AIMC Topic: Cyanobacteria

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Simultaneous feature engineering and interpretation: Forecasting harmful algal blooms using a deep learning approach.

Water research
Routine monitoring for harmful algal blooms (HABs) is generally undertaken at low temporal frequency (e.g., weekly to monthly) that is unsuitable for capturing highly dynamic variations in cyanobacteria abundance. Therefore, we developed a model inco...

Cyanobacteria cell prediction using interpretable deep learning model with observed, numerical, and sensing data assemblage.

Water research
Massive cyanobacterial blooms in river water causes adverse impacts on aquatic ecosystems and water quality. Complex and diverse data sources are available to investigate the cyanobacteria phenomena, including in situ data, synthetic measurements, an...

PhotoModPlus: A web server for photosynthetic protein prediction from genome neighborhood features.

PloS one
A new web server called PhotoModPlus is presented as a platform for predicting photosynthetic proteins via genome neighborhood networks (GNN) and genome neighborhood-based machine learning. GNN enables users to visualize the overview of the conserved...

A Convolutional Neural Network-Based Approach for the Rapid Annotation of Molecularly Diverse Natural Products.

Journal of the American Chemical Society
This report describes the first application of the novel NMR-based machine learning tool "Small Molecule Accurate Recognition Technology" (SMART 2.0) for mixture analysis and subsequent accelerated discovery and characterization of new natural produc...

Small Molecule Accurate Recognition Technology (SMART) to Enhance Natural Products Research.

Scientific reports
Various algorithms comparing 2D NMR spectra have been explored for their ability to dereplicate natural products as well as determine molecular structures. However, spectroscopic artefacts, solvent effects, and the interactive effect of functional gr...

Convolutional neural networks for automated annotation of cellular cryo-electron tomograms.

Nature methods
Cellular electron cryotomography offers researchers the ability to observe macromolecules frozen in action in situ, but a primary challenge with this technique is identifying molecular components within the crowded cellular environment. We introduce ...

Comparison of models for predicting the changes in phytoplankton community composition in the receiving water system of an inter-basin water transfer project.

Environmental pollution (Barking, Essex : 1987)
Inter-basin water transfer projects might cause complex hydro-chemical and biological variation in the receiving aquatic ecosystems. Whether machine learning models can be used to predict changes in phytoplankton community composition caused by water...

On-water remote monitoring robotic system for estimating the patch coverage of Anabaena sp. filaments in shallow water.

Environmental science. Processes & impacts
An on-water remote monitoring robotic system was developed for indirectly estimating the relative density of marine cyanobacteria blooms at the subtidal sandy-rocky beach in Balandra Cove, Baja California Sur, Mexico. The system is based on an unmann...

Prediction of enhanced coagulation with varied pre-oxidations for seasonal variations of cyanobacteria-dominated algae-laden water quality.

Journal of environmental management
Enhanced coagulation with pre-oxidation is a cost-effective approach for managing seasonal water quality pollution caused by algal outbreaks and deaths. The synergistic application of pre-oxidants and coagulants, coupled with intelligent and precise ...

Spatiotemporal Variation Assessment and Improved Prediction Of Cyanobacteria Blooms in Lakes Using Improved Machine Learning Model Based on Multivariate Data.

Environmental management
Cyanobacterial blooms in shallow lakes pose a significant threat to aquatic ecosystems and public health worldwide, highlighting the urgent need for advanced predictive methodologies. As impounded lakes along the Eastern Route of the South-to-North W...