MLDAAPP: Machine Learning Data Acquisition for Assessing Population Phenotypes
MLDAAPP is a tool for automated phenotypic data collection from images and videos, improving accuracy, scalability, and reducing human bias in biological studies.
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7 papers
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MLDAAPP is a tool for automated phenotypic data collection from images and videos, improving accuracy, scalability, and reducing human bias in biological studies.
R. S. Shahmohamadloo, S. M. Rudman, C. I. Clare…·J. M. Fryxell
The paper shows that intraspecific genetic variation significantly affects toxicity responses to microcystins in Daphnia magna, highlighting the need for diverse genotypes in environmental risk assessments.
R. S. Shahmohamadloo, S. P. Bhavsar, X. Ortiz Almirall…·P. K. Sibley
The paper assesses microcystin toxin accumulation in fish from Lake St. Clair during and after HAB events, finding high liver concentrations but low muscle levels, supporting safe consumption of fillets.
S. C. Chalmer, S. M. Rudman, M. K. Andersen…·H. A. MacMillan
This study examines how acclimation and adaptation affect chill tolerance and elemental stoichiometry in Drosophila, finding acclimation has a greater impact than adaptation.
R. S. Shahmohamadloo, S. P. Bhavsar, X. Ortiz Almirall…·P. K. Sibley
The paper assesses microcystin toxin levels in Lake Erie fish to evaluate human health risks and ecological impacts, finding low muscle risk but pervasive liver toxin accumulation.
S. M. Rudman, S. I. Greenblum, S. Rajpurohit…·P. Schmidt
The paper tracks phenotypic and genomic adaptation in Drosophila over ten generations, showing rapid and dynamic responses to environmental change.
D. Rennison, S. M. Rudman, D. Schluter
The paper examines parallel changes in gut microbiomes alongside local adaptation and speciation in stickleback fish, finding consistent microbial shifts associated with host divergence.