@article {70275, title = {Rethinking insecticide toxicology for the 21st century.}, journal = {Insect Biochem Mol Biol}, year = {2026}, month = {2026 Jan 29}, pages = {104502}, abstract = {

Insecticides remain indispensable for crop protection and food security, yet their widespread use may contribute to the global decline of beneficial insect populations. Efforts to mitigate these impacts are hampered by a fragmented understanding of how insects metabolise insecticides and how sublethal exposures affect physiology, behaviour, and fitness. Here, we synthesise current understanding of metabolic detoxification and highlight critical gaps: the tissue- and time-dependent dynamics of insecticide entry and processing, the triggers and architecture of xenobiotic transcriptional responses, the role of rapid non-transcriptional regulation, and the population-level consequences of sublethal effects. We also outline emerging experimental strategies for addressing these questions and propose a next-generation research pipeline centred on multi-endpoint phenomics across life stages and sentinel species, integrated with AI-driven predictive toxicology, as a framework for identifying safer chemicals. We propose an integrated framework unifying molecular, physiological, and ecological responses to sublethal exposure to guide the design of insecticides that maintain effective pest control while safeguarding insect biodiversity and the ecosystems it underpins.

}, issn = {1879-0240}, doi = {10.1016/j.ibmb.2026.104502}, url = {https://doi.org/10.1016/j.ibmb.2026.104502}, author = {G{\'a}ndara, Lautaro and Martelli, Felipe and Ravenscroft, Thomas and Crocker, Justin and Batterham, Philip} } @article {69526, title = {LarvaTagger: Manual and automatic tagging of drosophila larval behaviour.}, journal = {Bioinformatics}, year = {2024}, month = {2024 Jul 05}, abstract = {

MOTIVATION: As more behavioural assays are carried out in large-scale experiments on Drosophila larvae, the definitions of the archetypal actions of a larva are regularly refined. In addition, video recording and tracking technologies constantly evolve. Consequently, automatic tagging tools for Drosophila larval behaviour must be retrained to learn new representations from new data. However, existing tools cannot transfer knowledge from large amounts of previously accumulated data.We introduce LarvaTagger, a piece of software that combines a pre-trained deep neural network, providing a continuous latent representation of larva actions for stereotypical behaviour identification, with a graphical user interface to manually tag the behaviour and train new automatic taggers with the updated ground truth.

RESULTS: We reproduced results from an automatic tagger with high accuracy, and we demonstrated that pre-training on large databases accelerates the training of a new tagger, achieving similar prediction accuracy using less data.

AVAILABILITY: All the code is free and open source. Docker images are also available. See gitlab.pasteur.fr/nyx/LarvaTagger.jl.

SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btae441}, author = {Laurent, Francois and Blanc, Alexandre and May, Lilly and G{\'a}ndara, Lautaro and Cocanougher, Benjamin T and Jones, Benjamin M W and Hague, Peter and Barre, Chloe and Vestergaard, Christian L and Crocker, Justin and Zlatic, Marta and Jovanic, Tihana and Masson, Jean-Baptiste} }