AI Medical Compendium Journal:
Aquatic toxicology (Amsterdam, Netherlands)

Showing 1 to 4 of 4 articles

CLSSATP: Contrastive learning and self-supervised learning model for aquatic toxicity prediction.

Aquatic toxicology (Amsterdam, Netherlands) Jan 7, 2025
As compound concentrations in aquatic environments increase, the habitat degradation of aquatic organisms underscores the growing importance of studying the impact of chemicals on diverse aquatic populations. Understanding the potential impacts of di...
Neural Networks, Computer Deep Learning Water Pollutants, Chemical Aquatic Organisms Supervised Machine Learning
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Enhancing eco-sensing in aquatic environments: Fish jumping behavior automatic recognition using YOLOv5.

Aquatic toxicology (Amsterdam, Netherlands) Nov 1, 2024
Contemporary research on ichthyological behavior predominantly investigates underwater environments. However, the intricate nature of aquatic ecosystems often hampers subaqueous observations of fish behavior due to interference. Transitioning the obs...
Neural Networks, Computer Animals Ecosystem Environmental Monitoring Fishes Behavior, Animal
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Exposure to nitrate induces alterations in blood parameter responses, liver immunity, and lipid metabolism in juvenile turbot (Scophthalmus maximus).

Aquatic toxicology (Amsterdam, Netherlands) Aug 24, 2022
Nitrate (NO) pollution of waterbodies has attracted significant global attention as it poses a serious threat to aquatic organisms and human beings. This study aimed to evaluate the role of NO, an end product of biological nitrification processes, in...
Humans Tumor Necrosis Factor-alpha Animals RNA, Messenger Water Pollutants, Chemical Liver Lipid Metabolism Heat-Shock Proteins Nitrates Alanine Transaminase Aspartate Aminotransferases Immunoglobulin M Complement C3 Fish Proteins Peroxisome Proliferator-Activated Receptors Carnitine O-Palmitoyltransferase Sterol Regulatory Element Binding Protein 1 Flatfishes Complement C4 Liver X Receptors Muramidase
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Video-tracking of zebrafish (Danio rerio) as a biological early warning system using two distinct artificial neural networks: Probabilistic neural network (PNN) and self-organizing map (SOM).

Aquatic toxicology (Amsterdam, Netherlands) Jun 20, 2015
Biological early warning systems (BEWS) are becoming very important tools in ecotoxicological studies because they can detect changes in the behavior of organisms exposed to toxic substances. In this work, a video tracking system was fully developed ...
Neural Networks, Computer Algorithms Animals Environmental Monitoring Water Pollutants, Chemical Models, Statistical Zebrafish Sodium Hypochlorite Video Recording Behavior, Animal
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