AIMC Topic: Bacteria

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Sequence-enabled community-based microbial source tracking in surface waters using machine learning classification: A review.

Journal of microbiological methods
The development of Microbial Source Tracking (MST) technologies was borne out of necessity. This was largely due to the: 1) inadequacies of the fecal indicator bacterial paradigm, 2) fact that many fecal bacteria can survive and often grow in the env...

Microbial contamination and efficacy of disinfection procedures of companion robots in care homes.

PloS one
BACKGROUND: Paro and other robot animals can improve wellbeing for older adults and people with dementia, through reducing depression, agitation and medication use. However, nursing and care staff we contacted expressed infection control concerns. Li...

Predicting the concentration of total coliforms in treated rural domestic wastewater by multi-soil-layering (MSL) technology using artificial neural networks.

Ecotoxicology and environmental safety
Many indicators are involved in monitoring water quality. For instance, the fecal indicator bacteria are extremely important to detect the water quality. For this purpose, to better predict the total coliforms at the outlet of a Multi-Soil-Layering (...

Raman spectroscopy of potential bio-hazards commonly found in bio-aerosols.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Pathogenic bio-aerosols are a threat to public health today, and thus quick detection and identification is of paramount importance. In this study, Raman spectroscopy was used to test 14 types of pollens, one type of fungus and two types of bacteria ...

Machine-learning approach expands the repertoire of anti-CRISPR protein families.

Nature communications
The CRISPR-Cas are adaptive bacterial and archaeal immunity systems that have been harnessed for the development of powerful genome editing and engineering tools. In the incessant host-parasite arms race, viruses evolved multiple anti-defense mechani...

Bacterial Foraging Optimization Based on Self-Adaptive Chemotaxis Strategy.

Computational intelligence and neuroscience
Bacterial foraging optimization (BFO) algorithm is a novel swarm intelligence optimization algorithm that has been adopted in a wide range of applications. However, at present, the classical BFO algorithm still has two major drawbacks: one is the fix...

Predicting host taxonomic information from viral genomes: A comparison of feature representations.

PLoS computational biology
The rise in metagenomics has led to an exponential growth in virus discovery. However, the majority of these new virus sequences have no assigned host. Current machine learning approaches to predicting virus host interactions have a tendency to focus...

Photosynthetic protein classification using genome neighborhood-based machine learning feature.

Scientific reports
Identification of novel photosynthetic proteins is important for understanding and improving photosynthetic efficiency. Synergistically, genome neighborhood can provide additional useful information to identify photosynthetic proteins. We, therefore,...

Combination of an Artificial Intelligence Approach and Laser Tweezers Raman Spectroscopy for Microbial Identification.

Analytical chemistry
Raman spectroscopy is a nondestructive, label-free, highly specific approach that provides the chemical information on materials. Thus, it is suitable to be used as an effective analytical tool to characterize biological samples. Here we introduce a ...

Predicting postmortem interval based on microbial community sequences and machine learning algorithms.

Environmental microbiology
Microbes play an essential role in the decomposition process but were poorly understood in their succession and behaviour. Previous researches have shown that microbes show predictable behaviour that starts at death and changes during the decompositi...