Facebook Users’ Language Predicts Who’s at Risk for Dangerous Drinking

The language used in Facebook posts can identify people at risk of hazardous drinking and alcohol use disorders (AUDs), according to a new study. Social media platforms are a “low-cost treasure trove” of data, researchers claim, expanding the options for studying, screening, and helping people at risk. Social media content in recent years has been used to explore various public health phenomena. For example, language and “likes” have predicted depression, hospital visits, low birthweight, obesity, and life expectancy. Social media language has also been linked to patterns of alcohol consumption and related problems. For the study in Alcoholism: Clinical & Experimental Research, investigators explored how convincingly the language of Facebook could be used to identify risky drinking. They compared the accuracy of multiple predictive tools, including a new technique for processing language that has rarely been applied to health research.

COVID-19, MIS-C and Kawasaki Disease Share Same Immune Response

COVID-19, MIS-C and KD all share a similar underlying mechanism involving the over-activation of particular inflammatory pathways, UC San Diego study shows. Findings support novel drug targets for MIS-C.

Artificial Intelligence Agents Argue to Enhance the Speed of Materials Discovery

Researchers have developed a new artificial intelligence (AI)-powered approach to analyzing X-ray diffraction (XRD) data. The X-ray Crystallography companion Agent (XCA) approach assembles a group of AIs that debate each other while analyzing live streaming X-ray data. Once the AIs cast their final votes, the XCA approach uses the vote tally to interpret what the most likely atomic structure is and to suggest how confident the researchers should be of the AI analysis. The AI analysis matches human effectiveness but takes just seconds.

Retinal cell map could advance precise therapies for blinding diseases

Researchers have identified distinct differences among the cells comprising a tissue in the retina that is vital to human visual perception. The scientists from the National Eye Institute (NEI) discovered five subpopulations of retinal pigment epithelium (RPE)—a layer of tissue that nourishes and supports the retina’s light-sensing photoreceptors. Using artificial intelligence, the researchers analyzed images of RPE at single-cell resolution to create a reference map that locates each subpopulation within the eye.

UAH collaboration creates self-learning AI platform to discover new drugs

A cross-college collaboration at The University of Alabama in Huntsville (UAH) has developed a self-learning artificial intelligence (AI) platform that uses big data analytics to discover how new pharmaceutical drugs and various molecules work inside living cells.

Machine learning program for games inspires development of groundbreaking scientific tool

Scientists have developed a groundbreaking AI-based algorithm for modeling the properties of materials at the atomic and molecular scale. It should greatly speed up materials discovery.

VA, ORNL and Harvard develop novel method to identify complex medical relationships

A team of researchers from the Department of Veterans Affairs, Oak Ridge National Laboratory, Harvard’s T.H. Chan School of Public Health, Harvard Medical School and Brigham and Women’s Hospital has developed a novel, machine learning–based technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.

AI Could Predict Ideal Chronic Pain Patients for Spinal Cord Stimulation

Spinal cord stimulation is a minimally invasive FDA-approved treatment to manage chronic pain such as back and neck pain. The ability to accurately predict which patients will benefit from this treatment in the long term is unclear and currently relies on the subjective experience of the implanting physician. A study is the first to use machine-learning algorithms in the neuromodulation field to predict long-term patient response to spinal cord stimulation.

AI May Detect Earliest Signs of Pancreatic Cancer

An artificial intelligence (AI) tool developed by Cedars-Sinai investigators accurately predicted who would develop pancreatic cancer based on what their CT scan images looked like years prior to being diagnosed with the disease. The findings, which may help prevent death through early detection of one of the most challenging cancers to treat, are published in the journal Cancer Biomarkers.

PSC and Partners to Lead $7.5-Million Project to Allocate Access on NSF Supercomputers

The NSF has awarded $7.5 million over five years to the RAMPS project, a next-generation system for awarding computing time in the NSF’s network of supercomputers. RAMPS is led by the Pittsburgh Supercomputing Center and involves partner institutions in Colorado and Illinois.

Pain in the Neck? New Surgical Method Could be Game-changing

Anterior cervical discectomy and fusion is widely used to treat spinal disorders. The fusion involves placing a bone graft or “cage” and/or implants where the surgically removed damaged disc was originally located to stabilize and strengthen the area. The risk factors for cage migration are multifactorial and include patient, radiological characteristics, surgical techniques and postoperative factors. A study is the first to evaluate the effect of the range of motion, cage migration and penetration using variable angle screws and cervical spine models. The plate developed and tested by the researchers provided directional stability and excellent fusion, showing promising clinical outcomes for patients with degenerative cervical spine disease.

Can University of Oklahoma Research Team Clear Up Biases in Artificial Intelligence?

An American Meteorological Fellow, Amy McGovern has been studying severe weather phenomena since the late 1990s. During her career, she has witnessed a rapid emergence in the AI field, all while developing what she hopes are trustworthy AI methods to avert weather and climate disasters. Lately, however, McGovern and researchers from Colorado and Washington have noticed grave disparities in AI, noting that the methods are not objective, especially when it comes to geodiversity.

Seizure forecasting with wrist-worn devices possible for people with epilepsy, study shows

Despite medications, surgery and neurostimulation devices, many people with epilepsy continue to have seizures. The unpredictable nature of seizures is severely limiting. If seizures could be reliably forecast, people with epilepsy could alter their activities, take a fast-acting medication or turn up their neurostimulator to prevent a seizure or minimize its effects.

A new study in Scientific Reports by Mayo Clinic researchers and international collaborators found patterns could be identified in patients who wear a special wristwatch monitoring device for six to 12 months, allowing about 30 minutes of warning before a seizure occurred. This worked well most of the time for five of six patients studied.

Want to throw off your chatbot? Use figurative language

Computer scientists recently examined the performance of dialog systems, such as personal assistants and chatbots designed to interact with humans. The team found that when these systems are confronted with dialog that includes idioms or similes, their performance drops to between 10 and 20 percent. The research team also developed a partial remedy.

Engineering Researchers Receive $1 Million NSF Grant for First Networked-AI Testbed

Just like humans, autonomous robots need to communicate with one another to learn together and to accomplish a team mission such as search and rescue. Researchers are developing the nation’s first-of-its-kind testbed platform that connects robots using high-frequency radio waves (30 to 300 gigahertz). The robots will be able communicate at ultra-high speeds of gigabits per second by forming and directing ‘beams’ toward each other that also will enable them to see through objects as needed. They will see what the other robots are sensing in real-time, resulting in five times the eyes thanks to the nearly instantaneous exchange of high volumes of data.

Data Scientist Discusses Job Outlook in Era of Artificial Intelligence

Recent worker shortages and higher labor costs have resulted in more automated jobs, including service and professional jobs economists once considered safe. Predictions are mixed on job losses going forward, although the World Economic Forum (WEF) concluded in a 2020 report that “a new generation of smart machines, fueled by rapid advances in artificial intelligence and robotics, could potentially replace a large proportion of existing human jobs.”

Joaquin Carbonara, Buffalo State College professor of mathematics, weighed in on AI’s effect on the job market now and in the future.

Novel Tag Provides First Detailed Look into Goliath Grouper Behavior

A study is the first to reveal detailed behavior of massive goliath groupers. Until now, no studies have documented their fine-scale behavior. What is known about them has been learned from divers, underwater video footage, and observing them in captivity. Using a multi-sensor tag with a three axis accelerometer, gyroscope and magnetometer as well as a temperature, pressure and light sensor, a video camera and a hydrophone, researchers show how this species navigates through complex artificial reef environments, maintain themselves in high current areas, and how much time they spend in different cracks and crevices – none of which would be possible without the tag.

Key to resilient energy-efficient AI/machine learning may reside in human brain

A clearer understanding of how a type of brain cell known as astrocytes function and can be emulated in the physics of hardware devices, may result in artificial intelligence (AI) and machine learning that autonomously self-repairs and consumes much less energy than the technologies currently do, according to a team of Penn State researchers.

All About Eve

New AI model called EVE, developed by scientists at Harvard Medical School and Oxford University, outperforms other AI methods in determining whether a gene variant is benign or disease-causing.
When applied to more than 36 million variants across 3,219 disease-associated proteins and genes, EVE indicated more than 256,000 human gene variants of unknown significance that should be reclassified as benign or pathogenic.

AI Tool Pairs Protein Pathways with Clinical Side Effects, Patient Comorbidities to Suggest Targeted Covid-19 Treatments

Researchers led by Jeffrey Skolnick have designed a new AI-based “decision prioritization tool” that combines data on protein pathways with common Covid-19 side effects and known patient comorbidities. The tool offers possible targeted treatment options with existing FDA-approved drugs to foster better health outcomes for individuals fighting Covid-19.

LLNL joins Human Vaccines Project to accelerate vaccine development and understanding of immune response

Lawrence Livermore National Laboratory has joined the international Human Vaccines Project, bringing Lab expertise and computing resources to the consortium to aid development of a universal coronavirus vaccine and improve understanding of immune response.

AI-driven dynamic face mask adapts to exercise, pollution levels

Researchers reporting in ACS Nano have developed a dynamic respirator that modulates its pore size in response to changing conditions, such as exercise or air pollution levels, allowing the wearer to breathe easier when the highest levels of filtration are not required.

Researchers predict viewer interest, not just attention, in public screen content

We are constantly surrounded by screens that offer us information on the weather, current events or the latest offers from the corner shop. Yet most displays are updated manually, if at all. Researchers at Aalto University and the Finnish Center for Artificial Intelligence FCAI have developed a new, simpler way to choose and arrange public display content so that it really catches people’s attention.

Novel Insights on COVID-19 Vaccines and Virus Evolution, Artificial Intelligence in the Clinic, Miniaturization of Diagnostic Platforms, and Much More to Be Explored at the 2021 AACC Annual Scientific Meeting & Clinical Lab Expo

At the 2021 AACC Annual Scientific Meeting & Clinical Lab Expo, laboratory medicine experts will present the cutting-edge research and technology that is revolutionizing clinical testing and patient care.

Preparing for exascale: Argonne’s Aurora supercomputer to drive brain map construction

Argonne researchers are mapping the complex tangle of the brain’s connections — a connectome — by developing applications that will find their stride in the advent of exascale computing.

New machine learning method to analyze complex scientific data of proteins

Scientists have developed a method using machine learning to better analyze data from a powerful scientific tool: nuclear magnetic resonance (NMR). One way NMR data can be used is to understand proteins and chemical reactions in the human body. NMR is closely related to magnetic resonance imaging (MRI) for medical diagnosis.

Department of Energy Invests $1 Million in Artificial Intelligence Research for Privacy-Sensitive Datasets

The U.S. Department of Energy (DOE) announced $1 million for a one-year collaborative research project to develop artificial intelligence (AI) and machine learning (ML) algorithms for biomedical, personal healthcare, or other privacy-sensitive datasets.