cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21430/m3tevre64x
title: CD8 Depletion

description: Cell-mediated immunity is critical for control of M. tuberculosis infection. Although the importance of CD4 T cells in control of tuberculosis is well-established, the importance of CD8+ lymphocytes is less clear. Here, we investigated the requirement for all CD8+ lymphocytes (innate and adaptive) or just CD8 alpha/beta adaptive T cells in control of initial and early infection in cynomolgus macaques, using anti-CD8alpha or anti-CD8beta depleting antibodies in vivo. Flow cytometry and single-cell RNA-sequencing analysis of lung granulomas from control (undepleted) macaques at 6 weeks post-infection revealed a rich landscape of cytotoxic CD8+ lymphocytes. Anti-CD8alpha antibody resulted in susbstantial loss of all CD8+ lymphocytes from granulomas, while anti-CD8beta antibody selectively depleted classical CD8alpha beta T cells. In CD8-depleted granulomas, CD4 T cells and gamma delta T cells had increased expression of certain cytotoxic effectors, such as granzymes. However, these cells had reduced expression of perforin, granulysin, and/or CCL5, and thus displayed an incomplete cytotoxic profile. At 6 weeks post-infection, CD8 alpha depleted macaques had increased granuloma numbers, lung inflammation, and total thoracic bacterial burden, while CD8beta-depleted macaques showed increased lymph node bacterial burden. Mtb barcode analysis revealed that depletion of all CD8+ lymphocytes resulted in greater initial establishment of Mtb in the lungs, indicating a loosening of the airway bottleneck for infection, and increased dissemination of Mtb within lungs, to lymph nodes and to extrapulmonary sites. These data suggest that the total CD8+ lymphocyte population is critical for early control of Mtb infection in macaques and support the importance of targeting CD8+ lymphocytes in a vaccine strategy.
DOI: 10.21430/m3rn7m163w
title: Longitudinal analysis of SARS-CoV-2 seroprevalence

description: Samples from healthcare workers (HCWs) were collected to assess their seroprevalence using various serology platforms
DOI: 10.21430/m3xzn09qff
title: MoTrPAC 6-month rat endurance training

description: MoTrPAC is a national research consortium designed to discover and perform preliminary characterization of the range of molecular transducers (the molecular map) that underlie the effects of physical activity in humans. The programs goal is to study the molecular changes that occur during and after exercise and ultimately to advance the understanding of how physical activity improves and preserves health.
DOI: 10.21430/m3gxsgmpex
title: Sex and Gender Differences in Testing, Hospital Admission, Clinical Presentation, and Drivers of Severe Outcomes From COVID-19

description: To elucidate features of COVID-19 infection that differ between males and females from diagnosis to outcomes
DOI: 10.21430/m3y1wrsjz6
title: Neutralization Escape by SARS-CoV-2 Omicron Subvariant BA.4.6

description: To determine the ability of BA.4.6 to evade neutralizing antibodies that were induced by infection or vaccination.
DOI: 10.21430/m3ppvgc75e
title: Determinants and Mechanisms of the Low Fusogenicity and High Dependence on Endosomal Entry of Omicron Subvariants

description: To investigate the mechanisms governing Omicron subvariant entry.
DOI: 10.21430/m3ctynvl3v
title: SARS-CoV-2 Delta variant genomic variation associated with breakthrough infection in Northern California: A retrospective cohort study

description: To characterize the genomic variation within a circulating variant and identifying potential mutations associated with breakthrough infection among persons with Delta variant SARS-CoV-2 infection
DOI: 10.21430/m39gu9w1lh
title: Immunogenicity of COVID-19 mRNA Vaccines in Pregnant and Lactating Women

description: To evaluate the immunogenicity of COVID-19 mRNA vaccines, including against emerging SARS-CoV-2 variants of concern, in pregnant and lactating women.
DOI: 10.21430/m3xautdg0k
title: Coronavirus Disease 2019 Messenger RNA Vaccine Immunogenicity in Immunosuppressed Individuals

description: To assess the effect of immunosuppresive therapy on antibody responses following COVID-19 messenger RNA vaccination
DOI: 10.21430/m3ek86tjv6
title: Characterization of immune responses in fully vaccinated individuals after breakthrough infection with the SARS-CoV-2 delta variant

description: To evaluate the immune responses in fully vaccinated individuals with or without SARS-CoV-2 breakthrough infection.
DOI: 10.21430/m36hjyny0b
title: Neutralization escape of Omicron XBB, BR.2, and BA.2.3.20 subvariants

description: To examine the sensitivity of BR.2, BA.2.3.20, and XBB subvariants to neutralization by vaccine- and infection-induced sera samples and compare them to the ancestral D614G, BA.2, and BA.2.75 subvariants.
DOI: 10.21430/m3dt6lu7ew
title: Differential Kinetics of Immune Responses Elicited by Covid-19 Vaccines

description: To evaluate antibody and T-cell responses from peak immunity at 2 to 4 weeks after the second immunization in recipients of the messenger RNA (mRNA) vaccines or after the first immunization in recipients of the Ad26.COV2.S vaccine to 8 months.
DOI: 10.21430/m3yp5oulp0
title: Right Ventricular Abnormality in Patients Hospitalized With COVID-19 Infection During Omicron Variant Surge.

description: To study the association of in-hospital mortality with echocardiographic measures of right ventricular performance during the COVID-19 infection during the Omicron surge in New York City.
DOI: 10.21430/m3e2gti9wo
title: Coronavirus disease 2019 vaccine response in pregnant and lactating women: a cohort study

description: To evaluate the immunogenicity and reactogenicity of coronavirus disease 2019 messenger RNA vaccination in pregnant and lactating women.
DOI: 10.21430/m369ettadw
title: Methodological approaches to optimize multiplex oral fluid SARS-CoV-2 IgG assay performance and correlation with serologic and neutralizing antibody responses

description: To optimize performance of a magnetic microparticle-based multiplex immunoassay (MIA) for SARS-CoV-2 IgG measurement in saliva.
DOI: 10.21430/m3et0zgoyy
title: Significant Reduction in Vaccine Induced Antibody Levels and Neutralization Activity Among Healthcare Workers and Nursing Home Residents 6 Months Following Coronavirus Disease 2019 BNT162b2 mRNA Vaccination

description: To assess antibody decline following vaccination in Nursinghome residents and health care workers.
DOI: 10.21430/m3nwos0byj
title: Dissecting strategies to tune the therapeutic potential of SARS-CoV-2–specific monoclonal antibody CR3022

description: To explore the therapeutic potential of SARS-CoV-2–specific monoclonal antibody CR3022 and study the role of Fc effector function on the response to SARS-CoV-2 infection.
DOI: 10.21430/m36u7rskao
title: A Machine Learning Algorithm Predicts Duration of hospitalization in COVID-19 patients.

description: To develop machine learning algorithms that predict the likelihood of prolonged length of stay using electronic health record data from 966 COVID-19 patient.
DOI: 10.21430/m3p39udc8c
title: Progression and Resolution of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection in Golden Syrian Hamsters

description: To characterize progression of disease in depth in a robust coronavirus disease 2019 (COVID-19) animal model
DOI: 10.21430/m3gxf3v8pa
title: 124I-Iodo-DPA-713 Positron Emission Tomography in a Hamster Model of SARS-CoV-2 Infection

description: To evaluate the local immune response in a hamster lung model of SARS-CoV-2 infection by pulmonary Positron Emission Tomography with 124I-iodo-DPA-713.
DOI: 10.21430/m3pv8vpkp0
title: Exposure of progressive immune dysfunction by SARS-CoV-2 mRNA vaccination in patients with chronic lymphocytic leukemia: A prospective cohort study

description: To probe the underlying mechanisms of immune dysfunction in chronic lymphocytic leukemia (CLL) patients with varying degrees of disease progression by studying both humoral and cellular immune responses to SARS-CoV-2 mRNA vaccination.
DOI: 10.21430/m3e361589t
title: Conflict in the EMS Workforce: An Analysis of an Open-Ended Survey Question Reveals a Complex Assemblage of Stress, Burnout, and Pandemic-Related Factors Influencing Well-Being

description: To explore the extent to which added stressors of the pandemic exacerbated EMS workplace conflict.
DOI: 10.6084/m9.figshare.24164325
title: Miliary TB scoping review.xlsx

description: Data from a scoping review on miliary TB in children (protocol available at https://f1000research.com/articles/12-309)
DOI: 10.6084/m9.figshare.24164319
title: Supplementary materials for Paleoearthquakes at the junction of the Tokomaru and Northern Ōhāriu faults, implications for fault interactions in the southern North Island, New Zealand

description: Includes unit descriptions for Makahika-1 and Makahika-2 trench logs, photo-orthomosaics for Makahika-1 and Makahika-2, and the OxCal code used to model earthquake ages in Makahika-1 and at other sites in the southern North Island.
DOI: 10.5281/zenodo.8361372
title: Test AIM model data

description: AIM model data
DOI: 10.5281/zenodo.8361371
title: Test AIM model data

description: AIM model data
DOI: 10.5281/zenodo.8361377
title: Dataset for the Cost of Living Index as a Primary Driver of Homelessness

description: SPSS data file SPSS output file Excel data and sources file Excel data only file for use with python processing (program on Github and archived on Zenodo)
DOI: 10.25345/c5pc2tk76
title: MassIVE MSV000092896 - Ttttttttttttttttttttttttttttttttttttttteszt5
title: dklnsavhuisdahvsuidahvnudahvdhsaonuvgdsaougvd sauvgdoasiuvgouasigvd usiah udhsa
DOI: 10.5281/zenodo.8357446
title: Fortuitous Observations of Potential Stellar Relay Probe Positions with GBT - Results Data Table

description: This record contains the data table produced as part of "Fortuitous Observations of Potential Stellar Relay Probe Positions with GBT" by Palumbo et al.
DOI: 10.6084/m9.figshare.23736258
title: <strong>Map use experiment data</strong> (include all behavioural, ET and EEG metrics)

description: This data contains a total of seven files.The first file is anonymised participants' behaviour, ET and EEG metrics when using the map task (raw data for statistical tests in the manuscript). Each row represents the results for one participant under one map trial. The ET and EEG data recordings failed for subject #1, and the EEG data recordings failed for subjects #17 and #26.The second file is the material (NASA-TLX scale and post-experiment questionnaire) that participants needed to make after completing the map task. All data were collected on paper, with two photographs for a sample of one participant.The third and fourth files are a sample of one participants' raw ET data (exported from Tobii pro) and EEG data (collected via the nic2 system).The fifth file is the stimulus material used in the experiment, of which only one trial is shown as a sample.The sixth file is the NASA-TLX and self-rated difficulty results.The last file is the python code for statistical analysis. It should be noted that visualization and EEG ERP results need the input of all sample raw data, and given the size of the data, the generation of these results is not shown in the code. Please contact the authors for more detailed requests.<b>Please note that we have not disclosed all raw data given the volume of raw data and concerns about the privacy of participants. Please contact the authors at their email address for further enquiries.</b><br>
DOI: 10.6084/m9.figshare.23736258.v4
title: <strong>Map use experiment data</strong> (include all behavioural, ET and EEG metrics)

description: This data contains a total of seven files.The first file is anonymised participants' behaviour, ET and EEG metrics when using the map task (raw data for statistical tests in the manuscript). Each row represents the results for one participant under one map trial. The ET and EEG data recordings failed for subject #1, and the EEG data recordings failed for subjects #17 and #26.The second file is the material (NASA-TLX scale and post-experiment questionnaire) that participants needed to make after completing the map task. All data were collected on paper, with two photographs for a sample of one participant.The third and fourth files are a sample of one participants' raw ET data (exported from Tobii pro) and EEG data (collected via the nic2 system).The fifth file is the stimulus material used in the experiment, of which only one trial is shown as a sample.The sixth file is the NASA-TLX and self-rated difficulty results.The last file is the python code for statistical analysis. It should be noted that visualization and EEG ERP results need the input of all sample raw data, and given the size of the data, the generation of these results is not shown in the code. Please contact the authors for more detailed requests.<b>Please note that we have not disclosed all raw data given the volume of raw data and concerns about the privacy of participants. Please contact the authors at their email address for further enquiries.</b><br>
DOI: 10.6084/m9.figshare.23736258.v3
title: <strong>Map use experiment data</strong> (include all behavioural, ET and EEG metrics)

description: This data contains a total of seven files.The first file is anonymised participants' behaviour, ET and EEG metrics when using the map task (raw data for statistical tests in the manuscript). Each row represents the results for one participant under one map trial. The ET and EEG data recordings failed for subject #1, and the EEG data recordings failed for subjects #17 and #26.The second file is the material (NASA-TLX scale and post-experiment questionnaire) that participants needed to make after completing the map task. All data were collected on paper, with two photographs for a sample of one participant.The third and fourth files are a sample of one participants' raw ET data (exported from Tobii pro) and EEG data (collected via the nic2 system).The fifth file is the stimulus material used in the experiment, of which only one trial is shown as a sample.The sixth file is the NASA-TLX and self-rated difficulty results.The last file is the python code for statistical analysis. It should be noted that visualization and EEG ERP results need the input of all sample raw data, and given the size of the data, the generation of these results is not shown in the code. Please contact the authors for more detailed requests.<b>Please note that we have not disclosed all raw data given the volume of raw data and concerns about the privacy of participants. Please contact the authors at their email address for further enquiries.</b><br>
DOI: 10.5281/zenodo.6895820
title: Surface Area of River and Lakes (SARL)

description: The Surface Area of River and Lakes (SARL) dataset has been developed to show the 38-years of seasonal and permanent water surface area change in rivers and lakes.
DOI: 10.5281/zenodo.6895819
title: Surface Area of River and Lakes (SARL)

description: The Surface Area of River and Lakes (SARL) dataset has been developed to show the 38-years of seasonal and permanent water surface area change in rivers and lakes.
DOI: 10.6084/m9.figshare.23681783
title: Maximum Likelihood Estimation of Hierarchical Linear Models from Incomplete Data: Random Coefficients, Statistical Interactions, and Measurement Error

description: We consider two-level models where a continuous response <i>R</i> and continuous covariates <i>C</i> are assumed missing at random. Inferences based on maximum likelihood or Bayes are routinely made by estimating their joint normal distribution from observed data Robs and Cobs. However, if the model for <i>R</i> given <i>C</i> includes random coefficients, interactions, or polynomial terms, their joint distribution will be nonstandard. We propose a family of unique factorizations involving selected “provisionally known random effects” <i>u</i> such that h(Robs,Cobs|u) is normally distributed and <i>u</i> is a low-dimensional normal random vector; we approximate h(Robs,Cobs)=∫h(Robs,Cobs|u)g(u)du via adaptive Gauss-Hermite quadrature. For polynomial models, the approximation is exact but, in any case, can be made as accurate as required given sufficient computation time. The model incorporates random effects as explanatory variables, reducing bias due to measurement error. By construction, our factorizations solve problems of compatibility among fully conditional distributions that have arisen in Bayesian imputation based on the Gibbs Sampler. We spell out general rules for selecting <i>u</i>, and show that our factorizations can support fully compatible Bayesian methods of imputation using the Gibbs Sampler. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.23681783.v2
title: Maximum Likelihood Estimation of Hierarchical Linear Models from Incomplete Data: Random Coefficients, Statistical Interactions, and Measurement Error

description: We consider two-level models where a continuous response <i>R</i> and continuous covariates <i>C</i> are assumed missing at random. Inferences based on maximum likelihood or Bayes are routinely made by estimating their joint normal distribution from observed data Robs and Cobs. However, if the model for <i>R</i> given <i>C</i> includes random coefficients, interactions, or polynomial terms, their joint distribution will be nonstandard. We propose a family of unique factorizations involving selected “provisionally known random effects” <i>u</i> such that h(Robs,Cobs|u) is normally distributed and <i>u</i> is a low-dimensional normal random vector; we approximate h(Robs,Cobs)=∫h(Robs,Cobs|u)g(u)du via adaptive Gauss-Hermite quadrature. For polynomial models, the approximation is exact but, in any case, can be made as accurate as required given sufficient computation time. The model incorporates random effects as explanatory variables, reducing bias due to measurement error. By construction, our factorizations solve problems of compatibility among fully conditional distributions that have arisen in Bayesian imputation based on the Gibbs Sampler. We spell out general rules for selecting <i>u</i>, and show that our factorizations can support fully compatible Bayesian methods of imputation using the Gibbs Sampler. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.23681783.v1
title: Maximum Likelihood Estimation of Hierarchical Linear Models from Incomplete Data: Random Coefficients, Statistical Interactions, and Measurement Error

description: Abstract–We consider two-level models where a continuous response <i>R</i> and continuous covariates <i>C</i> are assumed missing at random. Inferences based on maximum likelihood or Bayes are routinely made by estimating their joint normal distribution from observed data <i>R<sub>obs</sub> </i> and <i>C<sub>obs</sub> </i>. However, if the model for <i>R</i> given <i>C</i> includes random coefficients, interactions, or polynomial terms, their joint distribution will be nonstandard. We propose a family of unique factorizations involving selected “provisionally known random effects” <i>u</i> such that h(Robs,Cobs|u) is normally distributed and <i>u</i> is a low-dimensional normal random vector; we approximate h(Robs,Cobs)=∫h(Robs,Cobs|u)g(u)du via adaptive Gauss-Hermite quadrature. For polynomial models, the approximation is exact but, in any case, can be made as accurate as required given sufficient computation time. The model incorporates random effects as explanatory variables, reducing bias due to measurement error. By construction, our factorizations solve problems of compatibility among fully conditional distributions that have arisen in Bayesian imputation based on the Gibbs Sampler. We spell out general rules for selecting <i>u</i>, and show that our factorizations can support fully compatible Bayesian methods of imputation using the Gibbs Sampler.
DOI: 10.57703/ce-45pqf
title: Surface Topography Challenge C27

description: Smoother surface
Measured by Zygo NewView 9000
DOI: 10.25345/c5jm23s3f
title: MassIVE MSV000092897 - Staphylococcus aureus LukAB stem domain unlatching influences toxin oligomerization
title: Staphylococcus aureus (S. aureus) is a serious global pathogen that causes a diverse range of invasive diseases and is notorious for antibiotic resistance. S. aureus utilizes a family of pore-forming toxins, known as bi component leukocidins, to evade the host immune response and promote infection. Among these is LukAB (leukocidin A, leukocidin B), a toxin that is secreted as a soluble heterodimer and assembles into an octameric beta barrel pore that is embedded in the host cell membrane, resulting in death of the host cell. The established cellular receptor for LukAB is CD11b of the Mac1 complex. LukAB variants from S. aureus clonal complexes (CC) 30 and 45 were recently described to use the proton channel hydrogen voltage gated channel 1 (HVCN1) as a receptor. Here we show that HVCN1 is an essential receptor used by all LukAB variants representing the major S. aureus clonal complexes, including CC8 LukAB, which belongs to the most prevalent lineage responsible for skin and soft tissue infections in the United States. We demonstrate that while each receptor is sufficient to recruit CC8 LukAB to the plasma membrane of phagocytes, both receptors are required for maximal lytic activity. Why LukAB requires two receptors, and how each of these receptors contribute to pore-formation remains unknown. To begin to resolve this, we performed an alanine scanning mutagenesis screen to identify mutations that allow CC8 LukAB to bypass the requirement for CD11b. We discovered thirty mutations primarily localized in the stem domain of LukA and LukB that enabled LukAB to exhibit enhanced cytotoxicity in the absence of CD11b. Using crosslinking, electron microscopy, and hydroxyl radical protein footprinting experiments we show these mutations alter the solvent accessibility of the stem domain which prime LukAB for oligomerization. Together, our data allow us to introduce a role for CD11b beyond toxin recruitment to the target cell: we propose a model in which CD11b binding unlatches the membrane penetrating stem domains of LukAB, and this change in flexibility promotes toxin oligomerization, driving pore-formation. The mass spectrometric raw files for the hydroxyl radical footprinting experiment are included here.
DOI: 10.25438/wes02.23546064
title: Biden and Trump Mentions in Facebook Advertising Data

description: <b>Time period: June 1, 2020 through Election Day 2020</b><i>Through its Cross-Platform Election Advertising Transparency Initiative (CREATIVE) funded in part through</i><i> </i><i>a grant from the NSF</i><i> (Award Number 2235006), the Wesleyan Media Project is actively working on identifying and summarizing federal election content from the general election periods in federal cycles (September through Election Day). In late 2020, we received a request from the collaboration between Facebook and academics studying the 2020 election to obtain our classifications of advertising data relevant to the presidential election (defined by mentions of either candidate). We already had September through Election Day content in hand and worked backward to acquire June through August 2020 content relevant to presidential mentions solely in service of this request. The data provided here, which cover the June 1 to Election Day period from 2020, apply many of our methods employed in CREATIVE but precedes many of the refinements that we have made in identifying and classifying federal advertising to the content relevant to presidential ads. In addition, in the provided data we did not conduct a lot of fine tuning just for the presidential race. If we had built methods solely for delivery of presidential advertising in particular, we likely would have chosen different methods. We would like to remind researchers that the earlier (June through August) period contains, for many races, primary advertising rather than general election advertising, and we did not examine carefully the differences between these two.</i>