Seibel, A. | bioRxiv Science

bioRxiv2024

Sexual Dimorphism in Jump Kinematics and Choreography in Peacock Spiders

Jumping requires a rapid release of energy to propel an animal. Terrestrial animals achieve this by relying on the power generated by muscles, or by storing and rapidly releasing elastic energy. Jumping spiders rely on hydraulic pressure and muscular action to propel their jump. Though males and females of jumping spiders vary in size, sex-specific differences in jumping have never been studied. We investigated sexual dimorphism in the jump kinematics of an Australian Peacock spider, Maratus splendens. We recorded locomotory jumps in males and females using high-speed videography (5000 frames per second). We determined the centre of mass of the animals using {micro}CT and tracked its displacement during a jump. We found that although females weighed more than twice as much as males, both had similar accelerations and take-off velocities. Males had shorter jump take-off duration, steeper take-off angle and experienced higher g-force compared to the females. We examine the jump choreography of male and female spiders and explore the factors behind the differences in their jump kinematics.

animal behavior and cognition↗

bioRxiv2020

Anatomical modeling of brain vasculature in two-photon microscopy by generalizable deep learning

Objective and Impact StatementSegmentation of blood vessels from two-photon microscopy (2PM) angiograms of brains has important applications in hemodynamic analysis and disease diagnosis. Here we develop a generalizable deep learning technique for accurate 2PM vascular segmentation of sizable regions in mouse brains acquired from multiple 2PM setups. The technique is computationally efficient, thus ideal for large-scale neurovascular analysis. IntroductionVascular segmentation from 2PM angiograms is an important first step in hemodynamic modeling of brain vasculature. Existing segmentation methods based on deep learning either lack the ability to generalize to data from different imaging systems, or are computationally infeasible for large-scale angiograms. In this work, we overcome both these limitations by a method that is generalizable to various imaging systems, and is able to segment large-scale angiograms. MethodsWe employ a computationally efficient deep learning framework with a loss function that incorporates a balanced binary-cross-entropy loss and a total variation regularization on the networks output. Its effectiveness is demonstrated on experimentally acquired in-vivo angiograms from mouse brains of dimensions up to 808x808x702 m. ResultsTo demonstrate the superior generalizability of our framework, we train on data from only one 2PM microscope, and demonstrate high-quality segmentation on data from a different microscope without any network tuning. Overall, our method demonstrates 10x faster computation in terms of voxels-segmented-per-second and 3x larger depth compared to the state-of-the-art. ConclusionOur work provides a generalizable and computationally efficient anatomical modeling framework for brain vasculature, which consists of deep learning based vascular segmentation followed by graphing. It paves the way for future modeling and analysis of hemodynamic response at much greater scales that were inaccessible before.

neuroscience↗