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4,924 results for “distinctiveness”
Antigen-specific CD4+ T cells exhibit distinct transcriptional phenotypes in the lymph node and blood following vaccination in humans
<p><strong>Abstract: </strong><br>SARS-CoV-2 infection and mRNA vaccination induce robust CD4+ T cell responses that are critical for the development of protective immunity. Here, we evaluated spike-specific CD4+ T cells in the blood and draining lymph node (dLN) of human subjects following BNT162b2 mRNA vaccination using single-cell transcriptomics. We analyze multiple spike-specific CD4+ T cell clonotypes, including novel clonotypes we define here using Trex, a new deep learning-based reverse epitope mapping method integrating single-cell T cell receptor (TCR) sequencing and transcriptomics to predict antigen-specificity. Human dLN spike-specific T follicular helper cells (TFH) exhibited distinct phenotypes, including germinal center (GC)-TFH and IL-10+ TFH, that varied over time during the GC response. Paired TCR clonotype analysis revealed tissue-specific segregation of circulating and dLN clonotypes, despite numerous spike-specific clonotypes in each compartment. Analysis of a separate SARS-CoV-2 infection cohort revealed circulating spike-specific CD4+ T cell profiles distinct from those found following BNT162b2 vaccination. Our findings provide an atlas of human antigen-specific CD4+ T cell transcriptional phenotypes in the dLN and blood following vaccination or infection.</p> <p><strong>More Information:</strong></p> <ul> <li><strong>Preprint:</strong> <a href="https://www.researchsquare.com/article/rs-3304466/v1">Research Square.</a></li> <li><strong>Sample information</strong>: data_inventory.csv file.</li> <li><strong>Code</strong> code_github_repo.zip or at the <a href="https://github.com/ncborcherding/COVID_TCR">original github repo</a></li> <li><strong>Interactive Portal</strong>: <a href="https://cellpilot.emed.wustl.edu/">CellPilot</a></li> </ul>
Data set of the manuscript titled: Follicular Immune Landscaping Reveals a distinct profile of FOXP3hi CD4+ T cells in Treated compared to Untreated HIV
<p>Multiplex imaging data were collected using a scanning confocal system (STELARIS, Leica) and proccessed with the Imaris and Fiji imaging programs. csv files incuding the position identifiers and intensities for each fluorochrome used were generated and data were further analysed using the FlowJo10 program. Neighboring analysis was performed using the G function and mean of minimum distances of relevant cell type pairs. </p>
Dataset of The distinct influence of different maternal mental health symptom profiles on infant sleep during the first year postpartum: a cross-sectional survey
<p>The distinct influence of different, but comorbid, maternal mental health difficulties, such as postpartum depression, anxiety, or childbirth-related posttraumatic stress disorder (CB-PTSD) on infant sleep is unknown, although maternal mental health was reported to be associated with infant sleep. This paper first aimed to associations between maternal mental health symptoms and infant sleep. Second, it aimed to exploratory obtain maternal mental health symptom profiles from maternal mental health symptoms. Finally, it aimed to investigate the distinct influence of these maternal mental health symptom profiles on infant sleep, when including mediators (i.e., maternal perception of infant temperament and method to fall asleep) and moderators (maternal educational level and infant age).</p> <p>This dataset contains data on the mental health (i.e., CB-PTSD, depression, anxiety) of 410 mothers with an infant aged between 3 to 12 months old. Information on infant sleep and temperament (negative emotionality) was collected via standardised maternal-report questionnaires (City BiTS, EPDS, HADS, BISQ, and IBQ-R very short form). Sociodemographic data such as maternal age, marital status, educational level, infant age, and week of gestation are reported.</p> <p>This dataset is related to: Sandoz, V.; Lacroix, A.; Stuijfzand, S.; Bickle Graz, M.; Horsch, A. Maternal Mental Health Symptom Profiles and Infant Sleep: A Cross-Sectional Survey. <em>Diagnostics</em> <strong>2022</strong>, <em>12</em>, 1625. https://doi.org/10.3390/diagnostics12071625. </p>
Data for The Generally Curious: Thematically Distinct Datasets of 4chan's /pol/ Discussion Forum's 'General Threads'
<p>Over the second half of the 2010s, the /pol/ (‘politically incorrect’) forum on the 4chan image board has emerged as a space within which various extreme political ideologies are discussed and cultivated, occasionally informing off-site acts of political extremism. While previous research has often studied this space as a unified whole, it is relevant to more specifically demarcate different publics within 4chan’s /pol/ board, apart from studying it as an ‘amorphous blob’. This paper focuses specifically on ‘generals’ - recurring threads with a specific thematic focus identified by a particular vernacular phrase or tag. By identifying them it is possible to subset the board’s archive into multiple distinct datasets comprising discussions about a particular topic, such as Donald Trump, the Syria war, or British politics. We provide a dataset containing 58,841 opening posts and 13,697,738 replies to those, divided over 329 thematically distinct ‘general thread’ collections. In this paper we outline our data collection and query protocol, the structure of the data and its rationale, as well as a number of suggested research uses for this new data.</p> <p> </p>
Supplementary Datasets for the publication "Increased Susceptibility of Rousettus aegyptiacus Bats to Respiratory SARS-CoV-2 Challenge Despite Its Distinct Tropism for Gut Epithelia in Bats"
<p>Increasing evidence suggests bats are the ancestral hosts of the majority of coronaviruses. In gen-eral, coronaviruses primarily target the gastrointestinal system, while some strains, especially Be-tacoronaviruses with the most relevant representatives SARS-CoV, MERS-CoV, and SARS-CoV-2, also cause severe respiratory disease in humans and other mammals. We previously reported the susceptibility of Rousettus aegyptiacus (Egyptian fruit bats) to intranasal SARS-CoV-2 infection. Here, we compared their permissiveness to an oral infection versus respiratory challenge (in-tranasal or orotracheal) by assessing virus shedding, host immune responses, tissue-specific pa-thology, and physiological parameters. While respiratory challenge with a moderate infection dose of 1 × 104 TCID50 caused a systemic infection with oral and nasal shedding of replica-tion-competent virus, the oral challenge only induced nasal shedding of low levels of viral RNA. Even after a challenge with a higher infection dose of 1 × 106 TCID50, no replication-competent vi-rus was detectable in any of the samples of the orally challenged bats. We postulate that SARS-CoV-2 is inactivated by HCl and digested by pepsin in the stomach of R. aegyptiacus, thereby decreasing the efficiency of an oral infection. Therefore, fecal shedding of RNA seems to depend on systemic dissemination upon respiratory infection. These findings may influence our general understanding of the pathophysiology of coronavirus infections in bats.</p>
Data from: Carbon accumulation of cool season sports turfgrass species in distinctive soil layers
<p>Carbon sequestered by turfgrasses may contribute to reducing atmospheric CO<sub>2 </sub>levels, to improved soil health and to increased turfgrass quality. Therfore in a field study conducted in the Netherlands, the amount of soil C accumulated by nine cool season turfgrass monocultures and 12 mixtures of turfgrass species during the first three years of establishment was analysed and compared. Thatch, mat and other soil layers and the layers were sampled and thickness of these layers was quantified. From these samples, dry matter, C and N concentrations, and CN ratio were measured.</p> <p>The study was conducted on a 3 years old turfgrass field of the turfgrass seed company DLF. The site was located in the Netherlands (51°32´N, 4°20´E), on a sandy soil (Hortic Anthrasol as described in the FAO/Unesco soil map of the world (2006)). The monocultures consisted of different varieties of the (sub)species <em>Lolium perenne (lp), Poa pratensis (Pp), Festuca arundinacea (Fa), Festuca rubra commutata (Frc), Festuca rubr trichophylla (Frt), Festuca rubra rubra (Frr), Festuca ovina duriuscala (Fod), Festuca ovina vulgaris (Fov), Agrostis stolonifera (As). </em>Varieties were treated as replicates per (sub)species, which resulted in some variation in the number of replicates, as not all species were available in the same number of varieties.<em> </em>Varieties of the<em> (s</em>ub)species and mixtures were on the market as commercial turfgrass seeds. </p> <p>In 2016 a soil profile sampler with a depth of 20 cm, a horizontal length of 10 cm and a width of 2 cm was used to take an undisturbed soil profile in each plot and the thickness of each layer, thatch, matt and remainder soil, was measured using the protocol as described in Evers et al. (2024). Plant biomass in the plots was quantified by taking cores of the top 20 cm of the soil with a core sampler (diameter 28 mm). Cores were divided into thatch, mat, the remainder soil till 10 cm depth, and 10-20 cm depth, respectively, based on the earlier measurement of layer thicknesses in the field. Sediment of each section was then carefully washed out with tap water, after which the remaining below-ground (dead and living) plant biomass was dried at 65°C until stable weight and weighed. Total C and N analyses were carried out at the General Instrumentation Department of Radboud University with a Vario Micro Cube Element Analyzer (Elementar, Langenselbold, Germany), from which C and N concentrations (in % of dry matter or in mg cm<sup>-3</sup> C from total plant biomass in a layer) and CN ratios were calculated.</p> <p>Statistical analyses were carried out using the open source program R version 3.5.2 (2018-12-20). Differences in thickness of thatch and mat as well as differences in the C accumulation and C- and N concentration in thatch, mat and soil layers between (sub)species of turfgrasses in were based on the calculated means per species. Normality of residuals and the equality of variances was checked with diagnostic plots and Levene’s test, respectively. Non-normal and heteroscedastic data were either log transformed in linear models from the car package, or general least square (gls) models using varIdent from the nlme package were used. All data were further analyzed with ANOVA-type3 from the car package, followed by the Tukey post hoc test of the emeans package. Correlations between thatch and mat thickness were analyzed with linear regression models in R of the ggplot package. Similar procedures were performed for correlation between thatch, mat or soil thickness and C accumulation as well as for the correlation between C concentration and N concentration on C accumulation in a particular layer.</p>
Supplementary Materials for 'Measuring and assessing indeterminacy and variation in the morphology-syntax distinction'
<p><strong>Supplementary materials for the article 'Measuring and assessing indeterminacy and variation in the morphology-syntax distinction' in <em>Linguistic Typology </em>(Vol. and No. TBD).</strong></p> <p>Abstract:</p> <p>We provide a discussion of some of the challenges in using statistical methods to investigate the morphology-syntax distinction cross-linguistically. The paper is structured around three problems related to the morphology-syntax distinction; (i) the boundary strength problem; (ii) the composition problem; (iii) the architectural problem.<br> The boundary strength problem refers to the possibility that languages vary in terms of how distinct morphology and syntax are or the degree to which morphology is autonomous. The composition problem refers to the possibility that languages vary in terms of how they distinguish morphology and syntax: what types of properties distinguish the two systems. The architecture problem refers to the possibility that languages vary in terms of whether a global distinction between morphology and syntax is motivated at all and the possibility that languages might partition phenomena in different ways.<br> This paper is concerned with providing an overarching review of the methodological problems involved in addressing these three issues. We illustrate the problems using three statistical methods: correlation matrices, random forests with different choices for the dependent variable, and hierarchical clustering with validation techniques.</p> <p> </p> <p>Overview of materials:</p> <ul> <li>SM1: csv with the data</li> <li>SM2: code and pdf for generating the correlation matrices</li> <li>SM3: code and pdf for the random forest analyses</li> <li>SM4: code and pdf for the clustering and cluster validation analyses</li> </ul>
Distinct brain networks involved in placebo analgesia between individuals with or without prior experience with opioids
<p><strong>ABSTACT</strong></p> <p>Placebo analgesia is defined as a psychobiological phenomenon triggered by the information surrounding an antalgic drug instead of its inherent pharmacological properties. Placebo analgesia is hypothesized to be formed through either verbal suggestions or conditioning. The present study aims at disentangling the neural correlates of expectations effects with or without conditioning through prior experience using the model of placebo analgesia.</p> <p>We will address this question by recruiting two groups of individuals holding comparable verbally-induced expectations regarding morphine analgesia but either (i) with or (ii) without prior experience with opioids. We will then contrast the two groups’ neurocognitive response to acute heat-pain induction following the injection of sham morphine using electroencephalography (EEG). Topographic ERP analyses of the N2 and P2 pain evoked potential components will allow to test the hypothesis that placebo analgesia involves distinct neural networks when induced by expectations with or without prior experience.</p>
Neurodevelopmental Patterns of Early Postnatal White Matter Maturation Represent Distinct Underlying Microstructure and Histology
<p>This dataset includes:</p> <ol> <li>T2w Template from the dHCP datasets.</li> <li>4D weekly average maps from the dHCP study: i) average T2w; ii) average T2w/T1w signal ratio; iii) average neurite density index [from NODDI]; iv) average free water map [from NODDI].</li> <li>NMF results - NeWMaPs from the dHCP study across multiple resolutions (ranging from 2-20 NMFs).</li> </ol> <p> </p>
Discrimination of Classical and Atypical BSE by a Distinct Immunohistochemical PrPSc Profile
<p>Bovine spongiform encephalopathy (BSE) is a fatal neurodegenerative disease in cattle belonging to the group of transmissible spongiform encephalopathies. Hallmark of the disease is the accumulation of the pathological prion protein (PrPSc) in the brain. Classical BSE (C-type) and two atypical BSE forms (L- and H-type) are known, and can be discriminated by biochemical characteristics. The data presented here underline that immunohistochemistry can also be used to identify type-specific PrPSc profiles which can be used for discriminatory purposes. For this brain samples from 21 cattle, intracerebrally inoculated with C-, H-, and L-type BSE, were used as well as three orally C-type BSE infected animals. Using six brain regions distinct lesion (H&E staining) and PrPSc profiles were determined. While the neuroanatomical distribution of lesions and the PrPSc accumulation were highly consistent between the groups, the topographic and cellular PrPSc profile revealed characteristic pattern for the different BSE types.</p>
Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes
<p><strong>Abstract</strong></p> <p>Brain ageing is a highly variable, spatially and temporally heterogeneous process, marked by numerous structural and functional changes. These can cause discrepancies between individuals’ chronological age and the apparent age of their brain, as inferred from neuroimaging data. Machine learning models, and particularly Convolutional Neural Networks (CNNs), have proven adept in capturing patterns relating to ageing induced changes in the brain. The differences between the predicted and chronological ages, referred to as brain age deltas, have emerged as useful biomarkers for exploring those factors which promote accelerated ageing or resilience, such as pathologies or lifestyle factors. However, previous studies rely only on structural neuroimaging for predictions, overlooking potentially informative functional and microstructural changes. Here we show that multiple contrasts derived from different MRI modalities can predict brain age, each encoding bespoke brain ageing information. By using 3D CNNs and UK Biobank data, we found that 57 contrasts derived from structural, susceptibility-weighted, diffusion, and functional MRI can successfully predict brain age. For each contrast, different patterns of association with non-imaging phenotypes were found, resulting in a total of 191 unique, statistically significant associations. Furthermore, we found that ensembling data from multiple contrasts results in both higher prediction accuracies and stronger correlations to non-imaging measurements. Our results demonstrate that other 3D contrasts and modalities, which have not been considered so far for the task of brain age prediction, encode different information about the ageing brain. We envision our work as being the starting point for future investigations into the causal links underpinning the observed brain age deltas and non-imaging measurement associations. For instance, drug effects can be monitored, given that certain medications correlated with accelerated brain ageing. Furthermore, continued development of brain age models could facilitate their deployment in clinical trials for recruitment and monitoring, and hospitals for diagnostic and screening tasks.</p> <p><strong>Data Description</strong></p> <p>This dataset contains the full correlation results with all nIDPs in the UK Biobank. These are presented in datasets split by sex in Female and Male subjects. For easier data manipulation, two smaller datasets have also been made available, containing just those correlation which pass the False Discovery Rate (FDR) threshold. </p> <p>As experiments were also conducted for ensembles using multiple contrasts, similar datasets are provided for those.</p> <p>Finally, global datasets are also provided. These are the concatenation of the associations contained in the Male and Female datasets.</p> <p><strong>Paper & Code</strong></p> <p>The original paper for this article can be accessed here:</p> <ul> <li><a href="https://ieeexplore.ieee.org/abstract/document/10196736">https://ieeexplore.ieee.org/abstract/document/10196736</a></li> </ul> <p>To access the codes relevant for this project, please access the project GitHub Repos:</p> <ul> <li><a href="https://github.com/AndreiRoibu/AgeMapper">https://github.com/AndreiRoibu/AgeMapper</a></li> </ul> <p>If using this work, please cite it based on the above paper, or using the following BibTex:</p> <pre><code class="language-markdown">@inproceedings{roibu2023brain, title={Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes}, author={Roibu, Andrei-Claudiu and Adaszewski, Stanislaw and Schindler, Torsten and Smith, Stephen M and Namburete, Ana IL and Lange, Frederik J}, booktitle={2023 10th IEEE Swiss Conference on Data Science (SDS)}, pages={17--25}, year={2023}, organization={IEEE}, doi={10.1109/SDS57534.2023.00010} }</code></pre> <p> </p> <p><strong>Data Access</strong></p> <p>The data for this project is freely available upon application at the UK Biobank. For more information regarding the individual nIDPs, please access the UK Biobank Showcase website at: https://biobank.ctsu.ox.ac.uk/showcase/search.cgi</p> <p><strong>Funding</strong></p> <p>ACR is supported by EPSRC Grant EP/S024093/1, F. Hoffmann-La Roche AG and a 2021 Industrial Fellowship offered by the Royal Commission for the Exhibition of 1851. SMS is supported by a Wellcome Trust Collaborative Award 215573/Z/19/Z. AILN is grateful for support from the Academy of Medical Sciences under the Springboard Awards scheme (SBF005/1136), and the Bill and Melinda Gates Foundation. FJL is supported by a Wellcome Trust Collaborative Award (215573/Z/19/Z). The WIN is supported by core funding from the Wellcome Trust (203139/Z/16/Z). The computational aspects were supported by the Wellcome Trust (203141/Z/16/Z) and the NIHR Oxford BRC. Corresponding authors: ACR (andreiroibu@icloud.com), SA (stanislaw.adaszewski@roche.com) and AILN (ana.namburete@cs.ox.ac.uk).</p>
Experimentally manipulated biota over a 30-40d period in two streams with distinctly different macrobiotic assemblages
Here we test the hypothesis that differences in macrobiotic assemblages can lead to differences in the quantity and quality of organic matter in benthic depositional environments among streams in montane Puerto Rico. We experimentally manipulated biota over a 30-40d period in two streams with distinctly different macrobiotic assemblages: one characterized by high densities of omnivorous shrimps (Decapoda: Atyidae and Xiphocarididae) and no predaceous fishes. To incorporate the natural hydrologic regime and to avoid confounding artifacts associated with cage enclosure/exclosure (e.g., high sedimentation), we used electricity as a mechanism for experimental exclusion, in situ. In each stream, shrimps and/or fishes were excluded from specific areas of rock substrata in four pools using electric "fences" attached to solar-powered fence chargers. In the stream lacking predaceous fishes (Sonadora), the unelectrified control treatment was almost exclusively dominated by high densities of omnivorous shrimps that constantly ingested fine particulate material from rock surfaces. Consequently, the control had significantly lower levels of inorganic sediments, organic material, carbon and nitrogen than the exclusion treatment, as well as less variability in these parameters. Tenfold more organic material (as ash-free dry mass, AFDM) and fivefold more nitrogen accrued in shrimp exclosures (10.6 g AFDM/m2, 0.2 g N/m2) than in controls (1.1 g AFDM/m2, 0.04 g N/m2). By reducing th quantity of fine particulate organic material and associated nitrogen in benthic environments, omnivorous shrimps potentially affect the the supply of this important resource to other trophic levels. The small amount of fine particulate organic matter (FPOM) that remained in control treatments (composed of sparse algal cells0 was of higher quality than that in shrimp exclosures. This is evidenced by the significantly lower carbon-to-nitrogen (C/N) ratio (an indicator of food quality, with relatively low C
Data Repository - Distinct Roles of Direct and Indirect Electrification in Pathways to a Renewables-dominated European Energy System
<p>This is the data repository to reproduce the scenario analysis of the paper "<a href="https://www.cell.com/one-earth/fulltext/S2590-3322(24)00037-X?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS259033222400037X%3Fshowall%3Dtrue">Distinct roles of direct and indirect electrification in pathways to a renewables-dominated European energy system</a>".</p> <p>The source code for the REMIND version used in this study is available at <a href="https://github.com/fschreyer/remind/tree/ElecH2_prod">https://github.com/fschreyer/remind/tree/ElecH2_prod</a>. The scenario config file that was used to start the specific model runs of the paper and that inlucdes all scenario-specific model settings can be found in the repository under <a href="https://github.com/fschreyer/remind/blob/ElecH2_prod/config/21_regions_EU11/scenario_config_ElecH2.csv">./config/21_regions_EU11/scenario_config_ElecH2.csv</a>. The repository is a fork with slight changes relative to the main release version available at <a href="https://github.com/remindmodel/remind/tree/v3.2.1">https://github.com/remindmodel/remind/tree/v3.2.1</a> and <a href="https://doi.org/10.5281/zenodo.7852740">https://doi.org/10.5281/zenodo.7852740</a>. The model documentation can be found at <a href="https://rse.pik-potsdam.de/doc/remind/3.2.0">https://rse.pik-potsdam.de/doc/remind/3.2.0</a>. </p> <p>Model output data as well as other data that were used in the study are stored in data.zip. Moreover, we added a PlotsData.zip file, which contains the data shown in the figures of the paper. The R script to produce the figures and analysis of the paper can be found in ElecH2paper_Plots.Rmd. We publish a comprehensive dataset of our model output which includes more data than what is needed to reproduce the figures of the paper. Those data can be helpful to compare and contextualize our scenarios or use them for further analyses. However, due to the scope and complexity of our modeling framework, these data need to be used with care. The data used for the analysis of this study have been thoroughly validated. However, we cannot always perform such validation for the whole dataset and data need to treated with caution in particular at high regional or sectoral resolution and with respect to aspects that were not in the focus of the study as there maybe artefacts or limitations of our modeling approach. Please contact us in case you would like to use our scenarios for further analyses. We welcome open and constructive exchange on our data. </p> <p> </p> <p>Contact:<br>Felix Schreyer<br>Potsdam Institute for Climate Impact Research<br>felix.schreyer@pik-potsdam.de</p>
Dataset for manuscript "During haptic communication, the central nervous system compensates distinctly for delay and noise"
<p>Data relating to the manuscript "Dataset for manuscript "During haptic communication, the central nervous system compensates distinctly for delay and noise". This includes the experiment dataset (in file experiment_dataset.csv) as well as the MATLAB functions used for the development of the simulation model (with main function main_delay.m)</p>
Data from: Validating a practical methodology for thatch - mat - soil distinction in turfgrass soils
<p><span>We described a <a name="_Hlk163223211"></a>practical method for the distinction of thatch, mat and soil layers in turfgrass soils, being a combination of visually and manually observable characteristics. For two widely different turfgrass species, we analyzed total organic matter (TOM) and dry bulk density (ρd) in thin slices of 6 mm (between 0 and 10 cm soil depth), resulting in clear patterns for both soil properties with increasing depth. Statistical analysis of TOM patterns resulted in similar boundary depths between calculated and observed layers, validating our practical method for the distinction of thatch, mat and soil layers as a reliable method. </span><span>Furthermore, we characterized thatch, mat and soil layer by different TOM fractions. <span>TOM was fractionalized into three distinctive and functional pools of organic matter: (1) visible organic matter (VOM), consisting of mainly non-decomposed plant structures, (2) decomposed organic matter (DOM), consisting of mainly decomposed plant structures with its associated microbial biomass, and (3) soil organic matter (SOM), being the background value or recalcitrant native organic matter in a soil and its local microbial biomass.</span></span></p> <p><span><span>We distinguished thatch, mat and soil layer based on visual and manual observable characteristics of the layers and a protocol as described in Evers et al. (2024) https://doi.org/10.1002/its2.148. This study was conducted on a well-established turfgrass demonstration field with monoculture plots of turfgrass varieties (turfgrass seed company DLF; Moerstraten, the Netherlands; 51°32'27'' N 4°20'54" E) 3.5 years after sowing, reflecting the result of organic matter accumulation over these initial years of turfgrass establishment. The climate regime was marine with cool to medium summer temperatures and mild winters (Cfb/Cfa according to the Köppen-Geiger climate classification system (Peel, et al., 2007)). The field was built on a sandy soil (Hortic Anthrasol as described in the FAO/UNESCO soil map of the world (2006)). Sampling of the soil took place in June 2016 before the field was sown. We tested our methodology with two turfgrass species, slender creeping red fescue (<em>Festuca rubra trichophylla </em>(Frt), variety Beudin of DLF) <span>as an example of a spreading turfgrass</span><em> </em><span>and perennial ryegrass (<em>Lolium perenne </em></span>(Lp)<em>,</em> variety Duparc of DLF) as an example of a bunch-type grass, as these two species were expected to differ widely in thatch and mat depth. The individual plot size for each variety was approximately 1 m<sup>2</sup> (0.8 x 1.2 m). Plots of each species were sampled at the end of February 2020. A subplot of 0.25 m<sup>2</sup> (0.5 m x 0.5 m) in the center of one plot per species was selected, to avoid contamination with other varieties (at least 90% pure monoculture), and it was marked with a metal frame. From the 25 cells of 5 x 5 cm in this frame, nine evenly dispersed cells were chosen to take a set of soil samples of 10 cm depth, using a core sampler with 2.8 cm diameter, for thatch-mat and mat-deeper soil boundary observation and TOM and <em>ρ</em><sub>d</sub> analyses. A<span>nother set of nine samples was taken next to the previous cells for VOM analyses. </span>Every fresh 10 cm soil core was first photographed (Canon Powershot S5 camera, 24 megapixels; Canon Europe, Amstelveen, the Netherlands), judged on thatch-mat and mat-deeper soil boundaries following the method described in supplementary information, and then sliced into 14 subsamples of 6 mm each plus a 16 mm subsample at the bottom, starting to measure just below the green canopy with an accurate ruler (Sola HK ¼ W12, EU-accuracy class 3), for analyzing TOM and <em>ρ</em><sub>d</sub>.in every subsample. TOM and <em>ρ</em><sub>d</sub> were analyzed after samples were dried at 105°C for 24 h. VOM was analyzed after a<span>ll sediment per slice was carefully washed off with tap water in a fine sieve (approximately 600 µm (27 mesh)), after which the remaining (dead and living) plant biomass, mainly roots and rhizomes, was dried at 65 °C for at least 48 h. SOM was determined separately in the bulk soil of the study site and is 2.6% of the dry matter. DOM was calculated via subtraction of VOM and SOM from TOM</span></span></span></p> <p><span>Based on the analyzed TOM content of the 14 slices per nine replicates, the boundaries of distinctive layers were calculated. For this, we rescaled the TOM results per replicate via the normalized function <em>F </em>(χ) = (χ-χ<sub>min</sub>)/(χ<sub>max</sub>-χ<sub>min</sub>) into values between 0 and 1 to overcome scale differences between replicates but keeping distributions the same. Per turfgrass species, the best fitted line, i.e., the smallest <em>rse</em>, and its 95%-confidence interval through all 135 points was iteratively calculated by non-linear least square regression with the nls function of R (version 3.5.2; 2018-12-20). For the fitting of the statistical models, either a logistic function (<em>F </em>(χ) = α/(1+e<sup>-(βχ+γ)</sup>) or a bell-shaped Gaussian function (<em>G </em>(χ) = αe<sup>-((χ-β)^2/2γ^2)</sup>) was used, based on the best fit for the respective turfgrass species. The characteristics of these mathematical functions were used to explain the TOM-dynamics in the soil. To this end, the turning points of the curves were calculated by finding where the first derivative of both functions equals zero, i.e., solving <em>F’</em> (χ) = 0 and <em>G’ </em>(χ) = 0 respectively. The inflection points of each curve were determined by analyzing the second derivative, i.e., identifying the points where the second derivative <em>F’’</em> (χ) or <em>G’’</em> (χ) = 0, indicating changes in concavity. The inflection points and turning points indicated changes in TOM content in the turfgrass soil profile as likely boundaries between distinctive soil layers. </span><span>Differences in parameters between distinctive soil layers and turfgrass species were determined based on the calculated means of parameters per nine replicates of soil slices Normality of residuals and the equality of variances was checked with diagnostic plots and Levene’s test, respectively. Normally distributed means were compared with one-way ANOVA for a 3-layered soil system, followed by either Tukey post hoc tests in case of equality of variance, or by the Games-Howell post hoc test in case of no equality of variance, or with a one sample t-test for a 2-layered soil system. </span></p>
Whole-genome genotype data for French Large White pigs from two distinct sampling times
<p>Genotype data at plink binary format for 36 pigs from the french Large White breed: 13 animals from the female line born in 2014 and 2015, 13 animals from the male line born between 2012 and 2016, and 10 animals from a common ancestral line, born in 1977. These genotypes were obtained from individual whole genome sequencing (WGS) data, whiwh are available at https://www.ebi.ac.uk/ena under the accession number PRJEB51909.</p> <p>Two different genotype datasets were obtained from the raw WGS:</p> <p>1) snp20_auto_cr (.bed/bim/fam): High quality autosomal SNPs, called by 3 different software, with a call rate of at least 90%</p> <p>2) all10_auto (.bed/bim/fam): All SNPs or indels called by at least one of 3 different software.</p> <p>More details about these datasets and their use can be found in the following study:</p> <p>Boitard et al (under revision): Whole-genome sequencing of cryo-preserved resources from French Large White pigs at two distinct sampling times reveals strong signatures of convergent and divergent selection between the dam and sire lines.</p>
Longitudinal characterization of circulating neutrophils uncovers distinct phenotypes associated with severity in hospitalized COVID-19 patients
<p>Code and data for the manuscript "Longitudinal characterization of circulating neutrophils uncovers distinct phenotypes associated with severity in hospitalized COVID-19 patients".</p> <p>Contains all code located at <a href="https://github.com/lasalletj/COVID_Neutrophils">https://github.com/lasalletj/COVID_Neutrophils</a> as well as additional data files needed to run the code.</p> <p>Three additional publicly available data objects are required to run the code from start to finish. The first, covid.combined_final.Robj, from the Sinha et al. Nature Medicine 2022 paper (<a href="https://doi.org/10.1038/s41591-021-01576-3">https://doi.org/10.1038/s41591-021-01576-3</a>), is downloadable from the following link: <a href="https://figshare.com/ndownloader/files/31562957">https://figshare.com/ndownloader/files/31562957</a>. The other two required objects, seurat_COVID19_Neutrophils_cohort2_rhapsody_jonas_FG_2020-08-18.rds and seurat_COVID19_freshWB-PBMC_cohort2_rhapsody_jonas_FG_2020-08-18.rds, are from the Schulte-Schrepping et al. Cell 2020 paper (<a href="https://doi.org/10.1016/j.cell.2020.08.001">https://doi.org/10.1016/j.cell.2020.08.001</a>), and can be downloaded from <a href="https://beta.fastgenomics.org/datasets/detail-dataset-ee4b1a0f339140ad82f861aea35076f1#Files">https://beta.fastgenomics.org/datasets/detail-dataset-ee4b1a0f339140ad82f861aea35076f1#Files</a> and <a href="https://beta.fastgenomics.org/datasets/detail-dataset-1ad2967be372494a9fdba621610ad3f3#Files">https://beta.fastgenomics.org/datasets/detail-dataset-1ad2967be372494a9fdba621610ad3f3#Files</a>, respectively.</p> <p>Any additional information required to reanalyze the data reported in this work paper is available from the Lead Contact, Moshe Sade-Feldman (msade-feldman@mgh.harvard.edu) upon request.</p>
Data supporting "Transformer Model Generated Bacteriophage Genomes are Compositionally Distinct from Natural Sequences"
<p>Sequence and composition data supporting doi: <a href="https://doi.org/10.1101/2024.03.19.585716" target="_blank" rel="noopener">10.1101/2024.03.19.585716</a>. Uncompressed file size is ~5.8GB.</p> <p>Data in zip files is organized by sequence provenance (generRNA, natural, or transformer (megaDNA)). Common file types between folders include:</p> <ul> <li>Multi-record fasta file: Sequence data for all sequences of a given provenance. For generRNA sequences, these are found within the `seq` column of file "MFE_distribution_Fig4a.csv"</li> <li>Composition files: Individual sequence level compositional metrics for sliding 120 bp windows. Only structural metrics were used in this study.</li> <li>Genomad: Results from the genomad pipeline (https://portal.nersc.gov/genomad/)</li> <li>Stats: Aggregate statistics for all sequences of a given provenance.</li> </ul> <p>The natural folder also has a metadata file detailing the taxonomy for all natural sequences.<br><br>Figure datasets are the cleaned (sometimes aggregated) datasets that underly specific figures in the manuscript. The figure designations are based on the order in: https://www.biorxiv.org/content/10.1101/2024.03.19.585716v1.</p>
Mass mortality among colony-breeding seabirds in the German Wadden Sea in 2022 due to distinct genotypes of HPAIV H5N1 clade 2.3.4.4b: data sets on phylogeographic analyses
<p>Highly pathogenic avian influenza viruses (HPAIV) of clade 2.3.4.4b of the H5 goose/Guangdong (gs/GD) lineage have repeatedly emerged in Germany since 2016. Both poultry holdings and wild birds have been heavily hit but the 2020-2021 and 2021-2022 HPAI winter seasons exceeded all previously recorded epizootics in Germany in terms of number of wild bird cases recorded, genetic diversity of viruses, and duration of virus activity. In past seasons regional massing of wild bird cases were seen at the German coasts of the Baltic and North Sea, but species mainly affected varied from season to season. In 2022 a new and, in Europe, unprecedented aspect was observed when several cormorant and seabird breeding colonies became affected since May at the Baltic Sea coast and in the Wadden Sea, respectively by HPAI H5N1 viruses.</p> <p> </p>
Data set for "Distinct contributions of whisker sensory cortex and tongue-jaw motor cortex in a goal-directed sensorimotor transformation"
<p>Data set for: Mayrhofer JM, El-Boustani S, Foustoukos G, Auffret M, Tamura K, Petersen CCH (2019) Distinct contributions of whisker sensory cortex and tongue-jaw motor cortex in a goal-directed sensorimotor transformation. Neuron https://doi.org/10.1016/j.neuron.2019.07.008</p> <p>There are 2 files in this upload:</p> <p>1. The file named "2019_Mayrhofer_Neuron.pdf" is the Open Access pdf file of the manuscript published in Neuron.</p> <p>2. The file named "Mayrhofer_data_code.zip" (~20 GB) is a zipped version of a folder "Mayrhofer_data_code" (~57 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. The analysis code is in a subfolder named "MatlabCode", and the specific code for generating each figure panel is in a sub-subfolder named "Figures_tjM1_paper". When running the code, you need to set the Matlab file path to be "Mayrhofer_data_code". In addition, you should add the folder "Mayrhofer_data_code" with subfolders in Matlab "Set Path". The figures will be saved in a subfolder named "Figures". Some parts of the code rely upon previous results, and need to be executed sequentially in the order of the figure panels in the journal publication.</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.