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198 results for “poses”
Does the Application of Cold Decreases the Pain Associated in the Pose of a Patch of Capsaicine at High Concentration on the Carrier Patients of Localized Neuropathic Pains ? - DIDOCAP
ClinicalTrials.gov study NCT02869867. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Early-life sleep disruption impairs subtle social behaviours in prairie voles: a pose-estimation study
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Do pseudogenes pose a problem for metabarcoding marine animal communities?
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Dataset for: Estimating pregnancy rate from blubber progesterone levels of a blindly biopsied beluga population poses methodological, analytical and statistical challenges
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Thin lines between native and invasive aquatic plants are common, posing challenges for response efforts
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mRI: multi-modal 3d human pose estimation dataset using mmwave, rgb-d, and inertial sensors
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OpenApePose: a database of annotated ape photographs for pose estimation
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Data from: Deforestation risks posed by oil palm expansion in the Peruvian Amazon
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The docked poses from the docking screens against the AlphaFold2 models of the σ2 and 5HT2A receptors
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Agricultural intensification heightens food safety risks posed by wild birds
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Example body pose estimation and tracking
<p>Example body pose tracking of representative C. elegans wild type, egl-4 (n477 lf) and egl-4 (ad450 gf) animals for both the early and late time points in our behavioral assay.</p>
Dataset for Neural Network 3D Body Pose Tracking and Prediction for Motion-to-Photon Latency Compensation in Distributed Virtual Reality
<p>Distributed Virtual Reality (DVR) systems enable geographically dispersed users to interact in a shared virtual environment. The realism of the interaction is crucial to increase the feeling of co-presence. Latency, produced either by hard- or software components of DVR applications, impedes reaching high realism levels of the DVR experience. For example, the time delay between the user's motion and the corresponding display rendering of the DVR system might lead to adverse effects such as a reduced sense of presence or motion sickness. One way of minimizing the latency is to predict user's motion and thus compensate for the inherent latency in the system. In order to address this problem, we propose a neural network 3D pose tracking and prediction system with latency guarantees for end-to-end avatar reconstruction. We evaluate and compare our system against multiple traditional methods and provide a thorough analysis on real-world human motion data. Datasets used in the paper experiments. Datasets used in paper experiments.</p>
Do novel insecticides pose a threat to beneficial insects?
Systemic insecticides, such as neonicotinoids, are a major contributor towards beneficial insect declines. This has led to bans and restrictions on neonicotinoid use globally, most noticeably in the Europe Union, where four commonly-used neonicotinoids are banned from outside agricultural use. While this might seem like a victory for conservation, restrictions on neonicotinoid use will only benefit insect populations if newly emerging insecticides do not have similar negative impacts on beneficial insects. Flupyradifurone and sulfoxaflor are two novel insecticides that have been registered for use globally, including within the European Union. These novel insecticides differ in their chemical class, but share the same mode of action as neonicotinoids, raising the question as to whether they have similar sub-lethal impacts on beneficial insects. Here, we conducted a systematic literature review of the potential sub-lethal impacts of these novel insecticides on beneficial insects, quantifying these effects with a meta-analysis. We demonstrate that both flupyradifurone and sulfoxaflor have significant sub-lethal impacts on beneficial insects at field-realistic levels of exposure. These results confirm that bans on neonicotinoid use will only protect beneficial insects if paired with significant changes to the agrochemical regulatory process. A failure to modify the regulatory process will result in a continued decline of beneficial insects, and the ecosystem services, on which global food production relies.
SDT Dataset | Sdt: A Synthetic Multi-Modal Dataset For Person Detection And Pose Classification
<p>The Synthetic Depth & Thermal (SDT) dataset consists of 40k synthetic and 8k real depth and thermal stereo images, depicting human behavior in indoor environments. Included samples show uniquely posed lying, sitting, and standing persons within four different room types (living room, bedroom, bathroom, and kitchen), recorded from an elevated position. Furthermore, a fourth control class with empty rooms is provided as well. Both parts of SDT are balanced sets of these four classes and room types. The synthetic part of the dataset is intended to be used as training (and validation) data for uni-/multi-modal pose classification or person detection models, while the real part can be used to assess the generalization performance. To facilitate supervised training, pose labels and person bounding boxes are provided for all images. The real images in the dataset were captured by a multi-modal stereo camera system, consisting of an Orbbec Astra depth camera and a FLIR Lepton 3.5 thermal camera, while synthetic images, which share the image characteristics of these cameras, were acquired through 3D rendering of virtual scenes within Blender and subsequent introduction of camera-specific noise.</p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].</p> <p>[1] C. Pramerdorfer, J. Strohmayer and M. Kampel, "Sdt: A Synthetic Multi-Modal Dataset For Person Detection And Pose Classification," <em>2020 IEEE International Conference on Image Processing (ICIP)</em>, Abu Dhabi, United Arab Emirates, 2020, pp. 1611-1615, doi: 10.1109/ICIP40778.2020.9191284.</p> <p>BibTeX citation:</p> <pre>@INPROCEEDINGS{9191284, author={Pramerdorfer, C. and Strohmayer, J. and Kampel, M.}, booktitle={2020 IEEE International Conference on Image Processing (ICIP)}, title={Sdt: A Synthetic Multi-Modal Dataset For Person Detection And Pose Classification}, year={2020}, volume={}, number={}, pages={1611-1615}, doi={10.1109/ICIP40778.2020.9191284}}</pre>
Data from: Planting exotic relatives has increased the threat posed by Dothistroma septosporum to the Caledonian pine populations of Scotland
To manage emerging forest diseases and prevent their occurrence in the future, it is essential to determine the origin(s) of the pathogens involved and identify the management practices that have ultimately caused disease problems. One such practice is the widespread planting of exotic tree species within the range of related native taxa. This can lead to emerging forest disease both by facilitating introduction of exotic pathogens, and by providing susceptible hosts on which epidemics of native pathogens can develop. We used microsatellite markers to determine the origins of the pathogen Dothistroma septosporum responsible for the current outbreak of Dothistroma needle blight (DNB) on native Caledonian Scots pine (Pinus sylvestris) populations in Scotland, and evaluated the role played by widespread planting of two exotic pine species in the development of the disease outbreak. We distinguished three races of D. septosporum in Scotland, one of low genetic diversity associated with introduced lodgepole pine (Pinus contorta), one of high diversity probably derived from the DNB epidemic on introduced Corsican pine (Pinus nigra subsp. laricio) in England, and a third of intermediate diversity apparently endemic on Caledonian Scots pine. These races differed for both growth rate and exudate production in culture. Planting of exotic pine stands in the UK appears to have facilitated the introduction of two exotic races of D. septosporum into Scotland which now pose a threat to native Caledonian pines both directly and through potential hybridisation and introgression with the endemic race. Our results indicate that both removal of exotic species from the vicinity of Caledonian pine populations, and restriction of movement of planting material are required to minimise the impact of the current DNB outbreak. They also demonstrate that planting exotic species that are related to native species reduces rather than enhances the resilience of forests to pathogens.
Data from: Migrating songbirds on stopover prepare for, and recover from, oxidative challenges posed by long-distance flight
Managing oxidative stress is an important physiological function for all aerobic organisms, particularly during periods of prolonged high metabolic activity, such as long-distance migration across ecological barriers. However, no previous study has investigated the oxidative status of birds at different stages of migration and whether that oxidative status depends on the condition of the birds. In this study, we compared (1) energy stores and circulating oxidative status measures in (a) two species of Neotropical migrants with differing migration strategies that were sampled at an autumn stopover site before an ecological barrier; and (b) a species of trans-Saharan migrant sampled at a spring stopover site after crossing an ecological barrier; and (2) circulating oxidative measures and indicators of fat metabolism in a trans-Saharan migrant after stopovers of varying duration (0–8 nights), based on recapture records. We found fat stores to be positively correlated with circulating antioxidant capacity in Blackpoll Warblers and Red-eyed Vireos preparing for fall migration on Block Island, USA, but uncorrelated in Garden Warblers on the island of Ponza, Italy, after a spring crossing of the Sahara Desert and Mediterranean Sea. In all circumstances, fat stores were positively correlated with circulating lipid oxidation levels. Among Garden Warblers on the island of Ponza, fat anabolism increased with stopover duration while oxidative damage levels decreased. Our study provides evidence that birds build antioxidant capacity as they build fat stores at stopover sites before long flights, but does not support the idea that antioxidant stores remain elevated in birds with high fuel levels after an ecological barrier. Our results further suggest that lipid oxidation may be an inescapable hazard of using fats as the primary fuel for flight. Yet, we also show that birds on stopover are capable of recovering from the oxidative damage they have accrued during migration, as lipid oxidation levels decrease with time on stopover. Thus, the physiological strategy of migrating songbirds may be to build prophylactic antioxidant capacity in concert with fuel stores at stopover sites before a long-distance flight, and then repair oxidative damage while refueling at stopover sites after long-distance flight.
Data from: Planting of neonicotinoid-treated maize poses risks for honey bees and other non-target organisms over a wide area without consistent crop yield benefit
Neonicotinoid insecticides are routinely used as seed treatments on most grain and oilseed crops in the USA, yet the extent and likelihood of spread of insecticide residues during planting has not previously been quantified. Honey bees, Apis mellifera, are highly mobile and highly sensitive to neonicotinoid residues, presenting an opportunity to estimate non-target exposures to neonicotinoids in mobile insects. We measured neonicotinoid dust drift during maize sowing and used sites of maize fields, apiary locations and honey bee foraging radii to estimate likelihood of forager exposure. We performed a concurrent multi-year field assessment of the pest management benefits of neonicotinoid-treated maize. Our results indicate that over 94% of honey bee foragers throughout the state of Indiana are at risk of exposure to varying levels of neonicotinoid insecticides, including lethal levels, during sowing of maize. We documented no benefit of the insecticidal seed treatments for crop yield during the study. Synthesis and applications. We demonstrate movement of neonicotinoid residues well beyond planted fields occurs during maize sowing in Indiana. Based on locations of maize fields and apiaries in the state, the likelihood of neonicotinoid exposure for foraging honey bees is high. Other non-target organisms are also likely to encounter neonicotinoid residues; we conservatively estimate that deposition of neonicotinoid residues on non-target lands and waterways will occur on over 42% of the state of Indiana during the period of maize sowing. However, we also demonstrate that the risk to pollinators and other non-target organisms may be rapidly and dramatically reduced without yield penalties, by aligning use rates of neonicotinoid insecticides with pest incidence.
SpaceAnimal: Pose estimation and tracking dataset for multi-animal behavior analysis on the China Space Station
<p>Pose estimation and tracking dataset for multi-animal behavior analysis on the China Space Station. Scientific Data, 2025</p>
GAABind: A Geometry-Aware Attention-Based Network for Accurate Protein-Ligand Binding Pose and Binding Affinity Prediction
<p>The preprocessed dataset for paper "GAABind: A Geometry-Aware Attention-Based Network for Accurate Protein-Ligand Binding Pose and Binding Affinity Prediction" with associated code at https://github.com/Mercuryhs/GAABind.</p><p>The dataset files are saved as .pkl file for the convenience of use.</p><p><strong>Paper Abstract</strong>:</p><p>Protein-ligand interactions are increasingly profiled at high-throughput, playing a vital role in lead compound discovery and drug optimization. Accurate prediction of binding pose and binding affinity constitutes a pivotal challenge in advancing our computational understanding of protein-ligand interactions. However, inherent limitations still exist, including high computational cost for conformational search sampling in traditional molecular docking tools, and the unsatisfactory molecular representation learning and intermolecular interaction modeling in deep learning-based methods. Here we propose a geometry-aware attention-based deep learning model, GAABind, which effectively predicts the pocket- ligand binding pose and binding affinity within a multi-task learning framework. Specifically, GAABind comprehensively captures the geometric and topological properties of both binding pockets and ligands, and employs expressive molecular representation learning to model intramolecular interactions. Moreover, GAABind proficiently learns the intermolecular many-body interactions and simulates the dynamic conformational adaptations of the ligand during its interaction with the protein through meticulously designed networks. We trained GAABind on the PDBbindv2020 and evaluated it on the CASF2016 dataset, the results indicate that GAABind achieves state-of-the-art performance in binding pose prediction and shows comparable binding affinity prediction performance. Notably, GAABind achieves a success rate of 82.8% in binding pose prediction, and the Pearson correlation between predicted and experimental binding affinities reaches up to 0.803. Additionally, we assessed GAABind's performance on the SARS-CoV-2 main protease cross-docking dataset. In this evaluation, GAABind demonstrates a notable success rate of 76.5% in binding pose prediction and achieves the highest Pearson correlation coefficient in binding affinity prediction compared with all baseline methods.</p>
Data and structures for "How accurately can we predict binding poses with AlphaFold models?
<p>Contains structures generated by AlphaFold, models from GPCRdb, and structures of proteins from PDB. </p> <p>Additionally computed rmsds for pockets, backbone, and poses, and scripts to create figures.</p> <p> </p> <p> </p>
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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.