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48,453 grants matching “machine learning”
Molecular characterization of extracellular vesicles for the spread of misfolded tau protein
$611,392Tsuneya Ikezu · Mayo Clinic Jacksonville · R01 · FY2024 · AG
Molecular characterization of extracellular vesicles for the spread of misfolded tau protein
$611,392Tsuneya Ikezu · Mayo Clinic Jacksonville · R01 · FY2023 · AG
Molecular characterization of extracellular vesicles for the spread of misfolded tau protein
$611,392Tsuneya Ikezu · Mayo Clinic Jacksonville · R01 · FY2022 · AG
Advanced diffusion MRI for evaluating early response to radiation treatment in cervical cancer
$611,372Rebecca Ann Rakow-Penner · University Of California, San Diego · R37 · FY2024 · CA
Integrative Analysis Methods for the dGTEx Initiative
$611,365Lin Chen · University Of Chicago · U01 · FY2024 · MH
Imaging genetics of laryngeal dystonia
$611,364Kristina Simonyan · Massachusetts Eye And Ear Infirmary · R01 · FY2022 · DC
A novel human T-cell platform to define biological effects of genome editing
$611,358Shengdar Tsai · St. Jude Children'S Research Hospital · U01 · FY2019 · EB
Modulation of Hippocampal Circuitry and Memory Function with Focused Ultrasound in Amnestic MCI
$611,356Susan Y Bookheimer · University Of California Los Angeles · R01 · FY2025 · AG
Osteoarthritis: Quantitative Evaluation of Whole Joint Disease with MRI
$611,185Garry E Gold · Stanford University · R01 · FY2021 · EB
Whole Transcriptome Studies of Blood to Predict Stroke Outcome
$611,184Boryana Stamova · University Of California At Davis · R01 · FY2024 · NS
MAPS: Mobile Assessment for the Prediction of Suicide
$611,178Nicholas B Allen · University Of Oregon · U01 · FY2022 · MH
Quantification of Tics in Tourette Syndrome
$611,090Christine A Conelea · University Of Minnesota · R01 · FY2023 · NS
Personalized Risk Prediction for Prevention and Early Detection of Postoperative Failure to Rescue
$611,060Maxime Cannesson · University Of California Los Angeles · R01 · FY2025 · EB
Accelerating viral outbreak detection in US cities using mechanistic models, machine learning and diverse geospatial data
$611,043Alison P Galvani · Yale University · R01 · FY2020 · AI
FOOD PROCESSING AND COOKING SHARE MANY COMMON GOALS, SUCH AS PRESERVING FOOD, IMPROVING TASTE, AND INCREASING FOOD NUTRITIONAL VALUE. WITH CHANGING LIFESTYLES, THERE HAS BEEN AN UNDENIABLE INCREASE IN CONSUMPTION OF PROCESSED FOODS AS WELL AS INCREASES IN NON-COMMUNICABLE DISEASES IN THE PAST SEVERAL DECADES. THIS HAS LED SOME TO SPECULATE THAT PROCESSED FOOD CONSUMPTION RESULTS IN NEGATIVE HEALTH CONSEQUENCES. HOWEVER, THERE IS A LACK OF COMPREHENSIVE KNOWLEDGE THAT COMPARES SIMILAR FOODS THAT HAVE BEEN INDUSTRIALLY PROCESSED OR PRE-PROCESSED (E.G. FROZEN OR REFRIGERATED PRODUCTS THAT NEED TO BE PARTIALLY PREPARED AT HOME) WITH HOME COOKING TO UNDERSTAND THE IMPACT OF PROCESSING OR PREPARATION METHOD ON FOOD PHYSICAL PROPERTIES (E.G. TEXTURE, MOUTHFEEL), CHEMICAL PROPERTIES (E.G. COMPOSITION OF MACRO- AND MICRO-NUTRIENTS), AND NUTRITIONAL PROPERTIES (DIGESTIBILITY OR AVAILABILITY OF NUTRIENTS IN THE HUMAN BODY AFTER THE FOOD IS CONSUMED). THE OVERALL GOAL OF THIS PROJECT IS TO DEVELOP A COMPREHENSIVE ASSESSMENT OF THE IMPACT OF PROCESSING OR COOKING METHODS ON THE PHYSICAL, CHEMICAL, AND NUTRITIONAL PROPERTIES OF A BROAD RANGE OF FOOD PRODUCTS. THE OUTCOMES OF THIS PROJECT WILL PROVIDE A HOLISTIC UNDERSTANDING OF THE PHYSICAL, CHEMICAL, AND STRUCTURAL PROPERTIES OF FOOD AS A FUNCTION OF PROCESSING OR COOKING METHODS AND THEIR INFLUENCE ON THE NUTRITIONAL PROPERTIES OF FOODS, INCLUDING PROTEIN AND STARCH DIGESTION AND THE RELEASE OF MICRONUTRIENTS.TO ACHIEVE THESE GOALS, THIS PROJECT INTEGRATES ADVANCES IN THERMAL AND NON-THERMAL FOOD PROCESSING, TO COMPARE THESE PROCESSING TECHNIQUES WITH CONVENTIONAL (HOME) COOKING METHODS TO DEVELOP FOODS WITH A RANGE OF STRUCTURAL AND CHEMICAL PROPERTIES. THIS RANGE OF FOOD PRODUCTS, INCLUDING PROTEIN- AND CARBOHYDRATE-RICH FOODS, FRUITS, VEGETABLES, AND JUICES, WILL BE ANALYZED USING STATE-OF-THE-ART APPROACHES TO ASSESS THEIR PHYSICOCHEMICAL CHARACTERISTICS AND NUTRITIONAL PROPERTIES. THE PHYSICOCHEMICAL PROPERTIES OF FOODS ANALYZED IN THIS RESEARCH WILL INCLUDE TEXTURE, RHEOLOGY, PARTICLE SIZE, PROXIMATE ANALYSIS (COMPOSITION), AMINO ACID AND FATTY ACID PROFILES, AND SALT CONTENT. THE NUTRITIONAL PROPERTIES OF FOODS WILL BE MEASURED USING DYNAMIC DIGESTION MODELS TO QUANTIFY THE RELEASE RATE OF MACRONUTRIENTS, STRUCTURAL BREAKDOWN, MICRONUTRIENT BIOACCESSIBILITY, AND TOTAL MACRONUTRIENT DIGESTION. THE RESULTING DATA WILL BE ANALYZED USING MACHINE LEARNING MODELS TO EVALUATE THE ROLES OF PROCESSING METHODS AND THE FOOD PROPERTIES ON THE FOOD NUTRITIONAL PROFILE. THESE MACHINE LEARNING MODELS WILL BE ABLE TO CLASSIFY FOODS INTO DIFFERENT CLASSES (E.G. POOR, LOW, MEDIUM, HIGH) OF NUTRITIONAL PROPERTIES BASED ON FOOD COMPOSITION AND PROCESSING. THIS INFORMATION WILL BE USEFUL FOR FOOD PRODUCERS, AS IT WILL ALLOW THEM TO OPTIMIZE THEIR FOOD PROCESSING AND FORMULATION TO IMPROVE THE NUTRITIONAL PROPERTIES OF PROCESSED AND PRE-PROCESSED FOODS. AS A RESULT, THE AMERICAN FOOD CONSUMERS WILL ALSO BENEFIT BY HAVING A,N INCREASED SUPPLY OF FOODS WITH BETTER NUTRITIONAL QUALITY, AS WELL AS WITH KNOWLEDGE IN THE DIFFERENCE IN NUTRITIONAL QUALITY BETWEEN PROCESSED AND HOME COOKED FOODS. THE FOUNDATIONAL SET OF KNOWLEDGE DEVELOPED IN THIS PROJECT WILL ALSO BE ABLE TO ADDRESS GAPS IN FOOD CLASSIFICATION BY PROVIDING A COMPREHENSIVE DATA SET THAT LINKS PROCESSING METHOD AND PHYSICOCHEMICAL PROPERTIES (INCLUDING COMPOSITION) TO FOOD NUTRITIONAL PROPERTIES. ULTIMATELY, THIS PROJECT SEEKS TO IMPROVE OUR UNDERSTANDING OF THE IMPACT OF FOOD PROCESSING ON FOOD NUTRITIONAL QUALITY TO INCREASE THE AVAILABILITY AND CONSUMPTION OF HEALTHY FOOD.
$611,000University Of California, Davis · · FY2025 · National Institute of Food and Agriculture
** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** FOODBORNE PATHOGENS ARE THE MAIN SOURCE OF FOODBORNE ILLNESSES AND CAUSE SIGNIFICANTTHREAT TO PUBLIC HEALTH AND GLOBAL ECONOMY.WITHOUT TIMELY INTERVENTION, THESE PATHOGENS CAN COMPLETELY DISRUPT THE SUPPLY CHAIN FOR CRITICAL FOODSTUFFS AND CONSEQUENTLY ELEVATE ECONOMIC STRESS AND PUBLIC ANXIETY. AMONG ALL THE LEADING FOODBORNE PATHOGENS,SALMONELLAHAS PROVEN TO BE THE COSTLIEST ($3.7 BILLION/YEAR)TO THE US ECONOMY.IN THIS PROPOSAL, OUR GOAL IS TO DEVELOP A DETECTION KIT AGAINSTTHE MOST PREVALENT SEROTYPES AND ACCOUNT FOR HALF OF ALL HUMAN INFECTIONS IN THE US.THE PROPOSED APPROACH OFFERS THE DETECTION OFSALMONELLAUNDER 6-HR; SURPASSINGTHE CURRENT METHODS WHICH CAN TAKEUP TO 5 DAYS. THIS IS IMPORTANT FOR RAPID AND ROUTINE SAMPLING TO AVOID?THE SPREAD OFSALMONELLA. WE ALSO PROPOSE TO DEVELOP MACHINE LEARNING ALGORITHMS FOR DATA ANALYSIS AND MOBILE APP FOR DATA TRANSMISSION. THE MOBILE APP FACILITATED DATA TRANSMISSION IS IMPORTANT FOR TAKING IMMEDIATE ACTIONS AND THE CONTAINMENT OF DISEASE TRANSMISSION, THUS PREVENTING FURTHER HEALTH RISKS AND ECONOMIC LOSS. THE PROPOSED DETECTION KIT AND TECHNOLOGIES OFFER A UNIVERSAL DIAGNOSTIC ASSAY WHICH CAN BE EMPLOYED BEYONDSALMONELLADETECTION. THEREFORE, THERE IS POTENTIAL FOR BROADER APPLICATIONS.
$611,000Research Foundation For The State University Of New York, The · · FY2024 · National Institute of Food and Agriculture
Predicting individual responses to treatment for alcohol use disorder.
$610,959M Lee Van Horn · University Of New Mexico · R01 · FY2023 · AA
Discovery of early immunologic biomarkers for risk of PTLDS through machine learning-assisted broad temporal profiling of humoral immune response
$610,933Neal Walter Woodbury · Arizona State University-Tempe Campus · R01 · FY2025 · AI
Biomedical Informatics Tools for Applied Perioperative Physiology
$610,880Maxime Cannesson · University Of California Los Angeles · R01 · FY2022 · EB
Quantitative assessment of pre-metastatic immune subversion as a risk factor for melanoma relapse
$610,829Svetomir Nenad Markovic · Mayo Clinic Rochester · R01 · FY2023 · CA
Immune Determinants of the Course of Mycobacterium tuberculosis infection and Disease
$610,717Padmini Salgame · Rutgers Biomedical And Health Sciences · U19 · FY2025 · AI
Sensor Hardware and Intelligent Tools for Assessing the Health Effects of Heat Exposure
$610,713Vicki Stover Hertzberg · Emory University · R01 · FY2024 · ES
A novel human T-cell platform to define biological effects of genome editing
$610,710Shengdar Tsai · St. Jude Children'S Research Hospital · U01 · FY2020 · AI
AI-driven low-cost ultrasound for automated quantification of hypertension, preeclampsia, and IUGR
$610,642Gari David Clifford · Emory University · R01 · FY2025 · HD
BIGDATA: F: Metric-space Positioning Systems for Symbolic Data Science
$610,560Manuel E Lladser · University Of Colorado At Boulder · · FY2018 · CSE