Phlebotomine sand flies associated with a Boa constrictor snake in a chicken coop: species identification, trypanosomatid infection, and control.

Phlebotomine sand flies are known vectors of Leishmania spp. to mammals, but also may transmit Sauroleishmania, and Trypanosoma spp. to reptiles and amphibians.

Automated identification of spotted-fever tick vectors using convolutional neural networks.

Ticks are key ectoparasites for the One Health approach, as they are vectors of pathogens that infect humans, domestic and wild animals. The bacteria Rickettsia rickettsii and R. parkeri are the aetiological agents of tick-borne spotted fever (SF) in South America, where Amblyomma sculptum, A.

Phytophagous, blood-suckers or predators? Automated identification of Chagas disease vectors and similar bugs using convolutional neural network algorithms.

Correct identification of blood-sucking bugs, such as triatomines, is important because they are vectors of Chagas' disease. Identifying these insects is often difficult for non-specialists. Deep learning is emerging as a solution for automated identification.

Automated identification of Chagas disease vectors using AlexNet pre-trained convolutional neural networks.

The 158 bug species that make up the subfamily Triatominae are the potential vectors of Trypanosoma cruzi, the etiological agent of Chagas disease. Despite recent progress in developing a picture-based automated system for identification of triatomines, an extensive and diverse image database is required for a broadly useful automated application for identifying these vectors. We evaluated performance of a deep-learning network (AlexNet) for identifying triatomine species from a database of dorsal images of adult insects.

AI-driven convolutional neural networks for accurate identification of yellow fever vectors.

Background: Identifying mosquito vectors is crucial for controlling diseases. Automated identification studies using the convolutional neural network (CNN) have been conducted for some urban mosquito vectors but not yet for sylvatic mosquito vectors that transmit the yellow fever. We evaluated the ability of the AlexNet CNN to identify four mosquito species: Aedes serratus, Aedes scapularis, Haemagogus leucocelaenus and Sabethes albiprivus and whether there is variation in AlexNet's ability to classify mosquitoes based on pictures of four different body regions.

Mercury distribution, bioaccumulation, and biomagnification in riparian ecosystems from a neotropical savanna floodplain, Araguaia River, central Brazil.

Litterfall is the main source of dry deposition of mercury (Hg) into the soil in forest ecosystems. The accumulation of Hg in soil and litter suggests the possibility of transfer to terrestrial invertebrates through environmental exposure or ingestion of plant tissues. We quantified total mercury (THg) concentrations in two soil layers (organic: 0-0.

Automated Identification of Cutaneous Leishmaniasis Lesions Using Deep-Learning-Based Artificial Intelligence.

The polymorphism of cutaneous leishmaniasis (CL) complicates diagnosis in health care services because lesions may be confused with other dermatoses such as sporotrichosis, paracocidiocomycosis, and venous insufficiency. Automated identification of skin diseases based on deep learning (DL) has been applied to assist diagnosis. In this study, we evaluated the performance of AlexNet, a DL algorithm, to identify pictures of CL lesions in patients from Midwest Brazil.