Toward Creating Transplantable Organs

UMD Researchers Perform Crucial Proof-of-Concept Experiment, Paving the Way for Growing Human Organs for Therapeutics and Transplantation

By Samantha Watters

University of Maryland – December 17, 2020 -- In a new paper published in Stem Cell Reports, Bhanu Telugu and co-inventor Chi-Hun Park of the University of Maryland (UMD) Department of Animal and Avian Sciences show for the first time that newly established stem cells from pigs, when injected into embryos, contributed to the development of only the organ of interest (the embryonic gut and liver), laying the groundwork for stem cell therapeutics and organ transplantation. Telugu’s start-up company, Renovate Biosciences Inc. (RBI), was founded with the goal of leveraging the potential of stem cells to treat terminal diseases that would otherwise require organ transplants, either by avoiding the need for transplants altogether or creating a new pipeline for growing transplantable human organs. With the number of people who suffer from organ failures and the 20 deaths per day in the U.S. alone purely from a lack of available organs for transplant, finding a new way to provide organs and therapeutic options to transplant patients is a critical need. In this paper, Telugu and his team are sharing their first steps towards growing fully transplantable human organs in a pig host.

“This paper is really about using the stem cells from pigs for the first time and showing that they actually can be injected into embryos and only go to the endodermal target organs like the liver, which is very important for delivering safe therapeutic solutions going forward,” says Telugu. “This is an important milestone. It’s a pipe dream in a way because a lot of things need to work out between here and full organ transplantation, but this paper sets the stage for all our future research. We can’t really just go and start working with humans in work like this, so we started with pig-to-pig transfer in this paper, working with the stem cells and putting them back into other pigs to track the process to make sure it is safe for liver production as proof-of-concept.”

Winning the Inventor Pitch Award at UMD Bioscience Day

Telugu and his team pitched this work at UMD Bioscience Day on behalf of his company, RBI, and received the Inventor Pitch Award and the UMD Invention of the Year Award in 2018. In order to protect the intellectual property, Telugu worked with the UMD Office of Technology Commercialization (OTC) to secure patents and open the work up for additional fundraising to carry this technology through the preclinical and clinical stages. The Maryland Stem Cell Foundation provided some funding to advance this work, and Telugu is thankful that Maryland funds technologies in the human stem cell space.

“There are many terminal cases where people need some sort of an organ replacement, like organ failure and degenerative diseases that cannot be cured by drugs,” explains Telugu. “The traditional paradigm is to find a donor organ, but as of today there are still thousands of patients waiting for transplants, and there is no keeping up with the demand. Researchers have thought for a long time that stem cells could help solve this problem, and these stem cells have the ability to go into a specific organ as opposed to those that go into any lineage. In this case, you can differentiate the cells and place them where they are needed to help rescue a diseased organ, eliminating the need for transplant or at least buying the patient some time. Just making the human liver and collecting them early from a neonatal piglet, the hepatocyte [liver] cells alone are a $3 billion opportunity per year. And in the future, we can move into organ transplantation, first with the liver, and then looking at other organs of interest like the pancreas and lungs.” 

According to Telugu, this has distinct advantages over other methods that researchers are currently using to create donor organs in pigs, since the organs Telugu and his team are working with are actually of human origin and are therefore more likely to be accepted when transplanted. “Transplant rejections are pretty common even between humans and humans,” says Telugu, “and if it is such a problem normally, you can imagine how an organ from a pig could be difficult to accept and may not essentially perform the same functions. Pig proteins may not function the same, so that remains a huge barrier for other methods that are not actually growing fully human organs like ours.”

This work has the potential to solve a major problem in the treatment of organ failure and other degenerative diseases, which is what Telugu and his work is all about. “Being a veterinarian by training, we always look at the problem and try to find solutions to them,” says Telugu. “Most animal scientists operate by looking for solutions, so integrating research and entrepreneurship to get this to the market where it is needed is essential. We are one of the few groups on the planet that are working in this space, and we have a great team of embryologists here at Maryland to do this work. We are uniquely positioned to accomplish this with both genome editing and stem cell biology expertise, and being able to prove the concept with this paper is a great first step towards our goals.”

The paper, entitled “Extra-embryonic endoderm (XEN) cells capable of contributing to embryonic chimeras established from pig embryos,” is published in Stem Cells Reports, DOI: 10.1016/j.stemcr.2020.11.011. 

UMD Researchers Perform Crucial Proof-of-Concept Experiment, Paving the Way for Growing Human Organs for Therapeutics and Transplantation | College of Agriculture & Natural Resources, University of Maryland

A Brain Reads Computer Code Differently

Neuroscientists find that interpreting code activates a general-purpose brain network, but not language-processing centers

From:  Massachusetts Institute of Technology

December 15, 2020 -- In some ways, learning to program a computer is similar to learning a new language. It requires learning new symbols and terms, which must be organized correctly to instruct the computer what to do. The computer code must also be clear enough that other programmers can read and understand it.

In spite of those similarities, MIT neuroscientists have found that reading computer code does not activate the regions of the brain that are involved in language processing. Instead, it activates a distributed network called the multiple demand network, which is also recruited for complex cognitive tasks such as solving math problems or crossword puzzles.

However, although reading computer code activates the multiple demand network, it appears to rely more on different parts of the network than math or logic problems do, suggesting that coding does not precisely replicate the cognitive demands of mathematics either.

"Understanding computer code seems to be its own thing. It's not the same as language, and it's not the same as math and logic," says Anna Ivanova, an MIT graduate student and the lead author of the study.

Evelina Fedorenko, the Frederick A. and Carole J. Middleton Career Development Associate Professor of Neuroscience and a member of the McGovern Institute for Brain Research, is the senior author of the paper, which appears today in eLife. Researchers from MIT's Computer Science and Artificial Intelligence Laboratory and Tufts University were also involved in the study.

Language and cognition

A major focus of Fedorenko's research is the relationship between language and other cognitive functions. In particular, she has been studying the question of whether other functions rely on the brain's language network, which includes Broca's area and other regions in the left hemisphere of the brain. In previous work, her lab has shown that music and math do not appear to activate this language network.

"Here, we were interested in exploring the relationship between language and computer programming, partially because computer programming is such a new invention that we know that there couldn't be any hardwired mechanisms that make us good programmers," Ivanova says.

There are two schools of thought regarding how the brain learns to code, she says. One holds that in order to be good at programming, you must be good at math. The other suggests that because of the parallels between coding and language, language skills might be more relevant. To shed light on this issue, the researchers set out to study whether brain activity patterns while reading computer code would overlap with language-related brain activity.

The two programming languages that the researchers focused on in this study are known for their readability -- Python and ScratchJr, a visual programming language designed for children age 5 and older. The subjects in the study were all young adults proficient in the language they were being tested on. While the programmers lay in a functional magnetic resonance (fMRI) scanner, the researchers showed them snippets of code and asked them to predict what action the code would produce.

The researchers saw little to no response to code in the language regions of the brain. Instead, they found that the coding task mainly activated the so-called multiple demand network. This network, whose activity is spread throughout the frontal and parietal lobes of the brain, is typically recruited for tasks that require holding many pieces of information in mind at once, and is responsible for our ability to perform a wide variety of mental tasks.

"It does pretty much anything that's cognitively challenging, that makes you think hard," Ivanova says.

Previous studies have shown that math and logic problems seem to rely mainly on the multiple demand regions in the left hemisphere, while tasks that involve spatial navigation activate the right hemisphere more than the left. The MIT team found that reading computer code appears to activate both the left and right sides of the multiple demand network, and ScratchJr activated the right side slightly more than the left. This finding goes against the hypothesis that math and coding rely on the same brain mechanisms.

Effects of experience

The researchers say that while they didn't identify any regions that appear to be exclusively devoted to programming, such specialized brain activity might develop in people who have much more coding experience.

"It's possible that if you take people who are professional programmers, who have spent 30 or 40 years coding in a particular language, you may start seeing some specialization, or some crystallization of parts of the multiple demand system," Fedorenko says. "In people who are familiar with coding and can efficiently do these tasks, but have had relatively limited experience, it just doesn't seem like you see any specialization yet."

In a companion paper appearing in the same issue of eLife, a team of researchers from Johns Hopkins University also reported that solving code problems activates the multiple demand network rather than the language regions.

The findings suggest there isn't a definitive answer to whether coding should be taught as a math-based skill or a language-based skill. In part, that's because learning to program may draw on both language and multiple demand systems, even if -- once learned -- programming doesn't rely on the language regions, the researchers say.

"There have been claims from both camps -- it has to be together with math, it has to be together with language," Ivanova says. "But it looks like computer science educators will have to develop their own approaches for teaching code most effectively."

The research was funded by the National Science Foundation, the Department of the Brain and Cognitive Sciences at MIT, and the McGovern Institute for Brain Research.

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Story Source:

Materials provided by Massachusetts Institute of Technology. Original written by Anne Trafton. Note: Content may be edited for style and length.

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Journal Reference:

1. Anna A Ivanova, Shashank Srikant, Yotaro Sueoka, Hope H Kean, Riva Dhamala, Una-May O'Reilly, Marina U Bers, Evelina Fedorenko. Comprehension of computer code relies primarily on domain-general executive brain regions. eLife, 2020; 9 DOI: 10.7554/eLife.58906

                     https://www.sciencedaily.com/releases/2020/12/201215131236.htm

How Did the Virus Get Here?

How did the virus get from China to Europe and North America?  On jet airplanes, of course.  How?  Very  possibly, it was transmitted by sick flight crew members who, themselves, exhibited no symptoms.

[On flights lasting from 211 minutes to 313 minutes,] “Researchers reported that the transmission from a sick passenger would be greater to those in nearby rows, but would only result in an additional 0.7 average infected people per flight.  In contrast,. A sick crew member would infect an estimated 4.6 passengers.”

From:  How Safe is Air Travel during a Pandemic? - YouTube at 3min 3 sec through 3min 17 sec

Artificial Intelligence for Weather Forecasting

An A.I.-powered computer model could someday provide more accurate forecasts for rain, snow and other weather events.

From: University of Washington

December 15, 2020 -- Today's weather forecasts come from some of the most powerful computers on Earth. The huge machines churn through millions of calculations to solve equations to predict temperature, wind, rainfall and other weather events. A forecast's combined need for speed and accuracy taxes even the most modern computers.

The future could take a radically different approach. A collaboration between the University of Washington and Microsoft Research shows how artificial intelligence can analyze past weather patterns to predict future events, much more efficiently and potentially someday more accurately than today's technology.

The newly developed global weather model bases its predictions on the past 40 years of weather data, rather than on detailed physics calculations. The simple, data-based A.I. model can simulate a year's weather around the globe much more quickly and almost as well as traditional weather models, by taking similar repeated steps from one forecast to the next, according to a paper published this summer in the Journal of Advances in Modeling Earth Systems.

"Machine learning is essentially doing a glorified version of pattern recognition," said lead author Jonathan Weyn, who did the research as part of his UW doctorate in atmospheric sciences. "It sees a typical pattern, recognizes how it usually evolves and decides what to do based on the examples it has seen in the past 40 years of data."

Although the new model is, unsurprisingly, less accurate than today's top traditional forecasting models, the current A.I. design uses about 7,000 times less computing power to create forecasts for the same number of points on the globe. Less computational work means faster results.

That speedup would allow the forecasting centers to quickly run many models with slightly different starting conditions, a technique called "ensemble forecasting" that lets weather predictions cover the range of possible expected outcomes for a weather event -- for instance, where a hurricane might strike.

"There's so much more efficiency in this approach; that's what's so important about it," said author Dale Durran, a UW professor of atmospheric sciences. "The promise is that it could allow us to deal with predictability issues by having a model that's fast enough to run very large ensembles."

Co-author Rich Caruana at Microsoft Research had initially approached the UW group to propose a project using artificial intelligence to make weather predictions based on historical data without relying on physical laws. Weyn was taking a UW computer science course in machine learning and decided to tackle the project.

"After training on past weather data, the A.I. algorithm is capable of coming up with relationships between different variables that physics equations just can't do," Weyn said. "We can afford to use a lot fewer variables and therefore make a model that's much faster."

To merge successful A.I. techniques with weather forecasting, the team mapped six faces of a cube onto planet Earth, then flattened out the cube's six faces, like in an architectural paper model. The authors treated the polar faces differently because of their unique role in the weather as one way to improve the forecast's accuracy.

The authors then tested their model by predicting the global height of the 500 hectopascal pressure, a standard variable in weather forecasting, every 12 hours for a full year. A recent paper, which included Weyn as a co-author, introduced WeatherBench as a benchmark test for data-driven weather forecasts. On that forecasting test, developed for three-day forecasts, this new model is one of the top performers.

The data-driven model would need more detail before it could begin to compete with existing operational forecasts, the authors say, but the idea shows promise as an alternative approach to generating weather forecasts, especially with a growing amount of previous forecasts and weather observations.

AI model shows promise to generate faster, more accurate weather forecasts -- ScienceDaily

Journal Reference:

1. Jonathan A. Weyn, Dale R. Durran, Rich Caruana. Improving Data‐Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere. Journal of Advances in Modeling Earth Systems, 2020; 12 (9) DOI: 10.1029/2020MS002109

John le Carré Has Died

David John Moore Cornwell (19 October 1931 – 12 December 2020), better known by his pen name John le Carré, was a British author of espionage novels. During the 1950s and 1960s, he worked for both the Security Service (MI5) and the Secret Intelligence Service (MI6). His third novel, The Spy Who Came in from the Cold (1963), became an international best-seller and remains one of his best-known works.

Following the success of this novel, he left MI6 to become a full-time author. His books include The Looking Glass War (1965), Tinker Tailor Soldier Spy (1974), Smiley's People (1979),  The Little Drummer Girl (1983), The Night Manager (1993), The Tailor of Panama (1996), The Constant Gardener (2001), A Most Wanted Man (2008), and Our Kind of Traitor (2010), all of which have been adapted for film or television.

Le Carré's first two novels, Call for the Dead (1961) and A Murder of Quality (1962), are mystery fiction. Each features a retired spy, George Smiley, investigating a death; in the first book, the apparent suicide of a suspected communist, and in second the second volume, a murder at a boy's public school. Although Call for the Dead evolves into an espionage story, Smiley's motives are more personal than political. Le Carré's third novel, The Spy Who Came in from the Cold (1963), became an international best-seller and remains one of his best-known works; following its publication, he left MI6 to become a full-time writer. Although le Carré had intended The Spy Who Came in from the Cold as an indictment of espionage as morally compromised, audiences widely viewed its protagonist, Alec Leamas, as a tragic hero. In response, le Carré's next book, The Looking Glass War, was a satire about an increasingly deadly espionage mission which ultimately proves pointless.

Most of le Carré's books are spy stories set during the Cold War (1945–91) and portray British Intelligence agents as unheroic political functionaries aware of the moral ambiguity of their work and engaged more in psychological than physical drama. The novels emphasise the fallibility of Western democracy and of the secret services protecting it, often implying the possibility of east–west moral equivalence. They experience little of the violence typically encountered in action thrillers and have very little recourse to gadgets. Much of the conflict is internal, rather than external and visible. The recurring character George Smiley, who plays a central role in five novels and appears as a supporting character in four more, was written as an "antidote" to James Bond, a character le Carré called "an international gangster" rather than a spy and whom he felt should be excluded from the canon of espionage literature. In contrast, he intended Smiley, who is an overweight, bespectacled bureaucrat who uses cunning and manipulation to achieve his ends, as an accurate depiction of a spy.

Le Carré lived in St Buryan, Cornwall, for more than 40 years; he owned a mile of cliff near Land's End.  Le Carré died from pneumonia at Royal Cornwall Hospital, Truro, on 12 December 2020, at age 89.

George Smiley and Related Novels 

·         Call for the Dead (1961), OCLC 751303381

·         A Murder of Quality (1962), OCLC 777015390

·         The Spy Who Came in from the Cold (1963), OCLC 561198531

·         The Looking Glass War (1965), OCLC 752987890

·         Tinker Tailor Soldier Spy (1974), ISBN 0-143-12093-X

·         The Honourable Schoolboy (1977), ISBN 0-143-11973-7

·         Smiley's People (1979), ISBN 0-340-99439-8

·         The Russia House (1989), ISBN 0-743-46466-4

·         The Secret Pilgrim (1990), ISBN 0-345-50442-9

·         A Legacy of Spies (2017), ISBN 978-0-735-22511-4

                                       https://en.wikipedia.org/wiki/John_le_Carr%C3%A9

Robots Goad Humans Into Taking Risks

“The robot made me do it”

From: University of Southampton

December 11, 2020 -- New research has shown robots can encourage people to take greater risks in a simulated gambling scenario than they would if there was nothing to influence their behaviours. Increasing our understanding of whether robots can affect risk-taking could have clear ethical, practical and policy implications, which this study set out to explore.

Dr Yaniv Hanoch, Associate Professor in Risk Management at the University of Southampton who led the study explained, "We know that peer pressure can lead to higher risk-taking behaviour. With the ever-increasing scale of interaction between humans and technology, both online and physically, it is crucial that we understand more about whether machines can have a similar impact."

This new research, published in the journal Cyberpsychology, Behavior, and Social Networking, involved 180 undergraduate students taking the Balloon Analogue Risk Task (BART), a computer assessment that asks participants to press the spacebar on a keyboard to inflate a balloon displayed on the screen. With each press of the spacebar, the balloon inflates slightly, and 1 penny is added to the player's "temporary money bank." The balloons can explode randomly, meaning the player loses any money they have won for that balloon and they have the option to "cash-in" before this happens and move on to the next balloon.

One-third of the participants took the test in a room on their own (the control group), one third took the test alongside a robot that only provided them with the instructions but was silent the rest of the time and the final, the experimental group, took the test with the robot providing instruction as well as speaking encouraging statements such as "why did you stop pumping?"

The results showed that the group who were encouraged by the robot took more risks, blowing up their balloons significantly more frequently than those in the other groups did. They also earned more money overall. There was no significant difference in the behaviours of the students accompanied by the silent robot and those with no robot.

Dr Hanoch said: "We saw participants in the control condition scale back their risk-taking behaviour following a balloon explosion, whereas those in the experimental condition continued to take as much risk as before. So, receiving direct encouragement from a risk-promoting robot seemed to override participants' direct experiences and instincts."

The researcher now believe that further studies are needed to see whether similar results would emerge from human interaction with other artificial intelligence (AI) systems, such as digital assistants or on-screen avatars.

Dr Hanoch concluded, "With the wide spread of AI technology and its interactions with humans, this is an area that needs urgent attention from the research community."

"On the one hand, our results might raise alarms about the prospect of robots causing harm by increasing risky behavior. On the other hand, our data points to the possibility of using robots and AI in preventive programs, such as anti-smoking campaigns in schools, and with hard to reach populations, such as addicts."

              https://www.sciencedaily.com/releases/2020/12/201211100646.htm

What Makes Up the Universe?

Fragments of energy -- not waves or particles -- may be the fundamental building blocks of the universe

The Conversation has an insightful article by Larry M. Silverberg about this new theory about particles and energy he has been working on with Jeffrey Eischen, published  December 9, 2020.

See:   Fragments of energy – not waves or particles – may be the fundamental building blocks of the universe (theconversation.com)