cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12853724.v2
title: Kinetics of SARS-CoV-2 specific IgM and IgG responses in COVID-19 patients

description: The emerging COVID-19 caused by SARS-CoV-2 infection poses severe challenges to global public health. Serum antibody testing is becoming one of the critical methods for the diagnosis of COVID-19 patients. We investigated IgM and IgG responses against SARS-CoV-2 nucleocapsid (N) and spike (S) protein after symptom onset in the intensive care unit (ICU) and non-ICU patients. 130 blood samples from 38 COVID-19 patients were collected. The levels of IgM and IgG specific to N and S protein were detected by ELISA. A series of blood samples were collected along the disease course from the same patient, including 11 ICU patients and 27 non-ICU patients for longitudinal analysis. N and S specific IgM and IgG (N-IgM, N-IgG, S-IgM, S-IgG) in non-ICU patients increased after symptom onset. N-IgM and S-IgM in some non-ICU patients reached a peak in the second week, while N-IgG and S-IgG continued to increase in the third week. The combined detection of N and S specific IgM and IgG could identify up to 75% of SARS-CoV-2 infected patients in the first week. S-IgG was significantly higher in non-ICU patients than in ICU patients in the third week. In contrast, N-IgG was significantly higher in ICU patients than in non-ICU patients. The increase of S-IgG positively correlated with the decrease of C-reactive protein (CRP) in non-ICU patients. N and S specific IgM and IgG increased gradually after symptom onset and can be used for detection of SARS-CoV-2 infection. Analysis of the dynamics of S-IgG may help to predict prognosis.
DOI: 10.6084/m9.figshare.22815189
title: Additional file 6 of Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy

description: Additional file 6. Supplementary Table S5: The KEGG pathway analysis results of 155 robust DEGs.
DOI: 10.6084/m9.figshare.22815189.v1
title: Additional file 6 of Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy

description: Additional file 6. Supplementary Table S5: The KEGG pathway analysis results of 155 robust DEGs.
DOI: 10.6084/m9.figshare.22815183
title: Additional file 4 of Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy

description: Additional file 4. Supplementary Table S3: 155 robust differentially expressed genes identified by RRA method and their calculated values.
DOI: 10.6084/m9.figshare.22815183.v1
title: Additional file 4 of Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy

description: Additional file 4. Supplementary Table S3: 155 robust differentially expressed genes identified by RRA method and their calculated values.
DOI: 10.6084/m9.figshare.22815186
title: Additional file 5 of Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy

description: Additional file 5. Supplementary Table S4: The GO functional enrichment analysis results of 155 robust DEGs.
DOI: 10.6084/m9.figshare.22815192
title: Additional file 7 of Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy

description: Additional file 7. Supplementary Table S6: Correlation coefficient of correlation heatmap related to cell type abundance.
DOI: 10.6084/m9.figshare.22815192.v1
title: Additional file 7 of Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy

description: Additional file 7. Supplementary Table S6: Correlation coefficient of correlation heatmap related to cell type abundance.
DOI: 10.6084/m9.figshare.22815177
title: Additional file 2 of Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy

description: Additional file 2. Supplementary Table S1: Basic information of selected microarray datasets.
DOI: 10.6084/m9.figshare.22815177.v1
title: Additional file 2 of Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy

description: Additional file 2. Supplementary Table S1: Basic information of selected microarray datasets.
DOI: 10.6084/m9.figshare.22815180
title: Additional file 3 of Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy

description: Additional file 3. Supplementary Table S2: Differentially expressed genes and their calculated values in the GSE43292, GSE97210, and GSE100927 datasets.
DOI: 10.6084/m9.figshare.22815180.v1
title: Additional file 3 of Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy

description: Additional file 3. Supplementary Table S2: Differentially expressed genes and their calculated values in the GSE43292, GSE97210, and GSE100927 datasets.
DOI: 10.6084/m9.figshare.22815195
title: Additional file 8 of Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy

description: Additional file 8. Supplementary Table S7: The P-value of correlation coefficient of correlation heatmap related to cell type abundance.
DOI: 10.6084/m9.figshare.22815195.v1
title: Additional file 8 of Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy

description: Additional file 8. Supplementary Table S7: The P-value of correlation coefficient of correlation heatmap related to cell type abundance.
DOI: 10.6084/m9.figshare.12853733
title: Identification of novel tetracycline resistance gene <i>tet</i>(X14) and its co-occurrence with <i>tet</i>(X2) in a tigecycline-resistant and colistin-resistant <i>Empedobacter stercoris</i>

description: Tigecycline is one of the last-resort antibiotics to treat severe infections. Recently, tigecycline resistance has sporadically emerged with an increasing trend, and Tet(X) family represents a new resistance mechanism of tigecycline. In this study, a novel chromosome-encoded tigecycline resistance gene, <i>tet</i>(X14), was identified in a tigecycline-resistant and colistin-resistant <i>Empedobacter stercoris</i> strain ES183 recovered from a pig fecal sample in China. Tet(X14) shows 67.14-96.39% sequence identity to the other variants [Tet(X) to Tet(X13)]. Overexpression of Tet(X14) in <i>Escherichia coli</i> confers 16-fold increase in tigecycline MIC (from 0.125 to 2 mg/L), which is lower than that of Tet(X3), Tet(X4) and Tet(X6). Structural modelling predicted that Tet(X14) shared a high homology with the other 12 variants with RMSD value from 0.003 to 0.055, and Tet(X14) can interact with tetracyclines by a similar pattern as the other Tet(X)s. <i>tet</i>(X14) and two copies of <i>tet</i>(X2) were identified on a genome island with abnormal GC content carried by the chromosome of ES183, and no mobile genetic elements were found surrounding, suggesting that <i>tet</i>(X14) might be heterologously obtained by ES183 via recombination. Blasting in Genbank revealed that Tet(X14) was exclusively detected on the chromosome of <i>Riemerella anatipestifer</i>, mainly encoded on antimicrobial resistance islands. <i>E. stercoris</i> and <i>R. anatipestifer</i> belong to the family <i>Flavobacteriaceae</i>, suggesting that the members of <i>Flavobacteriaceae</i> maybe the major reservoir of <i>tet</i>(X14). Our study reports a novel chromosome-encoded tigecycline resistance gene <i>tet</i>(X14). The expanded members of Tet(X) family warrants the potential large-scale dissemination and the necessity of continuous surveillance for <i>tet</i>(X)-mediated tigecycline resistance.
DOI: 10.6084/m9.figshare.12853733.v1
title: Identification of novel tetracycline resistance gene <i>tet</i>(X14) and its co-occurrence with <i>tet</i>(X2) in a tigecycline-resistant and colistin-resistant <i>Empedobacter stercoris</i>

description: Tigecycline is one of the last-resort antibiotics to treat severe infections. Recently, tigecycline resistance has sporadically emerged with an increasing trend, and Tet(X) family represents a new resistance mechanism of tigecycline. In this study, a novel chromosome-encoded tigecycline resistance gene, <i>tet</i>(X14), was identified in a tigecycline-resistant and colistin-resistant <i>Empedobacter stercoris</i> strain ES183 recovered from a pig fecal sample in China. Tet(X14) shows 67.14-96.39% sequence identity to the other variants [Tet(X) to Tet(X13)]. Overexpression of Tet(X14) in <i>Escherichia coli</i> confers 16-fold increase in tigecycline MIC (from 0.125 to 2 mg/L), which is lower than that of Tet(X3), Tet(X4) and Tet(X6). Structural modelling predicted that Tet(X14) shared a high homology with the other 12 variants with RMSD value from 0.003 to 0.055, and Tet(X14) can interact with tetracyclines by a similar pattern as the other Tet(X)s. <i>tet</i>(X14) and two copies of <i>tet</i>(X2) were identified on a genome island with abnormal GC content carried by the chromosome of ES183, and no mobile genetic elements were found surrounding, suggesting that <i>tet</i>(X14) might be heterologously obtained by ES183 via recombination. Blasting in Genbank revealed that Tet(X14) was exclusively detected on the chromosome of <i>Riemerella anatipestifer</i>, mainly encoded on antimicrobial resistance islands. <i>E. stercoris</i> and <i>R. anatipestifer</i> belong to the family <i>Flavobacteriaceae</i>, suggesting that the members of <i>Flavobacteriaceae</i> maybe the major reservoir of <i>tet</i>(X14). Our study reports a novel chromosome-encoded tigecycline resistance gene <i>tet</i>(X14). The expanded members of Tet(X) family warrants the potential large-scale dissemination and the necessity of continuous surveillance for <i>tet</i>(X)-mediated tigecycline resistance.
DOI: 10.6084/m9.figshare.12853736
title: The ongoing COVID-19 epidemic in Minas Gerais, Brazil: insights from epidemiological data and SARS-CoV-2 whole genome sequencing

description: The recent emergence of a coronavirus (SARS-CoV-2), first identified in the Chinese city of Wuhan in December 2019, has had major public health and economic consequences. Although 61,888 confirmed cases were reported in Brazil by 28 April 2020, little is known about the SARS-CoV-2 epidemic in this country. To better understand the recent epidemic in the second most populous state in southeast Brazil - Minas Gerais (MG) - we sequenced 40 complete SARS-CoV-2 genomes from MG cases and examined epidemiological data from three Brazilian states. Both the genome analyses and the geographical distribution of reported cases indicate for multiple independent introductions into MG. Epidemiological estimates of the reproductive number (R) using different data sources and theoretical assumptions suggest the potential for sustained virus transmission despite a reduction in R from the first reported case to the end of April 2020. The estimated date of SARS-CoV-2 introduction into Brazil was consistent with epidemiological data from the first case of a returned traveller from Lombardy, Italy. These findings highlight the nature of the COVID-19 epidemic in MG and reinforce the need for real-time and continued genomic surveillance strategies to better understand and prepare for the epidemic spread of emerging viral pathogens..
DOI: 10.6084/m9.figshare.12853736.v1
title: The ongoing COVID-19 epidemic in Minas Gerais, Brazil: insights from epidemiological data and SARS-CoV-2 whole genome sequencing

description: The recent emergence of a coronavirus (SARS-CoV-2), first identified in the Chinese city of Wuhan in December 2019, has had major public health and economic consequences. Although 61,888 confirmed cases were reported in Brazil by 28 April 2020, little is known about the SARS-CoV-2 epidemic in this country. To better understand the recent epidemic in the second most populous state in southeast Brazil - Minas Gerais (MG) - we sequenced 40 complete SARS-CoV-2 genomes from MG cases and examined epidemiological data from three Brazilian states. Both the genome analyses and the geographical distribution of reported cases indicate for multiple independent introductions into MG. Epidemiological estimates of the reproductive number (R) using different data sources and theoretical assumptions suggest the potential for sustained virus transmission despite a reduction in R from the first reported case to the end of April 2020. The estimated date of SARS-CoV-2 introduction into Brazil was consistent with epidemiological data from the first case of a returned traveller from Lombardy, Italy. These findings highlight the nature of the COVID-19 epidemic in MG and reinforce the need for real-time and continued genomic surveillance strategies to better understand and prepare for the epidemic spread of emerging viral pathogens..
DOI: 10.25402/fon.19145201
title: Supplementary Tables – Is Cyberknife Radiosurgery System effective and safe to patients? An umbrella review of the evidence.

description: <b>Supplementary
 Table A</b>: Is Cyberknife
 Radiosurgery System effective and safe to patients? An umbrella review of the
 evidence.
 <i>Literature Search
 strategy</i>
 
 <b>Supplementary
 Table B</b>: Is Cyberknife
 Radiosurgery System effective and safe to patients? An umbrella review of the
 evidence.
 <i>List of excluded
 studies</i>
 
 <b>Supplementary Information:</b> Is Cyberknife Radiosurgery System effective
 and safe to patients? An umbrella review of the evidence.
 <i>Treatment Outcomes
 of Cyberknife specific by tumors' location</i>
DOI: 10.25402/fon.19145201.v1
title: Supplementary Tables – Is Cyberknife Radiosurgery System effective and safe to patients? An umbrella review of the evidence.

description: <b>Supplementary
 Table A</b>: Is Cyberknife
 Radiosurgery System effective and safe to patients? An umbrella review of the
 evidence.
 <i>Literature Search
 strategy</i>
 
 <b>Supplementary
 Table B</b>: Is Cyberknife
 Radiosurgery System effective and safe to patients? An umbrella review of the
 evidence.
 <i>List of excluded
 studies</i>
 
 <b>Supplementary Information:</b> Is Cyberknife Radiosurgery System effective
 and safe to patients? An umbrella review of the evidence.
 <i>Treatment Outcomes
 of Cyberknife specific by tumors' location</i>
DOI: 10.6084/m9.figshare.22815201
title: Additional file 1 of Natural variation in Beauty Mark is associated with UV-based geographical adaptation in Gossypium species

description: Additional file 1: Table S1. Tajima’s D test for GhBM and GbBM sequences. Table S2. Cotton accessions used for sequence analysis. Table S3. Sequences of primers used in this study.
DOI: 10.6084/m9.figshare.22815201.v1
title: Additional file 1 of Natural variation in Beauty Mark is associated with UV-based geographical adaptation in Gossypium species

description: Additional file 1: Table S1. Tajima’s D test for GhBM and GbBM sequences. Table S2. Cotton accessions used for sequence analysis. Table S3. Sequences of primers used in this study.
DOI: 10.6084/m9.figshare.22815204
title: Additional file 2 of Natural variation in Beauty Mark is associated with UV-based geographical adaptation in Gossypium species

description: Additional file 2. Includes individual values for quantitative data depicted in Figs. 1, 3 and 4.
DOI: 10.6084/m9.figshare.22815204.v1
title: Additional file 2 of Natural variation in Beauty Mark is associated with UV-based geographical adaptation in Gossypium species

description: Additional file 2. Includes individual values for quantitative data depicted in Figs. 1, 3 and 4.
DOI: 10.6084/m9.figshare.22815228
title: Additional file 3 of Nonnegative matrix factorization analysis and multiple machine learning methods identified IL17C and ACOXL as novel diagnostic biomarkers for atherosclerosis

description: Additional file 3: Table S1. List of 223 prognostic related DEGs used in this study.
DOI: 10.6084/m9.figshare.22815231
title: Additional file 4 of Nonnegative matrix factorization analysis and multiple machine learning methods identified IL17C and ACOXL as novel diagnostic biomarkers for atherosclerosis

description: Additional file 4: Table S2. Identification of hub genes by using LASSO.
DOI: 10.6084/m9.figshare.22815231.v1
title: Additional file 4 of Nonnegative matrix factorization analysis and multiple machine learning methods identified IL17C and ACOXL as novel diagnostic biomarkers for atherosclerosis

description: Additional file 4: Table S2. Identification of hub genes by using LASSO.
DOI: 10.6084/m9.figshare.22815228.v1
title: Additional file 3 of Nonnegative matrix factorization analysis and multiple machine learning methods identified IL17C and ACOXL as novel diagnostic biomarkers for atherosclerosis

description: Additional file 3: Table S1. List of 223 prognostic related DEGs used in this study.
DOI: 10.6084/m9.figshare.22815234
title: Additional file 5 of Nonnegative matrix factorization analysis and multiple machine learning methods identified IL17C and ACOXL as novel diagnostic biomarkers for atherosclerosis

description: Additional file 5: Table S3. Identification of hub genes by using SVM-RFE.
DOI: 10.6084/m9.figshare.22815234.v1
title: Additional file 5 of Nonnegative matrix factorization analysis and multiple machine learning methods identified IL17C and ACOXL as novel diagnostic biomarkers for atherosclerosis

description: Additional file 5: Table S3. Identification of hub genes by using SVM-RFE.
DOI: 10.6084/m9.figshare.22815237
title: Additional file 6 of Nonnegative matrix factorization analysis and multiple machine learning methods identified IL17C and ACOXL as novel diagnostic biomarkers for atherosclerosis

description: Additional file 6: Table S4. Identification of hub genes by using RF.
DOI: 10.6084/m9.figshare.22815237.v1
title: Additional file 6 of Nonnegative matrix factorization analysis and multiple machine learning methods identified IL17C and ACOXL as novel diagnostic biomarkers for atherosclerosis

description: Additional file 6: Table S4. Identification of hub genes by using RF.
DOI: 10.6084/m9.figshare.20193843
title: Additional file 6 of Upregulated lncARAT in Schwann cells promotes axonal regeneration by recruiting and activating proregenerative macrophages

description: Additional file 6: Table S1. Sequence of primers, miRNA, and siRNA used in the study. Table S2. DElncRNAs in crushed sciatic nerve compared with intact contralateral nerve through RNA-seq analysis. Table S3. GO analysis of differentially expressed mRNAs. Table S4. The top10 lncRNAs ranked by fold change. Table S5. miRNA prediction sponged by lncARAT using miRDB tool ( http://mirdb.org/ ).
DOI: 10.6084/m9.figshare.20193843.v1
title: Additional file 6 of Upregulated lncARAT in Schwann cells promotes axonal regeneration by recruiting and activating proregenerative macrophages

description: Additional file 6: Table S1. Sequence of primers, miRNA, and siRNA used in the study. Table S2. DElncRNAs in crushed sciatic nerve compared with intact contralateral nerve through RNA-seq analysis. Table S3. GO analysis of differentially expressed mRNAs. Table S4. The top10 lncRNAs ranked by fold change. Table S5. miRNA prediction sponged by lncARAT using miRDB tool ( http://mirdb.org/ ).
DOI: 10.6084/m9.figshare.20193849
title: Additional file 2 of The complete mitochondrial genome of okra (Abelmoschus esculentus): using nanopore long reads to investigate gene transfer from chloroplast genomes and rearrangements of mitochondrial DNA molecules

description: Additional file 2.
DOI: 10.6084/m9.figshare.20193849.v1
title: Additional file 2 of The complete mitochondrial genome of okra (Abelmoschus esculentus): using nanopore long reads to investigate gene transfer from chloroplast genomes and rearrangements of mitochondrial DNA molecules

description: Additional file 2.
DOI: 10.6084/m9.figshare.20193873
title: Additional file 1 of Ferroptosis-related long non-coding RNA signature predicts the prognosis of bladder cancer

description: Additional file 1.
DOI: 10.6084/m9.figshare.20193873.v1
title: Additional file 1 of Ferroptosis-related long non-coding RNA signature predicts the prognosis of bladder cancer

description: Additional file 1.
DOI: 10.6084/m9.figshare.20193876
title: Additional file 2 of Ferroptosis-related long non-coding RNA signature predicts the prognosis of bladder cancer

description: Additional file 2.
DOI: 10.6084/m9.figshare.20193876.v1
title: Additional file 2 of Ferroptosis-related long non-coding RNA signature predicts the prognosis of bladder cancer

description: Additional file 2.
DOI: 10.6084/m9.figshare.20193861
title: Additional file 2 of Overexpression of GhABF3 increases cotton(Gossypium hirsutum L.) tolerance to salt and drought

description: Addtional file 2: Table S1. Primer sequence. Table S2. Expression of GhABF3 in Han682 germination salt-tolerant transcriptome sequencing.  Table S3. Transgene inheritance pattern of T2 plants. The expected ratio in the chi-square test was 3:1, χ20.05(1)=3.84.
DOI: 10.6084/m9.figshare.20193861.v1
title: Additional file 2 of Overexpression of GhABF3 increases cotton(Gossypium hirsutum L.) tolerance to salt and drought

description: Addtional file 2: Table S1. Primer sequence. Table S2. Expression of GhABF3 in Han682 germination salt-tolerant transcriptome sequencing.  Table S3. Transgene inheritance pattern of T2 plants. The expected ratio in the chi-square test was 3:1, χ20.05(1)=3.84.
DOI: 10.6084/m9.figshare.20193879
title: Additional file 3 of Ferroptosis-related long non-coding RNA signature predicts the prognosis of bladder cancer

description: Additional file 3.
DOI: 10.6084/m9.figshare.20193879.v1
title: Additional file 3 of Ferroptosis-related long non-coding RNA signature predicts the prognosis of bladder cancer

description: Additional file 3.
DOI: 10.6084/m9.figshare.20193882
title: Additional file 4 of Ferroptosis-related long non-coding RNA signature predicts the prognosis of bladder cancer

description: Additional file 4.
DOI: 10.6084/m9.figshare.20193882.v1
title: Additional file 4 of Ferroptosis-related long non-coding RNA signature predicts the prognosis of bladder cancer

description: Additional file 4.
DOI: 10.6084/m9.figshare.20193888
title: Additional file 2 of Hyb4mC: a hybrid DNA2vec-based model for DNA N4-methylcytosine sites prediction

description: Additional file 2: An example of the matrix output by the Attention layer.
DOI: 10.6084/m9.figshare.20193891
title: Additional file 3 of Hyb4mC: a hybrid DNA2vec-based model for DNA N4-methylcytosine sites prediction

description: Additional file 3: An example of the matrix output by the Sitecaps layer.
DOI: 10.6084/m9.figshare.20193891.v1
title: Additional file 3 of Hyb4mC: a hybrid DNA2vec-based model for DNA N4-methylcytosine sites prediction

description: Additional file 3: An example of the matrix output by the Sitecaps layer.
DOI: 10.6084/m9.figshare.20193888.v1
title: Additional file 2 of Hyb4mC: a hybrid DNA2vec-based model for DNA N4-methylcytosine sites prediction

description: Additional file 2: An example of the matrix output by the Attention layer.
DOI: 10.6084/m9.figshare.11805336
title: Additional file 2 of Modulation of volatile compound metabolome and transcriptome in grape berries exposed to sunlight under dry-hot climate

description: Additional file 2: Table S1. Total soluble solids and titratable acidity of grape berries during development.
DOI: 10.6084/m9.figshare.11805336.v1
title: Additional file 2 of Modulation of volatile compound metabolome and transcriptome in grape berries exposed to sunlight under dry-hot climate

description: Additional file 2: Table S1. Total soluble solids and titratable acidity of grape berries during development.
DOI: 10.6084/m9.figshare.11805339
title: Additional file 7 of Exosome-shuttled miR-216a-5p from hypoxic preconditioned mesenchymal stem cells repair traumatic spinal cord injury by shifting microglial M1/M2 polarization

description: Additional file 7: Table S1. The primer sequences used in this work.