Sorting out viruses with machine learning

Sorting out viruses with machine learning
Single virus particle detections using a solid-state nanopore. Credit: Osaka University

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, researchers 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

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Machine Learning HSA Personas Reveal Industry’s Over-Generalization of Consumer Needs

Lively releases a new report showing that HSAs need more personalized offerings to help account holders save for the long-term

Lively, Inc., creators of the modern Health Savings Account (HSA), today released its first HSA Persona Report, giving a view into how consumers are using their HSA funds. Findings show that the industry’s perception of the average HSA holder is vastly misunderstood, as a majority of people who use HSAs have just $1,050 in assets and are primarily motivated to save in case of unexpected healthcare costs. This indicates that account holders do not utilize an HSA for its long-term benefits and may need more offerings and education to start saving.

This press release features multimedia. View the full release here:

Lively 2020 HSA Persona Report (Graphic: Business Wire)

“HSAs are an incredible option because of their triple tax advantage: You put money in tax-free, earn interest or potential

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Researchers develop machine learning model that will support safe and accurate decision making for the Halifax Harbour

Researchers develop machine learning model that will support safe and accurate decision making for the Halifax Harbour
A Smart Buoy floating on the ocean. Credit: Dalhousie University

Researchers at Dalhousie and ocean data analytics innovation environment DeepSense have developed a machine learning method for predicting wind speed and wave height measurements. Such measurements support safe and more accurate decision making by the Halifax Port Authority and the Halifax Marine Pilots.

Results published in the Journal for Ocean Technology demonstrate how the team used data from smart buoys to provide predictions for use during periods of scheduled buoy maintenance and/or spontaneous sensor failures. These predictions will be valuable to the Port community in providing continuity of critical information used in the safe navigation of vessels within the Port of Halifax and the safe transfer of Halifax Marine Pilots between pilot boats and commercial vessels.

The DeepSense/SmartAtlantic project is a collaboration between the Center for Ocean Ventures and Entrepreneurship (COVE), DeepSense, the Halifax Port Authority (HPA) and the Canadian

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Researchers at the forefront of developing machine learning methods for chemical discovery

Credit: CC0 Public Domain

The discovery and formulation of new drugs, antivirals, antibiotics and in general chemicals with tailored properties is a long and painstaking process. Interdisciplinary research at the crossroads of biochemistry, physics and computer science can change this. The development of machine learning (ML) methods, combined with first principles of quantum and statistical mechanics and trained on increasingly available molecular big datasets, has the potential to revolutionize the process of chemical discovery.

“Chemical discovery and machine learning are bound to evolve together, but achieving true synergy between them requires solving many outstanding challenges,” says Alexandre Tkatchenko, Professor of Theoretical Chemical Physics at the University.

Machine learning to help identify drug candidates

The University initiated a collaboration with Belgian company Janssen Pharmaceuticals in spring 2020 to develop novel ML methods for identifying compounds that have a strong therapeutic potential (also called drug candidates). So far, ML approaches have been

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