Biology subjects

Weis, M. A.

Publications and source records attributed to Weis, M. A..

4 recordsLinked to original sources

bioRxiv2024

PriMAT: A robust multi-animal tracking model for primates in the wild

O_LIDetection and tracking of animals is an important first step for automated behavioral studies using videos. Animal tracking is currently done mostly using deep learning frameworks based on keypoints, which show remarkable results in lab settings with fixed cameras, backgrounds, and lighting. However, multi-animal tracking in the wild presents several challenges such as high variability in background and lighting conditions, complex motion, and occlusion. C_LIO_LIWe propose a multi-animal tracking model, PriMAT, for nonhuman primates in the wild. The model learns to detect and track primates and other objects of interest from labeled videos or single images using bounding boxes instead of keypoints. Using bounding boxes significantly facilitates data annotation and robustness. Our one-stage model is conceptually simple but highly flexible, and we add a classification branch that allows us to train individual identification. C_LIO_LITo evaluate the performance of our model, we applied it in two case studies with Assamese macaques (Macaca assamensis) and redfronted lemurs (Eulemur rufifrons) in the wild. We show that with only a few hundred frames labeled with bounding boxes, we can achieve robust tracking results. Combining these results with the classification branch for the lemur videos, our model shows an accuracy of 84% in predicting lemur identities. C_LIO_LIOur approach presents a promising solution for accurately tracking and identifying animals in the wild, offering researchers a tool to study animal behavior in their natural habitats. Our code, models, training images, and evaluation video sequences are publicly available1, facilitating their use for animal behavior analyses and future research in this field. C_LI

animal behavior and cognition↗

bioRxiv2022

Large-scale unsupervised discovery of excitatory morphological cell types in mouse visual cortex

Neurons in the neocortex exhibit astonishing morphological diversity which is critical for properly wiring neural circuits and giving neurons their functional properties. However, the organizational principles underlying this morphological diversity remain an open question. Here, we took a data-driven approach using graph-based machine learning methods to obtain a low-dimensional morphological "bar code" describing more than 30,000 excitatory neurons in mouse visual areas V1, AL and RL that were reconstructed from the millimeter scale MICrONS serial-section electron microscopy volume. Contrary to previous classifications into discrete morphological types (m-types), our data-driven approach suggests that the morphological landscape of cortical excitatory neurons is better described as a continuum, with a few notable exceptions in layers 5 and 6. Dendritic morphologies in layers 2-3 exhibited a trend towards a decreasing width of the dendritic arbor and a smaller tuft with increasing cortical depth. Inter-area differences were most evident in layer 4, where V1 contained more atufted neurons than higher visual areas. Moreover, we discovered neurons in V1 on the border to layer 5 which avoided deeper layers with their dendrites. In summary, we suggest that excitatory neurons morphological diversity is better understood by considering axes of variation than using distinct m-types.

neuroscience↗

bioRxiv2020

COPB2 haploinsufficiency causes a coatopathy with osteoporosis and developmental delay

Coatomer complexes function in the sorting and trafficking of proteins between subcellular organelles. Pathogenic variants in coatomer subunits or associated factors have been reported in multi-systemic disorders, i.e., coatopathies, that can affect the skeletal and central nervous systems. We have identified loss-of-function variants in COPB2, a component of the coatomer complex I (COPI), in individuals presenting with osteoporosis, fractures and developmental delay of variable severity. Because the role of COPB2 in bone has not been characterized, we studied the effect of COPB2 deficiency on skeletal development in mice and zebrafish. Copb2+/- mice showed low bone mass and decreased bone strength. In zebrafish, larvae carrying a copb2 heterozygous frameshift variant showed delayed mineralization. copb2-null embryos showed endoplasmic reticulum (ER) and Golgi disorganization, and embryonic lethality. COPB2 siRNA-treated fibroblasts showed delayed collagen trafficking with retention of type I collagen in the ER and Golgi, and altered distribution of Golgi markers. Our data suggest that COPB2 haploinsufficiency leads to disruption of intracellular collagen trafficking and osteoporosis, which may improve with ascorbic acid supplementation. This work highlights the role of COPI complex as a critical regulator of bone mass and identifies a new form of coatopathy due to COPB2 deficiency.

genetics↗

bioRxiv2018

Sparse reduced-rank regression for exploratory visualization of single cell patch-seq recordings

In genomics, transcriptomics, and related biological fields (collectively known as omics), it is common to work with n << p datasets with the dimensionality much larger than the sample size. In recent years, combinations of experimental techniques began to yield multiple sets of features for the same set of biological replicates. One example is Patch-seq, a method combining single-cell RNA sequencing with electrophysiological recordings from the same cells. Here we present a framework based on sparse reduced-rank regression for obtaining an interpretable visualization of the relationship between the transcriptomic and the electrophysiological data. We use an elastic net regularization penalty that yields sparse solutions and allows for an efficient computational implementation. Using several publicly available Patch-seq datasets, we show that sparse reduced-rank regression outperforms both sparse full-rank regression and non-sparse reduced-rank regression in terms of predictive performance, and can outperform existing methods for sparse partial least squares and sparse canonical correlation analysis in terms of out-of-sample correlations. We introduce a bibiplot visualization in order to display the dominant factors determining the relationship between transcriptomic and electrophysiological properties of neurons. We believe that sparse reduced-rank regression can provide a valuable tool for the exploration and visualization of paired multivariate datasets, including Patch-seq.

bioinformatics↗