Biology subjects

Henry, D.

Publications and source records attributed to Henry, D..

3 recordsLinked to original sources

bioRxiv2022

Automated detection of sow posture changes with millimeter-wave radars and deep learning

Automated behavioural monitoring is increasingly required for animal welfare and precision agriculture. In pig farming, detailed analyses of sow activity are essential to identify and reduce the risks of piglets being crushed during postural changes of their mothers. Here we introduce a new, non-invasive, fast and accurate method for monitoring sow behaviour based on millimeter-wave radars and deep learning analysis. We used our method to predict postural changes in crated sows and distinguish the dangerous one that lie down abruptly from those that lie down carefully using transient postures. Two radars were placed on a metal backing above the head and the upper part of the back of each of ten sows to monitor their activity during 5 hours. We analysed the radar data with a convolutional neural network and identified five postures. The average sensitivity was 96.9% for standing, 90.8% for lying, 91.4% for nursing, 87.6% for sitting, but only 11.9% for kneeling. However, the average specificity and accuracy were greater than 92% for the five postures. Interestingly, two of the ten sows occasionally moved directly from standing to lying, without using the transient postures sitting and kneeling, thereby displaying risky behaviours for their piglets. Our radar-based classifier is more accurate, faster and require less memory than current computer vision approaches. Using more sows will improve the algorithm performance and facilitate future applications for large scale deployment in animal farming. HighlightsO_LIAutomated behavioural analysis is a major challenge for precision farming. C_LIO_LIWe developed automated detection of lactating sow postures with radars and deep learning. C_LIO_LIWe identified five postures, including transitions risky for the piglets. C_LIO_LIOur method is accurate, fast and requires less memory than computer vision. C_LIO_LIRadars thus hold considerable promises for high through-put recording of livestock activity. C_LI

animal behavior and cognition↗

bioRxiv2020

A non-invasive radar system for automated behavioural tracking: application to sheep

Automated quantification of the behaviour of freely moving animals is increasingly needed in ethology, ecology, genetics and evolution. State-of-the-art approaches often require tags to identify animals, high computational power for data collection and processing, and are sensitive to environmental conditions, which limits their large-scale utilisation. Here we introduce a new automated tracking system based on millimetre-wave radars for real time robust and high precision monitoring of untagged animals. To validate our system, we tracked 64 sheep in a standard indoor behavioural test used for genetic selection. First, we show that the proposed radar application is faster and more accurate than conventional video and infrared tracking systems. Next, we illustrate how new behavioural estimators can be derived from the radar data to assess personality traits in sheep for behavioural phenotyping. Finally, we demonstrate that radars can be used for movement tracking at larger spatial scales, in the field, by adjusting operating frequency and radiated electromagnetic power. Millimetre-wave radars thus hold considerable promises for high-throughput recording of the behaviour of animals with various sizes and locomotor modes, in different types of environments.

animal behavior and cognition↗

bioRxiv2020

Expression and distribution of synaptotagmin isoforms in the zebrafish retina

Synaptotagmins belong to a large family of proteins. While various synaptotagmins have been implicated as Ca2+ sensors for vesicle replenishment and release at conventional synapses, their roles at retinal ribbon synapses remain incompletely understood. Zebrafish is a widely used experimental model for retinal research. We therefore investigated the homology between human, rat, mouse, and zebrafish synaptotagmins 1 to 10 using a bioinformatics approach. We also characterized the expression and distribution of various synaptotagmin (syt) genes in the zebrafish retina, using RT-PCR and in situ hybridization, focusing on the family members whose products likely underlie Ca2+-dependent exocytosis in the central nervous system (synaptotagmins 1, 2, 5 and 7). We find that most zebrafish synaptotagmins are well conserved and can be grouped in the same classes as mammalian synaptotagmins, based on crucial amino acid residues needed for coordinating Ca2+ binding and determining phospholipid binding affinity. The only exception is synaptotagmin 1b, which lacks 34 amino acid residues in the C2B domain and is therefore unlikely to bind Ca2+ there. Additionally, the products of zebrafish syt5a and syt5b genes share identity with mammalian class 1 and class 5 synaptotagmins. Zebrafish syt1, syt2, syt5 and syt7 paralogues are found in the zebrafish brain, eye, and retina, excepting syt1b, which is only present in the brain. The complementary expression pattern of the remaining paralogues in the retina suggests that syt1a and syt5a may underlie synchronous release and syt7a and syt7b may mediate asynchronous release or other Ca2+ dependent processes in different types of retinal neurons.

neuroscience↗