 of those strips over the fruit filling positioning them about 1/2 inch apart. Arrange the other 9 strips over the strips on the pie in a diagonal pattern. (If you want to make a woven lattice, put one strip of dough over the 9 strips on the pie and weave them by lifting up and folding to weave them together.)

Crimp the edge of the bottom crust and the lattice strips together with your fingers. Use a fork to make a decorative edge all the way around the pie. Use a pastry brush to brush each of the strips and the edge of the pie with cream. Sprinkle strips and the edge with the coarse sugar.

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Place pie on a baking sheet. Bake at 425 degrees, 25 minutes. Reduce oven temperature to 350 degrees. Use strips of foil to lightly cover the outer edge of the pie. Continue baking until the filling is bubbling hot and the crust richly golden, about 40 minutes more.

Cool completely on a wire rack. Serve at room temperature topped with whipped cream or ice cream. To rewarm the pie, simply set it in a 350-degree oven for about 15 minutes.

Nutrition information per serving (for 10 servings): 540 calories, 24 g fat, 11 g saturated fat, 34 mg cholesterol, 80 g carbohydrates, 43 g sugar, 4 g protein, 270 mg sodium, 7 g fiber

DOUBLE CRUST PIE DOUGH

Prep: 20 minutes

Chill: 1 hour

Makes: Enough for a double crust 10-inch pie

This is our familys favorite pie crust for ease of use with a flaky outcome. We use vegetable shortening for easy dough handling and maximum flakiness; unsalted butter adds rich flavor.

2 1/2 cups flour

1 tablespoon sugar

1 teaspoon salt

1/2 cup unsalted butter, very cold

1/2 cup trans-fat free vegetable shortening, frozen

Put flour, sugar and salt into a food processor. Pulse to mix well. Cut butter and shortening into small pieces; sprinkle them over the flour mixture. Pulse to blend the fats into the flour. The mixture will look like coarse crumbs.

Put ice cubes into about 1/2 cup water and let the water chill. Remove the ice cubes and drizzle about 6 tablespoons of the ice water over the flour mixture. Briefly pulse the machine just until the mixture gathers into a dough.

Dump the mixture out onto a sheet of wax paper. Gather into two balls, one slightly larger than the other. (Use this one later for the bottom crust.) Flatten the balls into thick disks. Wrap in plastic and refrigerate until firm, about 1 hour. (Dough will keep in the refrigerator for several days.)

Nutrition information per serving (for 10 servings): 291 calories, 20 g fat, 8 g saturated fat, 24 mg cholesterol, 25 g carbohydrates, 1 g sugar, 3 g protein, 235 mg sodium, 1 g fiber

GINGER PRALINE PUMPKIN PIE

Prep: 40 minutes

Cook: 1 1/2 hours

Makes: 8 servings

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Prebaking the crust helps ensure the proper texture in the finished pie. You can replace the ginger snap cookies here with just about any spice cookie; I also like to use speculoos cookies or homemade molasses cookies. The recipe calls for canned pumpkin pie mix, which has sugar and spice already.

Half recipe double crust pie dough, see recipe

Filling

2 large eggs

1 can (30 ounces; or two 15-ounce cans) pumpkin pie mix (with sugar and spices)

1/2 teaspoon each ground: cinnamon, ginger

1/4 teaspoon ground cloves

2/3 cup heavy whipping cream

2 tablespoons dark rum or 1 teaspoon vanilla

Topping

3 tablespoons butter, softened

2 tablespoons dark brown sugar

1/4 cup finely chopped crystallized ginger, about 1 1/2 ounces

1 cup roughly chopped or broken ginger snap cookies, about 2 ounces or 12 cookies

Whipped cream for garnish

For crust, heat oven to 425 degrees. Roll pie dough between 2 sheets of floured wax paper to an 11-inch circle. Remove the top sheet of paper. Use the bottom sheet to help you flip the dough into a 9-inch pie pan. Gently ease the dough into the pan, without stretching it; roll the edge of the dough under so it sits neatly on the edge of the pie dish; flatten attractively with a fork.

Line the bottom of the pie crust with a sheet of foil; fill the foil with pie weights or dried beans. Bake, 8 minutes. Remove the beans using the foil to lift them out of the crust. Return pie crust to the oven; bake until light golden in color, about 2 minutes. Cool. (Crust can be prebaked up to 1 day in advance; store in a cool, dry place.)

Reduce oven temperature to 350 degrees. For filling, whisk eggs in a large bowl until smooth. Whisk in pumpkin mix, cinnamon, ginger and cloves until smooth. Whisk in cream and rum or vanilla.

For topping, mix soft butter and brown sugar in a small bowl until smooth. Stir in crystallized ginger; gently stir in the cookies to coat them with the butter mixture.

Carefully pour pie filling into cooled crust. Set the pie pan on a baking sheet; slide into the center of the oven. Bake, 40 minutes. Remove pie from oven. Gently distribute the topping evenly around the outer rim of the pie, near the crust. Return the pie to the oven; bake until a knife inserted near the center is withdrawn clean, about 40 more minutes. Cool on a wire rack. Serve cold or at room temperature with whipped cream.

Nutrition information per serving: 481 calories, 27 g fat, 13 g saturated fat, 96 mg cholesterol, 58 g carbohydrates, 9 g sugar, 6 g protein, 433 mg sodium, 9 g fiber

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2 tricked-out pies to be thankful for: pear with cranberries and pumpkin with ginger praline - The Gazette

Ready, set, recipes! Here are our just published, fresh-out-the-mill recipes in one convenient place! These are the top vegan recipes of the day, and are now a part of the thousands of recipes on ourFood Monster App! We have cauliflower chocolate mousse, Mediterranean lentil salad, and the ultimate guacamole recipe, so if youre looking for something new and delicious, you are sure to find a new favorite!

Source: Cauliflower Chocolate Mousse

If youre looking for a much healthier and more nutritious version of chocolate mousse, youve got to try this Cauliflower Chocolate Mousse by Mitra Shirmohammadi. Theres more than a full serving of vegetables in each cup, its free from refined sugar, and also super allergy-friendly (no dairy, eggs, soy, or gluten). Cauliflower is one of those miraculous vegetables that works incredibly well in both sweet and savory dishes. Theres no way anyone can ever tell there are vegetables hidden in your chocolate mousse! If you have picky eaters at home, this cauliflower chocolate mousse is a great way to get them to eat more veggies without even realizing it!

Source: Cinnamon Raisin Bread

Vegan and gluten-free cinnamon raisin bread thats refined sugar free and packed with raisins and texture. Made extra cinnamon-y with a secret ingredient! Its easy to make and only takes 1-bowl (and plenty of cinnamon and raisins.) This Gluten-Free Cinnamon Raisin Bread is a little less table bread and a lot more dense bakery- style bread. Its hearty, naturally sweet, and packed with protein and fiber.The combination of ground cinnamon and cinnamon kombucha adds the perfect amount of spice. Its hearty enough to serve for a breakfast bread or nutritious snack but sweet enough to also function as a healthful dessert. For even more texture, try adding walnuts or almonds when folding in the raisins. Serve your slices of gluten-free Cinnamon Raisin Bread by Lauren Kirchmaier warm with a big smear of almond butter slathered on top.

Source: Vegan Scampi in Lemon Garlic White Wine Sauce

This is a really simple dish that tastes elegant. Hearts of palm are the perfect stand-in for scallops. They have a similar look when sliced and a briny quality reminiscent of seafoodgreat for those with a shellfish allergy. This Vegan Scampi in Lemon Garlic White Wine Sauce by Jenn Sebestyen is elegant and delicious!

Source: Savory Granola

The amazing thing about granola is that you can, just like banana ice cream, use the basic recipe, exchange the spices and make your own combination. This savory granola recipe is perfect for topping your salad and soups or just munching on as a midday. It only takes about 20-30 min to make and the ingredients are pretty simple. You have to try this Savory Granola by True Foods Blog!

Source: Chocolate Pie

Chocolate and pie what could be better? This Chocolate Pie by Lenia Patsi is the ideal dessert to make this weekend. Simple and decadent!

Source: Protein Apple Berry Crumble

Vegan apple berry crumble is the perfect Autumn dessertits cozy, comforting, cinnamon-spiced & absolutely irresistible! This isnt however a standard crumble recipe. This is a healthy, refined sugar and gluten free crumble with added protein. You are going to love this Protein Apple Berry Crumble by Vicky Coates!

Source: Mediterranean Lentil Salad

This Mediterranean Lentil Salad by Medha Swaminathan is the perfect reset after a weekend of eating things that seemingly all contain massive amounts of vegan cream, cheese, and/or cream cheese. Its super easy to make, so your food-fatigued body doesnt have to do much work. Plus, its really good for you.

Source: Gado Gado Salad With Nut-Free Sauce

A mixed vegetable Indonesian-style salad served with nut free sauce dressing. The medley of vegetables with potato and tofu added make this salad a tasty, nutritious, attractive and colorful dish. This Gado Gado Salad by Daphne Goh is not only naturally gluten free but also vegan, egg free, nut free and refined sugar free. For soy free, simply omit the fried tofu and add some pumpkins for protein.

Source: Crispy Flavorful Pickles

A super quick recipe for yummy, full-of-flavor pickles. Have these Crispy Flavorful Pickles by Caroline Ginolfion the side of your preferred vegan dinner!

Source: The Ultimate Guacamole

This is the The Ultimate Guacamole by Christina Bedetta. Serve it with chips and fresh vegetables or enjoy it in wrap form. Its easy to make and is a great addition to so many dishes. Made with creamy avocado, red onion, tomatoes, and spices there is no arguing that this is the ultimate guacamole recipe. With such delicious flavors and versatility, the possibilities are endless!

We also highly recommend downloading ourFood Monster App, which is available foriPhone, and can also be found onInstagramandFacebook. The app has more than 15,000 plant-based, allergy-friendly recipes, and subscribers gain access to new recipes every day. Check it out!

For more Vegan Food, Health, Recipe, Animal, and Life content published daily, dont forget to subscribe to theOne Green Planet Newsletter!

Being publicly-funded gives us a greater chance to continue providing you with high quality content. Pleasesupport us!

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From Mediterranean Lentil Salad to Cinnamon Raisin Bread: Our Top 10 Vegan Recipes of the Day! - One Green Planet

Googles quantum supremacy claim has now been disputed by its close competitor IBM. Not because Googles Sycamore quantum computers calculations are wrong, but because Google had underestimated what IBMs Summit, the most powerful supercomputer in the world, could do. Meanwhile, Googles paper, which had accidentally been leaked by a NASA researcher, has now been published in the prestigious science journal Nature. Googles claims are official now, and therefore can be examined in the way any new science claim should be examined: skeptically until all the doubts are addressed.

Previously, I have covered what quantum computing is, and in this article, I will move on to the key issue of quantum supremacy, the claim that IBM has challenged and what it really means. IBM concedes that Google has achieved an important milestone, but does not accept that it has achieved quantum supremacy.

IBM refuted Googles claim around the same time as Googles Nature paper was published. Google had claimed that IBMs supercomputer, Summit, would take 10,000 years to solve the problem Googles Sycamore had solved in a mere 200 seconds. IBM showed that Summit, with clever programming and using its huge disk space, could actually solve the problem in only 2.5 days. Sycamore still beat Summit on this specific problem by solving it 1,100 times faster, but not 157 million times faster, as Google had claimed. According to IBM, this does not establish quantum supremacy as that requires solving a problem a conventional computer cannot solve in a reasonable amount of time. Two and a half days is reasonable, therefore according to IBM quantum supremacy is yet to be attained.

The original definition of quantum supremacy was given by John Preskill, on which he now has second thoughts. Recently he wrote, supremacy, through its association with white supremacy, evokes a repugnant political stance. The other reason is that the word exacerbates the already overhyped reporting on the status of quantum technology.

Regarding IBMs claim that quantum supremacy has not yet been achieved, Scott Aaronson, a leading quantum computing scientist, wrotethat though Google should have foreseen what IBM has done, it does not invalidate Googles claim. The key issue is not that Summit had a special way to solve the specific quantum problem Google had chosen, but that Summit cannot scale: if Googles Sycamore goes from 53 to 60 qubits, IBM will require 33 Summits; if to 70 Qubits, a supercomputer the size of a city!

Why does Summit have to increase at this rate to match Sycamores extra qubits? To demonstrate quantum supremacy, Google chose the simulation of quantum circuits, which is similar to generating a sequence of truly random numbers. Classical computers can produce numbers that appear to be random, but it is a matter of time before they will repeat the sequence.

The resources disk space, memory, computing power classical computers require to solve this problem, in a reasonable time, increase exponentially with the size of the problem. For quantum computers, adding qubits linearly meaning, simply adding more qubits increases computing capacity exponentially. Therefore, just 7 extra qubits of Sycamore means IBM needs to increase the size of Summit 33 times. A 17-qubit increase of Sycamore needs Summit to increase by thousands of times. This is the key difference between Summit and Sycamore. For each extra qubit, a conventional computer will have to scale its resources exponentially, and this is a losing game for the conventional computer.

We have to give Google the victory here, not because IBM is wrong, but because the principle of quantum supremacy, that a quantum computer can work as designed, solve a specific problem, and beat a conventional computer in computational time has been established. The actual demonstrationa more precise definition of reasonable time and its physical demonstration is only of academic value. If 53 qubits can solve the problem, but with IBMs Summit still in the race, even if much slower, it is just a matter of time before it is well and truly beaten.

Of course, there are other ways that this particular test could fail. A new algorithm can be discovered that solves this problem faster, starting a fresh race. But the principle here is not a specific race but the way quantum computing will scale in solving a certain class of problems that classical or conventional computers cannot.

For problems that do not increase exponentially with size, the classical computers work better, are way cheaper, and do not require near absolute zero temperatures that quantum computers require. In other words, classical computers will coexist with quantum computers and not follow typewriters and calculators to the technology graveyards.

The key issue in creating viable quantum computers should not be confused with a race between classical computers and the new kid on the block. If we see the race as between two classes of computers only in terms of solving a specific problem, we are missing the big picture. It is simply that for classical computers, the solution time for a certain class of problems increases exponentially with the size of the problem, and beyond a certain size, we just cant solve them in any reasonable time. Quantum computers have the potential to solve such large problems requiring exponential computing power. This opens a way to solve these classes of problems other than the iffy route of finding new algorithms.

Are there such problems, and will they yield worthwhile technological applications? The Google problem, computing the future states of quantum circuits, was not chosen for any practical application. It was simply chosen to showcase quantum supremacy, defined as a quantum computer solving a problem that a classical computer cannot solve in a reasonable time.

Recently, a Chinese team led by Pan Jianwei has published a paper that shows another problema Boson sampling experiment with 20 photons can also be a pathway to show quantum supremacy. Both these problems are constructed not to showcase real-world applications, but simply to show that quantum computing works and can potentially solve real-world problems.

What are the classes of problems that quantum computers can solve? The first are those for which the late Nobel laureate Richard Feynman had postulated quantum computers as a simulation of the quantum world. Why do we need such simulations, after all, we live in the macro-world in which quantum effects are not visible? Though such effects may not visible to us, they are indeed all around us and affect us in different ways.

A number of such phenomena arise out of the interaction of the quantum world with the macro-world. It is now clear that using classical computers we cannot simulate, for instance, protein folding, as it involves the quantum world intersecting with the macro-world. A quantum computer could simulate the probability of how many possible ways such proteins could fold and the likely shapes they could take. This would allow us to build not only new materials but also medicines known as biologics. Biologics are large molecules used for treating cancer and auto-immune diseases. They work due to not only their composition but also their shapes. If we could work out their shapes, we could identify new proteins or new biological drug targets; or complex new chemicals for developing new materials. The other examples are solving real-life combinatorial problems such as searching large databases, cracking cryptographic problems, improved medical imaging, etc.

The business world IBM, Google, Microsoft is gung-ho on the possible use of quantum computers for such applications, and that is why they are all investing in it big time. Nature reported that in 2017 and 2018, at least $450 million was invested by venture capital in quantum computing, more than four times more than the preceding two years. Nation-states, notably the United States and China, are also investing billions of dollars each year.

But what if quantum computers do not lead to commercial benefits should we then abandon them? What if they are useful only for simulating quantum mechanics and understanding that world better? Did we build the Hadron Collider investing $13.25 billion, and with an annual running cost of $1 billion only because we expected discoveries that will have commercial value? Or, should society invest in knowing the fundamental properties of space and time including that of the quantum world? Even if quantum computers only give us a window to the quantum world, the benefits would be knowledge.

What is the price of this knowledge?

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IBM vs. Google and the race to quantum supremacy - Salon

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It should not surprise us that even in parts of the genome where we dont obviously see a functional code (i.e., one thats been evolutionarily fixed as a result of some selective advantage), there is a type of code, but not like anything weve previously considered as such. And what if it were doing something in three dimensions as well as the two dimensions of the ATGC code? A paper just published in BioEssays explores this tantalizing possibility

Isnt it wonderful to have a really perplexing problem to gnaw on, one that generates almost endless potential explanations. How about what is all that non-coding DNA doing in genomes?that 98.5% of human genetic material that doesnt produce proteins. To be fair, the deciphering of non-coding DNA is making great strides via the identification of sequences that are transcribed into RNAs that modulate gene expression, may be passed on transgenerationally (epigenetics) or set the gene expression program of a stem cell or specific tissue cell. Massive amounts of repeat sequences (remnants of ancient retroviruses) have been found in many genomes, and again, these dont code for protein, but at least there are credible models for what theyre doing in evolutionary terms (ranging from genomic parasitism to symbiosis and even exploitation by the very host genome for producing the genetic diversity on which evolution works); incidentally, some non-coding DNA makes RNAs that silence these retroviral sequences, and retroviral ingression into genomes is believed to have been the selective pressure for the evolution of RNA interference (so-called RNAi); repetitive elements of various named types and tandem repeats abound; introns (many of which contain the aforementioned types of non-coding sequences) have transpired to be crucial in gene expression and regulation, most strikingly via alternative splicing of the coding segments that they separate.

Still, theres plenty of problem to gnaw on because although we are increasingly understanding the nature and origin of much of the non-coding genome and are making major inroads into its function (defined here as evolutionarily selected, advantageous effect on the host organism), were far from explaining it all, andmore to the pointwere looking at it with a very low-magnification lens, so to speak. One of the intriguing things about DNA sequences is that a single sequence can encode more than one piece of information depending on what is reading it and in which direction viral genomes are classic examples in which genes read in one direction to produce a given protein overlap with one or more genes read in the opposite direction (i.e., from the complementary strand of DNA) to produce different proteins. Its a bit like making simple messages with reverse-pair words (a so-called emordnilap). For example: REEDSTOPSFLOW, which, by an imaginary reading device, could be divided into REED STOPS FLOW. Read backwards, it would give WOLF SPOTS DEER.

Now, if it is of evolutionary advantage for two messages to be coded so economically as is the case in viral genomes, which tend to evolve towards minimum complexity in terms of information content, hence reducing necessary resources for reproductionthen the messages themselves evolve with a high degree of constraint. What does this mean? Well, we could word our original example message as RUSH-STEM IMPEDES CURRENT, which would embody the same essential information as REED STOPS FLOW. However, that message, if read in reverse (or even in the same sense, but in different chunks) does not encode anything additional that is particularly meaningful. Probably the only way of conveying both pieces of information in the original messages simultaneously is the very wording REEDSTOPSFLOW: thats a highly constrained system! Indeed, if we studied enough examples of reverse-pair phrases in English, we would see that they are, on the whole, made up of rather short words, and the sequences are missing certain units of language such as articles (the, a); if we looked more closely, we might even detect a greater representation than average of certain letters of the alphabet in such messages. We would see these as biases in word and letter usage that would, a priori, allow us to have a stab at identifying such dual-function pieces of information.

Now lets return to the letters, words, and information encoded in genomes. For two distinct pieces of information to be encoded in the same piece of genetic sequence we would, similarly, expect the constraints to be manifest in biases of word and letter usagethe analogies, respectively, for amino acid sequences constituting proteins, and their three-letter code. Hence a sequence of DNA can code for a protein and, in addition, for something else. This something else, according to Giorgio Bernardi, is information that directs the packaging of the enormous length of DNA in a cell into the relatively tiny nucleus. Primarily it is the code that guides the binding of the DNA-packaging proteins known as histones. Bernardi refers to this as the genomic codea structural code that defines the shape and compaction of DNA into the highly-condensed form known as chromatin.

But didnt we start with an explanation for non-coding DNA, not protein-coding sequences? Yes, and in the long stretches of non-coding DNA we see information in excess of mere repeats, tandem repeats and remnants of ancient retroviruses: there is a type of code at the level of preference for the GC pair of chemical DNA bases compared with AT. As Bernardi reviews, synthesizing his and others groundbreaking work, in the core sequences of the eukaryotic genome, the GC content in structural organizational units of the genome termed isochores increased during the evolutionary transition between so-called cold-blooded and warm-blooded organisms. And, fascinatingly, this sequence bias overlaps with sequences that are much more constrained in function: these are the very protein-coding sequences mentioned earlier, and theymore than the intervening non-coding sequencesare the clue to the genomic code.

Protein-coding sequences are also packed and condensed in the nucleus particularly when theyre not in use (i.e., being transcribed, and then translated into protein) but they also contain relatively constant information on precise amino acid identities, otherwise they would fail to encode proteins correctly: evolution would act on such mutations in a highly negative manner, making them extremely unlikely to persist and be visible to us. But the amino acid code in DNA has a little catch that evolved in the most simple of unicellular organisms (bacteria and archaea) billions of years ago: the code is partly redundant. For example, the amino acid Threonine can be coded in eukaryotic DNA in no fewer than four ways: ACT, ACC, ACA or ACG. The third letter is variable and hence available for the coding of extra information. This is exactly what happens to produce the genomic code, in this case creating a bias for the ACC and ACG forms in warm-blooded organisms. Hence, the high constraint on this additional codewhich is also seen in parts of the genome that are not under such constraint as protein-coding sequencesis imposed by the packaging of protein-coding sequences that embody two sets of information simultaneously. This is analogous to our example of the highly-constrained dual-information sequence REEDSTOPSFLOW.

Importantly, however, the constraint is not as strict as in our English language example because of the redundancy of the third position of the triplet code for amino acids: a better analogy would be SHE*ATE*STU* where the asterisk stands for a variable letter that doesnt make any difference to the machine that reads the three-letter component of the four-letter message. One could then imagine a second level of information formed by adding D at these asterisk points, to make SHEDATEDSTUD (SHE DATED STUD). Next imagine a second reading machine that looks for meaningful phrases of a sensitive nature containing a greater than average concentration of Ds. This reading machine carries a folding machine with it that places a kind of peg at each D, kinking the message by 120 degrees in a plane. a point where the message should be bent by 120 degrees in the same plane, we would end up with a more compact, triangular, version. In eukaryotic genomes, the GC sequence bias proposed to be responsible for structural condensation extends into non-coding sequences, some of which have identified activities, though less constrained in sequence than protein-coding DNA. There it directs their condensation via histone-containing nucleosomes to form chromatin.

Figure. Analogy between condensation of a word-based message and condensation of genomic DNA in the cell nucleus. Panel A: Information within information, a sequence of words with a variable fourth space which, when filled with particular letters, generates a further message. One message is read by a three-letter reading machine; the other by a reading machine that can interpret information extending to the 4thvariableposition of the sequence. The second reader recognizes sensitive information that should be concealed, and at the points where a D appears in the 4th position, it folds the string of words, hence compressing the sensitive part and taking it out of view. This is an analogy for the principle of genomic 3D compression via chromatin, as depicted in panel B: a fluorescence image (via Fluorescence In-Situ Hybridization FISH) of the cell nucleus. H2/H3 isochores, which increased in GC content during evolution from cold-blooded to warm-blooded vertebrates, are compressed into a chromatin core, leaving L1 isochores (with lower GC content) at the periphery in a less-condensed state. The genomic code embodied in the high-GC tracts of the genome is, according to Bernardi [1], read by the nucleosome-positioning machinery of the cell and interpreted as sequence to be highly compressed in euchromatin. Acknowledgements: Panel A: concept and figure production: Andrew Moore; Panel B: A FISH pattern of H2/H3 and L1 isochores from a lymphocyte induced by PHAcourtesy of S. Sacconeas reproduced in Ref. [1].]

These regions of DNA may then be regarded as structurally important elements in forming the correct shape and separation of condensed coding sequences in the genome, regardless of any other possible function that those non-coding sequences have: in essence, this would be an explanation for the persistence in genomes of sequences to which no function (in terms of evolutionarily-selected activity), can be ascribed (or, at least, no substantial function).

A final analogythis time much more closely relatedmight be the very amino acid sequences in large proteins, which do a variety of twists, turns, folds etc. We may marvel at such complicated structures and ask but do they need to be quite so complicated for their function? Well, maybe they do in order to condense and position parts of the protein in the exact orientation and place that generates the three-dimensional structure that has been successfully selected by evolution. But with a knowledge that the genomic code overlaps protein coding sequences, we might even start to become suspicious that there is another selective pressure at work as well

Andrew Moore, Ph.D.Editor-in-Chief, BioEssays

Reference:

1. G.Bernardi. 2019. The genomic code: a pervasive encoding/moulding ofchromatin structures and a solution of the non-coding DNA mystery. BioEssays41:12. 1900106

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That Junk DNA Is Full of Information! - Advanced Science News

A tenure track (equivalent to Assistant Professor) or tenure-eligible (equivalent to Associate or Full Professor) position is available for an experimental or theoretical biophysical scientist to establish an independent research program in the Laboratory of Chemical Physics (LCP), NIDDK, NIH. We are especially interested in candidates who will develop a vigorous independent research program involving the application of physical methods to biomedical problems and have a demonstrated track record of research excellence that is complementary to ongoing research in LCP. Current research includes: solution state NMR spectroscopy with an emphasis on methods development, structural and kinetic characterization of sparsely-populated states and molecular assembly (Ad Bax and Marius Clore); solid state NMR spectroscopy with an emphasis on the study of amyloid fibrils, protein self-assembly, and protein folding (Robert Tycko); single molecule fluorescence spectroscopy with applications to protein folding, binding, and aggregation (Hoi Sung Chung); picosecond X-ray crystallography and scattering, as well as femtosecond spectroscopy (Philip Anfinrud); theory of single molecule force and fluorescence spectroscopies (Attila Szabo); theory and simulations with emphasis on models for protein folding, misfolding and self-assemblies (Robert Best); and drug discovery for sickle cell disease (William Eaton). Four of the eight LCP principal investigators are members of the US National Academy of Sciences.

The Laboratory is located on the main campus of the NIH in Bethesda, Maryland (https://www.niddk.nih.gov/research-funding/at-niddk/labs-branches/laboratory-chemical-physics/about) and is part of the intramural program of NIDDK (http://www.niddk.nih.gov/research-funding/at-niddk/labs-branches/Pages/default.aspx). The NIH Intramural Program provides a highly interactive and interdisciplinary environment that is conducive for carrying out high risk, basic research with state-of-the-art core facilities and access to collaborators in both the basic and clinical sciences in almost every major area of biology and medicine. Stable research support for NIDDK intramural scientists is based on accomplishments.

Applicants must have a PhD, MD/PhD or equivalent degree and have a demonstrated record of scientific achievement. Applicants may be U.S. citizens, resident aliens, or non-resident aliens with, or eligible to obtain, a valid employment-authorization visa. Applicants should electronically submit a curriculum vitae, bibliography, a summary of research accomplishments, copies of three most significant publications and a brief statement of future research goals. Junior applicants should arrange for three letters of reference to be sent directly to the Chair of the Search Committee. Senior applicants should provide the names of three reference letter writers. All applications should be submitted electronically to:

Dr. Wei Yang

Chair, LCP Search Committee

danica.day@nih.gov

Please include in your CV a description of mentoring and outreach activities, especially those involving women and racial/ethnic groups that are underrepresented in biomedical research.

The LCP Search Committee will review received applications on or around December 7, 2019.Applications will be accepted until the position is filled. Salary and benefits are commensurate with the experience of the applicant.

DHHS and NIH are equal opportunity employers

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Tenure-Track or Tenure-Eligible Position in the Laboratory of Chemical Physics job with National Institutes of Health | 28302 - Chemical &...

Summary

Doctors and scientists from Memorial Sloan Kettering report on an innovative technique for noninvasively watching where a targeted therapy is going in the body. It also allows them to see how much of the drugreaches the tumor.

Targeted therapy has become an important player in the collection of treatments for cancer. But sometimes its difficult for doctors to determine whether a persons tumor has the right target or how much of a drug is actually reaching it.

A multidisciplinary team of doctors and scientists from Memorial Sloan Kettering has discovered an innovative technique for noninvasively visualizing where a targeted therapy is going in the body. This method can also measure how much of it reaches the tumor. What makes this development even more exciting is that the drug they are studying employs an entirely new approach for stopping cancer growth. The work was published on October 24 in Cancer Cell.

This paper reports on the culmination of almost 15 years of research, says first author Naga Vara Kishore Pillarsetty, a radiochemist in the Department of Radiology. Everything about this drug from the concept to the clinical trials was developed completely in-house at MSK.

Our research represents a new role for the field of radiology in drug development, adds senior author Mark Dunphy, a nuclear medicine doctor. Its also a new way to provide precision oncology.

Our research represents a new role for the field of radiology in drug development.

The drug being studied, called PU-H71, was developed by the studys co-senior author Gabriela Chiosis. Dr. Chiosis is a member of the Chemical Biology Program in the Sloan Kettering Institute. PU-H71 is being evaluated in clinical trials for breast cancer and lymphoma, and the early results are promising.

We always hear about how DNA and RNA control a cells fate, Dr. Pillarsetty says. But ultimately it is proteins that carry out the functions that lead to cancer. Our drug is targeting a unique network of proteins that allow cancer cells to thrive.

Most targeted therapies affect individual proteins. In contrast, PU-H71 targets something called the epichaperome. Discovered and named by Dr. Chiosis, the epichaperome is a communal network of proteins called chaperones.

Chaperone proteins help direct and coordinate activities in cells that are crucial to life, such as protein folding and assembly. The epichaperome, on the other hand, does not fold. It reorganizes the function of protein networks in cancer, which enables cancer cells to survive under stress.

Previous research from Dr. Chiosis and Monica Guzman of Weill Cornell Medicine provided details on how PU-H71 works. The drug targets a protein called the heat shock protein 90 (HSP90). When PU-H71 binds to HSP90 in normal cells, it rapidly exits. But when HSP90 is incorporated into the epichaperome, the PU-H71 molecule becomes lodged and exits more slowly. This phenomenon is called kinetic selectivity. It helps explain why the drug affects the epichaperome. It also explains why PU-H71 appears to have fewer side effects than other drugs aimed at HSP90.

At the same time, this means that PU-H71 works only in tumors where an epichaperome has formed. This circumstanceled to the need for a diagnostic method to determine which tumors carry the epichaperome and, ultimately, who might benefit from PU-H71.

Communal Behavior within Cells Makes Cancers Easier to Target

Findings about proteins called molecular chaperones are shedding new light on possible approaches to cancer treatment.

In the Cancer Cell paper, the investigators report the development of a precision medicine tactic that uses a PET tracer with radioactive iodine. It is called [124I]-PU-H71 or PU-PET. PU-PET is the same molecule as PU-H71 except that it carries radioactive iodine instead of nonradioactive iodine. The radioactive version binds selectively to HSP90 within the epichaperome in the same way that the regular drug does. Ona PET scan, PU-PET displays the location of the tumor or tumors that carry the epichaperome and therefore are likely to respond to the drug. Additionally, when its given along with PU-H71, PU-PET can confirm that the drug is reaching the tumor.

This research fits into an area that is sometimes called theranostics or pharmacometrics, Dr. Dunphy says. We have found a very different way of selecting patients for targeted therapy.

He explains that with traditional targeted therapies, a portion of a tumor is removed with a biopsy and then analyzed. Biopsies can be difficult to perform if the tumor is located deep in the body. Additionally, people with advanced disease that has spread to other parts of the body may have many tumors, and not all of them may be driven by the same proteins. By using this imaging tool, we can noninvasively identify all the tumors that are likely to respond to the drug, and we can do it in a way that is much easier for patients, Dr. Dunphy says.

The researchers explain that this type of imaging also allows them to determine the best dose for each person. For other targeted therapies, doctors look at how long a drug stays in the blood. But that doesnt tell you how much is getting to the tumor, Dr. Pillarsetty says. By using this imaging agent, we can actually quantify how much of the drug will reach the tumor and how long it will stay there.

Plans for further clinical trials of PU-H71 are in the works. In addition, the technology reported in this paper may be applicable for similar drugs that also target the epichaperome.

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Bulls-Eye: Imaging Technology Could Confirm When a Drug Is Going to the Right Place - On Cancer - Memorial Sloan Kettering

DetailsCategory: Small MoleculesPublished on Tuesday, 08 October 2019 10:09Hits: 417

YTX-7739 represents a novel, first-in-class, potentially disease-modifying therapy

Data from Phase 1 study expected in the first quarter of 2020

CAMBRIDGE, MA, USA I October 07, 2019 IYumanity Therapeutics, a company focused on protecting the vitality of the mind by discovering and developing transformative brain-penetrating small molecule drugs to treat neurodegenerative diseases, today announced that the first subject cohort has been dosed in a Phase 1 clinical trial evaluating the safety and tolerability of YTX-7739 in healthy volunteers. YTX-7739, the companys lead investigational therapy, is designed to inhibit Stearoyl-CoA-Desaturase (SCD), a validated biologic target that has recently shown potential in neurodegenerative diseases by protecting cells from a-synuclein toxicity, a major driver of Parkinsons disease.

Developing effective therapies for patients with devastating neurodegenerative diseases has been challenging because too few hypotheses and novel targets have been explored, said Kenneth Rhodes, Ph.D., chief scientific officer at Yumanity Therapeutics. We advanced YTX-7739, an orally-active SCD inhibitor, into clinical development because of recent evidence established at Yumanity Therapeutics demonstrating its promise to protect cells from a-synuclein toxicity. We look forward to fully characterizing the potential clinical use of YTX-7739, which is clearly differentiated from currently available Parkinsons disease therapies that only address the symptoms, not the underlying causes.

The double-blind, placebo-controlled, dose-escalation, crossover study is intended to evaluate the safety, tolerability and pharmacokinetics of single ascending doses of YTX-7739 in adult healthy volunteers. A second study, exploring multiple ascending doses in adult healthy volunteers and patients with Parkinsons, will follow. Approximately 40 participants will be enrolled in this Phase 1 single ascending dose study. Following completion of the Phase 1 studies, Yumanity Therapeutics expects to advance YTX-7739 into a Phase 1b proof-of-concept clinical trial in the second half of 2020.

Since Yumanity Therapeutics inception, our goal has been to uncover novel pathways and targets to tackle significant medical challenges, said Richard Peters, M.D., Ph.D., chief executive officer of Yumanity Therapeutics. Moving from target identification of SCD to initial clinical development of YTX-7739 in just three years is a testament to the enormous potential of our discovery platform to reproducibly identify previously unexplored biology and new, druggable targets that have the potential to protect cells from neurodegeneration. This Phase 1 trial will provide important validation for the broad application of our technology to help address arguably the most important therapeutic challenges of our time.

About YTX-7739 YTX-7739 is Yumanity Therapeutics proprietary lead investigational therapy designed to penetrate the blood-brain barrier and inhibit the activity of a novel target that plays an important and previously unrecognized role in the neurotoxicity caused by the a-synuclein protein, a major driver of Parkinsons disease and related neurodegenerative disorders. Misfolding and aggregation of the a-synuclein protein triggers a cascade of events, ultimately resulting in neurotoxicity and the subsequent disorders in movement and cognition that affect patients living with these diseases. YTX-7739 has been shown to inhibit many of the key aspects of a-synuclein toxicity and the company is assessing its potential utility in Parkinsons disease.

About Parkinsons Disease Parkinsons disease is a progressive neurological disorder that affects the central nervous system and impacts both motor and non-motor functions. It is one of the most common age-related neurodegenerative diseases, affecting an estimated 0.5 to 1 percent of people 65 to 69 years of age, rising to 1 to 3 percent of the population over the age of 80.1 Symptom severity and disease progression differ between individuals, but typically include slowness of movement (bradykinesia), trembling in the extremities (tremors), stiffness (rigidity), cognitive or behavioral abnormalities, sleep disturbances and sensory dysfunction.2 There is no laboratory or blood test for Parkinsons disease, so diagnosis is made based on clinical observation.3 Currently, there is no cure and available treatments only address the symptoms of Parkinsons disease, not the underlying causes.

About Yumanity Therapeutics Yumanity Therapeutics is transforming drug discovery for neurodegenerative diseases caused by protein misfolding. Formed in 2014 by renowned biotech industry leader, Tony Coles, M.D., and protein folding science pioneer, Susan Lindquist, Ph.D., the company is focused on discovering disease-modifying therapies for patients with Parkinsons disease and related disorders, amyotrophic lateral sclerosis (ALS), and Alzheimers disease. Leveraging its proprietary discovery engine, Yumanity Therapeutics innovative new approach to drug discovery and development concentrates on reversing the cellular phenotypes and disease pathologies caused by protein misfolding. For more information, please visit yumanity.com.

1N Engl J Med. 2003;348:1356-1364 doi: 10.1056/NEJM2003ra020003 2J Neurol Neurosurg Psychiatry. 2008;79:368376. doi:10.1136/jnnp.2007.131045 3Cold Spring Harb Perspect Med. 2012;2:a008870

SOURCE: Yumanity Therapeutics

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Yumanity Therapeutics Initiates Phase 1 Clinical Trial of Lead Candidate YTX-7739 for the Treatment of Parkinson's Disease | Small Molecules | News...