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237 results for “animal models”
Spatially explicit models for decision-making in animal conservation and restoration
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Data from: Models for assessing local-scale co-abundance of animal species while accounting for differential detectability and varied responses to the environment
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Systematic review of validation of supervised machine learning models in accelerometer-based animal behaviour classification literature
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Multiscale heart image data for: Multiscale cardiac imaging spanning the whole heart and its internal cellular architecture in a small animal model
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Data from: Joint modelling of multi-scale animal movement data using hierarchical hidden Markov models
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Are researchers moving away from animal models as a result of poor clinical translation in the field of stroke? an analysis of opinion papers
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Monte-Carlo Simulation Models of Animal Movement
This dataset consists of a series of computer programs in the PASCAL language that implement a simple model of animal movement. Results of model use were published in: Porter, J.H. and J.L. Dooley, Jr. 1993. Animal dispersal patterns: a reassessment of simple mathematical models. Ecology 74:2436-2443. MOVESIM.PAS, MOVESV1.PAS and MOVESV21.PAS are the model. Input is a set of coordinates for sampling points. The model randomly selects a starting point, then selects a random distance and direction. The distance to the nearest sampling point is then calculated and the process is repeated. There are a number of associated programs. MOVESTAT.SAS - SAS program to list frequency histogram for MOVESIM output. RANDGEN.PAS - Generate a set of random sampling stations within a given area. FITIT.PAS - Estimate fit of different movement models to output. ADJUST1.PAS - Estimate the number of individuals that would have been caught at a given distance if sample points had been evenly distributed.
Brumadinho tailings dam, Brazil model animations of mudflow and risk to people plus GIS files
<p>The data set comprises animations of modelling the mudflow and risks to people carried out of the Brumadinho tailings dam failure which occurred in January 2019. Some additional GIS files are also included.</p>
The performance of permutations and exponential random graph models when analysing animal networks (R code and data)
<p>Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyse animal social network data are permutation-based tests of significance and exponential random graph models. However, the performance of these approaches when analysing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analysing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a two-level explanatory variable (e.g. sex) on the number and combined strength of an individual's network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations of individuals in groups. Exponential random graph models had a marginally lower rate of false positives than permutations in most cases. Phenotypic assortativity had a large influence on the false positive rate, and a smaller effect on the false negative rate for both methods in all network types. Aspects of within- and between-group network structure influenced error rates, but not to the same extent. In grouping-event based networks, increased sampling effort marginally decreased rates of false negatives, but increased rates of false positives for both analysis methods. These results provide guidelines for biologists analysing and interpreting their own network data using these methods.</p>
Data from: Accounting for genetic differences among unknown parents in microevolutionary studies: how to include genetic groups in quantitative genetic animal models
Quantifying and predicting microevolutionary responses to environmental change requires unbiased estimation of quantitative genetic parameters in wild populations. 'Animal models', which utilize pedigree data to separate genetic and environmental effects on phenotypes, provide powerful means to estimate key parameters and have revolutionized quantitative genetic analyses of wild populations. However, pedigrees collected in wild populations commonly contain many individuals with unknown parents. When unknown parents are non-randomly associated with genetic values for focal traits, animal model parameter estimates can be severely biased. Yet, such bias has not previously been highlighted and statistical methods designed to minimize such biases have not been implemented in evolutionary ecology. We first illustrate how the occurrence of non-random unknown parents in population pedigrees can substantially bias animal model predictions of breeding values and estimates of additive genetic variance, and create spurious temporal trends in predicted breeding values in the absence of local selection. We then introduce 'genetic group' methods, which were developed in agricultural science, and explain how these methods can minimize bias in quantitative genetic parameter estimates stemming from genetic heterogeneity among individuals with unknown parents. We summarize the conceptual foundations of genetic group animal models and provide extensive, step-by-step tutorials that demonstrate how to fit such models in a variety of software programs. Furthermore, we provide new functions in r that extend current software capabilities and provide a standardized approach across software programs to implement genetic group methods. Beyond simply alleviating bias, genetic group animal models can directly estimate new parameters pertaining to key biological processes. We discuss one such example, where genetic group methods potentially allow the microevolutionary consequences of local selection to be distinguished from effects of immigration and resulting gene flow. We highlight some remaining limitations of genetic group models and discuss opportunities for further development and application in evolutionary ecology. We suggest that genetic group methods should no longer be overlooked by evolutionary ecologists, but should become standard components of the toolkit for animal model analyses of wild population data sets.
Data from: A systematic review and meta-analysis of gene therapy in animal models of cerebral glioma: why did promise not translate to human therapy?
Background: The development of therapeutics is often characterized by promising animal research that fails to translate into clinical efficacy; this holds for the development of gene therapy in glioma. We tested the hypothesis that this is because of limitations in the internal and external validity of studies reporting the use of gene therapy in experimental glioma. Method: We systematically identified studies testing gene therapy in rodent glioma models by searching three online databases. The number of animals treated and median survival were extracted and studies graded using a quality checklist. We calculated median survival ratios and used random effects meta-analysis to estimate efficacy. We explored effects of study design and quality and searched for evidence of publication bias. Results: We identified 193 publications using gene therapy in experimental glioma, including 6,366 animals. Overall, gene therapy improved median survival by a factor of 1.60 (95% CI 1.53–1.67). Study quality was low and the type of gene therapy did not account for differences in outcome. Study design characteristics accounted for a significant proportion of between-study heterogeneity. We observed similar findings in a data subset limited to the most common gene therapy. Conclusion: As the dysregulation of key molecular pathways is characteristic of gliomas, gene therapy remains a promising treatment for glioma. Nevertheless, we have identified areas for improvement in conduct and reporting of studies, and we provide a basis for sample size calculations. Further work should focus on genes of interest in paradigms recapitulating human disease. This might improve the translation of such therapies into the clinic.
Data from: Vaccination method affects immune response and bacterial growth but not protection in the Salmonella Typhimurium animal model of typhoid
Understanding immune responses elicited by vaccines, together with immune responses required for protection, is fundamental to designing effective vaccines and immunisation programs. This study examines the effects of the route of administration of a live attenuated vaccine on its interactions with, and stimulation of, the murine immune system as well as its ability to increase survival and provide protection from colonisation by a virulent challenge strain. We assess the effect of administration method using the murine model for typhoid, where animals are infected with S. Typhimurium. Mice were vaccinated either intravenously or orally with the same live attenuated S. Typhimurium strain and data were collected on vaccine strain growth, shedding and stimulation of antibodies and cytokines. Following vaccination, mice were challenged with a virulent strain of S. Typhimurium and the protection conferred by the different vaccination routes was measured in terms of challenge suppression and animal survival. The main difference in immune stimulation found in this study was the development of a secretory IgA response in orally-vaccinated mice, which was absent in IV vaccinated mice. While both strains showed similar protection in terms of challenge suppression in systemic organs (spleen and liver) as well as survival, they differed in terms of challenge suppression of virulent pathogens in gut-associated organs. This difference in gut colonisation presents important questions around the ability of vaccines to prevent shedding and transmission. These findings demonstrate that while protection conferred by two vaccines can appear to be the same, the mechanisms controlling the protection can differ and have important implications for infection dynamics within a population.
Data from: Effect of sensor location on continuous intraperitoneal glucose sensing in an animal model
In diabetes research, the development of the artificial pancreas has been a major topic since continuous glucose monitoring became available in the early 2000's. A prerequisite for an artificial pancreas is fast and reliable glucose sensing. However, subcutaneous continuous glucose monitoring carries the disadvantage of slow dynamics. As an alternative, we explored continuous glucose sensing in the peritoneal space, and investigated potential spatial differences in glucose dynamics within the peritoneal cavity. As a secondary outcome, we compared the glucose dynamics in the peritoneal space to the subcutaneous tissue. Eight-hour experiments were conducted on 12 anesthetised non-diabetic pigs. Four commercially available amperometric glucose sensors (FreeStyle Libre, Abbott Diabetes Care Ltd., Witney, UK) were inserted in four different locations of the peritoneal cavity and two sensors were inserted in the subcutaneous tissue. Meals were simulated by intravenous infusions of glucose, and frequent arterial blood and intraperitoneal fluid samples were collected for glucose reference. No significant differences were discovered in glucose dynamics between the four quadrants of the peritoneal cavity. The intraperitoneal sensors responded faster to the glucose excursions than the subcutaneous sensors, and the time delay was significantly smaller for the intraperitoneal sensors, but we did not find significant results when comparing the other dynamic parameters.
Data from: Application of benchmark concentration (BMC) analysis on zebrafish data – a new perspective for quantifying toxicity in alternative animal models
Over the past decade, the zebrafish is increasingly being used as a model to screen for chemical-mediated toxicities including developmental toxicity (DT) and neurotoxicity (NT). One of the major challenges is lack of harmonization in data analysis approaches, thereby posing difficulty in comparing findings across laboratories. To address this, we sought to establish a unified data analysis strategy for both DT and NT data, by adopting the benchmark concentration (BMC) analysis. There are two critical aspects in the BMC analysis: having a toxicity endpoint amenable for BMC and selecting a proper benchmark response (BMR) for the endpoint. For the former, in addition to the typical endpoints in NT assay (e.g., hyper/hypo- response quantified by distance moved), we also used endpoints that assess the differences in movement patterns between chemical-treated embryos and control embryos. For the latter, we standardized the selection of BMR, which is analogous to minimum activity threshold, based on intrinsic response variations in the endpoint. When comparing our BMC results with a traditionally used LOAEL method (Lowest-Observed-Adverse-Effect Level), we found high active compound concordance (100% for DT vs 74% for NT); generally, the BMC was more sensitive than LOAEL (# of BMC more sensitive/# of concordant active compounds, 43/50 for DT vs 16/26 for NT). Using the BMC with standardized toxicity endpoints and an appropriate BMR, we may now have a unified data-analysis approach to comparing results across different zebrafish datasets, for a better understanding of strengths and challenges when using the zebrafish as a screening tool.
Data from: The chinchilla as a novel animal model of pregnancy
Several parameters are important when choosing the most appropriate animal to model human obstetrics, including gestation period, number of fetuses per gestation and placental structure. The domesticated long-tailed chinchilla (Chinchilla lanigera) is a well-suited and appropriate animal model of pregnancy that often will carry only one offspring and has a long gestation period of 105–115 days. Furthermore, the chinchilla placenta is of the haemomonochorial labyrinthine type and is therefore comparable to the human villous haemomonochorial placenta. This proof-of-concept study demonstrated the feasibility in laboratory settings, and demonstrated the potential of the pregnant chinchilla as an animal model for obstetric research and its potential usefulness for non-invasive measurements in the placenta. We demonstrate measurements of the placental and fetal metabolism (demonstrated in vivo by hyperpolarized MRI and in vitro by qPCR analyses), placental vessels (demonstrated ex vivo by contrast-enhanced CT angiography) and overall anatomy (demonstrated in vivo by whole-body CT).
Data from: Characterization of advanced glycation end products and their receptor (RAGE) in an animal model of myocardial infarction
Circulating advanced glycation end products (AGE) and their receptor, RAGE, are increased after a myocardial infarction (MI) episode and seem to be associated with worse prognosis in patients. Despite the increasing importance of these molecules in the course of cardiac diseases, they have never been characterized in an animal model of MI. Thus, the aim of this study was to characterize AGE formation and RAGE expression in plasma and cardiac tissue during cardiac remodeling after MI in rats. Adult male Wistar rats were randomized to receive sham surgery (n = 15) or MI induction (n = 14) by left anterior descending coronary artery ligation. The MI group was stratified into two subgroups based on postoperative left ventricular ejection fraction: low (MIlowEF) and intermediate (MIintermEF). Echocardiography findings and plasma levels of AGEs, protein carbonyl, and free amines were assessed at baseline and 2, 30, and 120 days postoperatively. At the end of follow-up, the heart was harvested for AGE and RAGE evaluation. No differences were observed in AGE formation in plasma, except for a decrease in absorbance in MIlowEF at the end of follow-up. A decrease in yellowish-brown AGEs in heart homogenate was found, which was confirmed by immunodetection of N-ε-carboxymethyl-lysine. No differences could be seen in plasma RAGE levels among the groups, despite an increase in MI groups over the time. However, MI animals presented an increase of 50% in heart RAGE at the end of the follow-up. Despite the inflammatory and oxidative profile of experimental MI in rats, there was no increase in plasma AGE or RAGE levels. However, AGE levels in cardiac tissue declined. Thus, we suggest that the rat MI model should be employed with caution when studying the AGE-RAGE signaling axis or anti-AGE drugs for not reflecting previous clinical findings.
3D photogrammetry as a low cost and noninvasive method for acoustic modeling of animal hearing
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RAW DATA for "Kalium channelrhodopsins effectively inhibit neurons in the small model animals"
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Seamless integration of bioelectronic interface in an animal model via in vivo polymerization of conjugated oligomers
<p>Leveraging the biocatalytic machinery of living organisms for fabricating functional bioelectronic interfaces, in vivo, defines a new class of micro-biohybrids enabling the seamless integration of technology with living biological systems. Previously, we have demonstrated the in vivo polymerization of conjugated oligomers forming conductors within the structures of plants. Here, we expand this concept by reporting that Hydra, an invertebrate animal, polymerizes the conjugated oligomer ETE-S both within cells that expresses peroxidase activity and within the adhesive material that is secreted to promote underwater surface adhesion. The resulting conjugated polymer forms electronically conducting and electrochemically active μm-sized domains, which are inter-connected resulting in percolative conduction pathways extending beyond 100 μm, that are fully integrated within the Hydra tissue and the secreted mucus. Furthermore, the introduction and in vivo polymerization of ETE-S can be used as a biochemical marker to follow the dynamics of Hydra budding (reproduction) and regeneration. This work paves the way for well-defined self-organized electronics in animal tissue to modulate biological functions and in vivo biofabrication of hybrid functional materials and devices.</p>
Figure 3 in Drag of suction cup tags on swimming animals: Modeling and measurement
Figure 3. An illustration of the Model B tag in the water tunnel. Both models were mounted to the load cell via an extension used to submerge the test piece upside-down below the surface of the water tunnel. A grounding board surrounded the extension where the test piece was mounted to create a uniform ceiling for the test section, but the space above the grounding board was flooded.
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.