The researchers utilize an advanced machine learning method known as next generation reservoir computing. While the past may be a fixed and unchangeable point, machine learning can sometimes make predicting the future easier. Researchers at The Ohio State University have recently discovered a new way to predict the behavior of spatiotemporal chaotic systems, such as changes in Earth's weather, that are particularly difficult for scientists to forecast using a new type of machine learning technique called next generation reservoir computing. The research, which was recently published in the journal Chaos: An Interdisciplinary Journal of Nonlinear Science, makes use of a brand-new, highly efficient algorithm that, when combined with next-generation reservoir computing, can learn spatiotemporal chaotic systems in a fraction of the time required by traditional machine learning algorithms. Researchers put their method to the test by predicting the behavior of an atmospheric weather model, a challenging problem that has been researched extensively in the past.

Scientists at the University of Georgia Center for Food Safety has developed a new approach to identify the animal source of some types of Salmonella outbreaks. The researchers have developed a machine learning approach. The study is published in the January 2019 issue of Emerging Infectious Diseases. Researchers Xiangyu Deng and Shaokang Zhang, along with a team of colleagues, used more than a thousand genomes to predict the animal sources of Salmonella Typhimurium. The project used experts from the Centers for Disease Control and Prevention, the Food & Drug Administration, the Minnesota Department of Health, and the Translational Genomics Research Institute.

Researchers from the U.S. Department of Energy's Lawrence Berkeley National Laboratory have created a new method using machine learning to accelerate the design of microbes that produce biofuel. To speed up the production of biofuels, the scientists developed a computer algorithm that begins with abundant data about the proteins and metabolites in a biofuel-producing microbial. However, the algorithm does not contain information about how the pathway actually work and instead uses data from previous experiments to learn how the pathway will behave. This new technique enables scientists to automatically predict the amount of biofuel produced by pathways that have been added to E. coli bacterial cells. A pathway is a series of chemical reactions in a cell that produce a specific compound, which researchers have sought to find ways to re-engineer and import from one microbe to another.

Blend two or three metals together and you get an alloy that usually looks and acts like a metal, with its atoms arranged in rigid geometric patterns. But once in a while, under just the right conditions, you get something entirely new: a futuristic alloy called metallic glass that's amorphous, with its atoms arranged every which way, much like the atoms of the glass in a window. Its glassy nature makes it stronger and lighter than today's best steel, plus it stands up better to corrosion and wear. Even though metallic glass shows a lot of promise as a protective coating and alternative to steel, only a few thousand of the millions of possible combinations of ingredients have been evaluated over the past 50 years, and only a handful developed to the point that they may become useful. Now a group led by scientists at the Department of Energy's SLAC National Accelerator Laboratory, the National Institute of Standards and Technology (NIST) and Northwestern University has reported a shortcut for discovering and improving metallic glass – and, by extension, other elusive materials - at a fraction of the time and cost.

Scientists then presented the algorithm with a new set of brain scans, some of which were from patients who currently had mild cognitive impairment. All of the scans, however, were taken before any of the patients had developed the disease. The algorithm was able to predict which patients would end up impaired with an accuracy of 84 percent. "This is an example how big data and open science brings tangible benefits to patient care," said Rosa-Neto to McGill News. The biggest benefit to patient care could be improved clinical trials studying the effectiveness of drugs for Alzheimer's, the most common form of dementia. "By using this tool, clinical trials could focus only on individuals with a higher likelihood of progressing to dementia within the time frame of the study," said Dr. Serge Gauthier, the study's co-lead author, to McGill News. "This will greatly reduce the cost and the time necessary to conduct these studies." Research was funded by the Canadian Consortium on Neurodegeneration in Aging (CCNA) and the Canadian Institutes of Health Research.