Mittal, R. | bioRxiv Science

bioRxiv2025

Is Aedes nr. albopictus synonymous with Aedes pseudalbopictus?

A recent study reported a putative cryptic species of Aedes albopictus, provisionally named Aedes nr. albopictus, from Tripura, India, based on molecular data from mitochondrial cytochrome c oxidase subunit I (COI) and nuclear internal transcribed spacer 2 (ITS2) regions. However, our reanalysis, incorporating COI and ITS2 sequences of the co-occurring species Aedes pseudalbopictus and employing maximum likelihood phylogenetic analysis, albeit with a limited number of available sequences, suggests that this lineage is conspecific with Ae. pseudalbopictus. Given the morphological similarities and overlapping habitat use of Ae. albopictus and Ae. pseudalbopictus in India and Southeast Asia, we hypothesize that the reported cryptic species could potentially be attributed to Ae. pseudalbopictus. Our findings underscore the need for comprehensive taxonomic assessments, integrating morphological and molecular analyses of all sympatric Stegomyia species, to ensure accurate species delimitation and support effective vector surveillance and control.

zoology↗

bioRxiv2023

Reconstructing the pressure field around a swimming fish using a physics-informed neural network

Hydrodynamic pressure is a physical quantity that is utilized by fish and many other aquatic animals to generate thrust and sense the surrounding environment. To advance our understanding of how fish react to unsteady flows, it is necessary to intercept the pressure signals sensed by their lateral line system. In this study, the authors propose a new, non-invasive method for reconstructing the instantaneous pressure field around a swimming fish from 2D particle image velocimetry (PIV) measurements. The method uses a physics-informed neural network (PINN) to predict an optimized solution for the velocity and pressure fields that satisfy in an [L]2 sense both the Navier Stokes equations and the constraints put forward by the measurements. The method was validated using a direct numerical simulation of a swimming mackerel, Scomber scombrus, and was applied to empirically obtained data of a turning zebrafish, Danio rerio. The results demonstrate that when compared to traditional methods that rely on directly integrating the pressure gradient field, the PINN is less sensitive to the spatio-temporal resolution of the velocity field measurements and provides a more accurate pressure reconstruction, particularly on the surface of the body.

animal behavior and cognition↗