Come mating season, fishes off the California coast sing songs of love in the evenings and before sunrise. They vocalize not so much as lone crooners but in choruses, in some cases loud enough to be heard from land. It's a technique of romance shared by frogs, insects, whales, and other animals when the time is right. For most of these vocal arrangements, the choruses are low-frequency. They're hard to distinguish from the sounds of ships passing in the night among others.

A group of Northeastern researchers is tapping into the power of machine learning to develop new models for identifying patients who may have post-acute sequelae of SARS-CoV-2 infection, or so-called "long COVID." Using electronic health records from the National COVID Cohort Collaborative, a federal database that compiles medical information about COVID-19 patients, researchers were able to develop models that helped identify COVID long haulers across a range of features--from past COVID diagnosis, to the types of medications they've been prescribed, according to new research published in Lancet Digital Health. The data harmonization effort drew from a variety of information sources to construct a picture of what long COVID looks like in the U.S.--and who is most likely to have it. Those sources include demographic data, healthcare visit details, diagnoses and medications for 97,995 adults with COVID-19, the study says. Patients most likely suffering from the post-infection illness, which is estimated to plague between 10-30% of people who contract COVID-19, are often characterized as having new or lingering symptoms that are present 90 days after being diagnosed with the viral infection--a criteria researchers also used to determine their base population in their analysis.

A group of researchers from the Boston University School of Public Health and the VA Boston Healthcare System utilized machine learning to streamline the diagnosis tool for post-traumatic stress disorder (PTSD). According to their new study, released in the journal Assessment, some of the questions imposed in the Structural Clinical Interview for the Diagnostic Statistical Manual of Mental Disorders, Fifth Edition (SCID-5) could be eliminated, leading to more relevancy of the veteran population. "Our study is only a first step--but an important one, because it shows that machine learning methods can be used to help inform efforts to make care more efficient, without sacrificing or degrading the quality of care provided," said co-author Jaimie Graudus, in a news release. The new research included data from the SCID-5 assessments related to more than 1,200 military soldiers, half of which were male and the rest female, who served during the Afghanistan and Iraq conflicts. The use of random forests, a form of machine-learning system, was also incorporated into the study.

Researchers from Queen Mary University of London have developed a machine learning algorithm that ranks drugs based on their efficacy in reducing cancer cell growth. The approach may have the potential to advance personalised therapies in the future by allowing oncologists to select the best drugs to treat individual cancer patients. The method, named Drug Ranking Using Machine Learning (DRUML), was published today in Nature Communications and is based on machine learning analysis of data derived from the study of proteins expressed in cancer cells. Having been trained on the responses of these cells to over 400 drugs, DRUML predicts the best drug to treat a given cancer model. Speaking of the new method, Professor Pedro Cutillas from Queen Mary University of London, who led the study, said: "DRUML predicted drug efficacy in several cancer models and from data obtained from different laboratories and in a clinical dataset. These are exciting results because previous machine learning methods have failed to accurately predict drug responses in verification datasets, and they demonstrate the robustness and wide applicability of our method."

The ongoing global pandemic has created an urgent need for rapid tests that can diagnose the presence of the SARS-CoV-2 virus, the pathogen that causes COVID-19, and distinguish it from other respiratory viruses. Now, common respiratory from Japan have demonstrated a new system for single-virion identification of common respiratory pathogens using a machine learning algorithm trained on changes in current across silicon nanopores. This work may lead to fast and accurate screening tests for diseases like COVID-19 and influenza. In a study published this month in ACS Sensors scientists at Osaka University have introduced a new system using silicon nanopores sensitive enough to detect even a single virus particle when coupled with a machine learning algorithm. In this method, a silicon nitride layer just 50 nm thick suspended on a silicon wafer has tiny nanopores added, which are themselves only 300 nm in diameter.

Scientists at a university in Scotland have developed a technique which could help to identify the source of food poisoning in a better way than curre

BROOKLYN, New York, Monday, April 27, 2020 – Likes, shares, followers, and comments are the currency of online social networks. Posts with high levels of engagement are prioritized by content curation algorithms, allowing social network "influencers" to monetize the size and loyalty of their audience. Yet not all engagement is organic, according to a team of researchers at New York University Tandon School of Engineering and Drexel University, who have published the first analysis of a robust underground ecosystem of "pods." These groups of users manipulate curation algorithms and artificially boost content popularity -- whether to increase the reach of promoted content or amplify rhetoric -- through a tactic known as "reciprocity abuse," whereby each member reciprocally interacts with content posted by other members of the group. The researchers also developed a machine learning tool to detect posts with a high likelihood of having gained popularity through pod engagement.

A multidisciplinary group of researchers from the University of California, Irvine created a machine learning model to predict the potential of large wildfires from the time of ignition. The decision classifier model uses a single dataset to predict whether a fire will be large roughly 50% of the time, outperforming more complex models tested by researchers that rely on multiple weather variables. Researchers trained the AI with air moisture data over the span of the average weather report using data from more than 1,100 fire events in Alaska between 2001 and 2017 from the Alaska Large Fire Database. Each fire was then labeled as small, medium, or large. The model is then able to predict approximately 40% of ignitions that lead to large wildfires that account for 75% of burned area during that time period.

A team of Australian researchers has designed a reliable strategy for testing physical abilities of humanoid robots--robots that resemble the human body shape in their build and design. Using a blend of machine learning methods and algorithms, the research team succeeded in enabling test robots to effectively react to unknown changes in the simulated environment, improving their odds of functioning in the real world. The findings, which were published in a joint publication of the IEEE and the Chinese Association of Automation Journal of Automatica Sinica in July, have promising implications in the broad use of humanoid robots in fields such as healthcare, education, disaster response and entertainment. "Humanoid robots have the ability to move around in many ways and thereby imitate human motions to complete complex tasks. In order to be able to do that, their stability is essential, especially under dynamic and unpredictable conditions," said corresponding author Dacheng Tao, Professor and ARC Laureate Fellow in the School of Computer Science and the Faculty of Engineering at the University of Sydney.

In recent years, machine learning has been proving a valuable tool for identifying new materials with properties optimized for specific applications. Working with large, well-defined data sets, computers learn to perform an analytical task to generate a correct answer and then use the same technique on an unknown data set. While that approach has guided the development of valuable new materials, they've primarily been organic compounds, notes Heather Kulik Ph.D. '09, an assistant professor of chemical engineering. Kulik focuses instead on inorganic compounds--in particular, those based on transition metals, a family of elements (including iron and copper) that have unique and useful properties. In those compounds--known as transition metal complexes--the metal atom occurs at the center with chemically bound arms, or ligands, made of carbon, hydrogen, nitrogen, or oxygen atoms radiating outward. Transition metal complexes already play important roles in areas ranging from energy storage to catalysis for manufacturing fine chemicals--for example, for pharmaceuticals.