Medications like dextromethorphan, used to treat coughs caused by cold and flu, could potentially be repurposed to help people quit smoking cigarettes, according to a study by Penn State College of Medicine and University of Minnesota researchers. They developed a novel machine learning method, where computer programs analyze data sets for patterns and trends, to identify the drugs and said that some of them are already being tested in clinical trials. Cigarette smoking is risk factor for cardiovascular disease, cancer and respiratory diseases and accounts for nearly half a million deaths in the United States each year. While smoking behaviors can be learned and unlearned, genetics also plays a role in a person's risk for engaging in those behaviors. The researchers found in a prior study that people with certain genes are more likely to become addicted to tobacco.

Scientists have been wondering about water for a long time. They think that if water is cooled down to really cold temperatures, like -100C, it might be able to turn into two types of liquid that are different densities. These two types of liquid don't mix, like oil and water, and they might help explain some of water's other strange behaviors, like how it becomes less dense when it gets colder. It's almost impossible to study this in a laboratory, though, because water turns into ice very quickly at these low temperatures. Now, researchers at the Georgia Institute of Technology have used machine learning models to better understand how water changes under different conditions.

Different forms of dementia could be spotted sooner and more easily by analysing recordings of patients' electrical brain activity using artificial intelligence (AI), according to new research. Scientists from the University of Surrey and the University of Newcastle have shown that it is possible to use electroencephalography (EEG) as a low-cost diagnostic tool to help clinicians identify different forms of dementia, including Lewy body, Alzheimer's and Parkinson's dementia. "Our study shows that using artificial intelligence analysis of EEG data as a diagnostic tool to identify dementia could be life-changing for many people. We have shown that by combining brain activity captured from patients with their eyes open and with their eyes closed, our machine learning algorithms can accurately detect different forms of dementia, including Lewy body dementia, which is often only found post-mortem. As a result, we believe that our method could allow people to be diagnosed and treated sooner.

"We recorded a beauty score for each species and correlated it with each fish's ecological characteristics – its size, whether it's carnivorous or herbivorous, nocturnal or diurnal, in the middle or at the bottom of the water column, etc.," Mouquet explains. The researchers then noted that the fish that are considered beautiful – those with sharp contrasts of luminosity (black/white) and colour (e.g. In addition, they represent only a small branch of the tree of life. The fish considered "less attractive" are by far the majority, with longer bodies, duller colours and less easily discernable patterns (for example, the bluish-grey fish found in the water column). The oldest of these species have been in existence for 100 million years and span a wider variety of ecological traits.

Another, newer option is deep brain stimulation, in which small pulses of electricity are delivered to the brain using an implanted electrode. If the implantation is successful, a patient's motor symptoms can be reduced significantly. This technology is made possible by an ever-growing understanding of brain anatomy and the roles played by its various parts. The subthalmic nucleus (STN), for example, is part of the basic movement circuitry in the brain. Pulses of electricity can disrupt this faulty firing.

Before you ever step into a new coffee shop, years of urban planning have gone into building that brick-and-mortar building in your neighborhood. Urban planning is a multi-faceted discipline in which planners collect data points from targeted areas to determine if a store, hospital or other types of buildings are likely needed in that area. It also involves determining if the needed infrastructure to support the building is there or needs to be added. The data provides context to planners and includes things such as geographic factors, socioeconomic statistics and human mobility. This kind of planning can take years to configure, but for first-year computer science doctoral student Dongjie Wang, that isn't efficient.

For the past couple of decades, there has been a loneliness pandemic, marked by rising rates of suicides and opioid use, lost productivity, increased health care costs and rising mortality. The COVID-19 pandemic, with its associated social distancing and lockdowns, have only made things worse, say experts. Accurately assessing the breadth and depth of societal loneliness is daunting, limited by available tools, such as self-reports. In a new proof-of-concept paper, published online September 24, 2020 in the American Journal of Geriatric Psychiatry, a team led by researchers at University of California San Diego School of Medicine used artificial intelligence technologies to analyze natural language patterns (NLP) to discern degrees of loneliness in older adults. "Most studies use either a direct question of ' how often do you feel lonely,' which can lead to biased responses due to stigma associated with loneliness or the UCLA Loneliness Scale which does not explicitly use the word'lonely,'" said senior author Ellen Lee, MD, assistant professor of psychiatry at UC San Diego School of Medicine.

For the past couple of decades, there has been a loneliness pandemic, marked by rising rates of suicides and opioid use, lost productivity, increased health care costs and rising mortality. The COVID-19 pandemic, with its associated social distancing and lockdowns, have only made things worse, say experts. Accurately assessing the breadth and depth of societal loneliness is daunting, limited by available tools, such as self-reports. In a new proof-of-concept paper, published online September 24, 2020 in the American Journal of Geriatric Psychiatry, a team led by researchers at University of California San Diego School of Medicine used artificial intelligence technologies to analyze natural language patterns (NLP) to discern degrees of loneliness in older adults. "Most studies use either a direct question of ' how often do you feel lonely,' which can lead to biased responses due to stigma associated with loneliness or the UCLA Loneliness Scale which does not explicitly use the word'lonely,'" said senior author Ellen Lee, MD, assistant professor of psychiatry at UC San Diego School of Medicine.

How old are you for your age? Scientists who study aging have begun to distinguish chronological age: how long it's been since a person was born, and so-called biological age: how much a body is "aged" and how close it is to the end of life. These researchers are uncovering ways to measure biological age, from grip strength to the lengths of protective caps on the ends of chromosomes, known as telomeres. Their goal: to construct a comprehensive set of metrics that predicts an individual's life span and health span -- the number of healthy years they have left -- and illuminates the drivers of, and treatments for, age-related diseases. A team led by David Sinclair, professor of genetics in the Blavatnik Institute at Harvard Medical School, has just taken another step toward this goal by developing two artificial intelligence-based clocks that use established measures of frailty to gauge both chronological and biological age in mice.

Starting a new life is difficult for criminals transitioning from prison back to regular society. To help those individuals, Purdue University Polytechnic Institute researchers are using artificial intelligence to uncover risky behaviors which could then help identify when early intervention opportunities could be beneficial. Results of a U.S. Department of Justice study indicated more than 80 percent of people in state prisons were arrested at least once in the nine years following their release. Almost half of those arrests came in the first year following release. Marcus Rogers and Umit Karabiyik of Purdue Polytechnic's Department of Computer and Information Technology, are leading an ongoing project focused on using AI-enabled tools and technology to reduce the recidivism rates for convicted criminals who have been released.