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30,334 results for “Responsiveness”
Disentangling responses to natural stressor and human impact gradients in river ecosystems across Europe
<p>1. Rivers are dynamic ecosystems in which both human impacts and climate-driven drying events are increasingly common. These anthropogenic and natural stressors interact to influence the biodiversity and functioning of river ecosystems. Disentangling ecological responses to these interacting stressors is necessary to guide management actions that support ecosystems adapting to global change.</p> <p>2. We analysed the independent and interactive effects of human impacts and natural drying on aquatic invertebrate communities—a key biotic group used to assess the health of European freshwaters. We calculated biological response metrics representing communities from 406 rivers in eight European countries: taxonomic richness, functional richness and redundancy, and two biomonitoring indices that indicate ecological status. We analysed metrics based on the whole community and a group of taxa with traits promoting resistance and/or resilience ('high RR') to drying. We also examined how responses vary across Europe in relation to climatic aridity.</p> <p>3. Most community metrics decreased independently in response to impacts and drying. A richness-independent biomonitoring index (the average score per taxon; ASPT) showed particular potential for use in biomonitoring, and should be considered alongside new metrics representing high RR diversity, to promote accurate assessment of ecological status.</p> <p>4. High RR taxonomic richness responded only to impacts, not drying. However, these predictors explained little variance in richness and other high RR metrics, potentially due to low taxonomic richness. Metric responsiveness could thus be enhanced by developing region-specific high RR groups comprising sufficient taxa with sufficiently variable impact sensitivities to indicate ecological status.</p> <p>5. Synthesis and applications. Our results inform recommendations guiding the development of metrics to assess the ecological status of dynamic river ecosystems—including those that sometimes dry—thus identifying priority sites requiring further investigation to identify the stressors responsible for environmental degradation. We recommend concurrent consideration of richness-independent biomonitoring indices (such as an ASPT) and new high RR richness metrics that characterize groups of resistant and resilient taxa for region-specific river types. Interactions observed between aridity, impacts and drying evidence that these new metrics should be adaptable, promoting their ability to inform management actions that protect river ecosystems responding to climate change.</p>
Phase response analyses support a relaxation oscillator model of locomotor rhythm generation in Caenorhabditis elegans
<p>This dataset contains all data and codes that are used in the manuscript entitled "Phase response analyses support a relaxation oscillator model of locomotor rhythm generation in <em>Caenorhabditis elegans</em>".</p> <p>The data include raw videos and intermediate data for optogenetic experiments of all strains, experimental conditions (illumination duration, illuminated region, fluid viscosity and date). Within the parent folder 'Videos', each subfolder represents data of a group of experiments using the same strain under the same condition, as indicated explicitly by the subfolder name. Within each subfolder, there are raw videos of freely moving worms perturbed by transient optogenetic perturbations and intermediate data which include locomotory information and the corresponding figure plots (kymographs) that were generated by analysing the raw videos with the image analysis software (also in the dataset)</p> <p>The codes include scripts for image data analysis and model simulations. The image data analysis codes include scripts specifically for generating phase portrait graphs, phase response curves, head oscillation stability plots, phase isochron map and vector field. The model simulation codes include scripts for model oscillators implementation, paramter estimation/optimization and simulations of optogenetic inhibition.</p>
Data & code repository for "A re-appraisal of the ENSO response to volcanism with paleoclimate data assimilation"
<p>This repository includes the data and code that can be used to reproduce the figures for the paper entitled <em>A re-appraisal of the ENSO response to volcanism with paleoclimate data assimilation</em>.</p>
Enhancement of unsteady frequency responses of electro-thermal resonance MEMS cantilever sensors (Data)
<p>Origin projects and figures used for the article "Enhancement of unsteady frequency responses of electro-thermal resonance MEMS cantilever sensors", published in the proceedings of the 30th Micromechanics and Microsystems Europe Workshop; 18.08.2019 to 20.08.2019; Wolfson College, Oxford, United Kingdom.</p>
Pre-vaccination and early B cell signatures of the antibody response to SARS-CoV-2 mRNA vaccine
<p>The data presented in Code repository for Kardava, L., Rachmaninoff, N., Lau, W. W., Buckner, C. M., Trihemasava, K., Blazkova, J., ... & Moir, S. (2022). Early human B cell signatures of the primary antibody response to mRNA vaccination. Proceedings of the National Academy of Sciences, 119(28), e2204607119.<a href="https://www.pnas.org/doi/epdf/10.1073/pnas.2204607119">https://www.pnas.org/doi/epdf/10.1073/pnas.2204607119</a> are made available here.</p> <p>All code to reproduce the figures can be found here: https://github.com/niaid/COVID_Vaccine_Bcells</p> <p><a href="https://zenodo.org/api/files/f93859d0-b062-4def-8b17-c0f21ee36f09/all_subjects_cd19_positive_and_keys.zip">all_subjects_cd19_positive_and_keys.zip</a> contains a CSV file of all CD19+ cells with flowSOM clusters shown. Accompanying files allow for matching of timepoint and subject information.</p> <p><a href="https://zenodo.org/api/files/f93859d0-b062-4def-8b17-c0f21ee36f09/All_subjects_FCS_files_deidentified.zip">All_subjects_FCS_files_deidentified.zip</a> contains the raw fcs files and is organized by timepoint and subject.</p>
Dataset related to article "Mechanisms of responsiveness to and resistance against trabectedin in murine models of human myxoid liposarcoma"
<p>This record contains data related to article "Mechanisms of responsiveness to and resistance against trabectedin in murine models of human myxoid liposarcoma".</p> <p>Myxoid liposarcoma (MLPS) is a rare soft-tissue sarcoma characterised by the expression of FUS-DDIT3 chimera. Trabectedin has shown significant clinical anti-tumour activity against MLPS. To characterise the molecular mechanism of trabectedin sensitivity and of resistance against it, we integrated genomic and transcriptomic data from treated mice bearing ML017 or ML017/ET, two patient-derived MLPS xenograft models, sensitive to and resistant against trabectedin, respectively. Longitudinal RNA-Seq analysis of ML017 showed that trabectedin acts mainly as a transcriptional regulator: 15 days after the third dose trabectedin modulates the transcription of 4883 genes involved in processes that sustain adipocyte differentiation. No such differences were observed in ML017/ET. Genomic analysis showed that prolonged treatment causes losses in 4p15.2, 4p16.3 and 17q21.3 cytobands leading to acquired-resistance against the drug. The results dissect the complex mechanism of action of trabectedin and provide the basis for novel combinatorial approaches for the treatment of MLPS that could overcome drug-resistance.</p>
Emergence and function of cortical offset responses in sound termination detection
<p>Offset responses in auditory processing appear after a sound terminates. They arise in neuronal circuits within the peripheral auditory system, but their role in the central auditory system remains unknown. Here we ask what the behavioral relevance of cortical offset responses is and what circuit mechanisms drive them. At the perceptual level, our results reveal that experimentally minimizing auditory cortical offset responses decreases the mouse performance to detect sound termination, assigning a behavioral role to offset responses. By combining <i>in vivo</i> electrophysiology in the auditory cortex and thalamus of awake mice, we also demonstrate that cortical offset responses are not only inherited from the periphery but also amplified and generated <i>de novo</i>. Finally, we show that offset responses code more than silence, including relevant changes in sound trajectories. Together, our results reveal the importance of cortical offset responses in encoding sound termination and detecting changes within temporally discontinuous sounds crucial for speech and vocalization.</p>
Data and code from: "A transcriptional rheostat couples past activity to future sensory responses" (Tsukahara, Brann, et al. 2021 Cell)
<p># A transcriptional rheostat couples past activity to future sensory responses</p> <p>Code and data to replicate analyses in Tsukahara, Brann et al. 2021 Cell <a href="https://doi.org/10.1016/j.cell.2021.11.022">https://doi.org/10.1016/j.cell.2021.11.022</a></p> <p>## Summary</p> <p>Animals traversing different environments encounter both stable background stimuli and novel cues, which are thought to be detected by primary sensory neurons and then distinguished by downstream brain circuits. Here we show that each of the ~1000 olfactory sensory neuron (OSN) subtypes in the mouse harbors a distinct transcriptome whose content is precisely determined by interactions between its odorant receptor and the environment. This transcriptional variation is systematically organized to support sensory adaptation: expression levels of more than 70 genes relevant to transforming odors into spikes continuously vary across OSN subtypes, dynamically adjust to new environments over hours, and accurately predict acute OSN-specific odor responses. The sensory periphery therefore separates salient signals from predictable background via a transcriptional rheostat whose moment-to-moment state reflects the past and constrains the future; these findings suggest a general model in which structured transcriptional variation within a cell type reflects individual experience.</p> <p>## Manuscript</p> <p>For more details, please see our Open Access manuscript: <a href="https://www.cell.com/cell/fulltext/S0092-8674(21)01337-4">https://www.cell.com/cell/fulltext/S0092-8674(21)01337-4</a></p> <p># Code</p> <p>1. The code here is a copy of that on GitHub: <a href="https://github.com/dattalab/Tsukahara_Brann_OSN">https://github.com/dattalab/Tsukahara_Brann_OSN</a>. Instructions for how to download and install it can be found in the README.md file.</p> <p>2. Data is available on the NCBI GEO (accession <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE173947">GSE173947</a>) and raw fastq files are available from the SRA (accession SRP318630).</p> <p>3. Supplementary data (imaging traces and example preprocessed AnnData object for the home-cage dataset) can be found in the data folders of the attached Tsukahara_Brann_OSN-zenodo.zip file.</p>
Pallandt_etal2021_JGRBG_Decomposition_SM_Response
<p>This repository provides access to an R script and supporting data files connected to the following manuscript:</p> <p>Pallandt M., Ahrens B., Koirala S., Lange H., Reichstein M., Schrumpf M., Zaehle S: "<em>Vertically divergent responses of SOC decomposition to soil moisture in a changing climate</em>" (accepted for publication on 27.12.2021 in the Journal of Geophysical Research - Biogeosciences).</p> <p>The manuscript contains an application of the DAMM model by Davidson et al. (2012), with soil moisture and soil temperature data from the CMIP5 ensemble (<a href="https://esgf-node.llnl.gov/projects/esgf-llnl/">https://esgf-node.llnl.gov/projects/esgf-llnl/</a>) and soil data from SoilGrids (<a href="https://soilgrids.org/">https://soilgrids.org/</a>).</p> <p>For more details, downloading the latest version, and code repository history, see <a href="https://gitlab.com/MarleenPallandt/pallandt_etal2021_jgrbg_decomposition_sm_response">https://gitlab.com/MarleenPallandt/pallandt_etal2021_jgrbg_decomposition_sm_response</a>.</p>
Thermal tolerance in Drosophila: repercussions for distribution, community coexistence and responses to climate change
<p>Here we combined controlled experiments and field surveys to determine if estimates of heat tolerance predict distributional ranges and phenology of different Drosophila species in southern South America. </p> <p>We contrasted thermal death time curves, which consider both magnitude and duration of the challenge to estimate heat tolerance, against the thermal range where populations are viable based on field surveys in an 8-yr longitudinal study. </p> <p>We observed a strong correspondence of the physiological limits, the thermal niche for population growth, and the geographic ranges across studied species, which suggests that the thermal biology of different species provides a common currency to understand how species will respond to warming temperatures both at a local level and throughout their distribution range. </p> <p>Our approach represents a novel analytical toolbox to anticipate how natural communities of ectothermic organisms will respond to global warming.</p>
In vitro cell cycle oscillations exhibit a robust and hysteretic response to changes in cytoplasmic density
<p>Cells control the properties of the cytoplasm to ensure proper functioning of biochemical processes. Recent studies showed that cytoplasmic density varies in both physiological and pathological states of cells undergoing growth, division, differentiation, apoptosis, senescence, and metabolic starvation. Little is known about how cellular processes cope with these cytoplasmic variations. Here, we study how a cell cycle oscillator comprising cyclin-dependent kinase (Cdk1) responds to changes in cytoplasmic density by systematically diluting or concentrating cycling <em>Xenopus</em> egg extracts in cell-like microfluidic droplets. We found that the cell cycle maintains robust oscillations over a wide range of deviations from the endogenous density: as low as 0.2× to more than 1.22× relative cytoplasmic density (RCD). A further dilution or concentration from these values arrested the system in a low or high steady state of Cdk1 activity, respectively. Interestingly, diluting an arrested cytoplasm of 1.22× RCD recovers oscillations at lower than 1× RCD. Thus, the cell cycle switches reversibly between oscillatory and stable steady states at distinct thresholds depending on the direction of tuning, forming a hysteresis loop. We propose a mathematical model which recapitulates these observations and predicts that the Cdk1/Wee1/Cdc25 positive feedback loops do not contribute to the observed robustness, supported by experiments. Our system can be applied to study how cytoplasmic density affects other cellular processes.</p>
Global indicators framework for socially responsible research and innovation (RRI): How to monitor public and researcher perspectives (Supplemental material)
<p>This data upload provides a detailed account of indicators that can be used to measure RRI progress around the world against the UNESCO Recommendation for Science and Scientific Researchers at the level of individual researchers and public opinion. This data upload provides supplemental material for an article in Open Research Europe entitled, 'Global indicators framework for socially responsible research and innovation (RRI): How to monitor public and researcher perspectives'.</p> <p>Abstract for main article:</p> <p>As calls for more socially responsible research and innovation (RRI) policies and practices grow more insistent, the need for high quality indicators that can be used to evaluate progress is becoming increasingly important. Given the global nature of science, such indicators need to be relevant to countries across all world regions. Moreover, the methodological quality of indicators is critical to provide a strong foundation for long-term comparative measurement of the impacts of different kinds of policy intervention. There is a practical challenge in this effort relating to the uneven mechanisms for data collection and analysis available in different countries. There is also a geopolitical challenge in gaining buy-in from countries with very different, and sometimes competing, agendas. Here, the 2017 UNESCO-led Recommendation on Science and Scientific Researchers is highlighted as an existing vehicle that can enable cooperation on globally comparative measurement of socially responsible research and innovation. In particular, the quadrennial monitoring of the implementation of this wide-ranging global policy instrument that has been ratified by 195 countries affords a unique opportunity to add value for these countries by linking RRI to the 2017 Recommendation while establishing benchmark indicators for RRI more generally. As a practical and methodological contribution to the global community of science and innovation policymakers, researchers and research and innovation stakeholders committed to socially responsible research, this report contains specific, detailed survey questions and response options focusing on the public opinion and individual researchers’ level of measurement. It provides details of sources of benchmark survey data that have readily available open data that can be used to benchmark the development of socially responsible research and innovation over time from the vantage points of the public and researchers around the world. The aim of this kind of indicators framework is to enable evidence-based practice in socially responsible research and innovation. Robust and practical RRI measurement on a global scale can reduce the risk that well-intentioned but ill-conceived RRI policy and practice interventions create undetected, unchecked, and unreformed negative impacts in practice.</p>
RNAseq sequences of the study "Transactive response DNA-binding Protein (TARDBP/TDP-43) regulates early HIV-1 entry and infection" (1/2)
<p>Each pair of FASTQ files corresponds to a specific sample condition:</p> <table> <thead> <tr> <th scope="col">Condition</th> <th scope="col">Sample</th> <th scope="col">FASTQ name R1</th> <th scope="col">FASTQ name R2</th> </tr> </thead> <tbody> <tr> <td>Cneg</td> <td>RNASEQ-AVF1</td> <td>RNASEQ-AVF1_S1_R1_001.fastq.gz</td> <td>RNASEQ-AVF1_S1_R2_001.fastq.gz</td> </tr> <tr> <td>Flag-wt-TDP-43</td> <td>RNASEQ-AVF2</td> <td>RNASEQ-AVF2_S2_R1_001.fastq.gz</td> <td>RNASEQ-AVF2_S2_R2_001.fastq.gz</td> </tr> <tr> <td>Flag-NLS-mut-TDP-43</td> <td>RNASEQ-AVF3</td> <td>RNASEQ-AVF3_S3_R1_001.fastq.gz</td> <td>RNASEQ-AVF3_S3_R2_001.fastq.gz</td> </tr> <tr> <td>Cneg</td> <td>RNASEQ-AVF4</td> <td>RNASEQ-AVF4_S4_R1_001.fastq.gz</td> <td>RNASEQ-AVF4_S4_R2_001.fastq.gz</td> </tr> <tr> <td>Scramble</td> <td>RNASEQ-AVF5</td> <td>RNASEQ-AVF5_S5_R1_001.fastq.gz</td> <td>RNASEQ-AVF5_S5_R2_001.fastq.gz</td> </tr> <tr> <td>TDP-43 siRNA A</td> <td>RNASEQ-AVF6</td> <td>RNASEQ-AVF6_S6_R1_001.fastq.gz</td> <td>RNASEQ-AVF6_S6_R2_001.fastq.gz</td> </tr> <tr> <td>TDP-43 siRNA B</td> <td>RNASEQ-AVF7</td> <td>RNASEQ-AVF7_S7_R1_001.fastq.gz</td> <td>RNASEQ-AVF7_S7_R2_001.fastq.gz</td> </tr> <tr> <td>TDP-43 siRNA C</td> <td>RNASEQ-AVF8</td> <td>RNASEQ-AVF8_S8_R1_001.fastq.gz</td> <td>RNASEQ-AVF8_S8_R2_001.fastq.gz</td> </tr> </tbody> </table> <p> </p>
RNAseq sequences of the study "Transactive response DNA-binding Protein (TARDBP/TDP-43) regulates early HIV-1 entry and infection" (2/2)
<p>Each pair of FASTQ files corresponds to a specific sample condition:</p> <table> <thead> <tr> <th scope="col">Condition</th> <th scope="col">Sample</th> <th scope="col">FASTQ name R1</th> <th scope="col">FASTQ name R2</th> </tr> </thead> <tbody> <tr> <td>TDP-43 siRNA D</td> <td>RNASEQ-AVF9</td> <td>RNASEQ-AVF9_S1_R1_001.fastq.gz</td> <td>RNASEQ-AVF9_S1_R2_001.fastq.gz</td> </tr> <tr> <td>Cneg</td> <td>RNASEQ-AVF10</td> <td>RNASEQ-AVF10_S2_R1_001.fastq.gz</td> <td>RNASEQ-AVF10_S2_R2_001.fastq.gz</td> </tr> <tr> <td>Flag-wt-TDP-43</td> <td>RNASEQ-AVF11</td> <td>RNASEQ-AVF11_S3_R1_001.fastq.gz</td> <td>RNASEQ-AVF11_S3_R2_001.fastq.gz</td> </tr> <tr> <td>Flag-NLS-mut-TDP-43</td> <td>RNASEQ-AVF12</td> <td>RNASEQ-AVF12_S4_R1_001.fastq.gz</td> <td>RNASEQ-AVF12_S4_R2_001.fastq.gz</td> </tr> <tr> <td>Cneg</td> <td>RNASEQ-AVF13</td> <td>RNASEQ-AVF13_S5_R1_001.fastq.gz</td> <td>RNASEQ-AVF13_S5_R2_001.fastq.gz</td> </tr> <tr> <td>Scramble</td> <td>RNASEQ-AVF14</td> <td>RNASEQ-AVF14_S6_R1_001.fastq.gz</td> <td>RNASEQ-AVF14_S6_R2_001.fastq.gz</td> </tr> <tr> <td>Oligos B+C</td> <td>RNASEQ-AVF15</td> <td>RNASEQ-AVF15_S7_R1_001.fastq.gz</td> <td>RNASEQ-AVF15_S7_R2_001.fastq.gz</td> </tr> <tr> <td>Oligos A+B+C</td> <td>RNASEQ-AVF16</td> <td>RNASEQ-AVF16_S8_R1_001.fastq.gz</td> <td>RNASEQ-AVF16_S8_R2_001.fastq.gz</td> </tr> </tbody> </table> <p> </p>
Reproducing nonlinear seismic response from in-situ soil dynamic parameters: application to the Delaney Park Downhole Array, Alaska
<p>The zip file named 'seismic data at DPDA.zip' contains all the seismic data used in our study.</p> <p>The DEEPSOIL file named 'ground response analysis at DPDA.dp' contains the numerical models used in our study.</p>
Germinal centre-driven maturation of B cell response to SARS-CoV-2 mRNA vaccination
<p>These are the<strong> processed</strong> BCR repertoire and transcriptomics data described in <a href="https://doi.org/10.1038/s41586-022-04527-1">Kim & Zhou et al., <em>Nature</em>, 2022</a>. The <strong>raw</strong> sequencing data new to this study are available on SRA under BioProject <a href="https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA777934">PRJNA777934</a>. This study also used BCR repertoire data from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., <em>Nature</em>, 2021</a> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA731610">PRJNA731610</a>) and <a href="https://doi.org/10.1016/j.immuni.2021.08.013">Schmitz, Turner & Liu et al., <em>Immunity</em>, 2021</a> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA741267">PRJNA741267</a>).</p> <p> </p> <p><strong>Code</strong></p> <p>Code along with Docker containers for reproducing the NGS data-based figures and analyses in the published paper can be <a href="https://github.com/julianqz/wustl_published/tree/main/nature_2022">found on GitHub</a>.</p> <p> </p> <p><strong>Metadata</strong></p> <p>File: WU368_kim_et_al_nature_2022_meta.tsv</p> <p>Notes:</p> <ul> <li>Sample breakdown by `sequence_type` (132 total) <ul> <li>73 bulk BCR sequencing samples (`bulk`) <ul> <li>57 new</li> <li>5 from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., <em>Nature</em>, 2021</a></li> <li>11 from <a href="https://doi.org/10.1016/j.immuni.2021.08.013">Schmitz, Turner & Liu et al., <em>Immunity</em>, 2021</a>.</li> </ul> </li> <li>56 10x Genomics single-cell VDJ + 5' gene expression samples (`tgx`)</li> <li>3 samples from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., <em>Nature</em>, 2021</a> (`mab`) corresponding to a total of 37 S-binding mAbs previously reported. These are not the same as the 2099 recombinant mAbs generated in this study (see below).</li> </ul> </li> <li>Sample collection time was originally recorded in days in the `timepoint` column. Timepoints were referenced in weeks in the manuscript, as shown in the `timepoint_ms` column.</li> <li>`bio_rep` and `tech_rep` = biological replicate and technical replicate respectively.</li> </ul> <p>Abbreviations:</p> <ul> <li>LN = lymph node</li> <li>BM = bone marrow</li> <li>PB = plasmablast</li> <li>GC = germinal centre</li> <li>LLPC = long-lived plasma cell</li> <li>NS = no sorting</li> <li>mAb = monoclonal antibody</li> </ul> <p> </p> <p><strong>Information on the 2099 recombinant mAbs generated in this study</strong></p> <p>File: WU368_kim_et_al_nature_2022_mabs.tsv</p> <p>Notes on columns:</p> <ul> <li>`h_sequence_id` and `l_sequence_id`: Sequence IDs of the heavy and light chains respectively.</li> <li>`elisa`: ELISA results for binding to SARS-CoV-2 S (`TRUE` = positive).</li> </ul> <p> </p> <p><strong>Processed BCR data - heavy chains</strong></p> <p>File: WU368_kim_et_al_nature_2022_bcr_heavy.tsv</p> <p><em>Analysis was based on heavy chain-based clonal inference.</em></p> <p>Notes on columns:</p> <p>The columns largely follow the <a href="https://changeo.readthedocs.io/en/stable/standard.html">AIRR-C Rearrangement format</a>. The main deviation is that CDR3s were used, as opposed to IMGT-defined "junctions". Nonetheless, junction-related columns are included here as some repositories such as <a href="https://gateway.ireceptor.org/login"><em>iReceptor</em></a> use these. Non-standard columns are noted below.</p> <ul> </ul> <ul> <li>`cell_id`: Only sequences from single-cell samples and the 37 mAbs from Turner & O'Halloran et al., <em>Nature</em>, 2021 have cell IDs following the format `[donor]_[sample]@[id]`. `NA` for bulk sequences.</li> <li>`sequence_id`: Sequence IDs follow the format `[donor]_[sample]@[id]`.</li> <li>`v_call_genotyped`: V gene annotation reassigned after individualized genotyping by <a href="https://tigger.readthedocs.io/en/stable/">TIgGER</a>.</li> <li>`germline_[vdj]_call`: Clonal consensus germline calls after corresponding clonal consensus sequence were reconstructed via <a href="https://changeo.readthedocs.io/en/stable/methods/germlines.html">`CreateGermlines.py --cloned` from Change-O</a>.</li> <li>`isotype`: IGH[ADEGM].</li> <li>`cdr3`: CDR3 nucleotide sequence.</li> <li>`cdr3_length`: CDR3 nucleotide sequence length.</li> <li>`cdr3_aa`: CDR3 amino acid sequence.</li> <li>`collapse_count`: Number of duplicate IMGT-aligned V(D)J sequences that were collapsed by <a href="https://alakazam.readthedocs.io/en/stable/topics/collapseDuplicates/">`alakazam::collapseDuplicates`</a>.</li> <li>`donor`, `timepoint`, `tissue`, `sorting`, `seq_type`: Propagated as is from the metadata file. <ul> <li>In `seq_type`, `tgx` corresponds to 10x Genomics data; `mab` corresponds specifically to the 37 S-binding mAbs from Turner & O'Halloran et al., <em>Nature</em>, 2021.</li> </ul> </li> <li>`timepoint_2`: Same as `timepoint`, except that `d28+d35` and `d201+d208` were treated as `d28` (week 4) and `d201` (week 29) respectively as described in Materials & Methods.</li> <li>`gex_anno`: Cell type identity annotation based on transcriptomic profiles. Mapped from `anno_leiden_0.18` from WU368_kim_et_al_nature_2022_gex_b_cells.h5ad.</li> <li>`compartment`: B cell compartment. <ul> <li>ABC = activated B cell. LNPC = lymph node plasma cell. RMB = resting memory B cell.</li> <li>Minor differences in terminology <ul> <li>The manuscript refers to the memory compartment as MBCs, whereas the terminology used in the data is RMB. As described in Materials & Methods, analysis involving the memory compartment used specifically d201 bulk-sequenced memory sorts from blood. To get these sequences, subset `s_pos_clone`, `seq_type`, `compartment`, and `timepoint_2` to, respectively, `TRUE`, `bulk`, `RMB`, and `d201`. </li> <li>The manuscript uses the term BMPC (bone marrow plasma cell), whereas the data uses the term LLPC.</li> </ul> </li> </ul> </li> <li>`clone_id`: B cell clonal lineage IDs follow the format `[donor]@[id]`.</li> <li>`s_pos_clone`: `TRUE` if a sequence belonged to a B cell clone that was designated as S-binding by virtue of containing one of the recombinant mAbs that tested positive via ELISA or one of the S-binding mAbs from Turner & O'Halloran et al., <em>Nature</em>, 2021.</li> <li>`expressed_id`: mAb IDs for the 2099 recombinant mAbs generated in this study (mapped from `mab_id` from WU368_kim_et_al_nature_2022_mabs.tsv) and the 37 mAbs from Turner & O'Halloran et al., <em>Nature</em>, 2021. `NA` for everything else.</li> <li>`elisa`: ELISA results for binding of recombinant mAbs to SARS-CoV-2 S. `TRUE` if positive. `NA` if not tested.</li> <li>`nuc_RS_19_312`: number of replacement and silent mutations between IMGT-numbered nucleotide positions 19-312 along IGHV sequences, calculated by <a href="https://shazam.readthedocs.io/en/stable/topics/calcObservedMutations/">`shazam::calcObservedMutations`</a>.</li> <li>`nuc_denom_19_312`: number of informative nucleotide positions for counting mutations, excluding non-A/T/G/C positions (such as "N", "-", ".").</li> <li>`nuc_RS_freq_19_312`: nucleotide-level mutation frequency (= nuc_RS_19_312 / nuc_denom_19_312).</li> </ul> <p> </p> <p><strong>Processed BCR data - light chains</strong></p> <p>File: WU368_kim_et_al_nature_2022_bcr_light.tsv</p> <p><em>Light chains were not used for heavy chain-based clonal inference or analysis.</em></p> <p> </p> <p><strong>Processed transcriptomics data</strong></p> <p>Files:</p> <ul> <li>WU368_kim_et_al_nature_2022_gex_all_cells.h5ad (clustering all cells)</li> <li>WU368_kim_et_al_nature_2022_gex_b_cells.h5ad (re-clustering only the B cells)</li> </ul> <p>Notes:</p> <ul> <li>The `h5ad` files can be imported into <a href="https://scanpy.readthedocs.io/en/stable/index.html">Scanpy</a> as an <a href="https://scanpy.readthedocs.io/en/stable/usage-principles.html#anndata">AnnData object</a>.</li> <li>Each `AnnData` object has 3 `.layers`, each representing a version of the count matrix. <ul> <li>`raw_counts`: Imported from `<a href="https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/6.0/using/aggregate">cellranger aggr</a>` output by `scanpy.read_10x_mtx`.</li> <li>`log_norm`: Log-noramlized expression values outputted by `scanpy.pp.normalize_total` followed by `scanpy.pp.log1p`.</li> <li>`scaled`: The `log_norm` layer scaled to unit variance and zero mean by `scanpy.pp.scale`. </li> </ul> </li> <li>The `gene_name` and `biotype` columns in `.var` were extracted from GENCODE v32 GTF.</li> <li>Columns in `.obs` (each row corresponds to a cell) <ul> <li>`n_feature`: The `n_genes_by_counts` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The number of genes expressed. This is before subsetting the genes.</li> <li>`n_umi`: The `total_counts` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The total UMI counts in a cell.</li> <li>`pct_mt`: The `pct_counts_mt` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The percentage of counts in mitochondrial genes.</li> <li>`n_hkg`: The number of housekeeping genes for which expression was detected.</li> <li>`n_gene_expressed`: The total number of genes for which expression was detected. This is after subsetting the genes.</li> <li>`pre_qc_bcr`: `TRUE` if a cell also had paired BCR data available. Produced by cross-referencing the cellular barcodes in `cell_barcodes.json` outputted by `cellranger vdj`. At this point the BCR data had not gone through the QC process in the BCR processing pipeline (hence `pre_qc`). </li> <li>`leiden_[resolution]`: Cluster assignment by `scanpy.tl.leiden`.</li> <li>`anno_leiden_[resolution]`: Cell type identity annotations based on transcriptomic profiles. This was mapped onto the `gex_anno` column in the processed heavy chain BCR data.</li> </ul> </li> <li>UMAP coordinates can be found in `.obsm["X_umap"]`.</li> <li>`.X` has been set to `None` in order to reduce file size.</li> </ul> <p>In addition, the preprocessed count matrix outputted by `<a href="https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/6.0/using/aggregate">cellranger aggr</a>` is available from <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE195673">GEO under BioProject PRJNA777934</a>.</p>
[Data from:] Genetic Analysis Reveals Three Novel QTLs Underpinning a Butterfly Egg-Induced Hypersensitive Response-Like Cell Death in Brassica Rapa
<p><strong>Background</strong></p> <p>Cabbage white butterflies (<em>Pieris</em> spp.) can be severe pests of <em>Brassica</em> crops such as Chinese cabbage, Pak choi (<em>Brassica rapa</em>) or cabbages (<em>B. oleracea</em>). Eggs of <em>Pieris</em> spp. can induce a hypersensitive response-like (HR-like) cell death which reduces egg survival in the wild black mustard (<em>B. nigra</em>). Unravelling the genetic basis of this egg-killing trait in <em>Brassica</em> crops could improve crop resistance to herbivory, reducing major crop losses and pesticides use. Here we investigated the genetic architecture of a HR-like cell death induced by <em>P. brassicae</em> eggs in <em>B. rapa.</em></p> <p><strong>Results</strong></p> <p>A germplasm screening of <em>B. rapa</em> 56 accessions, representing the genetic and geographical diversity of a <em>B. rapa</em> core collection, showed phenotypic variation for cell death. An image-based phenotyping protocol was developed to accurately measure size of HR-like cell death and was then used to identify two accessions that consistently showed weak (R-o-18) or strong cell death response (L58). Screening of 160 RILs derived from these two accessions resulted in three novel QTLs for P<em>ieris</em> b<em>rassicae-</em>induced cell death on chromosomes A02 (<em>Pbc1</em>), A03 (<em>Pbc2</em>), and A06 (<em>Pbc3</em>). The three QTLs <em>Pbc1-3</em> contain cell surface receptors, intracellular receptors and other genes involved in plant immunity processes, such as ROS accumulation and cell death formation. Synteny analysis with <em>A. thaliana</em> suggested that <em>Pbc1</em> and <em>Pbc2</em> are novel QTLs associated with this trait, while <em>Pbc3</em> contains also LecRK-I.1, a gene of <em>A. thaliana</em> previously associated with cell death induced by a <em>P. brassicae</em> egg extract.</p> <p><strong>Conclusions</strong></p> <p>This study provides the first genomic regions associated with the <em>Pieris</em> egg-induced HR-like cell death in a <em>Brassica</em> crop species. It is a step closer towards unravelling the genetic basis of an egg-killing crop resistance trait, paving the way for breeders to further fine-map and validate candidate genes.</p>
The height-dependent delayed ionospheric response to solar EUV - artificial run
<p>These artificial runs use the TIE-GCM v2.0 model in its 2.5 × 2.5 configuration as the real condition model run. In order to converge to stable initial condition the TIE GCM runs for 30 days prior to the final simulation starting at 21 September 2010. This initial run was configured with default parameters. Both model runs start at 21 October 2010 and are calculated with default parameters except for the switched off auroral parameterization as well as the switched off high-latitude potential model. The influence of noise in solar activity is reduced by applying an artificial noise free sinusoidal time series for the F10.7 input.</p>
Data for Sentis et al. Short-term thermal acclimation modulates predator functional response
<p>Data from the study Short-term thermal acclimation modulates predator functional response by Arnaud Sentis, Lukas Veselý, Marek Let, Martin Musil, Viktoriia Malinovska and Antonín Kouba. <br> The data represent the number of prey eaten for different initial prey densities, temperatures and acclimation times.<br> The first column "temperature" represents the experimental temperature.<br> The second column "acl.time" represents the duration of acclimation at each of the experimental temperature before the predation trials<br> The third column "PreyDensity" represents the initial prey density at the begining of the predation trial<br> The column "alive indiv." represents the number of prey alive at the end of the predation trial<br> The column "dead indiv." represents the number of prey dead but not eaten at the end of the predation trial<br> The column "indiv. into pieces" represents the number of dead prey with visible attack marks at the end of the predation trial<br> The column "PreyEaten" represents the number of prey eaten at the end of the predation trial<br> The column "PreyEatenNCM" represents the number of prey eaten and killed but not eaten at the end of the predation trial</p> <p>Each row represents a single observation (i.e. predation trial). <br> Predators and prey were used only once.</p>
Response to COVID-19: Clients' perspectives on the utilization of reproductive, maternal and child health care services in Nigeria
<p>This dataset was a part of a cross-sectional descriptive study in 320 rural communities in 32 Local Government Areas (LGAs) across the Federal Capital Territory (FCT), Abuja, and 9 out of the 36 Nigerian States in 2020. The dataset contains views of women who used primary health care centres in the selected LGAs for maternal and child health care, and family planning services before, during, and after the COVID-19 pandemic lockdown in Nigeria. The sample size was determined using the Yamane sample size formula. The estimated sample size per State was 384 (3,840 for nine States and FCT), with a 10% adjustment for non-response, the total sample size was 422 per State or location (4220 for the nine States and FCT). The data are stored in SPSS format. <br> The study was an initiative of UNFPA Nigeria. They coordinated it under the One UN Basket fund to respond to COVID-19 and implemented it under the supervision of three national NGOs: The Women’s Health and Action Research Centre (WHARC), Education as a Vaccine (EVA), and the Planned Parenthood Federation of Nigeria (PPFN). </p> <p> </p>
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.