Biology subjects

Barberis, L.

Publications and source records attributed to Barberis, L..

2 recordsLinked to original sources

bioRxiv2023

Thanks to repetition, dustbathing detection can be automated combining accelerometry and wavelet analysis

Dustbathing is performed by many groups of birds, including Galliformes. It consists of a well-defined orderly sequence of movements. Repetitive changes in body position during dustbathing can be automatically detected through data processing of body mounted accelerometer recordings, specifically the complex Morlet continuous wavelet transform. The approach was tested in 13 adult male Japanese quail (Coturnix japonica) fitted with a backpack containing a triaxial accelerometer and video-recorded during at least 6h. Rhythmicity (period 25-60s) in the y-axis acceleration vector is reflected as large power values, and is associated almost exclusively to dustbathing events. Thus, by implementing a threshold value we detected events automatically with an accuracy of 80% (range 66-100%). We show potential uses for characterizing temporal dynamics (e.g. daily rhythms) of dustbathing and for the assessment of intra- and inter-individual variability over long-term studies, even within large complex environments (e.g. natural environments or breeding facilities). Summary statementWe propose a method for automatically detecting dustbathing (i.e a behavior performed by many groups of birds, including Galliformes) from triaxial accerometer recoding using a wavelet technique.

animal behavior and cognition↗

bioRxiv2022

Predicting Dog Phenotypes from Genotypes

We analyze dog genotypes (i.e., positions of dog DNA sequences that often vary between different dogs) in order to predict the corresponding phenotypes (i.e., unique observed characteristics). More specifically, given chromosome data from a dog, we aim to predict the breed, height, and weight. We explore a variety of linear and non-linear classification and regression techniques to accomplish these three tasks. We also investigate the use of a neural network (both in linear and non-linear modes) for breed classification and compare the performance to traditional statistical methods. We show that linear methods generally outperform or match the performance of non-linear methods for breed classification. However, we show that the reverse is true for height and weight regression. Finally, we evaluate the results of all of these methods based on the number of input features used in the analysis. We conduct experiments using different fractions of the full genomic sequences, resulting in input sequences ranging from 20 SNPs to [~]200k SNPs. In doing so, we explore the impact of using a very limited number of SNPs for prediction. Our experiments demonstrate that these phenotypes in dogs can be predicted with as few as 0.5% of randomly selected SNPs (i.e., 992 SNPs) and that dog breeds can be classified with 50% balanced accuracy with as few as 0.02% SNPs (i.e., 40 SNPs).

genomics↗