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479 results for “human interaction”
Molecular dynamics simulations of the interaction of the quadruple mutant human CYP2J2 (R111A + R117A + R382A + R446A) with arachidonic acid (POSES 4-6)
<p><strong>Description of files in this dataset:</strong></p> <p><strong>MD_quadmut_CYP2J2_AA_StateX_repeatY.zip</strong> : Series of zipped directories for molecular dynamics simulations of arachidonic acid in the active site of the quadruple (R111A + R117A+R382A+R446A) mutant CYP2J2. X is the docking pose number that constitutes the starting point of the simulation (the 6 lowest-energy poses from docking were selected as the starting points of the simulations - this dataset is State(pose) 1). Y is the repeat (each simulation was repeated 3 times, hence there are 3 repeats per pose). </p> <p>Each directory contains the following sub-directories:</p> <p>001.leap : Amber parameter and coordinate files; PDBs; ligands; leap commands</p> <p>002.min : Minimisation stage</p> <p>003.heat : Heating stage</p> <p>004.equil: Equilibration stage</p>
Predicting COVID-19 Incidence Through Spatiotemporal Human Interactions
<p>This repository contains data (features) necessary to run STXGB model. STXGB is a spatiotemporal autoregressive model that predicts county-level new cases of COVID-19 in the coterminous US in 1- to 4-week prediction horizons using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features.</p>
Spatiotemporal Prediction of COVID-19 Cases using Inter- and Intra-County Proxies of Human Interactions (dataset)
<p>This repository contains data (features) necessary to run STXGB model and accompanies the paper titled "Spatiotemporal Prediction of COVID-19 Cases using Inter- and Intra-County Proxies of Human Interactions".</p> <p> </p> <p>STXGB is a spatiotemporal autoregressive model that predicts county-level new cases of COVID-19 in the coterminous US in 1- to 4-week prediction horizons using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features.</p>
Supplements for "Understanding the interaction between a human transferrin receptor aptamer-short double stranded RNA conjugate and its cell membrane target by in silico methods"
<p>Supplements for "Understanding the interaction between a human transferrin receptor aptamer-short double stranded RNA conjugate and its cell membrane target by in silico methods". </p> <p>This supplement includes the following files:</p> <p> </p> <p>1. Structures of the most stable Protein-Aptamer complexes predicted from HADDOCK</p> <p>Haddock_Cluster1.pdb <br> Haddock_Cluster2.pdb <br> Haddock_Cluster3.pdb </p> <p>2. Structure of the most stable conformation aligned with Protein-transferring complex PDB</p> <p>cluster1_aligned.pdb <br> transferrin_aligned.pdb </p> <p>3. MM-GBSA decomposition analysis of the three replicas for Protein-Aptamer</p> <p>aptamer_new_rep01_Decomp.dat <br> aptamer_new_rep02_Decomp.dat <br> aptamer_new_rep03_Decomp.dat </p> <p>4. MM-GBSA decomposition analysis of the three replicas for Protein-Aptamer-Conjugate<br> conjugate_new_rep01_Decomp.dat <br> conjugate_new_rep02_Decomp.dat <br> conjugate_new_rep03_Decomp.dat <br> </p> <p><br> <br> </p>
IG. 6. — A, Trunk vertebra of Alsophis sp. 2 from Pointe du Helleux archaeological site (Square 2 – crab layer) located on Grande-Terre Island; B, trunk vertebra of Erythrolamprus juliae cf. copeae (Parker, 1936) from Sainte-Rose La Ramée archaeological site (US 2058) located on Basse-Terre Island. Abbreviations: cd., condyle; ct., cotyle; di., diapophysis; h. k., hemal keel; m. c., medial constriction; n. a., neural arch; n. s., neural spine; p. c., precondylar constriction; p. d., paracotylar depression; p. n., postero-medial notch of the zygantrum; pa., parapophysis; pz. f., prezygapophyseal facet; pz. p., prezygapophyseal process; s. d., subcentral depression; s. r., subcentral ridge; s. t., sub-cotylar tubercle; zs., zygosphene. Scale bars: 4 mm in Fossil dipsadid snakes from the Guadeloupe Islands (French West-Indies) and their interactions with past human populations
IG. 6. — A, Trunk vertebra of Alsophis sp. 2 from Pointe du Helleux archaeological site (Square 2 – crab layer) located on Grande-Terre Island; B, trunk vertebra of Erythrolamprus juliae cf. copeae (Parker, 1936) from Sainte-Rose La Ramée archaeological site (US 2058) located on Basse-Terre Island. Abbreviations: cd., condyle; ct., cotyle; di., diapophysis; h. k., hemal keel; m. c., medial constriction; n. a., neural arch; n. s., neural spine; p. c., precondylar constriction; p. d., paracotylar depression; p. n., postero-medial notch of the zygantrum; pa., parapophysis; pz. f., prezygapophyseal facet; pz. p., prezygapophyseal process; s. d., subcentral depression; s. r., subcentral ridge; s. t., sub-cotylar tubercle; zs., zygosphene. Scale bars: 4 mm
FIG. 4 in Fossil dipsadid snakes from the Guadeloupe Islands (French West-Indies) and their interactions with past human populations
FIG. 4. — Cranial bones of Alsophis antillensis (Schlegel, 1837) from La Désirade and Marie-Galante islands: A, right maxilla from Pointe Gros Rempart 6 (Dec. 7) located on La Désirade Island; B, right palatine from Blanchard Cave (Layer 8) located Marie-Galante Island; C, D, left pterygoid anterior (C) and posterior (D) fragments from Blanchard Cave (layers 8 and 10) located Marie-Galante Island; E, left compound bone from Blanchard Cave (Layer 8) located Marie-Galante Island; F, right dentary from Pointe Gros Rempart 6 (Dec. 3) located on La Désirade Island. Abbreviations: c. p., choanal process; d. n., dorsal notch; di., diastema; e. p. m., ectopterygoid process of the maxilla; e. p. p., ectopterygoid process of the pterygoid; f. m. n., foramen for the maxillary nerve; g. f., glenoid
FIG. 2 in Fossil dipsadid snakes from the Guadeloupe Islands (French West-Indies) and their interactions with past human populations
FIG. 2. — Morphological variability among four specimens of Alsophis Fitzinger, 1843. From left-to-right: smallest and largest available specimens of Alsophis rijgersmaei Cope, 1869 and Alsophis antillensis (Schlegel, 1837) varieties A and B (of Duméril et al. 1854). The two figured vertebrae for each specimen correspond to the most different morphologies observed among the trunk vertebrae (anterior vertebra on the left and median vertebra on the right).
FIG. 7 in Fossil dipsadid snakes from the Guadeloupe Islands (French West-Indies) and their interactions with past human populations
FIG. 7. — Results of statistical analyses of fossil and modern dipsadid snake vertebrae on the Guadeloupe Islands: A, two first axes of the PCA conducted on the maximum number of specimens (first analysis); B, Mahalanobis distance tree obtained from the results of the LDA (first analysis); C, two first axes of the PCA conducted on the maximum number of measurements (second analysis); D, Mahalanobis distance tree obtained from the results of the LDA (second analysis).
Decoding host-microbiome interactions through co-expression network analysis within the non-human primate intestine
<p>Supplementary Table Captions:</p> <p>Supplementary Table S9. Evaluation and parameter determination of host and microbiome RNA read classification using simulation datasets</p> <p>Supplementary Table S10. 40 pathways significantly upregulated in the cecum as compared to the transverse colon</p> <p>Supplementary Table S11. Host-microbiome gene co-expression network edges</p> <p>Supplementary Table S12. Host-host gene co-expression network edges</p> <p>Supplementary Table S13. Microbiome-microbiome gene co-expression network edges</p> <p>Supplementary Table S14. List of genes included in each gene module identified from the gene co-expression network</p> <p>Supplementary Table S15. Results of enrichment analysis for each gene module identified from the gene co-expression network</p> <p>Supplementary Table S16. The top 32 bacterial species in terms of expression abundance based on metatranscriptome profiles</p> <p>Supplementary Table S17. Number of microbiome RNA reads annotated by the KEGG database</p> <p>Supplementary Table S18. Results of enrichment analysis of gene modules for each parameter</p> <p>Supplementary Table S19. Evaluation of modules in each parameter of Newman algorithm</p> <p>Supplementary Table S20. Evaluation of modules in each parameter of Louvain algorithm</p> <p>Supplementary Table S21. Evaluation of modules in each parameter of Leiden algorithm</p> <p>Supplementary Table S22. Evaluation of modules in each parameter of WGCNA</p>
Evaluation Tools for Human-AI Interactions Involving Older Adults with Mild Cognitive Impairments
<h1>Abstract</h1> <p>As artificial intelligence (AI) systems have already proven useful in human lives generally, there is an opportunity for specialized human-AI interaction (HAI) systems to support and provide care for older adults with mild cognitive impairment (MCI). However, the integration of this technology in this population must be thoughtfully designed to accommodate specific needs and limitations. This includes careful measurement of both humans and systems. We developed an evolving dataset categorizing relevant measurement tools into five groups: cognitive ability, demographics & personality, activity level, state of mind, and perceptions of the AI system. Each instance of the tool being used in the literature cataloged in the dataset is qualified in terms of how likely we would recommend using it in the domain of HAI for older adults with MCI based on contextual factors and internal reliability measures. This dataset will serve as a valuable resource for future research, aiding in the identification of promising areas and trends in AI systems for older adults with MCI as well as providing essential tools for future studies.</p> <h1>Methodology</h1> <p>This dataset was not derived through a typical literature review or survey process, but rather followed a more flexible research method. To collect resources for the dataset, we searched numerous databases to identify studies and review types of publications in journals and conferences between the dates of 2000 to 2022. For the papers that contained extensive reviews of literature or cited original tools, we would further look into the citations of those papers, taking us beyond our limited date range. The tools used were categorized into five groups to broadly distinguish their usage in a study, measuring:</p> <ol> <li>Cognitive ability</li> <li>Demographics, personality, and experiences</li> <li>Activity level</li> <li>State of mind</li> <li>Perceptions of the AI system</li> </ol> <p>Subsequently, we conducted an examination of their Cronbach’s 𝛼 scores to assess internal reliability. We created tiers based on how likely we would be to recommend using each tool in the domain of human-AI (HAI) with older adults with MCI, as follows:</p> <ul> <li>Tier 1 included tools with Cronbach’s 𝛼 ≥ 0.7 when used with older adults with MCI in experimental settings interacting with AI</li> <li>Tier 2 included tools with Cronbach’s 𝛼 ≥ 0.7 when used with older adults, with or without MCI, in experimental settings with or without AI interaction</li> <li>Tier * included tools that satisfy the criteria for Tier 1, but, to the best of our knowledge, lack reported Cronbach’s 𝛼 scores</li> <li>Tier 3 included all remaining tools that do not meet the criteria for Tier 1, 2, or *</li> </ul> <p>It should be emphasized that a tool may be found in one or more tiers because multiple studies used the same tool yet resulted in varying reliability scores, contexts, etc.</p> <h1>Contribute</h1> <p>Readers are encouraged to reach out to Adam Norton (adam[underscore]norton[at]uml.edu) to recommend additional tools and entries to the dataset.</p> <h1>Publication</h1> <p>This dataset is published as a short contribution to the Human-Robot Interaction (HRI) 2024 conference. The corresponding paper citation is below:</p> <p>Daisy M. Kiyemba, Jasmin Marwad, Elizabeth J. Carter, and Adam Norton. <strong>Evaluation Tools for Human-AI Interactions Involving Older Adults with Mild Cognitive Impairments</strong>. In Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’24), March 11–14, 2024, Boulder, CO, USA. ACM, New York, NY, USA, 4 pages. <a href="https://doi.org/10.1145/3610977.3637474" target="_blank" rel="noopener">https://doi.org/10.1145/3610977.3637474</a></p> <h1>Acknowledgements</h1> <p>This work was supported by the National Science Foundation (IIS-2112633) as part of the AI-CARING Institute: <a href="https://ai-caring.org/" target="_blank" rel="noopener">https://ai-caring.org/</a></p>
The AFFECT-HRI data set: physiological data for affective computing in human-robot interaction with anthropomorphic service robots
<p>We provide a comprehensive data set <strong>AFFECT-HRI </strong>containing physiological data labeled with human affect (i.e., mood and emotion) gathered during an empirical study consisting of a complex human-robot interaction (HRI). A realistic retail scenario served as an experimental environment. In prior research, we showed the necessity to combine the expertise of the research fields of psychology, computer science, and law in the design of a responsible human-centered HRI. Therefore, we implemented five conditions (neutral, transparency, liability, moral, and immoral) covering the perspectives from these three research fields and used two different anthropomorphic service robots. Our study followed a multi-method approach, resulting in a data set containing and combining objective physiological sensor data with subjective human-affect assessments. Additionally, the data set includes insights from 146 participants regarding affect, demographics, and socio-technical questionnaire ratings, as well as robot gestures and robot speech. Our study can be split into three scenes: a consultation regarding products, a request for sensitive personal information while opening a customer account, and a successful or failing handover when buying a mold remover. Thus, this data set offers for the first time the possibility to prove established or develop new emotion recognition methods and technological capabilities for HRI. Further, our data set provides the possibility to combine affective computing with research about robot behavior (gestures, speech, and handover), liability (questionnaire), transparency (questionnaire), and psychological aspects, allowing an encompassing, human-centered view of HRI.</p> <p>The detailed data descriptor has been published in Nature Scientific Data. For more details on the data set, please check the paper below.</p> <p><strong>Please cite the following paper if the dataset is used in a publication:</strong><br>Heinisch, J.S., Kirchhoff, J., Busch, P. <em>et al.</em> Physiological data for affective computing in HRI with anthropomorphic service robots: the AFFECT-HRI data set. <em>Sci Data</em> <strong>11</strong>, 333 (2024). https://doi.org/10.1038/s41597-024-03128-z</p> <p><strong>Acknowledgements</strong><br>This research was conducted as part of RoboTrust, a project of the Centre Responsible Digitality, supported by the Hessian Minister for Digital Strategy and Innovation. The authors would like to thank all participants for their participation in the study. We particularly want to thank Ruth Stock-Homburg for her support and for making Elenoide available. Further, we want to thank Mona Kegel, Vignesh Prasad, and all the research assistants who supported the study. We also thank the leap in time lab for serving as study location. A special thanks goes to Amer Altizini, who supported us by helping to prepare the data for publication. We want to thank Niklas Jungermann for his valuable comments on the statistical evaluation.</p>
Inferred protein interactions between coronavirus and human proteins
<p>This repository contains the protein-protein interactions inferred by mimicINT (<a href="https://github.com/TAGC-NetworkBiology/mimicINT" target="_blank" rel="noopener">https://github.com/TAGC-NetworkBiology/mimicINT</a>) between the proteins of seven human coronaviruses (HCoV-229E, HCoV-HKU1, HCoV-NL63, HCoV-OC43, MERS-CoV, SARS-CoV and SARS-CoV-2) and substantial fraction of the human proteome. This dataset was generated in the context of the RiPCoN project (H2020-SC1-PHE-CORONAVIRUS-2020, <a href="https://cordis.europa.eu/project/id/101003633" target="_blank" rel="noopener">https://cordis.europa.eu/project/id/101003633</a>).</p>
Interaction of human keratinocytes and nerve fiber terminals at the neuro-cutaneous unit
<p>These data set belongs to the following publication:</p> <p>Interaction of human keratinocytes and nerve fiber terminals at the neuro-cutaneous unit<br> Christoph Erbacher, Sebastian Britz, Philine Dinkel, Thomas Klein, Markus Sauer, Christian Stigloher, Nurcan Üçeyler</p> <p>Link to corresponding pre-print will be embedded upon upload.</p> <p>Please read the README.txt file before using these data sets</p> <p> </p>
FIG. 10 in The biometry of prehistoric Alpine sheep: exploring four millennia of human-sheep interaction by means of osteometry
FIG. 10. — LSI of sheep bone width measurements in statistically meaningful Early/Middle Bronze Age find complexes. For Barche di Solferino, see Figure 7.
FIG. 5 in The biometry of prehistoric Alpine sheep: exploring four millennia of human-sheep interaction by means of osteometry
FIG. 5. — LSI of sheep bone width measurements in broad chronological subdivision. For the results of the significance test, see Table 3.
FIG. 4 in The biometry of prehistoric Alpine sheep: exploring four millennia of human-sheep interaction by means of osteometry
FIG. 4. — The LSI median values of width measurements compared with the shoulder height of sheep in individual find complexes. Furthermore, sample size in shoulder height values is considered. Abbreviations: BA, Bronze Age; EBA, Early Bronze Age; EIA, Early Iron Age; IA, Iron Age; LBA, Late Bronze Age; LIA, Late Iron Age; MBA, Middle Bronze Age; NCA, Neolithic/Copper Age. Site numbering, see Tables 1, 2.
FIG. 9 in The biometry of prehistoric Alpine sheep: exploring four millennia of human-sheep interaction by means of osteometry
FIG. 9. — LSI of sheep bone width measurements in Neolithic/Copper Age archaeofaunas. For the results of the significance test, see Table 3.
FIG. 6 in The biometry of prehistoric Alpine sheep: exploring four millennia of human-sheep interaction by means of osteometry
FIG. 6. — LSI of sheep bone width measurements in several micro-regions (for the results of the significance test see Table 3). The sites are arranged in chronological orders and numbering refers to Table 1. Abbreviations: a, Northern Pre-Alps and Limestone Alps; b, Inn Valley; BA, Bronze Age; c,Val Venosta; d, Isarco Valley; e, Adige Valley and surroundings; EBA, Early Bronze Age; EIA, Early Iron Age; ELT, Early La Tène Period; f, Southern drop of the Alps with Lessinian Mountains and Northern Padanian Plain;IA, Iron Age; LBA, Late Bronze Age; LIA, Late Iron Age; LLT, Late La Tène Period; MBA, Middle Bronze Age; MLT, Middle La Tène Period; NCA, Neolithic/Copper Age.
FIG. 1 in The biometry of prehistoric Alpine sheep: exploring four millennia of human-sheep interaction by means of osteometry
FIG. 1. — The percentage of the main livestock species animals in Prehistoric Alpine find complexes., Neolithic/Copper Age;, Bronze Age;, Iron Age. Site numbering, see Table 1.
FIG. 11 in The biometry of prehistoric Alpine sheep: exploring four millennia of human-sheep interaction by means of osteometry
FIG. 11. — The Bronze Age sheep populations of the Northern Alpine Foreland and the Inn Valley in LSI comparison. For the results of the significance test, see Table 3.
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.