Biology subjects

Ruidiaz, M. E.

Publications and source records attributed to Ruidiaz, M. E..

3 recordsLinked to original sources

bioRxiv2025

Multi-Week Digital Home Cage Monitoring Reduces Noise and Enhances Reproducibility

Reproducibility is a persistent challenge in preclinical research. We used multi-week rodent machine vision home cage monitoring at three different pharmaceutical companies to examine factors governing replication of genotype differences in activity. Interlaboratory replication of genotype effects was surprisingly high. Longer study durations reduced noise, improving replication and reducing replicable sample sizes. These findings demonstrate the potential of long-term home cage digital monitoring as a method to enhance reproducibility.

animal behavior and cognition↗

bioRxiv2025

Phenotypic scoring of Canola Blackleg severity using machine learning image analysis

Canola blackleg is a fungal disease that causes significant yield loss and plant death of infected canola (Brassica napus L., Brassica rapa L., Brassica juncea L.) fields worldwide. One of the most effective methods for controlling blackleg is through the cultivation of resistant varieties. Consequently, scoring blackleg disease severity of infected plants is a key metric for identifying and selecting resistant varieties. Traditionally, blackleg severity is scored by expert raters who evaluate disease in stem cross sections using established rating scales and reference images; however, human raters are expensive and inconsistent in their scoring. Here, we introduce a machine learning algorithm based on deep learning models that can score blackleg severity from cross-section images of infected plants. We find that expert ratings are largely inconsistent across raters and across years for the same rater, creating substantial noise in susceptibility ratings. Meanwhile, our trained machine learning model performs more consistently than the median rater while maintaining a similar heritability as expert raters for the blackleg susceptibility trait. This model can be used to standardize blackleg susceptibility scoring across locations and years to improve canola breeding outcomes across affected regions. Core IdeasO_LICanola Blackleg is a fungal disease affecting yield of canola, and accurate scoring of Blackleg severity is important for tracking disease and breeding for resistant varieties. C_LIO_LIThe standard practice of utilizing expert raters is expensive, and scores assigned are inconsistent across raters and years. C_LIO_LIOur deep learning model for assigning blackleg severity scores is more accurate than the median expert rater, opening the door for improved breeding of new resistant varieties. C_LI

plant biology↗

bioRxiv2024

An integrated and scalable rodent cage system enabling continuous computer vision-based behavioral analysis and AI-enhanced digital biomarker development

1Home cage monitoring enables continuous observation of animals in familiar environments. It has large utility in preclinical testing, mechanistic studies, animal husbandry, and the general practice of the Replacement, Reduction, Refinement (3R) principles. Despite its acknowledged utility, home cage monitoring has not been broadly adopted. This is mainly due to the complexity of the tasks that must be solved to have a successful system that includes hardware and sensor development, data management, machine vision expertise, behavioral expertise, support, and user training. Here, we describe the Digital In Vivo System (DIV Sys), a modern end-to-end system for video-based rodent home cage monitoring. The DIV Sys consists of a cloud-based study design, monitoring, display, and visualization app (DIV App), local hardware for data acquisition cages (DAX), a machine learning model for tracking mice (mHydraNet) optimized for speed and accuracy, a study display and visualization app, and an advanced behavior quantification workbench (DIV Data). The platform seamlessly manages terabytes of video data in the cloud and is built around enterprise-level security and data standards. Collaborative tools enable teams across geographical locations to work together. As a demonstration of its utility, we used DIV Sys to analyze over a century of mouse videos across multiple geographic locations. We also characterized home cage behavior of 8 mouse strains and carried out customized video analysis. Together, we present a scalable home cage monitoring system for advanced behavior quantification for the rodent research community.

animal behavior and cognition↗