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Phytoplankton

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Photoautotrophic picoplankton - a review on their occurrence, role and diversity in Lake Balaton.

Biologia futura
Occurrence of the smallest phototrophic microorganisms (photoautotrophic picoplankton, APP) in Lake Balaton was discovered in the early 1980s. This triggered a series of systematic studies on APP and resulted in the setting of a unique long-term pico...

Shallow convective mixing promotes massive Noctiluca scintillans bloom in the northeastern Arabian Sea.

Marine pollution bulletin
The northeastern Arabian Sea (NEAS) experiences convective mixing during winter, but this mixing does not reach up to the silicicline, resulting in the limited supply of silicate (Si) compared to nitrate (N) and phosphate (P) to the mixed layer (ML) ...

High-throughput time-stretch imaging flow cytometry for multi-class classification of phytoplankton.

Optics express
Time-stretch imaging has been regarded as an attractive technique for high-throughput imaging flow cytometry primarily owing to its real-time, continuous ultrafast operation. Nevertheless, two key challenges remain: (1) sufficiently high time-stretch...

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...

Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering.

PloS one
Scanning flow cytometry (SFCM) is characterized by the measurement of time-resolved pulses of fluorescence and scattering, enabling the high-throughput quantification of phytoplankton morphology and pigmentation. Quantifying variation at the single c...

Combining high-throughput imaging flow cytometry and deep learning for efficient species and life-cycle stage identification of phytoplankton.

BMC ecology
BACKGROUND: Phytoplankton species identification and counting is a crucial step of water quality assessment. Especially drinking water reservoirs, bathing and ballast water need to be regularly monitored for harmful species. In times of multiple envi...

Modelling of ecological status of Polish lakes using deep learning techniques.

Environmental science and pollution research international
Since 2000, after the Water Framework Directive came into force, aquatic ecosystems' bioassessment has acquired immense practical importance for water management. Currently, due to extensive scientific research and monitoring, we have gathered compre...

Collaborating robots sample the primary production in the ocean.

Science robotics
Sampling genetic material from phytoplankton in open ocean eddies becomes more precise and efficient using a heterogeneous network of autonomous marine robots.

A novel random forest approach to revealing interactions and controls on chlorophyll concentration and bacterial communities during coastal phytoplankton blooms.

Scientific reports
Increasing occurrence of harmful algal blooms across the land-water interface poses significant risks to coastal ecosystem structure and human health. Defining significant drivers and their interactive impacts on blooms allows for more effective anal...

Hierarchical deep learning model to simulate phytoplankton at phylum/class and genus levels and zooplankton at the genus level.

Water research
Harmful algal blooms (HABs) have become a global issue, affecting public health and water industries in numerous countries. Because funds for monitoring HABs are limited, model development may be an alternative approach for understanding and managing...