AIMC Topic: Tsetse Flies

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Species distribution modeling to predict tsetse fly (Glossina spp.) habitat suitability in Kenya.

Parasites & vectors
BACKGROUND: African animal trypanosomosis (AAT) and human African trypanosomosis (HAT) are transmitted and spread primarily by tsetse flies (Glossina spp.) in sub-Saharan Africa. The animal disease poses significant challenges to agropastoral systems...

Machine Learning Predicts Non-Preferred and Preferred Vertebrate Hosts of Tsetse Flies (Glossina spp.) Based on Skin Volatile Emission Profiles.

Journal of chemical ecology
Tsetse fly vectors of African trypanosomosis preferentially feed on certain vertebrates largely determined by olfactory cues they emit. Previously, we established that three skin-derived ketones including 6-methyl-5-hepten-2-one, acetophenone and ger...

Deep learning approaches to landmark detection in tsetse wing images.

PLoS computational biology
Morphometric analysis of wings has been suggested for identifying and controlling isolated populations of tsetse (Glossina spp), vectors of human and animal trypanosomiasis in Africa. Single-wing images were captured from an extensive data set of fie...

Post hoc support vector machine learning for impedimetric biosensors based on weak protein-ligand interactions.

The Analyst
Impedimetric biosensors for measuring small molecules based on weak/transient interactions between bioreceptors and target analytes are a challenge for detection electronics, particularly in field studies or in the analysis of complex matrices. Prote...