AIMC Topic: Biological Monitoring

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Unsupervised deep clustering as a tool for the identification of dark taxa in biomonitoring.

Environmental monitoring and assessment
The identification of aquatic macroinvertebrates, particularly dark taxa like Chironomidae, due to their complex morphological features and unresolved taxonomy hinder the efficiency of routine biomonitoring. This study proposes an unsupervised deep c...

Sound evidence for biodiversity monitoring.

Science (New York, N.Y.)
Bioacoustics and artificial intelligence facilitate ecological studies of animal populations.

CRISPR-Cas-Based Biomonitoring for Marine Environments: Toward CRISPR RNA Design Optimization Via Deep Learning.

The CRISPR journal
Almost all of Earth's oceans are now impacted by multiple anthropogenic stressors, including the spread of nonindigenous species, harmful algal blooms, and pathogens. Early detection is critical to manage these stressors effectively and to protect ma...

Accurate image-based identification of macroinvertebrate specimens using deep learning-How much training data is needed?

PeerJ
Image-based methods for species identification offer cost-efficient solutions for biomonitoring. This is particularly relevant for invertebrate studies, where bulk samples often represent insurmountable workloads for sorting, identifying, and countin...

Biomonitoring and Digital Data Technology as an Opportunity for Enhancing Animal Study Translation.

ILAR journal
The failure of animal studies to translate to effective clinical therapeutics has driven efforts to identify underlying cause and develop solutions that improve the reproducibility and translatability of preclinical research. Common issues revolve ar...