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Human SAN model 2024 supplementary animations.
<p>This repository is associated with the manuscript: "<em>Computational investigation of structural (fibrosis) and functional (I<sub>CaL</sub> and I<sub>Na</sub>) causes of arrhythmogenesis in the human 3D sino-atrial node", </em>by Sanjay R Kharche<sup>*</sup>, Galina Yu Mironova, Andrew Atkinson, and Donald G Welsh<sup>*</sup>. This repository contains the animations referred to in the above manuscript. A description of the file names, figure labels, and draft figure legends is provided below.</p> <p>1) File name in repository: S1_supplementVideo1Control3D.gif</p> <p>Figure label: Figure S1.</p> <p>Draft figure legend: Animation of 3D SAN control activation sequence in the border (left) and no border (right) cases.</p> <p> </p> <p>2) File name in repository: S3_pacingInducedMacro2D_Supplement.gif</p> <p>Figure label: Figure S3.</p> <p>Draft figure legend: Animation of pacing induced macro re-entry, SEP block, and altered activation sequence in the 2D model under s<sub>Na</sub> = 0.6 and s<sub>Ca</sub> = 0.8 conditions. See Figure 7 and main manuscript for details.</p> <p> </p> <p>3) File name in repository: S4_supplementToFig6_sna0.8_sca0.8.gif</p> <p>Figure label: Figure S4.</p> <p>Draft figure legend: In relation to main manuscript Figure 6, animation of complex wave propagation under s<sub>Na</sub> = s<sub>Ca</sub> = 0.8 conditions. SEP block, macro-re-entry, as well as 2:1 propagation can be observed.</p> <p> </p> <p>4) File name in repository: S6_run1dir1_9_1_1_2DmacroAndAtrialSupplement.gif</p> <p>Figure label: Figure S6.</p> <p>Draft figure legend: Animation of pacing induced macro-reentry, SEP block, and altered activation sequence in the 2D model under s<sub>Na</sub> = s<sub>Ca</sub> = 1 and 10% fibrosis conditions. See Figure 11 and main manuscript for details.</p> <p> </p> <p>5) File name in repository: S7_run3dir1_9_1_1_2DmicroAndComplexSupplement.gif</p> <p>Figure label: Figure S7.</p> <p>Draft figure legend: Animation of pacing induced micro re-entry, SEP block, and altered activation sequence in the 2D model under s<sub>Na</sub> = s<sub>Ca</sub> = 1 and 10% fibrosis conditions. See Figure 12 and main manuscript for details.</p> <p> </p> <p>6) File name in repository: S8_nopacingMacroreentryCedar0.80.8HSANb1.gif</p> <p>Figure label: Figure S8.</p> <p>Draft figure legend: Macro re-entry in the 3D non pacing with border model. s<sub>Na</sub> = s<sub>Ca</sub> = 0.8. See <strong>Figure 14</strong> in main manuscript.</p> <p> </p> <p>7) File name in repository: S9_HumanSAN_0.8_0.8_nonPacingCedar_Border1.gif</p> <p>Figure label: Figure S9.</p> <p>Draft figure legend: Atrial (complex) re-entry in the 3D model with s<sub>Na</sub> = s<sub>Ca</sub> = 0.8, no pacing and 10% fibrosis. See Figure 14 in main MS.</p>
Including a spatial predictive process in band recovery models improves inference for Lincoln estimates of animal abundance
<p>Abundance estimation is a critical component of conservation planning, particularly for exploited species where managers set regulations to restrict harvest based on current population size. An increasingly common approach for abundance estimation is through integrated population modeling (IPM), which uses multiple data sources in a joint likelihood to estimate abundance and additional demographic parameters. Lincoln estimators are one commonly used IPM component for harvested species, which combine information on the rate and the total number of individuals harvested within an integrated band-recovery framework to estimate abundance at large scales.</p> <p>A major assumption of the Lincoln estimator is that banding and recoveries are representative of the whole population, which may be violated if major sources of spatial heterogeneity in survival or harvest rates are not incorporated into the model. We developed an approach to account for spatial variation in harvest rates using a spatial predictive process, which we incorporated into a Lincoln estimator IPM.</p> <p>We simulated data under different configurations of sample sizes, harvest rates, and sources of spatial heterogeneity in harvest rate to assess potential model bias in parameter estimates. We then applied the model to data collected from a field study of wild turkeys (<em>Meleagris gallapavo</em>) to estimate local and statewide abundance in Maine, USA.</p> <p>We found that the band recovery model that incorporated a spatial predictive process consistently provided estimates of adult and juvenile abundance with low bias across a variety of spatial configurations of harvest rate and sampling intensities. When applied to data collected on wild turkeys, a model that did not incorporate spatial heterogeneity underestimated the harvest rate in some sub-regions. Consistent with simulation results, this led to over-estimation of both local and statewide abundance.</p> <p>Our work demonstrates that a spatial predictive process is a viable mechanism to account for spatial variation in harvest rates and limit bias in abundance estimates. This approach could be extended to large-scale band recovery datasets and has applicability for the estimation of population parameters in other ecological models as well.</p>
Antagonism of CGRP Receptor: Central and Peripheral Mechanisms and Mediators in an Animal Model of Chronic Migraine
<p>This dataset includes the findings obtained in the study aimed at investigating in more depth the interplay between the neuropeptide CGRP and the inflammatory mediators within the mechanisms of neuronal sensitization in an animal model of chronic migraine, using olcegepant as a pharmacological probe. Male Sprague-Dawley rats were exposed to nitroglycerin (NTG, 10 mg/kg, i.p.) or NTG vehicle and treated with the CGRP receptor antagonist olcegepant (1 or 2 mg/kg, i.p.) or vehicle (1 ml/kg, i.p.) 1h before the orofacial formalin test. Additionally, sets of rats received NTG (5 mg/kg, i.p.) or vehicle (equivalent volume) co-administered with olcegepant (2 mg/kg i.p.) or its vehicle every other day over a 9-day period. Twenty-four hours after the last injection of NTG (or vehicle), a first set of rats underwent the orofacial formalin test. In a second set, we evaluated the gene expression of CGRP and gene and protein expression of pro-inflammatory cytokines in specific areas involved in migraine pain. We also assessed the CGRP and cytokine levels in serum.</p> <p>The in vivo and ex vivo assessments were:</p> <p>1) Pain-related behavior in the orofacial formalin test: the face rubbing was measured counting the seconds the animal spent grooming the injected area (upper lip, lateral to the nose) with the ipsilateral forepaw or hindpaw 0–6 min (Phase I) or 12–45 min (Phase II) after formalin injection (50 µl, s.c.). The observation time was divided into 15 blocks of 3 min each.</p> <p>2) mRNA expression levels: CGRP, tumor necrosis factor-alpha (TNF-alpha), interleukin-1beta (IL-1beta), transient receptor potential ankyrin 1 (TRPA1) in cervical spinal cord (CSC), medulla-pons and trigeminal ganglia (TGs). mRNA levels were measured by rt-PCR. The same RNA was used for miRNAs (Mir-34a-5p, Mir-382-5p, Mir-155-5p) extraction in the same areas. All samples were assayed in triplicate and gene expression levels were calculated according to 2−∆∆Ct = 2− (∆Ct gene − ∆Ct housekeeping gene) formula by using Ct (cycle threshold) values.</p> <p>3) Pro-inflammatory cytokines in medulla-pons, CSC and TGs were evaluated using ELISA procedure. CGRP, TNF-alpha and IL-1beta serum levels were measured using commercial ELISA kits.</p> <p>Results in brief</p> <p>Olcegepant attenuated nitroglycerin-induced trigeminal hyperalgesia in the second phase of the orofacial formalin test. Interestingly, it also reduced gene expression and protein levels of CGRP, pro-inflammatory cytokines, inflammatory-associated miRNAs (miR-155-5p, miR-382-5p and miR-34a-5p) and TRPA1 channels in medulla-pons area, cervical spinal cord and trigeminal ganglia. Similarly, olcegepant reduced the NTG-induced increase of CGRP and inflammatory.</p>
Enhanced mTORC1 signaling and protein synthesis in pathologic alpha-synuclein cellular and animal models of Parkinson’s disease
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How to build a dinosaur: musculoskeletal modelling and simulation of locomotor biomechanics in extinct animals
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Proteoglycan 4 (lubricin) is a highly sialylated glycoprotein associated with cardiac valve damage in animal models of infective endocarditis
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Including a spatial predictive process in band recovery models improves inference for Lincoln estimates of animal abundance
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Evaluation of the silkworm lemon mutant as an invertebrate animal model for human sepiapterin reductase deficiency
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Data from: Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements
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Evaluation of the silkworm lemon mutant as an invertebrate animal model for human sepiapterin reductase deficiency
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Discrete element models for understanding the biomechanics of fossorial animals
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Data to support publication figures and animation scripts at GitHub: Modeling weather-driven long-distance dispersal of spruce budworm moths (Choristoneura fumiferana)
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Integrated animal movement and spatial capture-recapture models: simulation, implementation, and inference
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Non-alcoholic fatty liver disease animal models database
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Weight loss, insulin resistance, and study design confound results in a meta-analysis of animal models of fatty liver
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Data from : In vivo mechanical characterisation of an arterial wall using an inverse analysis procedure: application on an animal model of intracranial aneurysm
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Data from: Animal models to understand the etiology and pathophysiology in polycystic ovary syndrome
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Evaluating Bayesian stable isotope mixing models of wild animal diet and the effects of trophic discrimination factors and informative priors
<blockquote> <p>1. Ecologists quantify animal diets using direct and indirect methods, including analysis of faeces, pellets, prey items and gut contents. For stable isotope analyses of diet, Bayesian stable isotope mixing models (BSIMMs) are increasingly used to infer the relative importance of food sources to consumers. Although a powerful approach, it has been hard to test BSIMM performance for wild animals because precise, direct dietary data are difficult to collect.<br> 2. We evaluated the performance of BSIMMs in quantifying animal diets when using δ13C and δ15N stable isotope ratios from the feathers and red blood cells of common buzzard Buteo buteo chicks. We analysed mixing model outcomes with various trophic discrimination factors (TDFs), with and without informative priors, and compared these to direct observations of prey provisioned to chicks by adults at nests, using remote cameras. <br> 3. Although BSIMMs with different TDFs varied markedly in their performance, the statistical package SIDER generated TDFs for both feathers and blood that resulted in model outputs that accorded well with direct observations of prey provisioning. Using feather TDFs derived from captive peregrines Falco peregrinus resulted in estimates of diet composition that were also similar to provisioned prey, though blood TDFs from the same study performed poorly. The inclusion of informative priors, based on conventional analysis of pellet and prey remains, markedly reduced model performance.<br> 4. BSIMMs can provide accurate assessments of diet in wild animals. TDF estimates from the SIDER package performed well. The inclusion of informative priors from conventional methods in Bayesian mixing models can transfer biases into model outcomes, leading to erroneous results.</p> </blockquote>
Multiscale heart image data for: Multiscale cardiac imaging spanning the whole heart and its internal cellular architecture in a small animal model
<p>Cardiac pumping depends on the morphological structure of the heart, but also on its sub-cellular (ultrastructural) architecture, which enables cardiac contraction. In cases of congenital heart defects, localized ultrastructural disruptions that increase the risk of heart failure are only starting to be discovered. This is in part due to a lack of technologies that can image the three dimensional (3D) heart structure, assessing malformations; and its ultrastructure, assessing disruptions. We present here a multiscale, correlative imaging procedure that achieves high-resolution images of the whole heart, using 3D micro-computed tomography (micro-CT); and its ultrastructure, using 3D scanning electron microscopy (SEM). We achieved uniform fixation and staining of the whole heart, without losing ultrastructural preservation on the same sample, enabling correlative multiscale imaging. Our approach enables multiscale studies in models of congenital heart disease and beyond.</p>
Data from: Joint modelling of multi-scale animal movement data using hierarchical hidden Markov models
1. Hidden Markov models are prevalent in animal movement modelling, where they are widely used to infer behavioural modes and their drivers from various types of telemetry data. To allow for meaningful inference, observations need to be equally spaced in time, or otherwise regularly sampled, where the corresponding temporal resolution strongly affects what kind of behaviours can be inferred from the data. 2. Recent advances in biologging technology have led to a variety of novel telemetry sensors which often collect data from the same individual simultaneously at different time scales, e.g. step lengths obtained from GPS tags every hour, dive depths obtained from time-depth recorders once per dive, or accelerations obtained from accelerometers several times per second. However, to date, statistical machinery to address the corresponding complex multi-stream and multi-scale data is lacking. 3. We propose hierarchical hidden Markov models as a versatile statistical framework that naturally accounts for differing temporal resolutions across multiple variables. In these models, the observations are regarded as stemming from multiple, connected behavioural processes, each of which operates at the time scale at which the corresponding variables were observed. 4. By jointly modelling multiple data streams, collected at different temporal resolutions, corresponding models can be used to infer behavioural modes at multiple time scales, and in particular help to draw a much more comprehensive picture of an animal's movement patterns, e.g. with regard to long-term vs. short-term movement strategies. 5. The suggested approach is illustrated in two real-data applications, where we jointly model i) coarse-scale horizontal and fine-scale vertical Atlantic cod (Gadus morhua) movements throughout the English Channel, and ii) coarse-scale horizontal movements and corresponding fine-scale accelerations of a horn shark (Heterodontus francisci) tagged off the Californian coast.
ScienceDex guides
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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.