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237 results for “animal models”
Data from: Use of hidden Markov capture-recapture models to estimate abundance in presence of uncertainty: application to estimating the prevalence of hybrids in animal populations
Estimating the relative abundance (prevalence) of different population segments is a key step in addressing fundamental research questions in ecology, evolution, and conservation. The raw percentage of individuals in the sample (naive prevalence) is generally used for this purpose, but it is likely to be subject to two main sources of bias. First, the detectability of individuals is ignored; second, classification errors may occur due to some inherent limits of the diagnostic methods. We developed a hidden Markov (also known as multievent) capture–recapture model to estimate prevalence in free‐ranging populations accounting for imperfect detectability and uncertainty in individual's classification. We carried out a simulation study to compare naive and model‐based estimates of prevalence and assess the performance of our model under different sampling scenarios. We then illustrate our method with a real‐world case study of estimating the prevalence of wolf (Canis lupus) and dog (Canis lupus familiaris) hybrids in a wolf population in northern Italy. We showed that the prevalence of hybrids could be estimated while accounting for both detectability and classification uncertainty. Model‐based prevalence consistently had better performance than naive prevalence in the presence of differential detectability and assignment probability and was unbiased for sampling scenarios with high detectability. We also showed that ignoring detectability and uncertainty in the wolf case study would lead to underestimating the prevalence of hybrids. Our results underline the importance of a model‐based approach to obtain unbiased estimates of prevalence of different population segments. Our model can be adapted to any taxa, and it can be used to estimate absolute abundance and prevalence in a variety of cases involving imperfect detection and uncertainty in classification of individuals (e.g., sex ratio, proportion of breeders, and prevalence of infected individuals).
Are researchers moving away from animal models as a result of poor clinical translation in the field of stroke? an analysis of opinion papers
<p>Objectives</p> <p>Despite decades of research using animals to develop pharmaceutical treatments for stroke patients, few therapeutic options exist. The vast majority of interventions successful in preclinical animal studies have turned out to have no efficacy in humans, or to be harmful to humans. In view of this we explore whether there is evidence of a move away from animal models in this field.</p> <p>Methods</p> <p>We used an innovative methodology, the analysis of opinion papers. Although we took a systematic approach to literature searching and data extraction, this is not a systematic review because the study involves the synthesis of opinions, not research evidence. Data were extracted from retrieved papers in chronological order and analysed qualitatively and descriptively.</p> <p>Results</p> <p>Eighty eligible papers, published between 1979 and 2018, were identified. Most authors were from academic departments of neurology, neuroscience or stroke research. Authors agreed that translational stroke research was in crisis. They held diverse views about the causes of this crisis, most of which did not fundamentally challenge the use of animal models. Some, however, attributed the translational crisis to animal-human species differences and one to a lack of human in vitro models. Most of the proposed solutions involved fine-tuning animal models but authors disagreed about whether such modifications would improve translation. A minority suggested using human in vitro methods alongside animal models. One proposed focusing only on human based in vitro methods.</p> <p>Conclusion</p> <p>Despite recognising that animal models have been unsuccessful in the field of stroke, most researchers exhibited a strong resistance to relinquishing them. Nevertheless there is an emerging challenge to the use of animal models, in the form of human focused in vitro approaches. For the sake of stroke patients there is an urgent need to revitalise translational stroke research and explore the evidence for these new approaches.</p>
Data from: Analysis of animal accelerometer data using hidden Markov models
Use of accelerometers is now widespread within animal biologging as they provide a means of measuring an animal's activity in a meaningful and quantitative way where direct observation is not possible. In sequential acceleration data, there is a natural dependence between observations of behaviour, a fact that has been largely ignored in most analyses. Analyses of acceleration data where serial dependence has been explicitly modelled have largely relied on hidden Markov models (HMMs). Depending on the aim of an analysis, an HMM can be used for state prediction or to make inferences about drivers of behaviour. For state prediction, a supervised learning approach can be applied. That is, an HMM is trained to classify unlabelled acceleration data into a finite set of pre-specified categories. An unsupervised learning approach can be used to infer new aspects of animal behaviour when biologically meaningful response variables are used, with the caveat that the states may not map to specific behaviours. We provide the details necessary to implement and assess an HMM in both the supervised and unsupervised learning context and discuss the data requirements of each case. We outline two applications to marine and aerial systems (shark and eagle) taking the unsupervised learning approach, which is more readily applicable to animal activity measured in the field. HMMs were used to infer the effects of temporal, atmospheric and tidal inputs on animal behaviour. Animal accelerometer data allow ecologists to identify important correlates and drivers of animal activity (and hence behaviour). The HMM framework is well suited to deal with the main features commonly observed in accelerometer data and can easily be extended to suit a wide range of types of animal activity data. The ability to combine direct observations of animal activity with statistical models, which account for the features of accelerometer data, offers a new way to quantify animal behaviour and energetic expenditure and to deepen our insights into individual behaviour as a constituent of populations and ecosystems.
Animations of iSOSIA glacier model results for Miage Glacier through the Holocene
<p>Animations of the results from a simulaton of Miage Glacier, Italy, through the Holocene (~12 ka to present) made using the iSOSIA glacial landscape evolution model (Egholm et al., 2011). iSOSIA is a glacial landscape evolution model that simulates erosion, transport, and deposition of sediment, and represents the feedbacks between supraglacial debris transport, ice flow and mass balance balance (Rowan et al., 2015; Scherler and Egholm, 2020).</p> <p>The subglacial model domain has a 50 m x 50 m cell size and was defined using the 30-m ASTER GDEM version 2 from which the estimated present-day ice thickness for all glaciers in the catchment with an area greater than 1 km<sup>2</sup> was subtracted (Farinotti et al., 2019). The model was forced using the Temp12k palaeotemperature composite of calibrated records of median annual air temperature for the latitude band 30–60°N which has a time step of 500 years (Kaufman et al., 2020). These data give mean annual air temperature at sea level and were extrapolated to the model domain using a lapse rate of –0.006°C m<sup>–1</sup>. Sediment was produced by hillslope erosion and transported from the catchment headwalls using a non-linear hillslope flux model (Roering et al., 1999).</p> <p> </p> <p>File contents:</p> <p><strong>Miage_icethick.mp4</strong> shows the change in ice thickness during the simulation.</p> <p><strong>Miage_velocity.mp4</strong> shows the change in depth-integrated ice flow (sliding and deformation) during the simulation.</p> <p><strong>Miage_supraglacialdebris.mp4</strong> shows the change in thickness of the sediment layer on the glacier surface during the simulation.</p> <p><strong>Miage_hillslope.mp4</strong> shows the change in sediment production and storage from hillslope erosion during the simulation.</p> <p> </p> <p>References:</p> <p>Egholm DL, Knudsen MF, Clark CD, Lesemann JE. 2011. Modeling the flow of glaciers in steep terrains: The integrated second-order shallow ice approximation (iSOSIA). Journal of Geophysical Research: Earth Surface <strong>116</strong> DOI: 10.1029/2010JF001900</p> <p>Farinotti D, Huss M, Fürst JJ, Landmann J, Machguth H, Maussion F, Pandit A. 2019. A consensus estimate for the ice thickness distribution of all glaciers on Earth. Nature Geoscience <strong>12</strong> : 168–173. DOI: 10.1038/s41561-019-0300-3</p> <p>Kaufman D et al. 2020. A global database of Holocene paleotemperature records. Scientific Data <strong>7</strong> : 115. DOI: 10.1038/s41597-020-0445-3</p> <p>Roering JJ, Kirchner JW, Dietrich WE. 1999. Evidence for nonlinear, diffusive sediment transport on hillslopes and implications for landscape morphology. Water Resources Research <strong>35</strong> : 853–870. DOI: 10.1029/1998WR900090</p> <p>Rowan AV, Egholm DL, Quincey DJ, Glasser NF. 2015. Modelling the feedbacks between mass balance, ice flow and debris transport to predict the response to climate change of debris-covered glaciers in the Himalaya. Earth and Planetary Science Letters <strong>430</strong> : 427–438. DOI: 10.1016/j.epsl.2015.09.004</p> <p>Scherler D, Egholm DL. 2020. Production and Transport of Supraglacial Debris: Insights From Cosmogenic <sup>10</sup>Be and Numerical Modeling, Chhota Shigri Glacier, Indian Himalaya. Journal of Geophysical Research: Earth Surface <strong>125</strong> DOI: 10.1029/2020JF005586</p>
The effects of exploratory behavior on physical activity in a common animal model of human disease, zebrafish (Danio rerio)
<p>Zebrafish (Danio rerio) are widely accepted as a multidisciplinary vertebrate model for neurobehavioral and clinical studies, and more recently have become established as a model for exercise physiology and behavior. Individual differences in activity level (e.g., exploration) have been characterized in zebrafish, however, how different levels of exploration correspond to differences in motivation to engage in swimming behavior has not yet been explored. We screened individual zebrafish in two tests of exploration: the open field and novel tank diving tests. The fish were then exposed to a tank in which they could choose to enter a compartment with a flow of water (as a means of testing voluntary motivation to exercise). After a 2-day habituation period, behavioral observations were conducted. We used correlative analyses to investigate the robustness of the different exploration tests. Due to the complexity of dependent behavioral variables, we used machine learning to determine the personality variables that were best at predicting swimming behavior. Our results show that contrary to our predictions, the correlation between novel tank diving test variables and open field test variables was relatively weak. Novel tank diving variables were more correlated with themselves than open field variables were to each other. Males exhibited stronger relationships between behavioral variables than did females. In terms of swimming behavior, fish that spent more time in the swimming zone spent more time actively swimming, however, swimming behavior was inconsistent across the time of the study. All relationships between swimming variables and exploration tests were relatively weak, though novel tank diving test variables had stronger correlations. Machine learning showed that three novel tank diving variables (entries top/bottom, movement rate, average top entry duration) and one open field variable (proportion of time spent frozen) were the best predictors of swimming behavior, demonstrating that the novel tank diving test is a powerful tool to investigate exploration. Increased knowledge about how individual differences in exploration may play a role in swimming behavior in zebrafish is fundamental to their utility as a model of exercise physiology and behavior.</p>
Data for: Inferring spatially-varying animal movement characteristics using a hierarchical continuous-time velocity model
<p>Understanding the spatial dynamics of animal movement is an essential component of maintaining ecological connectivity, conserving key habitats, and mitigating the impacts of anthropogenic disturbance. Altered movement and migratory patterns are often an early warning sign of the effects of environmental disturbance, and a precursor to population declines. Here, we present a hierarchical Bayesian framework based on Gaussian processes for analysing the spatial characteristics of animal movement. At the heart of our approach is a novel covariance kernel that links the spatially-varying parameters of a continuous-time velocity model with GPS locations from multiple individuals. We demonstrate the effectiveness of our framework by first applying it to a synthetic dataset, then by analysing telemetry data from the Serengeti wildebeest migration. Through application of our approach, we are able to identify the key pathways of the wildebeest migration as well as revealing the impacts of environmental features on movement behaviour.</p>
WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models
<p>#############</p> <h1>WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models</h1> <p>#############</p> <p>Authors: Valentin Gabeff, Marc Russwurm, Devis Tuia & Alexander Mathis</p> <p>Affiliation: EPFL</p> <p>Date: January, 2024</p> <p>Link to the article: <a href="https://link.springer.com/article/10.1007/s11263-024-02026-6">https://link.springer.com/article/10.1007/s11263-024-02026-6</a></p> <p>--------------------------------</p> <p>WildCLIP is a fine-tuned CLIP model that allows to retrieve camera-trap events with natural language from the Snapshot Serengeti dataset. This project intends to demonstrate how vision-language models may assist the annotation process of camera-trap datasets.</p> <p>Here we provide the processed Snapshot Serengeti data used to train and evaluate WildCLIP, along with two versions of WildCLIP (model weights).</p> <p>Details on how to run these models can be found in the project <a href="https://github.com/amathislab/wildclip">github repository</a>.</p> <h2>Provided data (images and attribute annotations): </h2> <p>The data consists of 380 x 380 image crops corresponding to the MegaDetector output of Snapshot Serengeti with a confidence threshold above 0.7. We considered only camera trap images containing single individuals.</p> <p>A description of the original data can be found on LILA <a href="https://lila.science/datasets/snapshot-serengeti">here</a>, released under the <a href="https://cdla.dev/permissive-1-0/" rel="nofollow">Community Data License Agreement (permissive variant)</a>.</p> <p>We warmly thank the authors of LILA for making the MegaDetector outputs publicly available, as well as for structuring the dataset and facilitating its access.</p> <h2>Adapted CLIP model (model weights): </h2> <p>WildCLIP models provided:</p> <ul> <li><strong>[New] WildCLIP_vitb16_t1.pth: </strong>CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1. Trained on both base and novel vocabulary (see paper for details).</li> <li><strong>[New] WildCLIP_vitb16_t1_lwf.pth: </strong>CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1, and with the additional VR-LwF loss. Trained on both base and novel vocabulary (see paper for details).</li> <li><strong>WildCLIP_vitb16_t1_base.pth:</strong> CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1. Model used for evaluation and trained on base vocabulary only. (previously named <em>WildCLIP_vitb16_t1.pth</em>)</li> <li><strong>WildCLIP_vitb16_t1t7_lwf_base.pth</strong>: CLIP model with the ViT-B/16 visual backbone trained on data with captions following templates 1 to 7, and with the additional VR-LwF loss. Model used for evaluation and trained on base vocabulary only. (previously named <em>WildCLIP_vitb16_t1t7_lwf.pth</em>)</li> </ul> <p>We also provide the CSV files containing the train / val / test splits. The train / test splits follow camera split from LILA (https://lila.science/datasets/snapshot-serengeti). The validation split is custom, and also at the camera level.</p> <ul> <li><strong>train_dataset_crops_single_animal_template_captions_T1T7_ID.csv</strong>: Train set with captions from templates 1 through 7 (column "all captions") or template 1 only (column "template 1")</li> <li><strong>val_dataset_crops_single_animal_template_captions_T1T7_ID.csv</strong>: Validation set with captions from templates 1 through 7 (column "all captions") or template 1 only (column "template 1")</li> <li><strong>test_dataset_crops_single_animal_template_captions_T1T8T10.csv</strong>: Test set with captions from templates 1, 8, 9 and 10 (columns "all captions")</li> </ul> <p>Details on how the models were trained can be found in the associated <a href="https://link.springer.com/article/10.1007/s11263-024-02026-6" target="_blank" rel="noopener">publication</a>.</p> <h2>References: </h2> <p>If you find our code, or weights, please cite:</p> <pre>@article{gabeff2024wildclip, title={WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models}, author={Gabeff, Valentin and Ru{\ss}wurm, Marc and Tuia, Devis and Mathis, Alexander}, journal={International Journal of Computer Vision}, pages={1--17}, year={2024}, publisher={Springer} }</pre> <p>If you use the adapted Snapshot Serengeti data please also cite their article:</p> <pre>@article{swanson2015snapshot, title={Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna}, author={Swanson, Alexandra and Kosmala, Margaret and Lintott, Chris and Simpson, Robert and Smith, Arfon and Packer, Craig}, journal={Scientific data}, volume={2}, number={1}, pages={1--14}, year={2015}, publisher={Nature Publishing Group} }</pre>
Dataset supporting the manuscript "Establishment of a Newborn Lamb Gut-Loop Model to Evaluate New Methods of Enteric Disease Control and Reduce Experimental Animal Use" (Baillou, Kasal-Hoc et al, Veterinary Sciences, 2021)
<p>These are the data supporting reported results in the publication "Establishment of a Newborn Lamb Gut-Loop Model to Evaluate New Methods of Enteric Disease Control and Reduce Experimental Animal Use" (Baillou, A, Kasal-Hoc N. et al, Veterinary Sciences, 2021). DOI not yet available.</p>
dataset related to article "Dysregulation of Muscle-Specific MicroRNAs as Common Pathogenic Feature Associated with Muscle Atrophy in ALS, SMA and SBMA: Evidence from Animal Models and Human Patients"
<p> CINZIA CAGNOLI 0000-0001-6863-6687, MICHELA TAIANA 0000-0001-8257-8831, MONICA NIZZARDO 0000-0001-5447-0882, STEFANIA CORTI 0000-0001-5425-969X, VIVIANA PENSATO 0000-0001-9798-2669, ANNA VENERANDO <a href="https://orcid.org/0000-0002-7489-1833">0000-0002-7489-1833</a>, CINZIA GELLERA <a href="https://orcid.org/0000-0002-3582-665X">0000-0002-3582-665X</a>, SILVIA FENU 0000-0002-5233-6580, DAVIDE PAREYSON 0000-0001-6854-765X, RICCARDO MASSON 0000-0002-9311-452X, LORENZO MAGGI 0000-0002-0932-5173, ELEONORA DALLA BELLA 0000-0001-6267-9651, GIUSEPPE LAURIA 0000-0001-9773-020X, RENATO MANTEGAZZA 0000-0002-9810-5737, PIA BERNASCONI 0000-0003-0869-2104, ANGELO POLETTI 0000-0002-8883-0468, SILVIA BONANNO 0000-0002-8823-6821, STEFANIA MARCUZZO 0000-0001-6893-6372.</p>
Spatially explicit models for decision-making in animal conservation and restoration
<p>Models are useful tools for understanding and predicting ecological patterns and processes. Under ongoing climate and biodiversity change, they can greatly facilitate decision-making in conservation and restoration and help designing adequate management strategies for an uncertain future. Here, we review the use of spatially explicit models for decision support and identify key gaps in current modelling in conservation and restoration. Of 650 reviewed publications, 217 publications had a clear management application and were included in our quantitative analyses. Overall, modelling studies were biased towards static models (79 %), towards the species and population level (80 %) and towards conservation (rather than restoration) applications (71 %). Correlative niche models were the most widely used model type. Dynamic models as well as the gene-to-individual level and the community-to-ecosystem level were underrepresented, and explicit cost optimisation approaches were only used in 10 % of the studies. We present a new model typology for selecting models for animal conservation and restoration, characterising model types according to organisational levels, biological processes of interest and desired management applications. This typology will help to more closely link models to management goals. Additionally, future efforts need to overcome important challenges related to data integration, model integration, and decision-making. We conclude with five key recommendations, suggesting that wider usage of spatially explicit models for decision support can be achieved by (1) developing a toolbox with multiple, easier-to-use methods, (2) improving calibration and validation of dynamic modelling approaches, and (3) developing best-practise guidelines for applying these models. Further, more robust decision-making can be achieved by (4) combining multiple modelling approaches to assess uncertainty, and (5) placing models at the core of adaptive management. These efforts must be accompanied by long-term funding for modelling and monitoring, and improved communication between research and practise to ensure optimal conservation and restoration outcomes.</p>
Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distributions
<p>Habitat selection studies are designed to generate predictions of species distributions or inference regarding general habitat associations and individual variation in habitat use. Such studies frequently involve either individually indexed locations gathered across limited spatial extents and analyzed using resource selection functions (RSF), or spatially extensive locational data without individual resolution typically analyzed using species distribution models. Both analytical methodologies have certain desirable features, but analyses that combine individual- and population-level inference with flexible non-linear functions may provide improved predictions while accounting for individual variation. Here, we describe how RSFs can be fit using hierarchical generalized additive models (HGAMs) using widely available software, providing a means to explore individual variation in habitat associations and to generate species distribution maps. We used GPS tracking data from Golden Eagles (Aquila chrysaetos) from across eastern North America with four environmental predictors to generate monthly distribution models. We considered three model structures that assumed different amounts of individual variation in the functional relationship between predictors and habitat use and used k-fold cross-validation to compare model performance. Models accounting for individual variability in shape and smoothness of functional responses performed best. Eagles exhibited the least amount of individual variation in response to land cover variables during winter months, with most individuals more closely adhering to the population-level trend. During summer months, eagles exhibited more substantial individual variation in shape and smoothness of the functional relationships, suggesting some need to account for individual variation in eagle habitat use for both inferential and predictive purposes, during this time of year. Because they allow users to blend flexible functions with random effects structures and are well-supported by a variety of software platforms, we believe that HGAMs provide a useful addition to the suite of analyses used for modeling habitat associations or predicting species distributions.</p>
Mammalian animal & human retinal organ culture as pre-clinical model to evaluate oxidative stress and antioxidant intraocular therapeutics
<p>Oxidative stress (OS) is involved in the pathogenesis of retinal neurodegenerative diseases like age-related macular degeneration (AMD) and diabetic retinopathy (DR) and an important target of therapeutic treatments. New therapeutics are tested in vivo despite limits in transferability and ethical concerns. Retina cultures using human tissue can deliver critical information and significantly reduce the number of animal experiments along with increased transferability. We cultured up to 32 retina samples derived from one eye, analyzed models’ quality, induced OS, and tested efficiency of antioxidative therapeutics. Bovine, porcine, rat, and human retinae were cultured in different experimental settings for 3-14 d. OS was induced by high-glucose or hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) and treated by Scutellarin, pigment epithelium-derived factor (PEDF), and/or granulocyte macrophage-colony stimulating factor (GM-CSF). Tissue morphology, cell viability, inflammation, and glutathione level were determined. Retina samples showed only moderate necrosis (23.83±5.05 increased to 27.00±1.66 AU PI-staining over 14 d) after 14 days in culture. OS was successfully induced (reduced ATP content of 288.3±59.9 vs. 435.7±166.8 nM ATP in controls); antioxidants reduced OS-induced apoptosis (from 124.20±51.09 to 60.80±319.66 cells/image after Scutellarin-treatment). Enhanced mammalian animal and human retina cultures allow reliable, highly transferable research on OS-triggered age-related diseases and pre-clinical testing during drug development.</p>
Prevention of Post mEniscectomy Osteoarthritis: From New Animal Model to Patient pRofiLing
ClinicalTrials.gov study NCT06906094. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Application of Nurse-Patient Interaction Model Based on Animated Cartoons in Postoperative Analgesia for Preschool Children With Congenital Heart Disease.
ClinicalTrials.gov study NCT07319260. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data for: Inferring spatially-varying animal movement characteristics using a hierarchical continuous-time velocity model
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Data from: Analysis of animal accelerometer data using hidden Markov models
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Data from: Evaluating Bayesian stable isotope mixing models of wild animal diet and the effects of trophic discrimination factors and informative priors
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Data from: Use of hidden Markov capture-recapture models to estimate abundance in presence of uncertainty: application to estimating the prevalence of hybrids in animal populations
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Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distributions
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The effects of exploratory behavior on physical activity in a common animal model of human disease, zebrafish (Danio rerio)
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ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.