cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21430/m36n5njvxz
title: 35th Multicenter Airway Research Collaboration (MARC-35): Genetic analysis of infants

description: In a multicenter prospective cohort study of infants with severe bronchiolitis, we sought to identify genetic loci associated with asthma susceptibility and metabolites. We identified genetic loci and genetically driven metabolites that are associated with both genetic risk factors for asthma and childhood asthma development.
DOI: 10.21430/m3grs6dfe3
title: Immunity to seasonal coronavirus spike proteins does not protect from SARS-CoV-2 challenge in a mouse model

description: Mice were vaccinated with the spike proteins of 229E, NL63, OC43, HKU1 and SARS-CoV-2 (expressed as soluble trimers) with an oil-in-water emulsion adjuvant in a prime-boost regimen. Control mice were vaccinated with an irrelevant recombinant antigen, recombinant influenza virus hemagglutinin (HA).
DOI: 10.21430/m3bg3tevvt
title: mRNA vaccines induce rapid antibody responses in mice

description: This study investigates how quickly mRNA vaccines elicit antibody responses compared to other vaccine modalities
DOI: 10.21430/m3ze4kt4ou
title: Increased viral variants in children and young adults with impaired humoral immunity and persistent SARS-CoV-2 infection: A consecutive case series

description: To demostrate that immunocompromised pediatric and young adult patients are susceptible to prolonged viral infections with prolonged infectious virus shedding and mutation accumulation.
DOI: 10.21430/m3jrrvrsu4
title: Adverse Events After SARS-CoV-2 mRNA Vaccination Among Patients With Inflammatory Bowel Disease

description: To evaluate adverse events (AE) after messenger RNA vaccination in 246 adults with IBD participating in a longitudinal vaccine registry.
DOI: 10.21430/m3v0uomk3e
title: Nucleocapsid Antigenemia Is a Marker of Acute SARS-CoV-2 Infection

description: To determine if nucleocapsid antigenemia is a sensitive and specific marker of acute SARS-CoV-2 infection
DOI: 10.6084/m9.figshare.22118315.v1
title: Lagos State Health Facility Assessment Datasets_ HSCL

description: As part of the technical assistance of Health Systems Consult Limited (HSCL) to strengthen the strategic purchasing function of the Lagos State Health Management Agency (LASHMA), HSCL conducted a health facilities’ assessment to provide the data required for an effective roll-out of the Lagos State Health Scheme (LSHS).
DOI: 10.21430/m3urlefsyd
title: C57BL/6J Mice Are Not Suitable for Modeling Severe SARS-CoV-2 Beta and Gamma Variant Infection

description: To determine if mice infected with SARS-CoV-2 variants of concern without hACE2 can serve as models for severe COVID-19
DOI: 10.21430/m3t2140mm5
title: Modeling the Impact of Vaccination Strategies for Nursing Homes in the Context of Increased Severe Acute Respiratory Syndrome Coronavirus 2 Community Transmission and Variants

description: To examine the impact of community prevalence, the Delta variant, staff vaccination coverage, and booster vaccines for residents on outbreak dynamics in nursing homes
DOI: 10.21430/m3jlicy0tg
title: Variable cellular responses to SARS-CoV-2 in fully vaccinated patients with multiple myeloma

description: To determine whether seronegative vaccinated MM patients have detectable SARSCoV-2 B and T cell responses after SARS-CoV-2 vaccination
DOI: 10.21430/m3jwmd4xhu
title: Sex Differences in Lung Imaging and SARS-CoV-2 Antibody Responses in a COVID-19 Golden Syrian Hamster Model

description: To explore sex differences using Golden Syrian hamsters in the pathogenesis of a human isolate of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
DOI: 10.21430/m37rn5ilvc
title: A bacterial extracellular vesicle-based intranasal vaccine against SARS-CoV-2 protects against disease and elicits neutralizing antibodies to wild-type and Delta variants

description: To evaluate efficacy of intranasal vaccination of golden Syrian hamsters using novel technology (bacterial extracellular vesicle-based SARS-CoV-2 vaccine)
DOI: 10.21430/m39efsk7ei
title: Modeling in higher dimensions to improve diagnostic testing accuracy: theory and examples for multiplex saliva-based SARS-CoV-2 antibody assays

description: To showcase the power of mathematical modeling in diagnostic classification and highlight a method that can be adopted broadly in public health and clinical settings.
DOI: 10.21430/m39vjjkemo
title: A safe and highly efficacious measles virus-based vaccine expressing SARS-CoV-2 stabilized prefusion spike

description: To develop a safe, efficacious, and durable SARS-CoV-2 vaccine, using a measles virus (rMeV) vaccine strain as the backbone
DOI: 10.21430/m36yyrega0
title: Early Prediction and Longitudinal Modeling of Preeclampsia from Multiomics

description: The study developed a machine learning model for early prediction of preeclampsia (in first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from longitudinal cohorts of normotensive and preeclamptic pregnant women. The model had high accuracy in early pregnancy using urine metabolome and plasma proteome. A model using only nine urine metabolites had the highest accuracy and was validated on an independent cohort. Univariate analysis further confirmed statistical significance of all metabolites and proteins chosen by the prediction models. Longitudinal modeling revealed biological pathways associated with preeclampsia and confirmed known pathological alterations while suggesting novel associations between the immune and proteomic dynamics. The study identified several biological pathways associated with preeclampsia including steroid hormone biosynthesis (both early in pregnancy and over gestation), tryptophan and caffeine metabolisms (over gestation) and arachidonic acid metabolism (in early pregnancy). Integration with clinical variables further improved prediction accuracy of the urine metabolome model. The findings could lead to a simple, early diagnostic test for preeclampsia.
DOI: 10.34740/kaggle/dsv/5032238
title: COVID Vaccination in World (updated daily)
DOI: 10.6084/m9.figshare.21995621
title: Some structural features of the peptide profile of myelin basic protein-hydrolyzing antibodies in schizophrenic patients

description: Shotgun MS analysis of IgG peptides from the blood serum of patients with acute schizophrenia (noted 210421ZMG_SCH*) and healthy people (noted 210421ZMG_C*). Samples from schizophrenia patients exhibited proteolytic activity against myelin basic protein and samples from healthy individuals were with negligible or “null” proteolytic activity.
DOI: 10.6084/m9.figshare.21995621.v2
title: Some structural features of the peptide profile of myelin basic protein-hydrolyzing antibodies in schizophrenic patients

description: Shotgun MS analysis of IgG peptides from the blood serum of patients with acute schizophrenia (noted 210421ZMG_SCH*) and healthy people (noted 210421ZMG_C*). Samples from schizophrenia patients exhibited proteolytic activity against myelin basic protein and samples from healthy individuals were with negligible or “null” proteolytic activity.
DOI: 10.6084/m9.figshare.21995621.v1
title: Some structural features of the peptide profile of myelin basic protein-hydrolyzing antibodies in schizophrenic patients

description: Shotgun MS analysis of IgG peptides from the blood serum of patients with acute schizophrenia (noted 210421ZMG_SCH*) and healthy people (noted 210421ZMG_C*). Samples from schizophrenia patients exhibited proteolytic activity against myelin basic protein and samples from healthy individuals were with negligible or “null” proteolytic activity.
DOI: 10.6084/m9.figshare.22128377
title: Supplementary Material for: Visual Cue to Reduce False No-Show Registration in Riyadh Primary Healthcare: A Randomized Controlled Trial

description: <b><i>Introduction:</i></b> Preliminary field visits to the Saudi Ministry of Health’s primary healthcare centers (PHCs) confirmed a proportion of prior reported no-show appointments to be false due to staff registering patient arrivals inappropriately. We sought to investigate whether visual cue reminders would prime the staff to register patients properly. <b><i>Methods:</i></b> A randomized controlled trial was conducted in 2019 involving 35 PHCs in Riyadh, Saudi Arabia. Visual posters were installed in the intervention PHCs wherever patient arrivals were registered. The primary outcome was patient no-show appointments. Staff who registered appointment arrivals were observed and interviewed. Multilevel logistic regression analyzed the change in no-shows across an intervention group and a control group. <b><i>Results:</i></b> The intervention group had lower odds ratio (OR) of no-shows than the control group, although not significantly (OR 0.81, 95% confidence interval [CI] = 0.50–1.31). The period during Ramadan saw fewer no-shows than pre-Ramadan (OR 1.60, 95% CI = 1.55–1.66). Compared to family medicine appointments, no-shows were higher for the smoking cessation clinic (OR 3.95, 95% CI = 3.43–4.54), dental appointments (OR 2.14, 95% CI = 1.97–2.32), and vaccine appointments (OR 1.31, 95% CI = 1.27–1.35). Qualitative analysis shows that PHCs’ appointment processing was arbitrary without a unified structure. <b><i>Conclusion:</i></b> The reduction in no-show rates using visual cues was not significant. However, certain confounders, if not addressed prior to intervention implementation, can lead to cognitive overload and burnout, inviting unwanted behavior. Further implications are discussed.
DOI: 10.17632/shcnmv5dhx.1
title: Source Code in R for :  Sociodemographically Stratified Exploration of Pancreatic Cancer Incidence in Younger US Patients:  Implication of Cannabis Exposure as a Potential Risk Factor

description: Computational Source Code for above paper
DOI: 10.17632/y32txvjhpf.1
title: Major Datasets for:  Sociodemographically Stratified Exploration of Pancreatic Cancer Incidence in Younger US Patients:  Implication of Cannabis Exposure as a Potential Risk Factor

description: Major datasets to accompany paper of the above title.
DOI: 10.17632/4s87k4v5n7.1
title: Input Data to Accompany: Sociodemographically Stratified Exploration of Pancreatic Cancer Incidence in Younger US Patients:  Implication of Cannabis Exposure as a Potential Risk Factor

description: Input data to accompany paper of the above title.
DOI: 10.17632/wjb5f622n4.1
title: Source Data Files to Accompany: Sociodemographically Stratified Exploration of Pancreatic Cancer Incidence in Younger US Patients:  Implication of Cannabis Exposure as a Potential Risk Factor

description: Source data files for the paper of the above title
DOI: 10.5281/zenodo.7657181
title: Open-Unmix-Pytorch LabelNoise

description: SDXDB21 LabelNoise Baseline <pre>We split the training data into train and valid. For valid, the following songs were used:</pre> <pre>bc1f2967-f834-43bd-aadc-95afc897cfe7 cc3e4991-6cce-40fe-a917-81a4fbb92ea6 ed90a89a-bf22-444d-af3d-d9ac3896ebd2 f4b735de-14b1-4091-a9ba-c8b30c0740a7 bc964128-da16-4e4c-af95-4d1211e78c70 cc7f7675-d3c8-4a49-a2d7-a8959b694004 f40ffd10-4e8b-41e6-bd8a-971929ca9138</pre> <pre> The following commands were used to create the models:</pre> <pre><code class="language-bash">OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=3 python train.py \ --root /sdxdb23_labelnoise_v1.0 \ --dataset trackfolder_fix \ --target-file vocals.wav \ --interferer-files bass.wav drums.wav other.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=4 python train.py \ --root /sdxdb23_labelnoise_v1.0 \ --dataset trackfolder_fix \ --target-file bass.wav \ --interferer-files vocals.wav drums.wav other.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=5 python train.py \ --root /sdxdb23_labelnoise_v1.0 \ --dataset trackfolder_fix \ --target-file drums.wav \ --interferer-files bass.wav vocals.wav other.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=7 python train.py \ --root /sdxdb23_labelnoise_v1.0 \ --dataset trackfolder_fix \ --target-file other.wav \ --interferer-files bass.wav drums.wav vocals.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap</code></pre>
DOI: 10.5281/zenodo.7657180
title: Open-Unmix-Pytorch LabelNoise

description: SDXDB21 LabelNoise Baseline <pre>We split the training data into train and valid. For valid, the following songs were used:</pre> <pre>bc1f2967-f834-43bd-aadc-95afc897cfe7 cc3e4991-6cce-40fe-a917-81a4fbb92ea6 ed90a89a-bf22-444d-af3d-d9ac3896ebd2 f4b735de-14b1-4091-a9ba-c8b30c0740a7 bc964128-da16-4e4c-af95-4d1211e78c70 cc7f7675-d3c8-4a49-a2d7-a8959b694004 f40ffd10-4e8b-41e6-bd8a-971929ca9138</pre> <pre> The following commands were used to create the models:</pre> <pre><code class="language-bash">OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=3 python train.py \ --root /sdxdb23_labelnoise_v1.0 \ --dataset trackfolder_fix \ --target-file vocals.wav \ --interferer-files bass.wav drums.wav other.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=4 python train.py \ --root /sdxdb23_labelnoise_v1.0 \ --dataset trackfolder_fix \ --target-file bass.wav \ --interferer-files vocals.wav drums.wav other.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=5 python train.py \ --root /sdxdb23_labelnoise_v1.0 \ --dataset trackfolder_fix \ --target-file drums.wav \ --interferer-files bass.wav vocals.wav other.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=7 python train.py \ --root /sdxdb23_labelnoise_v1.0 \ --dataset trackfolder_fix \ --target-file other.wav \ --interferer-files bass.wav drums.wav vocals.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap</code></pre>
DOI: 10.6084/m9.figshare.22128389.v1
title: solidago_noindel_miss0.8_maf0.05.recode.vcf

description: This is the Variant Call Format (VCF) file that used for analyzing evolutionary history of 77 <em>Solidago canadensis </em>individuals from both the native (the USA) and the introduced (China, Japan, and Switzerland) areas.
DOI: 10.6084/m9.figshare.22128389
title: solidago_noindel_miss0.8_maf0.05.recode.vcf

description: This is the Variant Call Format (VCF) file that used for analyzing evolutionary history of 77 <em>Solidago canadensis </em>individuals from both the native (the USA) and the introduced (China, Japan, and Switzerland) areas.
DOI: 10.6084/m9.figshare.22126793
title: Foursquare_data

description: Foursquare data downloaded from https://sites.google.com/site/yangdingqi/home/foursquare-dataset This dataset includes long-term (about 22 months from Apr. 2012 to Jan. 2014) global-scale check-in data collected from Foursquare, and also two snapshots of user social networks before and after the check-in data collection period (see more details in our paper). The check-in dataset contains 22,809,624 checkins by 114,324 users on 3,820,891 venues. The social network data contains 363,704 (old) and 607,333 (new) friendships. Due to frequent requests, we also include the raw check-in dataset containing 90,048,627 checkins by 2,733,324 users on 11,180,160 venues.
DOI: 10.17632/y23ymcc6hn.1
title: Urban trees of Ibague

description: Measurements of urban trees of Ibagué in a census (2014-2017) and a partial re-measurement in 2019-2020-.
DOI: 10.17632/vw2zfv84nx.1
title: Helicobacter pylori

description: https://doi.org/10.1016/j.sjbs.2021.09.024
DOI: 10.17632/5tw9fhzkg9.1
title: Type I interferon-dependent IFIT3 signaling is critical for viral clearance in airway neutrophils

description: Purified blood neutrophils were transmigrated in vitro as previously described (Dobosh et al, STAR Methods) using ALI/ARDS mBAL supernatant obtained by mechanical dissociation on ice using an 18G needle and syringe, followed by differential centrifugation at 800g and 3,000g to obtain the cell- and bacteria-free supernatant. A2 neutrophils were transmigrated, as described above, into ALI/ARDS mBAL supplemented with either 10𝜇g/mL of IgG control (Biolegend) or 10𝜇g/mL of Anifrolumab (anti-IFNAR1, Creative biolabs). Treatment with IgG control antibody or Anifrolumab was continued at the same concentration for the first hour of incubation for SARS-CoV-2.
RNA from non infected neutrophils was isolated using the Nucleospin RNA kit (Takara). RNA isolated from in vitro samples was sequenced on the Illumina NextSeq500 at 75bp paired-end with a target of 20 million reads per sample. FASTQ files were checked for quality and raw sequencing data was aligned to the human reference genome (GRCh38) using STAR (Version 2.5.2) and quantmode was used to generate raw transcript counts.
DOI: 10.5281/zenodo.7657264
title: Open-Unmix Pytorch Bleeding

description: SDXDB21 Bleeding Baseline <pre>We split the training data into train and valid. For valid, the following songs were used:</pre> <pre>bc1f2967-f834-43bd-aadc-95afc897cfe7 cc3e4991-6cce-40fe-a917-81a4fbb92ea6 ed90a89a-bf22-444d-af3d-d9ac3896ebd2 f4b735de-14b1-4091-a9ba-c8b30c0740a7 bc964128-da16-4e4c-af95-4d1211e78c70 cc7f7675-d3c8-4a49-a2d7-a8959b694004 f40ffd10-4e8b-41e6-bd8a-971929ca9138</pre> <pre>The following commands were used to create the models:</pre> <pre><code class="language-bash">OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=3 python train.py \ --root /sdxdb23_bleeding_v1.0 \ --dataset trackfolder_fix \ --target-file vocals.wav \ --interferer-files bass.wav drums.wav other.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=4 python train.py \ --root /sdxdb23_bleeding_v1.0 \ --dataset trackfolder_fix \ --target-file bass.wav \ --interferer-files vocals.wav drums.wav other.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=5 python train.py \ --root /sdxdb23_bleeding_v1.0 \ --dataset trackfolder_fix \ --target-file drums.wav \ --interferer-files bass.wav vocals.wav other.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=7 python train.py \ --root /sdxdb23_bleeding_v1.0 \ --dataset trackfolder_fix \ --target-file other.wav \ --interferer-files bass.wav drums.wav vocals.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap</code></pre>
DOI: 10.5281/zenodo.7657263
title: Open-Unmix Pytorch Bleeding

description: SDXDB21 Bleeding Baseline <pre>We split the training data into train and valid. For valid, the following songs were used:</pre> <pre>bc1f2967-f834-43bd-aadc-95afc897cfe7 cc3e4991-6cce-40fe-a917-81a4fbb92ea6 ed90a89a-bf22-444d-af3d-d9ac3896ebd2 f4b735de-14b1-4091-a9ba-c8b30c0740a7 bc964128-da16-4e4c-af95-4d1211e78c70 cc7f7675-d3c8-4a49-a2d7-a8959b694004 f40ffd10-4e8b-41e6-bd8a-971929ca9138</pre> <pre>The following commands were used to create the models:</pre> <pre><code class="language-bash">OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=3 python train.py \ --root /sdxdb23_bleeding_v1.0 \ --dataset trackfolder_fix \ --target-file vocals.wav \ --interferer-files bass.wav drums.wav other.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=4 python train.py \ --root /sdxdb23_bleeding_v1.0 \ --dataset trackfolder_fix \ --target-file bass.wav \ --interferer-files vocals.wav drums.wav other.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=5 python train.py \ --root /sdxdb23_bleeding_v1.0 \ --dataset trackfolder_fix \ --target-file drums.wav \ --interferer-files bass.wav vocals.wav other.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=7 python train.py \ --root /sdxdb23_bleeding_v1.0 \ --dataset trackfolder_fix \ --target-file other.wav \ --interferer-files bass.wav drums.wav vocals.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap</code></pre>
DOI: 10.6084/m9.figshare.22128446
title: Dunham et al "Effects of honey locust seed pods on the behavior and nutrient intake of zoo-housed François langurs and prehensile-tailed porcupines"

description: Dataset for Dunham et al "Effects of honey locust seed pods on the behavior and nutrient intake of zoo-housed François langurs and prehensile-tailed porcupines" published in Zoo Biology.
DOI: 10.6084/m9.figshare.22128446.v1
title: Dunham et al "Effects of honey locust seed pods on the behavior and nutrient intake of zoo-housed François langurs and prehensile-tailed porcupines"

description: Dataset for Dunham et al "Effects of honey locust seed pods on the behavior and nutrient intake of zoo-housed François langurs and prehensile-tailed porcupines" published in Zoo Biology.
DOI: 10.5281/zenodo.7657233
title: Behind the Scenes of COVID-19 Vaccine Hesitancy: Psychological Predictors in an Italian Community Sample

description: Dataset associated with the paper: "Behind the Scenes of COVID-19 Vaccine Hesitancy: Psychological Predictors in an Italian Community Sample".
DOI: 10.5281/zenodo.7657232
title: Behind the Scenes of COVID-19 Vaccine Hesitancy: Psychological Predictors in an Italian Community Sample

description: Dataset associated with the paper: "Behind the Scenes of COVID-19 Vaccine Hesitancy: Psychological Predictors in an Italian Community Sample".
DOI: 10.17632/dxmzjhf7sn.1
title: Nickel Nanoparticles: Applications and Antimicrobial Role against Methicillin-Resistant Staphylococcus aureus Infections

description: Methicillin-resistant Staphylococcus aureus (MRSA) has evolved vast antibiotic resistance.
These strains contain numerous virulence factors facilitating the development of severe infections.
Considering the costs, side effects, and time duration needed for the synthesis of novel drugs,
seeking efficient alternative approaches for the eradication of drug-resistant bacterial agents seems
to be an unmet requirement. Nickel nanoparticles (NiNPs) have been applied as prognostic and
therapeutic cheap agents to various aspects of biomedical sciences. Their antibacterial effects are
exerted via the disruption of the cell membrane, the deformation of proteins, and the inhibition
of DNA replication. NiNPs proper traits include high-level chemical stability and binding affinity,
ferromagnetic properties, ecofriendliness, and cost-effectiveness. They have outlined pleomorphic
and cubic structures. The combined application of NiNPs with CuO, ZnO, and CdO has enhanced
their anti-MRSA effects. The NiNPs at an approximate size of around 50 nm have exerted efficient antiMRSA effects, particularly at higher concentrations. NiNPs have conferred higher antibacterial effects
against MRSA than other nosocomial bacterial pathogens. The application of green synthesis and
low-cost materials such as albumin and chitosan enhance the efficacy of NPs for therapeutic purposes.
DOI: 10.48544/3d26fb71-3367-4f07-ba09-50ac964b7627
title: Animation: why infrastructures are complex systems