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NIH to support 85 new grants featuring high-risk, high-reward research – National Institutes of Health

News Release

Tuesday, October 6, 2020

The 2020 Directors Awards will feature highly innovative biomedical research by investigators at all career stages.

The National Institutes of Health has awarded 85 grants through its High-Risk, High-Reward Research (HRHR) Program that will fund highly innovative and unusually impactful biomedical or behavioral research proposed by extraordinarily creative scientists. Examples of supported research include understanding the role of neighborhoods on urban substance abuse, brain-machine interfaces that allow learning by both brain and machine, engineering multi-organs in a dish, and exploiting latent immune pathways to treat disease. The 85 awards total approximately $251 million over five years, pending available funds.

The High-Risk, High-Reward Research program catalyzes scientific discovery by supporting research proposals that, due to their inherent risk, may struggle in the traditional peer-review process despite their transformative potential. Program applicants are encouraged to think outside the box and to pursue trailblazing ideas in any area of research relevant to the NIHs mission to advance knowledge and enhance health.

The breadth of innovative science put forth by the 2020 cohort of early career and seasoned investigators is impressive and inspiring," said NIH Director Francis S. Collins, M.D., Ph.D. I am confident that their work will propel biomedical and behavioral research and lead to improvements in human health.

The High-Risk, High-Reward Research Program is part of the NIH Common Fund, which oversees programs that pursue major opportunities and gaps throughout the research enterprise that are of great importance to NIH and require collaboration across the agency to succeed. The High-Risk, High-Reward Research program manages the following four awards, including two awards aimed specifically to support researchers in the early stages of their careers:

NIH issued10 Pioneer awards,53 New Innovator awards,nine Transformative Research awards, and 13 Early Independence awards for 2020. Funding for the awards comes from the NIH Common Fund; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Cancer Institute; National Human Genome Research Institute; National Institute of Biomedical Imaging and Bioengineering; National Institute of Dental and Craniofacial Research; National Institute of General Medical Sciences; National Institute of Mental Health; National Institute of Neurological Disorders and Stroke; and National Institute on Aging.

About the NIH Common Fund: The NIH Common Fund encourages collaboration and supports a series of exceptionally high-impact, trans-NIH programs. Common Fund programs are managed by the Office of Strategic Coordination in the Division of Program Coordination, Planning, and Strategic Initiatives in the NIH Office of the Director in partnership with the NIH Institutes, Centers, and Offices. More information is available at the Common Fund website:https://commonfund.nih.gov.

About the National Institutes of Health (NIH):NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit http://www.nih.gov.

NIHTurning Discovery Into Health

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Admissions at SCTIMST in Thiruvananthapuram: Apply by October 15 – Mathrubhumi English

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Admissions at SCTIMST in Thiruvananthapuram: Apply by October 15 - Mathrubhumi English

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Global and Asia Pacific L Citrulline Market to Witness Huge Growth by 2027 Best Companies included in report Nutra Green Biotechnology, Kyowa, Wuhan…

Global Coronavirus pandemic has impacted all industries across the globe, L Citrulline market being no exception. As Global economy heads towards major recession post 2009 crisis, Cognitive Market Research has published a recent study which meticulously studies impact of this crisis on Global L Citrulline market and suggests possible measures to curtail them. This press release is a snapshot of research study and further information can be gathered by accessing complete report. To Contact Research Advisor Mail us @ [emailprotected] or call us on +1-312-376-8303.

The global L Citrulline market research report is anticipated to rise at a considerable rate during forecast period, between 2020 and 2027. The global L Citrulline market report study provides intelligence studies ensuring relevant and fact-based research which help clients understand the significance and impact of market dynamics. This research report covers the current status and future prospects for the global L Citrulline market. Report offers the detailed L Citrulline market overview, development, and segment by type, application and region. In addition, L Citrulline market research report introduces the market competition overview among the major companies and companies profiles.

Global L Citrulline Market: Product analysis: Food Grade L Citrulline, Pharma Grade L Citrulline, Cosmestic Grade L Citrulline

Global L Citrulline Market: Application analysis: Food & Beverages, Cosmestics, Pharmaceuticals, Agriculture/Animal Feeds, Other

Some of the key players operating in this market include Nutra Green Biotechnology, Kyowa, Wuhan Soleado Technology, Zhaoqing City Dingkang Pharmaceutical, Ansun Bioengineering, Shanghai Join Ray Biotechnology, MH2 Biochemical, Premium Ingredient, Ningbo Yore Chemipharma . Manufacturers are facing continued downward pressure on demand, production and revenues as the COVID-19 pandemic strengthens. Manufacturing in the Euro-area experienced a substantial deterioration in its business cycle as the impact of COVID-19 hit both the demand and supply sides of the technology industry.

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The business is particularly defenseless given that the greater part of its workforce is utilized on location employments that are impossible remotely. Also, given the idea of the business, manufacturers should be creating social distancing in workplaces that are typically worker-dense (e.g., manufacturing plants, warehouses, material movements and logistics, etc.). Furthermore, manufacturers should be prepared for major supply chain disruptions. This will influence the OEMs, however will likewise wave all through flexibly chain, influencing manufactures by driving reduced demand for materials and parts.

There is hardly any place in the world that has remained unaffected by the brutality of the Covid-19 pandemic; almost every manufacturing company is suffering from ruthless Novel Coronavirus Disease. To encompass the pandemic, many nations and Governments around the world has imposed a lockdown, restricting the gatherings and the movement of people. Lockdown has multiple consequences, which further stretch the troubles for various sector like reverse migration, disruption of supply chains, manufacturing sector. As the government have close down shops, stores and malls that helps to slow the spread of the virus, which is the major factor that is affecting the industry.

The global L Citrulline market research report examined on the basis of the various parameters such as Porters Five Force Model, SWOT Analysis which provides the precise information about the global L Citrulline market. Furthermore, in-depth analysis of the global L Citrullinemarket research report helps to identify the drivers, restraints, and opportunity regarding current market scenario.This report offers the detailed information regarding the global L Citrulline market. Report covers the brief summary of product, which defines the scope of the report in the L Citrulline market. Along with that, production methods used in it are also covered in the report. In addition, global L Citrulline market research report analyzes the diverse dynamics which are influencing the global L Citrulline market.

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About Us:Cognitive Market Research is one of the finest and most efficient Market Research and Consulting firm. The company strives to provide research studies which include syndicate research, customized research, round the clock assistance service, monthly subscription services, and consulting services to our clients. We focus on making sure that based on our reports, our clients are enabled to make most vital business decisions in easiest and yet effective way. Hence, we are committed to delivering them outcomes from market intelligence studies which are based on relevant and fact-based research across the global market.Contact Us: +1-312-376-8303Email: [emailprotected]Web: https://www.cognitivemarketresearch.com/

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Global and Asia Pacific L Citrulline Market to Witness Huge Growth by 2027 Best Companies included in report Nutra Green Biotechnology, Kyowa, Wuhan...

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2020 Ducati Multistrada V4 to come with front and rear radar – public presentation on November fourth – Paul Tan’s Automotive News

Now in its fourth generation, the 2020 Ducati Multistrada V4 has entered the production line at Ducatis works in Borgo Panigale, Italy and is scheduled for its public unveiling this November fourth. Aside from the use of the Desmosedici Stradale V-four engine, also seen in the Panigale V4 and Streetfighter V4, the Multistrada V4 will be the first production motorcycle to use front and rear radar.

The radar system, developed in conjunction with the Department of Electronics, Information and Bioengineering of the Politecnico di Milano University, is part of Ducatis Advanced Rider Assistance Systems (ARAS) that enhances rider safety. The system features front- and rear-facing radars and each has a weight of 190 grammes, measuring 70 x 60 x 20 mm, or about the size of a box of cigarettes.

The front radar is linked to the Multistrada V4s Adaptive Cruise Control (ACC) and controls braking and acceleration to automatically adjust distance to the vehicle in front. Available with four preset distances, the system is operable between 30 to 160 km/h.

Based on systems currently used in four-wheeled vehicles, the ACC system has been modified to suit the requirements of motorcycles, notably in terms of acceleration and deceleration. These parameters are limited to ensure the rider can maintain constant control of the Multistrada V4 in any situation.

For the rear of the Multistrada V4, the rear radar monitors the bikes blind spot, notably to the rear and the outside viewing angles of the rear-view mirrors. The system already the rider to the presence of vehicles in the blind spot as well as vehicles approaching from behind at high speed.

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Vanderbilt researchers develop publicly available COVID-19 animal susceptibility prediction tool; suggests increased risk to horses – Vanderbilt…

A Vanderbilt team of experts in virology, genetics, structural biology, chemistry, physiology, medicine, immunology and pharmacology have together developed technology to understand and predict animal susceptibility to SARS-CoV-2, the scientific name for the strain of coronavirus causing COVID-19. providing evidence that horses and camels may be at increased risk of the virus. The group has also released a publicly available tool to enable people to understand the likelihood of other animals susceptibility.

The article, Predicting susceptibility to SARS-CoV-2 infection based on structural differences in ACE2 across species, was published in the Federation of American Societies for Experimental Biology (FASEB) Journal on Oct. 5.

The investigators applied a combination of sophisticated genetic sequence alignment and structural analysis of ACE2, the receptor protein for SARS-CoV-2, to a variety of known susceptible and non-susceptible species. Through the analysis they identified five particular amino acid sites within the protein that distinguish virus susceptibility or resistance, and using these sites developed an algorithm to predict susceptibility of unknown species. The algorithm has been made public on a website where people can upload the aligned ACE2 sequence of animals with unknown susceptibility to generate a COVID-19 susceptibility score.

Jacquelyn Brown, a staff scientist at the Vanderbilt Institute for Integrative Biosystems Research and Education, initiated the project. When I first learned that COVID-19 had crossed the species barrier into cats and dogs, I became worried about other animals that might act as reservoirs for the disease or be at risk, explained Brown, an avid equestrian who practices medieval mounted archery. Since MERS infects camels, I was concerned about what would happen if my horse could get it?! Horses have massive lungs and a sensitive respiratory system, and we humans often touch their noses and mouths.

206,000 horses live on horse farms and properties in Tennessee and 3.2 million of the states 10 million farm acres are devoted to the horse industry. Brown proposed a collaborative research project on the topic to Gordon A. Cain University Professor John Wikswo, who holds appointments in physics, biomedical engineering, and molecular physiology and biophysics.

As the director of VIIBRE, an institute established to foster and enhance interdisciplinary research in the biophysical sciences, bioengineering and medicine at Vanderbilt, Wikswo immediately assembled a trans-institutional team spanning Vanderbilt schools and colleges and Vanderbilt University Medical Center. I speak each disciplines language well enough to make the necessary connections, Wikswo said. This proved to be an outstanding group brought together by their interests and skills that produced an important result in very short order.

The project gave meaning to each researcher, at a time when we all were searching for ways to contribute to fighting COVID-19, noted Wenbiao Chen.

The work could not have been achieved without the collaboration of many researchers. The multidisciplinary approach revealed how much information can be wrung from the same basic information, noted Wenbiao Chen, the papers co-corresponding author and associate professor of molecular physiology and biophysics. We found potential targets by sequence comparison but wouldnt have been able to interpret our findings without structural information. The project gave meaning to each researcher, at a time when we all were searching for ways to contribute to fighting COVID-19.

Understanding the animals we should more closely scrutinize based on their susceptibility to COVID-19 can help us protect people, pets, wildlife, livestock and our food sources, said Matthew Alexander, assistant professor of medicine. The algorithm the team developed is particular to SARS-CoV-2 because it focuses on its particular receptor binding protein ACE2, but the approach is broadly applicable to predicting susceptibility to other viruses or during future outbreaks.

There is also the opportunity to investigate if the identified five sites on ACE2 that most distinguish susceptible from non-susceptible species can be used as targets to develop drugs that inhibit these sites specifically. I hope that our results will inspire future research on both rational drug design and closer examination of at-risk species, said Meena Madhur, the papers co-corresponding author, associate professor of medicine and associate director of the Vanderbilt Institute for Infection, Immunology and Inflammation at VUMC.

Of note, the work and collaboration were conducted remotely, with an analysis of publicly available data. This experimental approach of using extensive and rapidly accumulating publicly available data in new ways allowed us to efficiently answer a timely question without having to generate new datasets. The collaboration was fun and rewarding, and we were able to answer an important question that none of us could have solved alone, Alexander, the papers co-first author said. Wikswo pointed out that while the source data was public, the project required massive calculations of how different versions of the virus would bind to each animals ACE2.

Members of the collaborative project also include Distinguished Research Professor of Chemistry Jens Meiler, Clara Schoeder, co-first author and postdoctoral scholar, , Charles Duncan Smart, graduate student in molecular physiology and biophysics, Chris Moth, computational chemist in the biological sciences department, and Tony Capra, research associate professor of biological sciences.

The work was supported by National Institutes of Health grants F32HL144048-01, DK117147, UH3TR002097 and U01TR002383, U19AI117905, U01AI150739, and R01AI141661, R35GM127087, and DP2HL137166 and American Heart Association grants 20PRE35080177 and EIA34480023

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AI tool could predict how drugs will react in the body – Futurity: Research News

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A new deep learning-based tool called Metabolic Translator may soon give researchers a better handle on how drugs in development will perform in the human body.

When you take a medication, you want to know precisely what it does. Pharmaceutical companies go through extensive testing to ensure that you do.

Metabolic Translator, a computational tool that predicts metabolites, the products of interactions between small molecules like drugs and enzymes could help improve the process.

The new tool takes advantage of deep-learning methods and the availability of massive reaction datasets to give developers a broad picture of what a drug will do. The method is unconstrained by rules that companies use to determine metabolic reactions, opening a path to new discoveries.

When youre trying to determine if a compound is a potential drug, you have to check for toxicity, says Lydia Kavraki, a professor of computer science, a professor of bioengineering, mechanical engineering, and electrical and computer engineering, and director of Rices Ken Kennedy Institute, as well ascoauthor of the new paper in Chemical Science.

You want to confirm that it does what it should, but you also want to know what else might happen, she says.

The researchers trained Metabolite Translator to predict metabolites through any enzyme, but measured its success against the existing rules-based methods that are focused on the enzymes in the liver. These enzymes are responsible for detoxifying and eliminating xenobiotics, like drugs, pesticides, and pollutants. However, metabolites can form through other enzymes as well.

Our bodies are networks of chemical reactions, says graduate student and lead author Eleni Litsa. They have enzymes that act upon chemicals and may break or form bonds that change their structures into something that could be toxic, or cause other complications. Existing methodologies focus on the liver because most xenobiotic compounds are metabolized there. With our work, were trying to capture human metabolism in general.

The safety of a drug does not depend only on the drug itself but also on the metabolites that can be formed when the drug is processed in the body, Litsa says.

The rise of machine learning architectures that operate on structured data, such as chemical molecules, make the work possible, she says.

Transformer was introduced in 2017 as a sequence translation method that has found wide use in language translation and is based on SMILES (for simplified molecular-input line-entry system), a notation method that uses plain text rather than diagrams to represent chemical molecules.

What were doing is exactly the same as translating a language, like English to German, Litsa says.

Due to the lack of experimental data, the lab used transfer learning to develop Metabolite Translator. They first pre-trained a Transformer model on 900,000 known chemical reactions and then fine-tuned it with data on human metabolic transformations.

The researchers compared Metabolite Translator results with those from several other predictive techniques by analyzing known SMILES sequences of 65 drugs and 179 metabolizing enzymes.

Though they trained Metabolite Translator on a general dataset not specific to drugs, it performed as well as commonly used rule-based methods that have been specifically developed for drugs. But it also identified enzymes not commonly involved in drug metabolism and not found by existing methods.

We have a system that can predict equally well with rule-based systems, and we didnt put any rules in our system that require manual work and expert knowledge, Kavraki says. Using a machine learning-based method, we are training a system to understand human metabolism without the need for explicitly encoding this knowledge in the form of rules. This work would not have been possible two years ago.

Rice University and the Cancer Prevention and Research Institute of Texas supported the research.

Source: Rice University

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