Predicting Drosophila Body Orientation from a Translational Trajectory using an Artificial Neural Network

Body orientation is a key variable in the analysis of insect flight behavior, yet it remains difficult to measure across the full extent of a trajectory in most experimental settings. Although modern tracking systems reliably capture the position and velocity of the center of mass, resolving body yaw orientation typically requires dedicated hardware confined to a small, purpose-built volume, and is impractical for large-scale or long-duration studies. Here, we develop a data-driven estimator that predicts body yaw orientation directly from translational flight trajectory data. We trained a fully connected feedforward artificial neural network (ANN) on a dataset in which both flight trajectory and body orientation were recorded simultaneously in freely flying \textit{Drosophila}, using a time-delay embedding of ground velocity, air velocity, and inferred thrust vectors as input features. To improve generalization across arbitrary coordinate frames, we augmented the training data with random rotational transformations. Evaluated on a withheld test set of 3,313 trajectories (101,576 frames), the rotation-augmented model achieved a median mean absolute angular error of 10.51 degrees, with accurate heading recovery across the full [pi,-pi) range. The estimator provides a practical tool for recovering body orientation information from existing trajectory datasets in which only center-of-mass motion was recorded, extending the behavioral and computational analysis of insect navigation to previously inaccessible data. ### Competing Interest Statement The authors have declared no competing interest. National Institutes of Health, https://ror.org/01cwqze88, 1R01NS136988 U.S. National Science Foundation, https://ror.org/021nxhr62, 2112085

Isoflurane causes muscle contraction in Drosophila melanogaster despite inducing hyperpolarized state | microPublication

Environmental statistics and sensory experience shape patch foraging strategies in Drosophila larvae

Animals foraging in patchy environments must balance exploiting current resources with exploring for better alternatives to maximize resource intake and to survive. However, the neural and computational mechanisms underlying such adaptive decisions have just recently begun to be understood. Using Drosophila larvae as an experimentally tractable model, we combine long-timescale behavioral tracking in controlled patchy environments with varying statistics, along with quantitative analysis and computational modeling, to dissect foraging decision strategies. We show that larvae flexibly adjust their behavior according to both the quality and valence of available resources, shaped by prior foraging experience. A simple integration model recapitulates larval patch-leaving behavior, with model parameters tuned by environmental statistics and foraging history. Together, these findings establish Drosophila larvae as a powerful system for studying adaptive foraging and for uncovering the neural circuit mechanisms that implement experience-dependent foraging decisions. ### Competing Interest Statement The authors have declared no competing interest. Deutsche Forschungsgemeinschaft, EXC 2117-422037984 International Human Frontier Science Program Organization, RGP006/2025

Current Evidence for Sleep States in Drosophila: Findings and Implications - Current Sleep Medicine Reports

Sleep is an essential biological behavior, with its absence leading to severe consequences, including death. In mammals, sleep consists of distinct states&