cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12186294
title: Additional file 4 of Transcriptome analysis reveals differential immune related genes expression in Ruditapes philippinarum under hypoxia stress: potential HIF and NF-κB crosstalk in immune responses in clam

description: Additional file 4. 75 differentially expressed unigenes, including 43 upregulated and 32 downregulated unigenes indentified between hypoxia stress group and normoxia control group (qvalue 1).
DOI: 10.6084/m9.figshare.12186291
title: Additional file 3 of Transcriptome analysis reveals differential immune related genes expression in Ruditapes philippinarum under hypoxia stress: potential HIF and NF-κB crosstalk in immune responses in clam

description: Additional file 3. Kyoto Encyclopedia of Genes and Genomes (KEGG) assignment of unigenes in transcriptome of R. philippinarum under hypoxia stress.
DOI: 10.6084/m9.figshare.12186285
title: Additional file 1 of The characteristics of the frequent exacerbator with chronic bronchitis phenotype and non-exacerbator phenotype in patients with chronic obstructive pulmonary disease: a meta-analysis and system review

description: Additional file 1: Figure S1. Sensitivity analysis of Difference of FEV1%pred between the FE-CB and the NE phenotypes. Forest plots of the sensitivity analysis for difference of FEV1%pred between the FE-CB and the NE phenotypes. Figure S2. Difference of the quantity of cigarettes smoked (pack-years) between the FE-CB and the NE phenotypes. Forest plots of the difference of the quantity of cigarettes smoked (pack-years) between the FE-CB and the NE phenotypes. Figure S3. Difference of CAT score between the FE-CB and the NE phenotypes. Forest plots of the difference of cat score between the FE-CB and the NE phenotypes. Figure S4. Sensitivity analysis of CAT between the FE-CB and the NE phenotypes. Forest plots of the sensitivity analysis for CAT between the FE-CB and the NE phenotypes. Difference of mMRC score between the FE-CB and the NE phenotypes. Forest plots of the difference of mMRC score between the FE-CB and the NE phenotypes. Figure S6. Sensitivity analysis of mMRC between the FE-CB and the NE phenotypes. Forest plots of the sensitivity analysis for mMRC between the FE-CB and the NE phenotypes. Figure S7. Difference of BMI between the FE-CB and the NE phenotypes. Forest plots of the difference of BMI between the FE-CB and the NE phenotypes. Figure S8. Sensitivity analysis of BMI between the FE-CB and the NE phenotypes. Forest plots of the sensitivity analysis of BMI between the FE-CB and the NE phenotypes. Figure S9. Difference of BODEx between the FE-CB and the NE phenotypes. Forest plots of the difference of BODEx between the FE-CB and the NE phenotypes. Figure S10. Difference of Charlson comorbidity index between the FE-CB and the NE phenotypes. Forest plots of the difference of Charlson comorbidity index between the FE-CB and the NE phenotypes. Flow Diagram. PRISMA 2009 Flow Diagram. The screening procedure the study. Table S1. Excluded list. List of excluded full-text articles. Table S2. Other indices. Other indices in different phenotyp. Text S1. Literature Search. The full details of the databases searched to identify the studies.
DOI: 10.6084/m9.figshare.12186291.v1
title: Additional file 3 of Transcriptome analysis reveals differential immune related genes expression in Ruditapes philippinarum under hypoxia stress: potential HIF and NF-κB crosstalk in immune responses in clam

description: Additional file 3. Kyoto Encyclopedia of Genes and Genomes (KEGG) assignment of unigenes in transcriptome of R. philippinarum under hypoxia stress.
DOI: 10.6084/m9.figshare.12186294.v1
title: Additional file 4 of Transcriptome analysis reveals differential immune related genes expression in Ruditapes philippinarum under hypoxia stress: potential HIF and NF-κB crosstalk in immune responses in clam

description: Additional file 4. 75 differentially expressed unigenes, including 43 upregulated and 32 downregulated unigenes indentified between hypoxia stress group and normoxia control group (qvalue 1).
DOI: 10.6084/m9.figshare.12186285.v1
title: Additional file 1 of The characteristics of the frequent exacerbator with chronic bronchitis phenotype and non-exacerbator phenotype in patients with chronic obstructive pulmonary disease: a meta-analysis and system review

description: Additional file 1: Figure S1. Sensitivity analysis of Difference of FEV1%pred between the FE-CB and the NE phenotypes. Forest plots of the sensitivity analysis for difference of FEV1%pred between the FE-CB and the NE phenotypes. Figure S2. Difference of the quantity of cigarettes smoked (pack-years) between the FE-CB and the NE phenotypes. Forest plots of the difference of the quantity of cigarettes smoked (pack-years) between the FE-CB and the NE phenotypes. Figure S3. Difference of CAT score between the FE-CB and the NE phenotypes. Forest plots of the difference of cat score between the FE-CB and the NE phenotypes. Figure S4. Sensitivity analysis of CAT between the FE-CB and the NE phenotypes. Forest plots of the sensitivity analysis for CAT between the FE-CB and the NE phenotypes. Difference of mMRC score between the FE-CB and the NE phenotypes. Forest plots of the difference of mMRC score between the FE-CB and the NE phenotypes. Figure S6. Sensitivity analysis of mMRC between the FE-CB and the NE phenotypes. Forest plots of the sensitivity analysis for mMRC between the FE-CB and the NE phenotypes. Figure S7. Difference of BMI between the FE-CB and the NE phenotypes. Forest plots of the difference of BMI between the FE-CB and the NE phenotypes. Figure S8. Sensitivity analysis of BMI between the FE-CB and the NE phenotypes. Forest plots of the sensitivity analysis of BMI between the FE-CB and the NE phenotypes. Figure S9. Difference of BODEx between the FE-CB and the NE phenotypes. Forest plots of the difference of BODEx between the FE-CB and the NE phenotypes. Figure S10. Difference of Charlson comorbidity index between the FE-CB and the NE phenotypes. Forest plots of the difference of Charlson comorbidity index between the FE-CB and the NE phenotypes. Flow Diagram. PRISMA 2009 Flow Diagram. The screening procedure the study. Table S1. Excluded list. List of excluded full-text articles. Table S2. Other indices. Other indices in different phenotyp. Text S1. Literature Search. The full details of the databases searched to identify the studies.
DOI: 10.6084/m9.figshare.12186279
title: Additional file 1 of Transcriptome analysis reveals differential immune related genes expression in Ruditapes philippinarum under hypoxia stress: potential HIF and NF-κB crosstalk in immune responses in clam

description: Additional file 1. Gene Ontology (GO) categorization of the unigenes in cellular component, biological process, and molecular function from transcriptome of R. philippinarum.
DOI: 10.6084/m9.figshare.12186279.v1
title: Additional file 1 of Transcriptome analysis reveals differential immune related genes expression in Ruditapes philippinarum under hypoxia stress: potential HIF and NF-κB crosstalk in immune responses in clam

description: Additional file 1. Gene Ontology (GO) categorization of the unigenes in cellular component, biological process, and molecular function from transcriptome of R. philippinarum.
DOI: 10.6084/m9.figshare.12186222
title: Additional file 3 of Factors influencing patient and health care delays in Oropharyngeal Cancer

description: Additional file 3: Table 9. Primary health-care (PHC) delays exceeding 3 months
DOI: 10.6084/m9.figshare.12186231
title: Additional file 1 of Whole genome sequencing of Nontuberculous Mycobacterium (NTM) isolates from sputum specimens of co-habiting patients with NTM pulmonary disease and NTM isolates from their environment

description: Additional file 1: Table S1. Characteristics on environmental specimens. The numbers of collected specimens, culture-positive, morphologically distinct isolates from each habitat. Table S2. NTM species Identified and Sequencing Results. Description on the source of each sample and its sequencing quality (total read counts, mean depth, 20x coverage, number of SNPs), species, clonality, and morphologic feature.
DOI: 10.6084/m9.figshare.12186216
title: Additional file 2 of Factors influencing patient and health care delays in Oropharyngeal Cancer

description: Additional file 2: Table 8. Patient delays exceeding 6 months
DOI: 10.6084/m9.figshare.12186216.v1
title: Additional file 2 of Factors influencing patient and health care delays in Oropharyngeal Cancer

description: Additional file 2: Table 8. Patient delays exceeding 6 months
DOI: 10.6084/m9.figshare.12186222.v1
title: Additional file 3 of Factors influencing patient and health care delays in Oropharyngeal Cancer

description: Additional file 3: Table 9. Primary health-care (PHC) delays exceeding 3 months
DOI: 10.6084/m9.figshare.12186231.v1
title: Additional file 1 of Whole genome sequencing of Nontuberculous Mycobacterium (NTM) isolates from sputum specimens of co-habiting patients with NTM pulmonary disease and NTM isolates from their environment

description: Additional file 1: Table S1. Characteristics on environmental specimens. The numbers of collected specimens, culture-positive, morphologically distinct isolates from each habitat. Table S2. NTM species Identified and Sequencing Results. Description on the source of each sample and its sequencing quality (total read counts, mean depth, 20x coverage, number of SNPs), species, clonality, and morphologic feature.
DOI: 10.6084/m9.figshare.12186207
title: Additional file 1 of Factors influencing patient and health care delays in Oropharyngeal Cancer

description: Additional file 1: Table 7. Patients who received palliative treatment
DOI: 10.6084/m9.figshare.12186207.v1
title: Additional file 1 of Factors influencing patient and health care delays in Oropharyngeal Cancer

description: Additional file 1: Table 7. Patients who received palliative treatment
DOI: 10.6084/m9.figshare.12186192
title: Additional file 3 of IL-33, diet-induced obesity, and pulmonary responses to ozone

description: Additional file 3: Table S2. Bacterial taxa that were significantly affected by housing in chow-fed mice. Table indicates outcome from a comparison within the chow fed mice of all same housed versus cohoused mice. Only taxa with p
DOI: 10.6084/m9.figshare.12186192.v1
title: Additional file 3 of IL-33, diet-induced obesity, and pulmonary responses to ozone

description: Additional file 3: Table S2. Bacterial taxa that were significantly affected by housing in chow-fed mice. Table indicates outcome from a comparison within the chow fed mice of all same housed versus cohoused mice. Only taxa with p
DOI: 10.6084/m9.figshare.12186183
title: Additional file 2 of IL-33, diet-induced obesity, and pulmonary responses to ozone

description: Additional file 2: Table S1. Bacterial taxa that were significantly different in chow- versus HFD-fed mice. Weanling mice were fed standard mouse chow or high fat diets for 12 weeks after which a fecal pellet was collected. Fecal DNA was extracted from the pellets and 16S rRNA sequencing was performed. Table indicates outcome from a comparison of all chow-fed versus all HFD-fed mice regardless of whether they were WT or ST2−/− or whether they were same housed or cohoused. Only taxa with p
DOI: 10.6084/m9.figshare.12186174
title: Additional file 2 of Maladaptive plasticity facilitates evolution of thermal tolerance during an experimental range shift

description: Additional file 2.Callosobruchus maculatus Thermal Tolerance Raw Aata.
DOI: 10.6084/m9.figshare.12186183.v1
title: Additional file 2 of IL-33, diet-induced obesity, and pulmonary responses to ozone

description: Additional file 2: Table S1. Bacterial taxa that were significantly different in chow- versus HFD-fed mice. Weanling mice were fed standard mouse chow or high fat diets for 12 weeks after which a fecal pellet was collected. Fecal DNA was extracted from the pellets and 16S rRNA sequencing was performed. Table indicates outcome from a comparison of all chow-fed versus all HFD-fed mice regardless of whether they were WT or ST2−/− or whether they were same housed or cohoused. Only taxa with p
DOI: 10.6084/m9.figshare.12186174.v1
title: Additional file 2 of Maladaptive plasticity facilitates evolution of thermal tolerance during an experimental range shift

description: Additional file 2.Callosobruchus maculatus Thermal Tolerance Raw Aata.
DOI: 10.6084/m9.figshare.12186156
title: Additional file 1 of Genomic analysis of a riboflavin-overproducing Ashbya gossypii mutant isolated by disparity mutagenesis

description: Additional file 1: Table S1. All 33 homozygous mutations detected in the coding sequences of the MT genome. Table S2. All 1377 heterozygous mutations detected in coding sequences of the MT genome. Table S3. Gene Ontology (GO) enrichment analysis of the genes containing mutations. Table S4. Genes assigned over-represented Gene Ontology. Table S5. Genes assigned under-represented Gene Ontology.
DOI: 10.6084/m9.figshare.12186156.v1
title: Additional file 1 of Genomic analysis of a riboflavin-overproducing Ashbya gossypii mutant isolated by disparity mutagenesis

description: Additional file 1: Table S1. All 33 homozygous mutations detected in the coding sequences of the MT genome. Table S2. All 1377 heterozygous mutations detected in coding sequences of the MT genome. Table S3. Gene Ontology (GO) enrichment analysis of the genes containing mutations. Table S4. Genes assigned over-represented Gene Ontology. Table S5. Genes assigned under-represented Gene Ontology.
DOI: 10.6084/m9.figshare.12185760.v1
title: COVID-19 serial interval estimates based on confirmed cases in public reports from 86 Chinese cities

description: COVID-19
DOI: 10.6084/m9.figshare.12185742
title: Data file from Exposure to high male density causes maternal stress and female-biased sex ratios in a mammal

description: A shift from the traditional perspective that maternal stress is invariably costly has instigated recent interest into its adaptive role in offspring sex allocation. Stress generated by social instability has been linked to offspring sex ratio biases that favour the production of female offspring, which converges with the theoretical prediction that mothers in the poor condition are better off investing in daughters rather than sons. However, previous research has failed to disentangle two different processes: the passive consequence of maternal stress on sex-specific mortality and the adaptive effect of maternal stress at the time of conception. Here, I show that exposure to high male density social conditions leads to elevated stress hormone levels and female-biased <i>in utero</i> offspring sex ratios in house mice (<i>Mus musculus domesticus</i>), and identify that sex-specific offspring production—not sex-specific mortality—is the mechanism accounting for these sex ratio skews. This outcome reflects the optimal fitness scenario for mothers in a male-dominated environment: the production of daughters, who are guaranteed high mate availability, minimizes male–male competition for their sons. Overall, this study supports the idea that maternal stress has the potential to be adaptive and advances our understanding of how exposure to different social conditions can influence sex allocation in mammals.
DOI: 10.6084/m9.figshare.12185715
title: Online Resource 6 Rev.zip from From a discrete model of chemotaxis with volume-filling to a generalized Patlak–Keller–Segel model

description: We present a discrete model of chemotaxis whereby cells responding to a chemoattractant are seen as individual agents whose movement is described through a set of rules that result in a biased random walk. In order to take into account possible alterations in cellular motility observed at high cell densities (i.e. volume-filling), we let the probabilities of cell movement be modulated by a decaying function of the cell density. We formally show that a general form of the celebrated Patlak–Keller–Segel (PKS) model of chemotaxis can be formally derived as the appropriate continuum limit of this discrete model. The family of steady-state solutions of such a generalized PKS model are characterized and the conditions for the emergence of spatial patterns are studied via linear stability analysis. Moreover, we carry out a systematic quantitative comparison between numerical simulations of the discrete model and numerical solutions of the corresponding PKS model, both in one and in two spatial dimensions. The results obtained indicate that there is excellent quantitative agreement between the spatial patterns produced by the two models. Finally, we numerically show that the outcomes of the two models faithfully replicate those of the classical PKS model in a suitable asymptotic regime.
DOI: 10.6084/m9.figshare.12185715.v2
title: Online Resource 6 Rev.zip from From a discrete model of chemotaxis with volume-filling to a generalized Patlak–Keller–Segel model

description: We present a discrete model of chemotaxis whereby cells responding to a chemoattractant are seen as individual agents whose movement is described through a set of rules that result in a biased random walk. In order to take into account possible alterations in cellular motility observed at high cell densities (i.e. volume-filling), we let the probabilities of cell movement be modulated by a decaying function of the cell density. We formally show that a general form of the celebrated Patlak–Keller–Segel (PKS) model of chemotaxis can be formally derived as the appropriate continuum limit of this discrete model. The family of steady-state solutions of such a generalized PKS model are characterized and the conditions for the emergence of spatial patterns are studied via linear stability analysis. Moreover, we carry out a systematic quantitative comparison between numerical simulations of the discrete model and numerical solutions of the corresponding PKS model, both in one and in two spatial dimensions. The results obtained indicate that there is excellent quantitative agreement between the spatial patterns produced by the two models. Finally, we numerically show that the outcomes of the two models faithfully replicate those of the classical PKS model in a suitable asymptotic regime.
DOI: 10.6084/m9.figshare.12185715.v1
title: Online Resource 6 Rev.zip from From a discrete model of chemotaxis with volume-filling to a generalized Patlak–Keller–Segel model. 2 January 2020 2 April 2020

description: We present a discrete model of chemotaxis whereby cells responding to a chemoattractant are seen as individual agents whose movement is described through a set of rules that result in a biased random walk. In order to take into account possible alterations in cellular motility observed at high cell densities (i.e. volume-filling), we let the probabilities of cell movement be modulated by a decaying function of the cell density. We formally show that a general form of the celebrated Patlak–Keller–Segel (PKS) model of chemotaxis can be formally derived as the appropriate continuum limit of this discrete model. The family of steady-state solutions of such a generalized PKS model are characterized and the conditions for the emergence of spatial patterns are studied via linear stability analysis. Moreover, we carry out a systematic quantitative comparison between numerical simulations of the discrete model and numerical solutions of the corresponding PKS model, both in one and in two spatial dimensions. The results obtained indicate that there is excellent quantitative agreement between the spatial patterns produced by the two models. Finally, we numerically show that the outcomes of the two models faithfully replicate those of the classical PKS model in a suitable asymptotic regime.
DOI: 10.6084/m9.figshare.12185700
title: Simulation code from Reconstructing ecological networks with noisy dynamics

description: Codes used in numerical simulations
DOI: 10.6084/m9.figshare.12185691
title: Supplement S3: High Mechanical Stimulus Sensitivity Analysis from Quantification of uncertainty in a new network model of pulmonary arterial adventitial fibroblast pro-fibrotic signalling

description: Heat map of the sensitivity analysis under high mechanical stimulus
DOI: 10.6084/m9.figshare.12185694
title: Animal tuning curve data from An inter-species computational analysis of vibrotactile sensitivity in Pacinian and Herbst corpuscles

description: The attached spreadsheet includes the simulated tuning curves for each animal, as output from the model
DOI: 10.6084/m9.figshare.12185688
title: Supplement S1: PAAF Network from Quantification of uncertainty in a new network model of pulmonary arterial adventitial fibroblast pro-fibrotic signalling

description: Reaction list of PAAF model pathways
DOI: 10.6084/m9.figshare.12185691.v2
title: Supplement S3: High Mechanical Stimulus Sensitivity Analysis from Quantification of uncertainty in a new network model of pulmonary arterial adventitial fibroblast pro-fibrotic signalling

description: Heat map of the sensitivity analysis under high mechanical stimulus
DOI: 10.6084/m9.figshare.12185682
title: Supplement S2: Data used for Validation from Quantification of uncertainty in a new network model of pulmonary arterial adventitial fibroblast pro-fibrotic signalling

description: Independent set of input-output experiments and references used for model validation
DOI: 10.6084/m9.figshare.12185694.v1
title: Animal tuning curve data from An inter-species computational analysis of vibrotactile sensitivity in Pacinian and Herbst corpuscles

description: The attached spreadsheet includes the simulated tuning curves for each animal, as output from the model
DOI: 10.6084/m9.figshare.12185691.v1
title: Supplement S3: High Mechanical Stimulus Sensitivity Analysis from Quantification of uncertainty in a new network model of pulmonary arterial adventitial fibroblast pro-fibrotic signalling. 16 March 2020

description: Heat map of the sensitivity analysis under high mechanical stimulus
DOI: 10.6084/m9.figshare.12185682.v1
title: Supplement S2: Data used for Validation from Quantification of uncertainty in a new network model of pulmonary arterial adventitial fibroblast pro-fibrotic signalling. 16 March 2020

description: Independent set of input-output experiments and references used for model validation
DOI: 10.6084/m9.figshare.12185682.v2
title: Supplement S2: Data used for Validation from Quantification of uncertainty in a new network model of pulmonary arterial adventitial fibroblast pro-fibrotic signalling

description: Independent set of input-output experiments and references used for model validation
DOI: 10.6084/m9.figshare.12185688.v2
title: Supplement S1: PAAF Network from Quantification of uncertainty in a new network model of pulmonary arterial adventitial fibroblast pro-fibrotic signalling

description: Reaction list of PAAF model pathways
DOI: 10.6084/m9.figshare.12185670
title: 3D drawing of the segment. Sub-linear case from Flexural tensegrity of segmental beams. 31 January 2020 26 March 2020

description: AutoCAD file used for the 3D printing of the prototype for the sub-linear case.
DOI: 10.6084/m9.figshare.12185688.v1
title: Supplement S1: PAAF Network from Quantification of uncertainty in a new network model of pulmonary arterial adventitial fibroblast pro-fibrotic signalling. 16 March 2020

description: Reaction list of PAAF model pathways
DOI: 10.6084/m9.figshare.12185667
title: 3D drawing of the segment. Linear case from Flexural tensegrity of segmental beams. 31 January 2020 26 March 2020

description: AutoCAD file used for the 3D printing of the prototype for the linear case.
DOI: 10.6084/m9.figshare.12185664
title: 3D drawing of the segment. Super-linear case from Flexural tensegrity of segmental beams. 31 January 2020 26 March 2020

description: AutoCAD file used for the 3D printing of the prototype for the super-linear case.
DOI: 10.6084/m9.figshare.12185670.v1
title: 3D drawing of the segment. Sub-linear case from Flexural tensegrity of segmental beams. 31 January 2020 26 March 2020

description: AutoCAD file used for the 3D printing of the prototype for the sub-linear case.