AIMC Topic: Cyanobacteria

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From sample to sonde to Sentinel-2: insights from a multi-scale chlorophyll-a monitoring effort in the Hudson River, New York.

Environmental monitoring and assessment
Monitoring cyanobacteria and other nuisance phytoplankton in the Hudson River is of great interest given its societal and ecological importance. Satellite remote sensing provides a cost-effective method to monitor chlorophyll-a (chl-a), a common prox...

Predicting cyanobacteria removal efficiency in flocculation-DAF: Improving interpretable automated machine learning with CVAE data augmentation.

Water research
Flocculation-dissolved air flotation (DAF) is an efficient and widely adopted technique for cyanobacteria separation. However, optimizing its removal efficiency remains challenging due to complex interdependencies among water quality, cyanobacterial ...

An Integrated Machine Learning and Remote Sensing Method for Predicting Cyanobacterial Blooms: A Case Study in China's lakes along a large-scale water diversion project.

Environmental management
Cyanobacterial blooms in lakes are a complex and challenging environmental issue worldwide. However, many existing studies on cyanobacterial bloom prediction were constrained by limited data availability, which poses significant challenges to the dev...

Automatic detection of harmful cyanobacterial genera using deep CNN models and artemisinin optimization.

Scientific reports
Concerns over the spread of Cyanobacteria, which can lead to dangerous blooms that harm drinking water quality and, therefore, the health of plants and animals, are being raised by global warming. Traditional methods for assessing the amount of toxic...

Cyanobacteria Join the Kahalalide Conversation: Genome and Metabolite Evidence for Structurally Related Peptides.

Journal of the American Chemical Society
Kahalalide F is a cyclic depsipeptide with notable anticancer properties, initially discovered from the green alga sp. and its molluscan predator . Recent studies have pinpointed a bacterial endosymbiont of the green alga, Endobryopsis kahalalidefa...

In Silico Design and Analysis of Cyanobacterial Pseudo Natural Products.

Journal of natural products
Marine cyanobacteria produce natural products (NPs) with potent and selective bioactivity against a broad range of diseases. However, like many NPs, most exhibit poor drug-like physicochemical properties, and the discovery of structurally novel NPs i...

Spatiotemporal dynamics of cyanobacterial blooms: Integrating machine learning and feature selection techniques with uncrewed aircraft systems and autonomous surface vessel data.

Journal of environmental management
Cyanobacterial blooms pose significant threats to aquatic ecosystems and public health due to their ability to release harmful toxins, degrade water quality, disrupt aquatic habitats, and endanger human and animal health through contact or consumptio...

Combining diffusion and transformer models for enhanced promoter synthesis and strength prediction in deep learning.

mSystems
UNLABELLED: In the field of synthetic biology, the engineering of synthetic promoters that outperform their natural counterparts is of paramount importance, which can optimize the expression of exogenous genes, enhance the efficiency of metabolic pat...

Machine learning models for segmentation and classification of cyanobacterial cells.

Photosynthesis research
Timelapse microscopy has recently been employed to study the metabolism and physiology of cyanobacteria at the single-cell level. However, the identification of individual cells in brightfield images remains a significant challenge. Traditional inten...

Spatiotemporal variation in biomass abundance of different algal species in Lake Hulun using machine learning and Sentinel-3 images.

Scientific reports
Climate change and human activities affect the biomass of different algal and the succession of dominant species. In the past, phytoplankton phyla inversion has been focused on oceanic and continental shelf waters, while phytoplankton phyla inversion...