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1,102 results for “human use”
Data from: Blockade of dengue virus transmission from viremic blood to Aedes aegypti mosquitoes using human monoclonal antibodies
Background <p class="CxSpFirst">Dengue is the most prevalent arboviral disease of humans. Virus neutralizing antibodies are likely to be critical for clinical immunity after vaccination or natural infection. A number of human monoclonal antibodies (mAbs) have previously been characterized as able to neutralize the infectivity of dengue virus (DENV) for mammalian cells in cell-culture systems.</p> <p class="CxSpLast"> </p> Methodology/Principle findings <p class="CxSpFirst">We tested the capacity of 12 human mAbs, each of which had previously been shown to neutralize DENV in cell-culture systems, to abrogate the infectiousness of dengue patient viremic blood for mosquitoes. Seven of the twelve mAbs (1F4, 14c10, 2D22, 1L12, 5J7, 747(4)B7, 753(3)C10), almost all of which target quaternary epitopes, inhibited DENV infection of <i>Ae. aegypti</i>. The mAbs 14c10, 747(4)B7 and 753(3)C10 could all inhibit transmission of DENV in low microgram per mL concentrations. An Fc-disabled variant of 14c10 was as potent as its parent mAb.</p> <p class="CxSpLast"> </p> Conclusions/Significance <p class="CxSpFirst">The results demonstrate that mAbs can neutralize infectious DENV derived from infected human cells, in the matrix of human blood. Coupled with previous evidence of their ability to prevent DENV infection of mammalian cells, such mAbs could be considered attractive antibody classes to elicit with dengue vaccines, or alternatively, for consideration as therapeutic candidates.</p>
Chromosome images used for "Centromere detection of human metaphase chromosome images using a candidate based method"
<p>Chromosome image data used in the paper "Centromere detection of human metaphase chromosome images using a candidate based method". Images are in tiff format</p>
Chromosome images used for "Centromere detection of human metaphase chromosome images using a candidate based method"
<p>Chromosome image data used in the paper "Centromere detection of human metaphase chromosome images using a candidate based method". Images are in tiff format.</p>
Human pan-body age- and sex-specific molecular phenomena inferred from public transcriptome data using machine learning - Data
<p>Expression data used in manuscript <i>Human pan-body age- and sex-specific molecular phenomena inferred from public transcriptome data using machine learning</i></p>
Pacing of primary murine cardiomyocytes expressing human TRPV1 using infra-red laser
<h2>Abstract</h2> <p>The expression of human TRPV1 in cardiomyocytes allowed us to induce action potentials (APs) by pulse irradiation with infra-red (IR) diod laser. Mice cardiomyocytes were transformed by AAV-based vectors bearing construction of TRPV1 with mRuby (for expression detection). We selected cells having their own intrisic AP generation and then set them up for measuring membrane potential in a current-clamp mode. Cells were irradiated using 2 or 7 Hz laser pulses. Laser trigger pulses were recorded along with cell potential. In the dataset <em><strong>05-12-22.zip</strong></em>, there are some records where electrical stimulation were used alone or together with IR pulses. Experimental conditions are summarized in the file <em><strong>experiment-descriptions.tsv</strong></em>.</p> <h2>Primary cell production and transformation</h2> <p>Experiments were carried out using C57Bl/6J mice (The Jackson Laboratory, #000664, RRID: IMSR_JAX:000664). The mixed mouse primary neonatal cardiomyocyte cell culture was obtained using a neonatal heart dissociation kit (Miltenyi Biotec, 130-098-373) according to the manufacturer’s instructions. The cells were cultured in DMEM/F12, 1:1 mixture (BioloT, 1.3.7.2.) supplemented with 10% FBS, penicillin 100 U/ml /streptomycin 100 mg/ml, and L-glutamine 0.365 g/l. The culture was seeded on 10mm coverslips coated with 10 mg/ml gelatin diluted in PBS and maintained at 37℃ in 5% CO2. For transient expression of the hTRPV1 channel, a reporter protein, and a fluorescent Ca2+ sensor GCaMP6s, we used AAV-based vectors with the encoded genes above. We used AAV-DJ serotype at a MOI of 12,000 VG/cells for cTnT_hTRPV1(sh)_P2A_mRuby based viruses, and a MOI of 2,500 VG/cells for cTnT_GCaMP6s ones. The cells were infected on the next day after plating, and the transgene expression peak was observed on the third day after the infection.</p> <h2>Distant heating system</h2> <p>The system was equipped with a fiber coupled laser diode (LD) 4PN-117 (SemiNex) as a powerful heating laser, providing radiation at a wavelength of 1375 nm with an average power of up to 4.3 W through a multimode fiber with a core diameter of 105 μm and 0.22NA. The LD was mounted onto a TEC-controlled plate “264 TEC HP LaserMount” (A.I.), which was operated by TEC driver TECSource 5305 (A.I.); current stabilization and control for LD were performed with LD driver LaserSource 4320 (A.I.). The laser was controlled via the TTL output from the HEKA EPC-10 amplifier. Different laser intensities, and pulse widths were used. Laser power, <em><strong>P</strong></em>, can be computed from trigger voltage, <em><strong>U</strong></em>, by an equation: <em><strong>P [W] = -0.28 + 1.42 * U [V]</strong></em></p> <h2>Electrophysiology of single cardiac cells </h2> <p>Patch electrodes were pulled from hard borosilicate capillary glass (Sutter Instruments flaming/brown micropipette puller) and filled with an intracellular solution consisting of (in mM) K-gluconate, 100; KCl, 40; HEPES, 10; NaCl, 8; MgATP, 4; MgGTP, 0.3; phosphocreatine, 10 (pH 7.3 with KOH). Cells were identified visually using IR-video microscopy using a Hamamatsu ORCA-Flash4.0 V3 Digital sCMOS camera (Hamamatsu Photonics) expression of wild type or mutant TRPV1 was confirmed by the presence of red flourescent protein. Coverslips were placed in a recording chamber continuously perfused with heated Tyrode's solution. Whole-cell recordings were taken at 32°C in current-clamp mode using a HEKA EPC-10 amplifier (List Elektronik) with a sampling rate of 100 μs. Steady state current was injected to achieve a membrane potential of approximately −60 to −90 mV. For experiments using the IR laser the optic fiber was placed near the cell, and light from a green laser diode was shone onto the cell to check correct positioning. </p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 2
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP. </p> <p>This Zenodo repository provides data on following land-use classes: grazing land characterized by open wooded lands (GL-owl)</p>
Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex
<p><span>Recent advances in multi-electrode array technology have made it possible to monitor large neuronal ensembles at cellular resolution in animal models. In humans, however, c</span>urrent approaches restrict recordings to few neurons per penetrating electrode or combine the signals of thousands of neurons in local field potential (LFP) recordings. Here, we describe a new probe variant and set of techniques which enable simultaneous recording from over 200 well-isolated cortical single units in human participants during intraoperative neurosurgical procedures using silicon Neuropixels probes. We characterized a diversity of extracellular waveforms with eight separable single unit classes, with differing firing rates, locations along the length of the electrode array, waveform spatial spread, and modulation by LFP events such as inter-ictal discharges and burst suppression. While some challenges remain in creating a turn-key recording system, high-density silicon arrays provide a path for studying human-specific cognitive processes and their dysfunction at unprecedented spatiotemporal resolution. </p>
Land use fractions and human impact index in 101 Canadian lake watersheds
<p>Land use fractions (urban, mines, agriculture, pasture, forestry, managed grassland, water and natural landscape) and associated human impact index in 101 lake watersheds sampled as part the NSERC Canadian Lake Pulse Network project. Land use and human impact index were calculated as described in Huot et al. (2019).</p> <p>Lakes IDs with respective locations (longitude and latitude coordinates) and Continental Basin allocations can be found here: <a href="https://doi.org/10.5281/zenodo.4701262">https://doi.org/10.5281/zenodo.4701262</a></p> <p>Reference</p> <p>Huot, Y., C. A. Brown, G. Potvin, and others. 2019. The NSERC Canadian Lake Pulse Network: A national assessment of lake health providing science for water management in a changing climate. Sci. Total Environ. <strong>695</strong>: 133668. doi:10.1016/j.scitotenv.2019.133668</p>
Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback [Dataset]
<p>Dataset used for the paper submitted to RO-MAN 2022</p> <p>Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback<br> Giorgio Nicola, Enrico Villagrossi, Nicola Pedrocchi</p>
Custom language model checkpoints used in "Testing the limits of natural language models for predicting human language judgments"
<p>Checkpoint files for an RNN, LSTM, BILSTM and n-gram models used the paper "Testing the limits of natural language models for predicting human language judgments"</p>
Evaluating 3D Human Motion Capture using Apple ARKit against the Vicon System: A Dataset
<p><strong>A journal paper which was published in Applied Sciences gives detailed information about the dataset.</strong></p> <p>Reimer, L.M.; Kapsecker, M.; Fukushima, T.; Jonas, S.M. Evaluating 3D Human Motion Capture on Mobile Devices. Appl. Sci. <em>(2022)</em></p> <p><a href="https://www.mdpi.com/2076-3417/12/10/4806">https://www.mdpi.com/2076-3417/12/10/4806</a></p> <p>Please cite the corresponding paper when using the dataset.</p> <p> </p> <p>A dataset containing anonymized exercise data for eight exercises from ten subject. The exercise data was recorded with two iPads 11" (2021 version, Apple Inc., Cupertino, CA, USA) and a Vicon system. The two iPads were positioned frontal and in a 30° angle to the left side of the subject. The Vicon system used 14 cameras and captured the motion using the Full-body Plug-in-gait model.</p> <p>The dataset contains 220 files, 22 per subject. The structure of the dataset contains 10 folders, one per subject. Each folder contains two subfolders: ARKit and Vicon. Each ARKit folder holds two CSV files. Each Vicon folder holds 16 files, two per exercise: a .csv file with the motion data and a .xcp file containing meta data about the recording, including the camera setup and start/stop timestamps.</p> <p>Du to export problems, the ARKit files for the Side View do not always contain all joint data. The upper body joints are only available for three out of the ten subjects for the Side View.</p>
Screening routine for integrative dynamic structural biology using SAXS and intramolecular FRET and DEER-EPR on hGBP1 (human guanalyte binding protein 1)
<p>Initial and selected ensemble for major and minor species of the human guanalyte binding protein 1 with scripts for the reading routine to combine and analyse jointly SAXS, EPR and FRET data.</p>
Data of: Imputation-free reconstructions of three-dimensional chromosome architectures in human diploid single-cells using allele-specified contacts
<p>These files are results obtained in<br><span><span><span><span>Imputation-free reconstructions of three-dimensional chromosome architectures in human diploid single-cells using allele-specified contacts</span></span></span></span><br>by Yoshito Hirata, Arisa H. Oda, Chie Motono, Masanori Shiro & Kunihiro Ohta.</p> <p>There are 33 files for the corresponding each reconstruction of three-dimensional chromosomone structures<br>for each cell.<br>There are 3D structures for 15 GM cells and 18 PBMC cells, which are obtained from the single cell Hi-C data of Tan et al. Science (2018).</p> <p>For each file, there are 6 columns:<br>The first column corresponds to the allele (0: maternal, 1: paternal)<br>The second column corresponds to the chromosome (1-22: chromosome's number, 23: X, 24: Y)<br>The third column corrsponds to the base point.<br>The fourth column, the fifth column and the sixth column correspond to x-, y-, and z-axes of our reconstruction.</p>
Prediction and Visualization of Human Transmembrane Proteins using AlphaFold and Protein Language Models
<p><strong>Description:</strong> <strong>TMvis</strong> ("TMvis496.tar.gz") is a dataset containing 496 3D-structures of predicted human transmembrane proteins (TMP) and their predicted membrane embedding. The method TMbed [1], based on the protein language model ProtT5 [2] predicted 4.967 TMP for the human proteome (20,375 proteins, UniProt [3] version April 2022; excluding TITIN_HUMAN due to length). For these proteins, we obtained AlphaFold [4] structures from AlphaFoldDB [5] with an average per-residue confidence score (pLDDT) of more than 90%. This resulted in the 496 proteins of TMvis, as can be found in "TMvis496.fasta". The membrane embedding was predicted using the methods ANVIL [6], PPM3 [7], and per-residue TMbed predictions. As the three methods are based on different approaches, we decided to publish results for all. The figure “TMvis_project_overview.png” provides a graphical overview for each step described above.</p> <p><strong>TMvis Folder Structure:</strong> TMvis is separated into “alpha” containing predicted alpha-helical TMPs, and “beta” containing predicted beta-barrel TMPs. Within these folders, each protein is assigned one folder, identifiable by the respective unique UniProt ID. Each protein folder consists of:<br> - “UniprotID.fasta” with UniProt ID, sequence, TMbed per-residue prediction<br> - “AF-UniprotID-F1-model_v2.pdb” with the AlphaFold structure<br> - “AF-UniprotID-F1-model_v2.cif” with the AlphaFold structure<br> - “AF-UniprotID-F1-model_v2_ANVIL.pdb” with predicted ANVIL membrane embedding<br> - “AF-UniprotID-F1-model_v2_ppm.pdb” predicted PPM3 membrane embedding</p> <p>TMvis <br> | <br> ├── alpha <br> │ │ <br> │ ├── A0A087X1C5 <br> │ │ ├── A0A087X1C5.fasta <br> │ │ ├── AF-A0A087X1C5-F1-model_v2.pdb <br> │ │ ├── AF-A0A087X1C5-F1-model_v2.cif <br> │ │ ├── AF-A0A087X1C5-F1-model_v2_ANVIL.pdb <br> │ │ └── AF-A0A087X1C5-F1-model_v2_ppm.PDB <br> │ └── ... <br> └── beta <br> └── P45880</p> <p><strong>TMvis visualization:</strong> The 3D-visualization of every protein in the dataset TMvis can be easily accessed using the Jupyter Notebook “TMvis.ipynb”. It contains detailed descriptions the different membrane prediction tools ANVIL, PPM3, and TMbed as well as the respective code. Additionally, it allows to visualize the per-residue confidence scores (pLDDT) of AlphaFold.</p> <p>——————————————————————————————————————————————————————————————————————————</p> <p><strong>References:</strong></p> <p>[1] TMbed - TMbed Bernhofer, Michael, and Burkhard Rost. 2022. “TMbed – Transmembrane Proteins Predicted through Language Model Embeddings.” bioRxiv.</p> <p>[2] ProtT5 - A. Elnaggar et al., "ProtTrans: Towards Cracking the Language of Lifes Code Through Self-Supervised Deep Learning and High Performance Computing," in IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2021.3095381.</p> <p>[3] UniProt - UniProt Consortium (2021). UniProt: the universal protein knowledgebase in 2021. Nucleic acids research, 49(D1), D480–D489.</p> <p>[4] AlphaFold - AlphaFold Jumper, John, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, et al. 2021. “Highly Accurate Protein Structure Prediction with AlphaFold.” Nature 596 (7873): 583–89.</p> <p>[5] Alphafold DB - Varadi, Mihaly, Stephen Anyango, Mandar Deshpande, Sreenath Nair, Cindy Natassia, Galabina Yordanova, David Yuan, et al. 2022. “AlphaFold Protein Structure Database: Massively Expanding the Structural Coverage of Protein-Sequence Space with High-Accuracy Models.” Nucleic Acids Research 50 (D1): D439–44.</p> <p>[6] ANVIL - ANVIL Postic, Guillaume, Yassine Ghouzam, Vincent Guiraud, and Jean-Christophe Gelly. 2016. “Membrane Positioning for High- and Low-Resolution Protein Structures through a Binary Classification Approach.” Protein Engineering, Design & Selection: PEDS 29 (3): 87–91.</p> <p>[7] PPM3 - PPM3 Lomize, Mikhail A., Irina D. Pogozheva, Hyeon Joo, Henry I. Mosberg, and Andrei L. Lomize. 2012. “OPM Database and PPM Web Server: Resources for Positioning of Proteins in Membranes.” Nucleic Acids Research 40 (Database issue): D370–76.</p> <p>——————————————————————————————————————————————————————————————————————————</p> <p><strong>License:</strong></p> <p>This work is licensed under a Creative Commons Attribution 4.0 International License (CC-BY 4.0).</p> <p> </p>
Strains used in the paper "Bacteriophage cultivation for commensal human gut bacteria"
<p>Sequences of 16S rRNA genes of 411 strains for taxonomic detection;</p> <p>Genomic sequence of of 42 strains for taxonomic detection;</p> <p>Genomic sequence of Bacteroides fragilis and Parabacteroides merdae strains used for genomic analysis in phage-host range analysis experiments. </p>
Congruence among multiple indices of habitat preference for species facing human-induced rapid environmental change: A case study using the Brewer's sparrow
<p>Accurate evaluations of habitat preference are key to understanding optimal conditions for wildlife survival and reproduction. Habitat selection, however, usually is evaluated using a single index of preference, and congruence among multiple, relevant indices of preference is examined rarely.</p> <p>We assessed the concordance between patterns of habitat preference using three different indices of breeding site preference in a migratory songbird. Specifically, we compared the chronology of territorial establishment, pair formation, and reproductive initiation of the Brewer's sparrow (<em>Spizella breweri</em>) along a gradient of surface disturbance associated with natural gas development in Wyoming, USA during 2019.</p> <p>We expected all three indices to demonstrate a preference for breeding sites with less surface disturbance, where reproductive success typically is higher. By contrast, all indices suggested suboptimal preference with respect to surface disturbance, with some discrepancy among them. The chronology of settlement and pairing did not vary across the disturbance gradient, whereas nest initiation tended to occur earlier at sites with more disturbance.</p> <p>If the pattern of suboptimal selection of breeding sites that we identified is generalizable across other populations of migratory birds affected by energy development, the resultant lower fitness in those areas may exacerbate population declines.</p> <p>Our results suggest that traditional, single-index approaches to the study of habitat selection, if chosen carefully, may provide adequate inference on habitat preferences. Different metrics, however, can lead to at least subtle differences in patterns of habitat selection. The simultaneous examination of multiple indices of preference across a diversity of systems would help clarify the contexts under which preference metrics can become decoupled.</p>
Fig. 1. The 80 in Importance of Srepok Wildlife Sanctuary, Cambodia, for the endangered green peafowl: implications of co-occurrence near human use areas
Fig. 1. The 80-point count listening post locations within the core and outer core area of Srepok Wildlife Sanctuary.
Figure 9. Classification accuracy regardless the ethnic group (Total accuracy 75%)-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>Our experiments show that, the impact of ethnic group on the accuracy of emotions<br> recognition is a positive where the accuracy of emotion recognition considering ethnic group is<br> 83.3% as shown in Figure 8, and we got 75% of accuracy regardless ethnic group as shown in<br> Figure 9.</p>
Figure 8. Classification accuracy of emotions considering the ethnic group-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>To study the accuracy of emotion recognition for our approach regardless the ethnic group<br> we used 108 images for the training representing six emotions of six persons. For testing, we used<br> 36 images representing six emotions of six persons.<br> On the other hand, to study the accuracy of emotion recognition for our approach<br> considering the ethnic group we used 36 images for training for each ethnic group representing six<br> emotions of six persons, and test the classifier by using 12 images representing six emotions of six<br> persons.</p>
Figure 5 in Improved diagnostic sensitivity of human strongyloidiasis using point-of-care mixed recombinant antigen-based immunochromatography
Figure 5. The intensity values of NIE (a), SsIR (b), and NIE-SsIR (c) ICT kits were evaluated using an in-house strip reader (red line indicates cut-off intensity value). Groups I, II, and III represented healthy controls, proven strongyloidiasis, and other parasitic infections, respectively. Any value above the cut-off value (red horizontal lines) is negative. Receiver operator characteristic (ROC) area analyses of the NIE, SsIR, and NIE-SsIR ICT kits to compare accuracy of all kits with gold standard methods (d).
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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.