AIMC Topic: Microcystis

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Tracking putative viruses and virus-host associations across distinct phases of a -dominated bloom.

mSystems
Viruses significantly impact microbial community composition and function. Yet their role in the fate of freshwater cyanobacterial harmful algal blooms (cHABs), an increasing threat to freshwater systems, remains poorly understood. Here, we address t...

Machine-Learning-Based Prediction of Algal Density Using Algal Volatile Organic Compounds for Bloom Early Warning.

Environmental science & technology
Harmful algal blooms (HABs) pose severe threats to aquatic ecosystems, yet rapid and accurate prediction of algal density remains challenging. As integrated metabolites are released throughout the algal growth, algal volatile organic compounds (AVOCs...

Multi-modal learning-based algae phyla identification using image and particle modalities.

Water research
Algal blooms in freshwater, which are exacerbated by urbanization and climate change, pose significant challenges in the water treatment process. These blooms affect water quality and treatment efficiency. Effective identification of algal proliferat...

Algal classification and Chlorophyll-a concentration determination using convolutional neural networks and three-dimensional fluorescence data matrices.

Environmental research
In recent years, the frequency of harmful algal blooms has increased, leading to the release of large quantities of toxins and compounds that cause unpleasant odors and tastes, significantly compromising drinking water quality. Chlorophyll-a (Chl-a) ...

Assessment of estrogenic potential from exudates of microcystin-producing and non-microcystin-producing Microcystis by metabolomics, machine learning and E-screen assay.

Journal of hazardous materials
Cyanobacterial blooms, often dominated by Microcystis aeruginosa, are capable of producing estrogenic effects. It is important to identify specific estrogenic compounds produced by cyanobacteria, though this can prove challenging owing to the complex...

Employing hybrid deep learning for near-real-time forecasts of sensor-based algal parameters in a Microcystis bloom-dominated lake.

The Science of the total environment
Harmful cyanobacterial blooms (CyanoHABs) are increasingly impacting the ecosystem of lakes, reservoirs and estuaries globally. The integration of real-time monitoring and deep learning technology has opened up new horizons for early warnings of Cyan...

Cyanobacterium removal and control of algal organic matter (AOM) release by UV/HO pre-oxidation enhanced Fe(II) coagulation.

Water research
Harmful algal blooms in source water are a worldwide issue for drinking water production and safety. UV/HO, a pre-oxidation process, was firstly applied to enhance Fe(II) coagulation for the removal of Microcystis aeruginosa [M. aeruginosa, 2.0 (±0.5...

The allelopathic effect and safety evaluation of 3,4-Dihydroxybenzalacetone on Microcystis aeruginosa.

Pesticide biochemistry and physiology
Harmful algal blooms (HABs) has been a serious problem in recent years, because of large quantities of cyanobacterial in eutrophic water. We studied the effects of 3,4-Dihydroxybenzalacetone (DBL) and other four compounds (vanillic acid, ferulic acid...

Key drivers of microcystin-producing cyanobacteria in South Korean eutrophic waters determined with data-driven models.

Journal of environmental management
The rise in cyanobacterial harmful algal blooms (CHABs), driven by eutrophication and climate change, necessitates understanding cyanotoxin conditions to mitigate risks. However, limited studies have explored the influencing factors of cyanobacterial...