Biology subjects

Qureshi, Y.

Publications and source records attributed to Qureshi, Y..

3 recordsLinked to original sources

bioRxiv2025

Machine Learning Reveals Immediate Disruption in Mosquito Flight when exposed to Olyset Nets

Insecticide-treated nets (ITNs) remain a critical intervention in controlling malaria transmission, yet the behavioural adaptations of mosquitoes in response to these interventions are not fully understood. This study examined the flight behaviour of insecticide-resistant (IR) and insecticide-susceptible (IS) Anopheles gambiae strains around an Olyset net (OL), a permethrin-impregnated ITN, versus an untreated net (UT). Using machine learning (ML) models, we classified mosquito flight trajectories with high accuracy (0.838) and ROC AUC (0.925). Contrary to assumptions that behavioural changes at OL would intensify over time, our findings show an immediate onset of convoluted, erratic flight paths for both IR and IS mosquitoes around the treated net. SHAP analysis identified three key predictive features of OL exposure: frequency of zero-crossings in flight angle change, first quartile of flight angle change, and zero-crossings in horizontal velocity. These suggest disruptive flight patterns, indicating insecticidal irritancy. While IS mosquitoes displayed rapid, disordered trajectories and mostly died within 30 minutes, IR mosquitoes persisted throughout the 2-hour experiments but exhibited similarly disturbed behaviour, suggesting resistance does not fully mitigate disruption. Our findings challenge literature suggesting permethrins repellency in solution form, instead supporting an irritant or contactdriven effect when incorporated into net fibres. This study highlights the value of ML-based trajectory analysis for understanding mosquito behaviour, refining ITN configurations and evaluating novel active ingredients aimed at disrupting mosquito flight behaviour. Future work should extend these methods to other ITNs to further illuminate the complex interplay between mosquito behaviour and insecticidal intervention.

animal behavior and cognition↗

bioRxiv2024

Discrimination of inherent characteristics of susceptible and resistant strains of Anopheles gambiae by explainable Artificial Intelligence Analysis of Flight Trajectories

Understanding mosquito behaviours is vital for development of insecticide-treated bednets (ITNs), which have been successfully deployed in sub-Saharan Africa to reduce disease transmission, particularly malaria. However, rising insecticide resistance (IR) among mosquito populations, owing to genetic and behavioural changes, poses a significant challenge. We present a machine learning pipeline that successfully distinguishes between IR and insecticide-susceptible (IS) mosquito behaviours by analysing trajectory data. Data driven methods are introduced to accommodate common tracking system shortcomings that occur due to mosquito positions being occluded by the bednet or other objects. Trajectories, obtained from room-scale tracking of two IR and two IS strains around a human-baited, untreated bednet, were analysed using features such as velocity, acceleration, and geometric descriptors. Using these features, an XGBoost model achieved a balanced accuracy of 0.743 and a ROC AUC of 0.813 in classifying IR from IS mosquitoes. SHAP analysis helped decipher that IR mosquitoes tend to fly slower with more directed flight paths and lower variability than IS--traits that are likely a fitness advantage by enhancing their ability to respond more quickly to bloodmeal cues. This approach provides valuable insights based on flight behaviour that can reveal the action of interventions and insecticides on mosquito physiology.

animal behavior and cognition↗

bioRxiv2019

Retromer repletion with AAV9-VPS35 restores endosomal function in the mouse hippocampus

Retromer has emerged as a master conductor of endosomal trafficking, and VPS35 and other retromer-related proteins are found to be deficient in late-onset Alzheimers disease (AD). Depleting VPS35 in neurons impairs retromer function, affecting for example the trafficking of the amyloid-precursor protein (APP) and the glutamate receptor GluA1. Whether VPS35 repletion, after chronic in vivo depletion, can rescue these impairments remains unknown. Here we set out to address this question by using a viral vector approach for VPS35 repletion. First, we completed a series of studies using neuronal cultures in order to optimize AAV9-VPS35 delivery, and to understand how exogenous VPS35 expression affects its endogenous levels as well as its binding to other retromer proteins. Next, we completed a series of studies in wildtype mice to determine the optimum protocol for in vivo delivery of AAV9-VPS35 to the hippocampus. We relied on this information to deliver AAV9-VPS35 to the hippocampus of mice genetically engineered to have chronic, neuronal-selective, VPS35 depletion. VPS35 repletion in the hippocampus was found to normalize APP cleavage and to restore glutamate receptor levels. Unexpectedly, chronic VPS35 depletion in neurons caused glial activation, similar to the pattern observed in AD, which was also partially normalized by VPS35 repletion. Taken together, these studies strengthen the mechanistic link between retromer and AD, and have therapeutic implications.

neuroscience↗