Artificial intelligence (AI): a new window to revamp the vector-borne disease control.

Artificial intelligence (AI) facilitates scientists to devise intelligent machines that work and behave like humans to resolve difficulties and problems by utilizing minimal resources. The Healthcare sector has benefited due to this. Mosquito-transmitted diseases pose a significant health risk. Despite all advances, present strategies for curbing these diseases still depend largely on controlling the mosquito vectors. This strategy demands an army of entomology experts for thorough monitoring, determining, and finally eradicating the targeted mosquito population. Deep learning (DL) algorithms may substitute such unmanageable processes. The current review focuses on how AI, with particular emphasis on deep learning, demonstrates effectiveness in quick detection, identification, monitoring, and finally controlling the target mosquito populations with minimal resources. It accelerates the pace of operation and data exploration on ongoing evolutionary status, tendency to feed blood, and age grading of mosquitoes. The successful combination of computer and biological sciences will provide practical insight and generate a new research niche in this study area.

Morphological and genetic analyses of the first record of longrakered trevally, Ulua mentalis (Perciformes: Carangidae) and of the pinjalo snapper, Pinjalo pinjalo (Perciformes: Lutjanidae) in the Odisha coast, Bay of Bengal.

Identification of fish species have so far been carried out mostly by classical morpho-taxonomy. In the present study, however, an attempt has been taken to identify two species of fishes Ulua mentalis and Pinjalo pinjalo of order Perciformes which happens to be the first record in Odisha coast Bay of Bengal, India during the year 2015, using DNA barcoding technique for reconfirmation over conventional morpho-taxonomy. During recent past, study of molecular-taxonomical profile of mitochondrial DNA in general and Cytochrome Oxidase subunit I (COI) gene in particular has gained enormous importance for accurate identification of species. In the present study, the partial COI sequence of Ulua mentalis and Pinjalo pinjalo were generated. Analysis using the COI gene produced phylogenetic trees in concurrence with other multi gene studies and we came across the identical phylogenetic relationship considering Neighbor-Joining and Maximum Likelihood tree. Moreover, these molecular data set further testified in Bayesian framework to reevaluate the exact taxonomic groupings within the family. Surprisingly, Ulua mentalis and Pinjalo pinjalo seems to be closely related to their sister taxa.