Biology subjects

Lorimer, T.

Publications and source records attributed to Lorimer, T..

4 recordsLinked to original sources

bioRxiv2025

GrimACE: Automated, multimodal cage-side assessment of pain and well-being in mice

Pain and welfare monitoring is essential for ethical animal testing, but current cage-side assessments are qualitative and subjective. Here we present the GrimACE, the first fully standardised and automated cage-side monitoring tool for mice, the most widely used animals in research. The GrimACE uses computer vision to provide automated mouse grimace scale (MGS) assessment together with pose estimation in a dark, safe environment. We validated the system by analysing pain after brain surgeries (craniotomies) with head implants under two analgesia regimes. Human-expert and automated MGS scores showed very high correlation (Pearson's r=0.87). Both expert and automated scores revealed that a moderate increase in pain can be detected for up to 48 hours after surgeries, but that both a single dose of meloxicam (5mg/kg s.c.) or 3 doses of buprenorphine (0.1mg/kg) + meloxicam (5mg/kg s.c.) provide adequate and comparable pain management. Simultaneous pose estimation demonstrated that mice receiving buprenorphine + meloxicam showed increased movement 4h after surgery, indicative of hyperactivity, a well-known side-effect of opioid treatment. Significant weight loss was also detected in the buprenorphine + meloxicam treatment group compared to the meloxicam-only group. Additionally, detailed BehaviorFlow analysis and automated MGS scoring of control animals suggests that habituation to the GrimACE system is unnecessary, and that measurements can be repeated multiple times, ensuring standardised post-operative recovery monitoring.

neuroscience↗

bioRxiv2021

Underwater dual-magnification imaging for automated lake plankton monitoring

We present an approach for automated in-situ monitoring of phytoplankton and zooplankton communities based on a dual magnification dark-field imaging microscope/camera. We describe the Dual Scripps Plankton Camera (DSPC) system and associated image processing, and assess its capabilities in detecting and characterizing plankton species of different size and taxonomic categories, and in measuring their abundances in both laboratory and field applications. In the laboratory, body size and abundance estimates by the DSPC significantly and robustly scale with the same measurements derived by traditional microscopy. In the field, a DSPC installed permanently at 3 m depth in Lake Greifensee (Switzerland), delivered images of plankton individuals, colonies, and heterospecific aggregates without disrupting natural arrangements of interacting organisms, their microenvironment or their behavior at hourly timescales. The DSPC was able to track the dynamics of taxa in the size range between [~]10 m to [~] 1 cm, covering virtually all the components of the planktonic food web (including parasites and potentially toxic cyanobacteria). Comparing data from the field-deployed DSPC to traditional sampling and microscopy revealed a general overall agreement in estimates of plankton diversity and abundances, despite imaging limitations in detecting small phytoplankton species and rare and large zooplankton taxa (e.g. carnivorous zooplankton). The most significant disagreements between traditional methods and the DSPC resided in the measurements of community properties of zooplankton, organisms that are heterogeneously distributed spatially and temporally, and whose demography appeared to be better captured by automated imaging. Time series collected by the DSPC depicted ecological succession patterns, algal bloom dynamics and circadian fluctuations with a temporal frequency and morphological resolution that would have been impossible with traditional methods. We conclude that the DSPC approach is suitable for stable long-term deployments, and robust for both research and water quality monitoring. Access to high frequency, reproducible and real-time data of a large spectrum of the planktonic ecosystem might represent a breakthrough in both applied and fundamental plankton ecology.

ecology↗

bioRxiv2020

Tracking changes in behavioural dynamics using prediction error

Automated analysis of video can now generate extensive time series of pose and motion in freely-moving organisms. This requires new quantitative tools to characterize behavioural dynamics. For the model roundworm Caenorhabditis elegans, body pose can be accurately quantified from video as coordinates in a single low-dimensional space. We focus on this well-established case as an illustrative example and propose a method to reveal subtle variations in behaviour at high time resolution. Our data-driven method, based on empirical dynamic modeling, quantifies behavioural change as prediction error with respect to a time-delay-embedded attractor of behavioural dynamics. Because this attractor is constructed from a user-specified reference data set, the approach can be tailored to specific behaviours of interest at the individual or group level. We validate the approach by detecting small changes in the movement dynamics of C. elegans at the initiation and completion of delta turns. We then examine an escape response initiated by an aversive stimulus and find that the method can track return to baseline behaviour in individual worms and reveal variations in the escape response between worms. We suggest that this general approach - defining dynamic behaviours using reference attractors and quantifying dynamic changes using prediction error - may be of broad interest and relevance to behavioural researchers working with video-derived time series.

bioinformatics↗

bioRxiv2019

A Universal Phase Transition in Plankton Trait Dynamics

Key ecological traits, like cell size, often follow scale-free or self-similar distributions. This indicates that these systems might operate near a critical (i.e. second-order) phase transition where macroscopic system behaviour is largely decoupled from microscopic system details, allowing an extremely simple, yet accurate and robust mathematical system characterisation. However, how trait-distribution scaling results from a critical transition has not yet been explicitly demonstrated. Here, we demonstrate that a generic class of cell growth and division models exhibits a critical transition from a growth-dominated to a division-dominated phase. We find experimental evidence for this transition, both in the population dynamics and in the moment scaling of chlorophyll distributions, for prokaryotic and eukaryotic phytoplankton growth under different light intensities. Our approach offers testable predictions of the response of unicellular trait-distributions to perturbations.

ecology↗