cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21366029
title: Additional file 4 of Circulating N-formylmethionine and metabolic shift in critical illness: a multicohort metabolomics study

description: Additional file 4. Full metabolomics results from VITdAL-ICU and RoCI Cohorts. Six Individual Worksheets include Day 0 t test VITdAL-ICU: t test results of Day 0 data from the VITdAL-ICU cohort; Day 0 VITdAL-ICU: Linear regression results of Day 0 data from the VITdAL-ICU cohort adjusted for age, sex, baseline 25(OH)D, SAPS II, and admission diagnosis; Day 0,3,7 VITdAL-ICU: Mixed effects regression results of Day 0,3,7 data from the VITdAL-ICU cohort adjusted for age, sex, baseline 25(OH)D, absolute increase in 25(OH)D at day 3, SAPS II, plasma day, admission diagnosis, and an individual subject-specific random-intercept; Day 0,3,7 VITdAL-ICU Placebo: Mixed effects regression results of Day 0,3,7 data from the VITdAL-ICU cohort in patients who received Placebo adjusted for age, sex, baseline 25(OH)D, absolute increase in 25(OH)D at day 3, SAPS II, plasma day, admission diagnosis, and an individual subject-specific random-intercept; Day 0 t test RoCI: t test results of Day 0 data from the RoCI cohort; Day 0 RoCI: Linear regression results of Day 0 data from the RoCI cohort adjusted for age, sex, race, and APACHE II score. T-statistics are presented for t tests. Beta coefficients are presented for linear regression and mixed effects analysis. In all worksheets, raw p values are presented. A false discovery rate adjusted p value (q value) threshold of 0.05 was used to identify all significant differences.
DOI: 10.6084/m9.figshare.21366029.v1
title: Additional file 4 of Circulating N-formylmethionine and metabolic shift in critical illness: a multicohort metabolomics study

description: Additional file 4. Full metabolomics results from VITdAL-ICU and RoCI Cohorts. Six Individual Worksheets include Day 0 t test VITdAL-ICU: t test results of Day 0 data from the VITdAL-ICU cohort; Day 0 VITdAL-ICU: Linear regression results of Day 0 data from the VITdAL-ICU cohort adjusted for age, sex, baseline 25(OH)D, SAPS II, and admission diagnosis; Day 0,3,7 VITdAL-ICU: Mixed effects regression results of Day 0,3,7 data from the VITdAL-ICU cohort adjusted for age, sex, baseline 25(OH)D, absolute increase in 25(OH)D at day 3, SAPS II, plasma day, admission diagnosis, and an individual subject-specific random-intercept; Day 0,3,7 VITdAL-ICU Placebo: Mixed effects regression results of Day 0,3,7 data from the VITdAL-ICU cohort in patients who received Placebo adjusted for age, sex, baseline 25(OH)D, absolute increase in 25(OH)D at day 3, SAPS II, plasma day, admission diagnosis, and an individual subject-specific random-intercept; Day 0 t test RoCI: t test results of Day 0 data from the RoCI cohort; Day 0 RoCI: Linear regression results of Day 0 data from the RoCI cohort adjusted for age, sex, race, and APACHE II score. T-statistics are presented for t tests. Beta coefficients are presented for linear regression and mixed effects analysis. In all worksheets, raw p values are presented. A false discovery rate adjusted p value (q value) threshold of 0.05 was used to identify all significant differences.
DOI: 10.6084/m9.figshare.21366041
title: Additional file 8 of Circulating N-formylmethionine and metabolic shift in critical illness: a multicohort metabolomics study

description: Additional file 8. Additional metabolomics results from VITdAL-ICU and RoCI Cohorts. Nine individual worksheets include Linear regression results of Day 0 data from the VITdAL-ICU cohort adjusted for age, sex, baseline 25(OH)D, SAPS II, admission diagnosis, with additional adjustment for total bilirubin, creatinine, sepsis or propofol exposure. Day 0 data from the VITdAL-ICU cohort is also shown adjusted for age, sex, baseline 25(OH)D, and admission diagnosis but without SAPS II. Linear regression results of Day 0 data from the RoCI cohort are shown adjusted for age, sex, baseline 25(OH)D, SAPS II, admission diagnosis, with additional adjustment for total bilirubin, creatinine, or sepsis. In all worksheets, raw p values are presented. A false discovery rate adjusted p value (q value) threshold of 0.05 was used to identify all significant differences.
DOI: 10.6084/m9.figshare.21366041.v1
title: Additional file 8 of Circulating N-formylmethionine and metabolic shift in critical illness: a multicohort metabolomics study

description: Additional file 8. Additional metabolomics results from VITdAL-ICU and RoCI Cohorts. Nine individual worksheets include Linear regression results of Day 0 data from the VITdAL-ICU cohort adjusted for age, sex, baseline 25(OH)D, SAPS II, admission diagnosis, with additional adjustment for total bilirubin, creatinine, sepsis or propofol exposure. Day 0 data from the VITdAL-ICU cohort is also shown adjusted for age, sex, baseline 25(OH)D, and admission diagnosis but without SAPS II. Linear regression results of Day 0 data from the RoCI cohort are shown adjusted for age, sex, baseline 25(OH)D, SAPS II, admission diagnosis, with additional adjustment for total bilirubin, creatinine, or sepsis. In all worksheets, raw p values are presented. A false discovery rate adjusted p value (q value) threshold of 0.05 was used to identify all significant differences.
DOI: 10.6084/m9.figshare.21366068
title: Additional file 1 of The resistomes of Mycobacteroides abscessus complex and their possible acquisition from horizontal gene transfer

description: Additional file 1: Table S1.General Information on MABC Genomes. Table S2. Amount of Antibiotic Resistance Proteins (ARPs) Detected in MABCper Antibiotic Classes. Table S3.Distribution of Antibiotic Resistance Proteins (ARPs) Associated withResistance to Multiple Antibiotic Classes. Table S4. Distribution ofAntibiotic Resistance Proteins (ARPs) Associated with Beta-lactam Resistance. TableS5. Distribution ofAntibiotic Resistance Proteins (ARPs) Associated with AminoglycosideResistance. Table S6. Distribution of Antibiotic Resistance Proteins(ARPs) Associated with Glycopeptide Resistance. Table S7. Distributionof Antibiotic Resistance Proteins (ARPs) Associated with TetracyclineResistance. Table S8. Distribution of Antibiotic Resistance Proteins(ARPs) Associated with Phenicol Resistance. Table S9. Distribution ofAntibiotic Resistance Proteins (ARPs) Associated with FluoroquinoloneResistance. Table S10. Distributionof Antibiotic Resistance Proteins (ARPs) Associated with Resistance to OtherAntibiotic Classes. Table S11. Detailed Information on Test ofRecombination. Table S12. List of Antibiotic Resistance Proteins (ARPs)that Underwent Selection. Table S13. Distribution of AntibioticResistance Proteins (ARPs) Pseudogene in MABC. Table S14. List ofAntibiotic Resistance Proteins (ARPs) with Homologs in ACLAME.
DOI: 10.6084/m9.figshare.21366068.v1
title: Additional file 1 of The resistomes of Mycobacteroides abscessus complex and their possible acquisition from horizontal gene transfer

description: Additional file 1: Table S1.General Information on MABC Genomes. Table S2. Amount of Antibiotic Resistance Proteins (ARPs) Detected in MABCper Antibiotic Classes. Table S3.Distribution of Antibiotic Resistance Proteins (ARPs) Associated withResistance to Multiple Antibiotic Classes. Table S4. Distribution ofAntibiotic Resistance Proteins (ARPs) Associated with Beta-lactam Resistance. TableS5. Distribution ofAntibiotic Resistance Proteins (ARPs) Associated with AminoglycosideResistance. Table S6. Distribution of Antibiotic Resistance Proteins(ARPs) Associated with Glycopeptide Resistance. Table S7. Distributionof Antibiotic Resistance Proteins (ARPs) Associated with TetracyclineResistance. Table S8. Distribution of Antibiotic Resistance Proteins(ARPs) Associated with Phenicol Resistance. Table S9. Distribution ofAntibiotic Resistance Proteins (ARPs) Associated with FluoroquinoloneResistance. Table S10. Distributionof Antibiotic Resistance Proteins (ARPs) Associated with Resistance to OtherAntibiotic Classes. Table S11. Detailed Information on Test ofRecombination. Table S12. List of Antibiotic Resistance Proteins (ARPs)that Underwent Selection. Table S13. Distribution of AntibioticResistance Proteins (ARPs) Pseudogene in MABC. Table S14. List ofAntibiotic Resistance Proteins (ARPs) with Homologs in ACLAME.
DOI: 10.6084/m9.figshare.21366131
title: Additional file 1 of Metagenomic analysis reveals differences in the co-occurrence and abundance of viral species in SARS-CoV-2 patients with different severity of disease

description: Additional file 1: Table S1. Patients included in the study. Information of samples included in study, sample ID, age, and sex of patients, place of collection, date of sample and start of the symptoms, clinical status, comorbidities, sample type, and diagnostic test for other respiratory viruses if performed. Table S2. General sequencing data and SARS-CoV-2 sequenced genome characteristics. Number of reads (original total reads, filtered reads, SARS-CoV-2-specific reads, reads used for metagenomic analysis) and results of SARS-CoV-2 genome sequencing. Table S3. Respiratory viruses found in SARS-CoV-2 positive samples. List of samples with homologs to other (non-SARS-CoV-2) human respiratory viruses. Table S4. Other human (non-respiratory) viruses, found in SARS-CoV-2 positive samples. List of samples with homologs to other non-respiratory human viruses. Table S5. Viruses of other vertebrates found in SARS-CoV-2 infected patients. Contains list of samples with homologs to viruses of other vertebrate species found in SARS-CoV-2 infected patients. Table S6. Non-vertebrate viruses found in SARS-CoV-2 infected persons. Contains list of samples with homologs to non-vertebrate viruses. Table S7. Non-SARS-CoV-2 viruses with &gt; 25% genome coverage. Contains list of viruses, where more than 25% genome was sequenced, from those found in SARS-CoV-2 positive samples.
DOI: 10.6084/m9.figshare.21366131.v1
title: Additional file 1 of Metagenomic analysis reveals differences in the co-occurrence and abundance of viral species in SARS-CoV-2 patients with different severity of disease

description: Additional file 1: Table S1. Patients included in the study. Information of samples included in study, sample ID, age, and sex of patients, place of collection, date of sample and start of the symptoms, clinical status, comorbidities, sample type, and diagnostic test for other respiratory viruses if performed. Table S2. General sequencing data and SARS-CoV-2 sequenced genome characteristics. Number of reads (original total reads, filtered reads, SARS-CoV-2-specific reads, reads used for metagenomic analysis) and results of SARS-CoV-2 genome sequencing. Table S3. Respiratory viruses found in SARS-CoV-2 positive samples. List of samples with homologs to other (non-SARS-CoV-2) human respiratory viruses. Table S4. Other human (non-respiratory) viruses, found in SARS-CoV-2 positive samples. List of samples with homologs to other non-respiratory human viruses. Table S5. Viruses of other vertebrates found in SARS-CoV-2 infected patients. Contains list of samples with homologs to viruses of other vertebrate species found in SARS-CoV-2 infected patients. Table S6. Non-vertebrate viruses found in SARS-CoV-2 infected persons. Contains list of samples with homologs to non-vertebrate viruses. Table S7. Non-SARS-CoV-2 viruses with &gt; 25% genome coverage. Contains list of viruses, where more than 25% genome was sequenced, from those found in SARS-CoV-2 positive samples.
DOI: 10.6084/m9.figshare.21366171
title: Additional file 3 of Pesticide residue exposure provides different responses of the microbiomes of distinct cultures of the stored product pest mite Acarus siro

description: Additional file 3: Table S1. The list of samples, factors and files deposited at NCBI (PRJNA774490) describing the microbiome of Acarus siro. Table S2. Identification of OTUs based on RDP and comparison to GenBank using Blatstn in Acarus siro microbiome. The identified taxonomy levels and thresholds (Tr) are shown. The most similar hits in GenBank are presented (Blast hits) with their percentual similarity to compared sequences (ident %). Table S3. The list of samples with standardized read numbers of Acarus siro microbiome, the total numbers of standardized reads (N) and frequency in the samples (F) are provided. Table S4. The list of almost complete 16S DNA sequences used for the identification of Sodalis-like symbiont of Acarus siro. Table S5. The list of samples of Acarus siro microbiome, diversity indexes and numbers of reads based on qPCR (universal primers). The reads were recalculated per mite.
DOI: 10.6084/m9.figshare.21366171.v1
title: Additional file 3 of Pesticide residue exposure provides different responses of the microbiomes of distinct cultures of the stored product pest mite Acarus siro

description: Additional file 3: Table S1. The list of samples, factors and files deposited at NCBI (PRJNA774490) describing the microbiome of Acarus siro. Table S2. Identification of OTUs based on RDP and comparison to GenBank using Blatstn in Acarus siro microbiome. The identified taxonomy levels and thresholds (Tr) are shown. The most similar hits in GenBank are presented (Blast hits) with their percentual similarity to compared sequences (ident %). Table S3. The list of samples with standardized read numbers of Acarus siro microbiome, the total numbers of standardized reads (N) and frequency in the samples (F) are provided. Table S4. The list of almost complete 16S DNA sequences used for the identification of Sodalis-like symbiont of Acarus siro. Table S5. The list of samples of Acarus siro microbiome, diversity indexes and numbers of reads based on qPCR (universal primers). The reads were recalculated per mite.
DOI: 10.6084/m9.figshare.19744933.v3
title: Chatbot Ai-aun

description: Chatbot Ai-aun
DOI: 10.6084/m9.figshare.21366186.v1
title: Parasitic plants indirectly regulate decomposition of soil organic matter

description: Datasets, Parasitic plants indirectly regulate decomposition of soil organic matter, Yuan, Yongge; Lin, Xinru; Chen, Gelv; van Kleunen, Mark; Li, Junmin
DOI: 10.6084/m9.figshare.21366186
title: Parasitic plants indirectly regulate decomposition of soil organic matter

description: Datasets, Parasitic plants indirectly regulate decomposition of soil organic matter, Yuan, Yongge; Lin, Xinru; Chen, Gelv; van Kleunen, Mark; Li, Junmin
DOI: 10.5281/zenodo.7227055
title: Pharokka v 1.1.0 Benchmarking Input Output and Script

description: This record contains all benchmarking input FASTA and output files for the Pharokka manuscript. Enterobacteria Phage Lambda (Genbank accession J02459), Staphylococcus phage SAOMS1 (Genbank accessionMW460250) and 673 crAss-like metagenome assembled phage genomes from the human gut taken from Yutin et al 2021 (https://doi.org/10.1038/s41467-021-21350-w). Each input was benchmarked with 3 runs: 1) Pharokka v1.1.0 using PHANOTATE as a gene predictor 2) Pharokka v1.1.0 specifying Prodigal as gene predictor, and 3) Prokka v1.14.6 using a version of the PHROGs HMM database that has been reformatted for use with Prokka found at the following URLs (http://s3.climb.ac.uk/ADM_share/all_phrogs.hmm.gz https://millardlab.org/2021/11/21/phage-annotation-with-phrogs/ ). Benchmarking was conducted on an Intel® Xeon® CPU E5-4610 v2 @ 2.30GHz specifying 16 threads for Pharokka and 16 cpus for Prokka. Coding densities for each contig in the Prokka output were calculated using the python script calc_gff_coding_density_prokka.py available in the tarball. Prokka coding densities are included in the Prokka_CDS_Coding_Densities directory.
DOI: 10.5281/zenodo.7227056
title: Pharokka v 1.1.0 Benchmarking Input Output and Script

description: This record contains all benchmarking input FASTA and output files for the Pharokka manuscript. Enterobacteria Phage Lambda (Genbank accession J02459), Staphylococcus phage SAOMS1 (Genbank accessionMW460250) and 673 crAss-like metagenome assembled phage genomes from the human gut taken from Yutin et al 2021 (https://doi.org/10.1038/s41467-021-21350-w). Each input was benchmarked with 3 runs: 1) Pharokka v1.1.0 using PHANOTATE as a gene predictor 2) Pharokka v1.1.0 specifying Prodigal as gene predictor, and 3) Prokka v1.14.6 using a version of the PHROGs HMM database that has been reformatted for use with Prokka found at the following URLs (http://s3.climb.ac.uk/ADM_share/all_phrogs.hmm.gz https://millardlab.org/2021/11/21/phage-annotation-with-phrogs/ ). Benchmarking was conducted on an Intel® Xeon® CPU E5-4610 v2 @ 2.30GHz specifying 16 threads for Pharokka and 16 cpus for Prokka. Coding densities for each contig in the Prokka output were calculated using the python script calc_gff_coding_density_prokka.py available in the tarball. Prokka coding densities are included in the Prokka_CDS_Coding_Densities directory.
DOI: 10.5281/zenodo.7227091
title: Pharokka v 1.1.0 Benchmarking Input Output and Script

description: This record contains all benchmarking input FASTA and output files for the Pharokka manuscript. Enterobacteria Phage Lambda (Genbank accession J02459), Staphylococcus phage SAOMS1 (Genbank accessionMW460250) and 673 crAss-like metagenome assembled phage genomes from the human gut taken from Yutin et al 2021 (https://doi.org/10.1038/s41467-021-21350-w). Each input was benchmarked with 3 runs: 1) Pharokka v1.1.0 using PHANOTATE as a gene predictor 2) Pharokka v1.1.0 specifying Prodigal as gene predictor, and 3) Prokka v1.14.6 using a version of the PHROGs HMM database that has been reformatted for use with Prokka found at the following URLs (http://s3.climb.ac.uk/ADM_share/all_phrogs.hmm.gz https://millardlab.org/2021/11/21/phage-annotation-with-phrogs/ ). Benchmarking was conducted on an Intel® Xeon® CPU E5-4610 v2 @ 2.30GHz specifying 16 threads for Pharokka and 16 cpus for Prokka. Coding densities for each contig in the Prokka output were calculated using the python script calc_gff_coding_density_prokka.py available in the tarball. Prokka coding densities are included in the Prokka_CDS_Coding_Densities directory.
DOI: 10.6084/m9.figshare.19744933.v4
title: Chatbot Ai-aun

description: Chatbot Ai-aun
DOI: 10.5519/qd.iko1ok4v
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (6 records)
DOI: 10.5519/qd.br8y309c
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.57770/gmpshi
title: Stomatin modulates adipogenesis through the ERK pathway and regulates fatty acid uptake and lipid droplet growth

description: Regulation of fatty acid uptake, lipid production and storage, and metabolism of lipid droplets (LDs), is closely related to lipid homeostasis, adipocyte hypertrophy and obesity. We report here that stomatin, a major constituent of lipid raft, participates in adipogenesis and adipocyte maturation by modulating related signaling pathways. In adipocyte-like cells, increased stomatin promotes LD growth or enlargements by facilitating LD-LD fusion. It also promotes fatty acid uptake from extracellular environment by recruiting effector molecules, such as FAT/CD36 translocase, to lipid rafts to promote internalization of fatty acids. Stomatin transgenic mice fed with high-fat diet exhibit obesity, insulin resistance and hepatic impairments; however, such phenotypes are not seen in transgenic animals fed with regular diet. Inhibitions of stomatin by gene knockdown or OB-1 inhibit adipogenic differentiation and LD growth through downregulation of PPARγ pathway. Effects of stomatin on PPARγ involves ERK signaling; however, an alternate pathway may also exist.
DOI: 10.34740/kaggle/dsv/4345293
title: Agriculture and Livestock in Oman
DOI: 10.21227/7kde-wd64
title: Non-IID IoT Person Detection (NIPD)

description: Federated learning (FL), as a privacy-preserving distributed machine learning algorithm is being rapidly applied in wireless communication networks, which enables IoT clients to obtain well-trained models while keeping data local. As object detection is in the security field, it can quickly retrieve the object of interest in images and videos, and combined with FL algorithms, it can be deployed on edge devices with limited computing power to process video data directly at the edge, reducing the transmission pressure on the network, relieving the computational burden on the central server, and reducing the processing latency of the video surveillance system. However, due to the different deployment scenarios of different devices, the data collected by the devices present independent and identically distributed (non-IID) characteristics, and the global model aggregated out is not effective if FL is performed. Meanwhile, the current research lacks publicly available data sets for real scenario target detection, which is not conducive to the study of the non-IID problem on wireless Internet of Things (IoT) edge devices. To this end, we open source a non-IID IoT person detection (NIPD) data set, which is collected from 5 different cameras with a total of 10,000 surveillance images of real scenes containing 55,160 people. We have built an experimental platform based on this data set and conducted some experiments and reflections. Our data set can effectively test the ability of IoT camera devices to solve real-world problems by FL algorithm, and also serve as a benchmark to solve the non-IID data distribution problem in real-world applications.
DOI: 10.6084/m9.figshare.20425580
title: Levels of evidence for the association between different food groups/items consumption and the risk of various cancer sites: an umbrella review

description: This study aimed to determine the level of evidence on the association between food groups/items consumption and the risk of different cancer sites from the meta-analyses/pooled analyses of observational studies. A systematic search was executed in Scopus, PubMed/MEDLINE, and Web of Science. The criteria from the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) Expert Report were adopted for evidence grading. In summary, there was convincing evidence for the association between fibre intake and decreased risk of colon and breast cancer. Also, consumption of dairy products, milk, fruits, and fibre was associated with a probable decreased risk of breast cancer. Consumption of whole grains, dairy products, milk, fruits, vegetables, and fibre had a probable inverse association with the incidence risk of gastrointestinal tract cancers. More qualified studies are needed to find reliable findings on the association between various food groups/items consumption and the risk of different cancer sites.
DOI: 10.6084/m9.figshare.20425580.v1
title: Levels of evidence for the association between different food groups/items consumption and the risk of various cancer sites: an umbrella review

description: This study aimed to determine the level of evidence on the association between food groups/items consumption and the risk of different cancer sites from the meta-analyses/pooled analyses of observational studies. A systematic search was executed in Scopus, PubMed/MEDLINE, and Web of Science. The criteria from the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) Expert Report were adopted for evidence grading. In summary, there was convincing evidence for the association between fibre intake and decreased risk of colon and breast cancer. Also, consumption of dairy products, milk, fruits, and fibre was associated with a probable decreased risk of breast cancer. Consumption of whole grains, dairy products, milk, fruits, vegetables, and fibre had a probable inverse association with the incidence risk of gastrointestinal tract cancers. More qualified studies are needed to find reliable findings on the association between various food groups/items consumption and the risk of different cancer sites.
DOI: 10.6084/m9.figshare.21366222
title: Transcriptome analysis reveals the effect of pasteurized colostrum on the growth traits of Holstein calves

description: Quantitative genetic count values and normalized TPM values for a total of 8 Holstein female calves
DOI: 10.5281/zenodo.7227268
title: ramdaq_testdata

description: Downsampled FASTQ files of scRNA-seq for testing scRNA-seq data processing workflow