AI-Driven Test System Detects Bacteria In Water


"Clean water and health care and school and food and tin roofs and cement floors, all of these things should constitute a set of basics that people must have as birthrights."1 Obtaining clean water is a critical problem for much of the world's population. Testing and confirming a clean water source typically requires expensive test equipment and manual analysis of the results. For regions in the world in which access to clean water is a continuing problem, simpler test methods could dramatically help prevent disease and save lives. To apply artificial intelligence (AI) techniques to evaluating the purity of water sources, Peter Ma, an Intel Software Innovator, developed an effective system for identifying bacteria using pattern recognition and machine learning.

Artificial Intelligence may help identify bacteria quickly, accurately


Microscopes enhanced with artificial intelligence (AI) could help in the quick and accurate diagnosis of the deadly blood infections, which may improve patients' odds of survival, according to a study. The bacteria that most often cause bloodstream infections include the rod-shaped bacteria including Escherichia coli or E.coli, the round clusters of Staphylococcus species, and the pairs or chains of Streptococcus species.

Less Than 10% of Bovine i E. coli /i Strains Affect Human Health


Using software to compare genetic information in bacterial isolates from animals and people, researchers have predicted that less than 10% of Escherichia coli 0157:H7 strains are likely to have the potential to cause human disease. In this study, the researchers applied machine learning to predict the zoonotic potential of bacterial isolates from the United Kingdom and the United States. "[O]ne of the cattle isolates (apart from outbreak trace-back isolates) achieved very high human association probabilities ( 0.9), potentially indicating that those posing a serious zoonotic threat are very rare," the authors write. As a consequence, experts could use targeted control strategies, including vaccination or eradication, in cattle carrying strains of high zoonotic potential, in order to better protect human health.

Researchers Use Machine Learning to Detect Pathogenic Bacteria in Cattle


A team of researchers has found a new way to detect dangerous strains of bacteria, potentially preventing outbreaks of food poisoning. The team developed a method that utilizes machine learning and tested it with isolates of Escherichia coli strains. The team utilized machine learning, a form of artificial intelligence that allows computers to learn and analyze patterns. If researchers can quickly identify cattle herds carrying dangerous strains of E.coli, that particular herd can be treated or isolated before an outbreak occurs.

The Roslin Institute (University of Edinburgh) - News


Machine learning can predict strains of bacteria likely to cause food poisoning outbreaks, research has found. The study – which focused on harmful strains of E. coli bacteria – could help public health officials to target interventions and reduce risk to human health. The team trained the software on DNA sequences from strains isolated from cattle herds and human infections in the UK and the US. The study highlights the potential of machine learning approaches for identifying these strains early and prevent outbreaks of this infectious disease.

Characterization of graphs for protein structure modeling and recognition of solubility Artificial Intelligence

This paper deals with the relations among structural, topological, and chemical properties of the E.Coli proteome from the vantage point of the solubility/aggregation propensity of proteins. Each E.Coli protein is initially represented according to its known folded 3D shape. This step consists in representing the available E.Coli proteins in terms of graphs. We first analyze those graphs by considering pure topological characterizations, i.e., by analyzing the mass fractal dimension and the distribution underlying both shortest paths and vertex degrees. Results confirm the general architectural principles of proteins. Successively, we focus on the statistical properties of a representation of such graphs in terms of vectors composed of several numerical features, which we extracted from their structural representation. We found that protein size is the main discriminator for the solubility, while however there are other factors that help explaining the solubility degree. We finally analyze such data through a novel one-class classifier, with the aim of discriminating among very and poorly soluble proteins. Results are encouraging and consolidate the potential of pattern recognition techniques when employed to describe complex biological systems.