cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11447622
title: Proteome database of Aeromonas hydrophila AH1

description: Resistant to most common antibiotics and cold temperatures, <i>Aeromonas hydrophila</i> is a microorganism that could emerge as a human pathogen of concern. More importantly, its ability to live in aerobic and anaerobic environments endow it with capability in infecting body wounds that would result in morbidity. Finally, ability to use a variety of organic substrates as carbon source further positions <i>A. hydrophila</i> as a potential pathogen given its ability to subsist on small amounts of nutrients in the interstitial fluid that bathe human cells and tissues. This work sought to illuminate the fundamental biology of <i>A. hydrophila</i> AH1 through parsing the UniProt proteome file of the bacterium using an in-house MATLAB software. Specifically, a proteome database comprising protein name, amino acid sequence, number of residues, molecular weight and nucleotide sequence was built that would help guide experiments to deduce the nutritional and environmental conditions that would be most conducive for the growth of this versatile microorganism. Finally, proteomic information would serve as a useful reference for thinking about possible interventions or therapeutic strategies, especially as more research confirms <i>A. hydrophila</i> as an emerging human pathogen. <br><br>
DOI: 10.6084/m9.figshare.11447622.v1
title: Proteome database of Aeromonas hydrophila AH1

description: Resistant to most common antibiotics and cold temperatures, <i>Aeromonas hydrophila</i> is a microorganism that could emerge as a human pathogen of concern. More importantly, its ability to live in aerobic and anaerobic environments endow it with capability in infecting body wounds that would result in morbidity. Finally, ability to use a variety of organic substrates as carbon source further positions <i>A. hydrophila</i> as a potential pathogen given its ability to subsist on small amounts of nutrients in the interstitial fluid that bathe human cells and tissues. This work sought to illuminate the fundamental biology of <i>A. hydrophila</i> AH1 through parsing the UniProt proteome file of the bacterium using an in-house MATLAB software. Specifically, a proteome database comprising protein name, amino acid sequence, number of residues, molecular weight and nucleotide sequence was built that would help guide experiments to deduce the nutritional and environmental conditions that would be most conducive for the growth of this versatile microorganism. Finally, proteomic information would serve as a useful reference for thinking about possible interventions or therapeutic strategies, especially as more research confirms <i>A. hydrophila</i> as an emerging human pathogen. <br><br>
DOI: 10.6084/m9.figshare.11447655.v2
title: The cost-effectiveness of guideline-driven use of drug-eluting stents: propensity-score matched analysis of a seven-year multicentre experience

description: <b>Background:</b> In routine clinical practice, the implantation of a drug-eluting stent (DES) versus a bare metal stent (BMS) for percutaneous coronary intervention (PCI) has been guided by criteria for appropriate use. The cost-effectiveness (CE) of adopting these guidelines, however, is not clear, and was investigated from the perspective of the Australian healthcare payer. <b>Methods and results:</b> Baseline and 12-month follow-up data of 12,710 PCI patients enrolled in the Melbourne Interventional Group (MIG) registry between 2004 and 2011 were analysed. Costs inputs were derived from a clinical costing database and published sources. Propensity-score-matching was performed for DES and BMS groups within sub-groups. Incremental cost-effectiveness ratios (ICERs) were evaluated for all patients, and sub-groups of patients with ‘0’, 1, 2, or ≥3 indications for a DES. The incremental cost per target vessel revascularization avoided for the overall population was $24,683, and for patients with 0, 1, and 2 indications for a DES was $44,635, $33,335, and $23,788, respectively. However, for those with &gt;3 indications, DES compared with BMS was associated with cost savings. At willingness to pay thresholds of $45,000–$75,000, the probability of cost-effectiveness of DES for the overall cohort was 71–91%, ‘0’ indications, 49–67%, 1 indication, 56–82%, 2 indications, 70–90%, and ≥3 indications, 97–99%. <b>Conclusions:</b> The cost-effectiveness of DES compared with BMS increased with increasing risk profile of patients from those who had 1, 2, to ≥3 indications for a DES. When compared with BMS, DES was least cost effective among patients with ‘0’ indications for a DES. Based on these results, selective use of DES implantation is supported. These findings may be useful for evidence-based clinical decision-making.
DOI: 10.6084/m9.figshare.11447655.v1
title: The cost-effectiveness of guideline-driven use of drug-eluting stents: propensity-score matched analysis of a seven-year multicentre experience

description: <b><i>Background:</i></b> In routine clinical practice, the implantation of a drug-eluting stent (DES) versus a bare metal stent (BMS) for percutaneous coronary intervention (PCI) has been guided by criteria for appropriate use. The cost-effectiveness (CE) of adopting these guidelines, however, is not clear, and was investigated from the perspective of the Australian healthcare payer. <b><i>Methods and Results:</i></b> Baseline and 12-month follow-up data of 12,710 PCI patients enrolled in the Melbourne Interventional Group (MIG) registry between 2004 and 2011 were analysed. Costs inputs were derived from a clinical costing database and published sources. Propensity-score-matching was performed for DES and BMS groups within sub-groups. Incremental cost-effectiveness ratios (ICERs) were evaluated for all patients, and sub-groups of patients with ‘0’, 1, 2, or ≥3 indications for a DES. While the incremental cost per TVR avoided for these groups were $24,683, $44,635, $33,335, and $23,788, respectively, for those with ≥3 indications, DES compared with BMS was associated with cost savings. At willingness to pay thresholds of $45,000-$75,000, probability of cost-effectiveness of DES for the overall cohort was 71-91%, ‘0’ indications, 49-67%, 1 indication, 56-82%, 2 indications, 70-90%, and ≥3 indications, 97-99%. <b><i>Conclusions:</i></b> The cost-effectiveness of DES compared with BMS increased with increasing risk profile of patients from those who had 1, 2, to ≥3 indications for a DES. When compared with BMS, DES was least cost effective among patients with ‘0’ indications for a DES. Based on these results, selective use of DES implantation is supported. These findings may be useful for evidence-based clinical decision-making.
DOI: 10.6084/m9.figshare.11447325
title: MOESM1 of Weight status and obesity-related dietary behaviours among culturally and linguistically diverse (CALD) children in Victoria, Australia

description: Additional file 1: Table S1. Descriptive statistics of Victorian primary school children by CALD background.
DOI: 10.6084/m9.figshare.11447325.v1
title: MOESM1 of Weight status and obesity-related dietary behaviours among culturally and linguistically diverse (CALD) children in Victoria, Australia

description: Additional file 1: Table S1. Descriptive statistics of Victorian primary school children by CALD background.
DOI: 10.6084/m9.figshare.11447301
title: MOESM1 of Investigation of somatic CNVs in brains of synucleinopathy cases using targeted SNCA analysis and single cell sequencing

description: Additional file 1: Table S1. Summary of demograhics of cases used, and all experiments performed on each. These are iindicated by “x” in relevant cell, with the following exceptions: The % SNCA mosaicism is provided by cell type for the SN and Cingulate cortex. The region(s) from which single cells were sequenced are shown in the relevant cells. The GCI grade is shown in the relevant cells. Note that the single DLB case was reported with ILBD in SN experiments. Empty cells signify that data not available, or experiment not performed. PMI = post mortem interval. OC = occipital cortex, PUT = putamen. sc = single cell. MPS = mate pair sequencing. AD = Alzheimer’s disease.
DOI: 10.6084/m9.figshare.11447301.v1
title: MOESM1 of Investigation of somatic CNVs in brains of synucleinopathy cases using targeted SNCA analysis and single cell sequencing

description: Additional file 1: Table S1. Summary of demograhics of cases used, and all experiments performed on each. These are iindicated by “x” in relevant cell, with the following exceptions: The % SNCA mosaicism is provided by cell type for the SN and Cingulate cortex. The region(s) from which single cells were sequenced are shown in the relevant cells. The GCI grade is shown in the relevant cells. Note that the single DLB case was reported with ILBD in SN experiments. Empty cells signify that data not available, or experiment not performed. PMI = post mortem interval. OC = occipital cortex, PUT = putamen. sc = single cell. MPS = mate pair sequencing. AD = Alzheimer’s disease.
DOI: 10.6084/m9.figshare.11447271
title: MOESM2 of Parallel selection on ecologically relevant gene functions in the transcriptomes of highly diversifying salmonids

description: Additional file 2. Orthogroups under diversifying selection (FDR
DOI: 10.6084/m9.figshare.11447271.v1
title: MOESM2 of Parallel selection on ecologically relevant gene functions in the transcriptomes of highly diversifying salmonids

description: Additional file 2. Orthogroups under diversifying selection (FDR
DOI: 10.6084/m9.figshare.11447265
title: MOESM1 of Parallel selection on ecologically relevant gene functions in the transcriptomes of highly diversifying salmonids

description: Additional file 1. Total set of orthogroups with associated GO terms and results of relaxed and intensified selection analyses.
DOI: 10.6084/m9.figshare.11447265.v1
title: MOESM1 of Parallel selection on ecologically relevant gene functions in the transcriptomes of highly diversifying salmonids

description: Additional file 1. Total set of orthogroups with associated GO terms and results of relaxed and intensified selection analyses.
DOI: 10.6084/m9.figshare.11447193
title: MOESM1 of The development and evaluation of a self-marking unit to estimate malaria vector survival and dispersal distance

description: Additional file 1. MMRR field trial dataset. Contains data on individual recaptured mosquitoes, pupae numbers and mosquito trapping totals (marked and unmarked).
DOI: 10.6084/m9.figshare.11447193.v1
title: MOESM1 of The development and evaluation of a self-marking unit to estimate malaria vector survival and dispersal distance

description: Additional file 1. MMRR field trial dataset. Contains data on individual recaptured mosquitoes, pupae numbers and mosquito trapping totals (marked and unmarked).
DOI: 10.6084/m9.figshare.11447157
title: MOESM2 of Diet and irradiation effects on the bacterial community composition and structure in the gut of domesticated teneral and mature Queensland fruit fly, Bactrocera tryoni (Diptera: Tephritidae)

description: Additional file 2. Representative set of 16S rRNA gene sequences of OTUs from teneral and mature Bactrocera tryoni.
DOI: 10.6084/m9.figshare.11447160
title: MOESM3 of Diet and irradiation effects on the bacterial community composition and structure in the gut of domesticated teneral and mature Queensland fruit fly, Bactrocera tryoni (Diptera: Tephritidae)

description: Additional file 3: Table S4. OTU Table.
DOI: 10.6084/m9.figshare.11447157.v1
title: MOESM2 of Diet and irradiation effects on the bacterial community composition and structure in the gut of domesticated teneral and mature Queensland fruit fly, Bactrocera tryoni (Diptera: Tephritidae)

description: Additional file 2. Representative set of 16S rRNA gene sequences of OTUs from teneral and mature Bactrocera tryoni.
DOI: 10.6084/m9.figshare.11447160.v1
title: MOESM3 of Diet and irradiation effects on the bacterial community composition and structure in the gut of domesticated teneral and mature Queensland fruit fly, Bactrocera tryoni (Diptera: Tephritidae)

description: Additional file 3: Table S4. OTU Table.
DOI: 10.6084/m9.figshare.11447139
title: MOESM2 of Joint learning improves protein abundance prediction in cancers

description: Additional file 2: Supplementary Tables. Table S1. The five-fold Pearsonâ€™s correlations of the generic, gene-specific and trans-tissue models. Table S2. The five-fold correlations of models using different numbers of features. Table S3. The weighting ratios to stack the generic, gene-specific and trans-tissue models and the corresponding prediction correlations in breast. Table S4. The weighting ratios to stack the generic, gene-specific and trans-tissue models and the corresponding prediction correlations in ovary. Table S5. The five-fold correlations of models using different numbers of samples. Table S6. The correlations and RMSEs across 32 overlapping ovarian cancer samples measured at both JHU and PNNL. Table S7. The correlations and RMSEs of our predictions on the held-out testing dataset of 82 ovarian cancer samples during the NCI-CPTAC DREAM challenge. Table S8. The feature importance of all genes in breast. Table S9. The feature importance of all genes in ovary. Table S10. The list of driver genes and their clusters in the breast functional network. Table S11. The list of driver genes and their clusters in the ovary functional network.
DOI: 10.6084/m9.figshare.11447139.v1
title: MOESM2 of Joint learning improves protein abundance prediction in cancers

description: Additional file 2: Supplementary Tables. Table S1. The five-fold Pearsonâ€™s correlations of the generic, gene-specific and trans-tissue models. Table S2. The five-fold correlations of models using different numbers of features. Table S3. The weighting ratios to stack the generic, gene-specific and trans-tissue models and the corresponding prediction correlations in breast. Table S4. The weighting ratios to stack the generic, gene-specific and trans-tissue models and the corresponding prediction correlations in ovary. Table S5. The five-fold correlations of models using different numbers of samples. Table S6. The correlations and RMSEs across 32 overlapping ovarian cancer samples measured at both JHU and PNNL. Table S7. The correlations and RMSEs of our predictions on the held-out testing dataset of 82 ovarian cancer samples during the NCI-CPTAC DREAM challenge. Table S8. The feature importance of all genes in breast. Table S9. The feature importance of all genes in ovary. Table S10. The list of driver genes and their clusters in the breast functional network. Table S11. The list of driver genes and their clusters in the ovary functional network.
DOI: 10.6084/m9.figshare.11447085
title: MOESM4 of Dynamics of microbiota during mechanical ventilation in aspiration pneumonia

description: Additional file 4: Figure S2–1, S2–2. Unsupervised hierarchical clustering of genus for samples of saliva and tracheal aspirate.
DOI: 10.6084/m9.figshare.11447085.v1
title: MOESM4 of Dynamics of microbiota during mechanical ventilation in aspiration pneumonia

description: Additional file 4: Figure S2–1, S2–2. Unsupervised hierarchical clustering of genus for samples of saliva and tracheal aspirate.
DOI: 10.6084/m9.figshare.11447076
title: MOESM3 of Dynamics of microbiota during mechanical ventilation in aspiration pneumonia

description: Additional file 3: Figure S1–1, S1–2. Proportions of bacterial flora in saliva and tracheal aspirate (individual cases).
DOI: 10.6084/m9.figshare.11447076.v1
title: MOESM3 of Dynamics of microbiota during mechanical ventilation in aspiration pneumonia

description: Additional file 3: Figure S1–1, S1–2. Proportions of bacterial flora in saliva and tracheal aspirate (individual cases).
DOI: 10.6084/m9.figshare.11447064
title: MOESM2 of Dynamics of microbiota during mechanical ventilation in aspiration pneumonia

description: Additional file 2: Appendix S2. In-house database containing 5878 16S rDNA sequences of type strains.
DOI: 10.6084/m9.figshare.11447064.v1
title: MOESM2 of Dynamics of microbiota during mechanical ventilation in aspiration pneumonia

description: Additional file 2: Appendix S2. In-house database containing 5878 16S rDNA sequences of type strains.
DOI: 10.6084/m9.figshare.11447052
title: MOESM1 of Epigenetic therapy of myelodysplastic syndromes connects to cellular differentiation independently of endogenous retroelement derepression

description: Additional file 1: Table S1. Details of patients recruited in this study. Table S2. Details of patient samples used in this study. Table S3. PCR primers used in this study. Table S4. 479 elements differentially expressed between healthy and dysplastic HSCs. Table S5. Interferon-stimulated genes (ISG) and LTR elements (IS-LTR) used in this study. Table S6. 91 transcripts differentially expressed between responders and failures prior to 5-AZA treatment.
DOI: 10.6084/m9.figshare.11447052.v1
title: MOESM1 of Epigenetic therapy of myelodysplastic syndromes connects to cellular differentiation independently of endogenous retroelement derepression

description: Additional file 1: Table S1. Details of patients recruited in this study. Table S2. Details of patient samples used in this study. Table S3. PCR primers used in this study. Table S4. 479 elements differentially expressed between healthy and dysplastic HSCs. Table S5. Interferon-stimulated genes (ISG) and LTR elements (IS-LTR) used in this study. Table S6. 91 transcripts differentially expressed between responders and failures prior to 5-AZA treatment.
DOI: 10.6084/m9.figshare.11447016
title: MOESM3 of An improved catalogue of putative synaptic genes defined exclusively by temporal transcription profiles through an ensemble machine learning approach

description: Additional file 3: New catalogue of putative synaptic genes (XLS 39 kb)
DOI: 10.6084/m9.figshare.11447016.v1
title: MOESM3 of An improved catalogue of putative synaptic genes defined exclusively by temporal transcription profiles through an ensemble machine learning approach

description: Additional file 3: New catalogue of putative synaptic genes (XLS 39 kb)
DOI: 10.6084/m9.figshare.11447013
title: MOESM2 of An improved catalogue of putative synaptic genes defined exclusively by temporal transcription profiles through an ensemble machine learning approach

description: Additional file 2: New synaptic genes & references (XLS 21 kb)
DOI: 10.6084/m9.figshare.11447013.v1
title: MOESM2 of An improved catalogue of putative synaptic genes defined exclusively by temporal transcription profiles through an ensemble machine learning approach

description: Additional file 2: New synaptic genes & references (XLS 21 kb)
DOI: 10.6084/m9.figshare.11447007
title: MOESM1 of An improved catalogue of putative synaptic genes defined exclusively by temporal transcription profiles through an ensemble machine learning approach

description: Additional file 1: List of genes in the original training set (XLS 41 kb)
DOI: 10.6084/m9.figshare.11447004
title: MOESM6 of Hypothesis: protein and RNA attributes are continuously optimized over time

description: Additional file 6: Tables S1 and S2. Median protein turnover values for GO categories based on mass spectrometry proteomics data from arrested human HeLa and differentiated mouse muscle C2C12 cells. Probability indicates difference to average turnover of all proteins.
DOI: 10.6084/m9.figshare.11447007.v1
title: MOESM1 of An improved catalogue of putative synaptic genes defined exclusively by temporal transcription profiles through an ensemble machine learning approach

description: Additional file 1: List of genes in the original training set (XLS 41 kb)
DOI: 10.6084/m9.figshare.11447004.v1
title: MOESM6 of Hypothesis: protein and RNA attributes are continuously optimized over time

description: Additional file 6: Tables S1 and S2. Median protein turnover values for GO categories based on mass spectrometry proteomics data from arrested human HeLa and differentiated mouse muscle C2C12 cells. Probability indicates difference to average turnover of all proteins.
DOI: 10.6084/m9.figshare.11446947
title: MOESM1 of Validating a screening agar for linezolid-resistant enterococci

description: Additional file 1: Table S1. Detailed data of the 48 enterococcal isolates from the test series to identify the ideal linezolid concentration to screen for LRE and for the 400 samples sent to the NRC for subsequent strain characterization and typing.
DOI: 10.6084/m9.figshare.11446947.v1
title: MOESM1 of Validating a screening agar for linezolid-resistant enterococci

description: Additional file 1: Table S1. Detailed data of the 48 enterococcal isolates from the test series to identify the ideal linezolid concentration to screen for LRE and for the 400 samples sent to the NRC for subsequent strain characterization and typing.
DOI: 10.6084/m9.figshare.11446935
title: MOESM18 of Root system traits impact early fire blight susceptibility in apple (Malus × domestica)

description: Additional file 18. Hub genes in the “C3” co-expression module.
DOI: 10.6084/m9.figshare.11446935.v1
title: MOESM18 of Root system traits impact early fire blight susceptibility in apple (Malus × domestica)

description: Additional file 18. Hub genes in the “C3” co-expression module.
DOI: 10.6084/m9.figshare.11446929
title: MOESM17 of Root system traits impact early fire blight susceptibility in apple (Malus Ã— domestica)

description: Additional file 17. Pathways showing enrichment in the common 454 DEGs.
DOI: 10.6084/m9.figshare.11446929.v1
title: MOESM17 of Root system traits impact early fire blight susceptibility in apple (Malus Ã— domestica)

description: Additional file 17. Pathways showing enrichment in the common 454 DEGs.
DOI: 10.6084/m9.figshare.11446926
title: MOESM16 of Root system traits impact early fire blight susceptibility in apple (Malus Ã— domestica)

description: Additional file 16. Functional annotations and expression patterns of common 454 DEGs.
DOI: 10.6084/m9.figshare.11446926.v1
title: MOESM16 of Root system traits impact early fire blight susceptibility in apple (Malus Ã— domestica)

description: Additional file 16. Functional annotations and expression patterns of common 454 DEGs.
DOI: 10.6084/m9.figshare.11446920
title: MOESM15 of Root system traits impact early fire blight susceptibility in apple (Malus Ã— domestica)

description: Additional file 15. DEGs between control and fire blight infected samples in RAC-1 and RAC-4 at 4 and 8 dai.
DOI: 10.6084/m9.figshare.11446920.v1
title: MOESM15 of Root system traits impact early fire blight susceptibility in apple (Malus Ã— domestica)

description: Additional file 15. DEGs between control and fire blight infected samples in RAC-1 and RAC-4 at 4 and 8 dai.
DOI: 10.6084/m9.figshare.11446911
title: MOESM14 of Root system traits impact early fire blight susceptibility in apple (Malus Ã— domestica)

description: Additional file 14. DEGs between RAC-1 and RAC-4 from control samples at 4 and 8 dai.
DOI: 10.6084/m9.figshare.11446911.v1
title: MOESM14 of Root system traits impact early fire blight susceptibility in apple (Malus Ã— domestica)

description: Additional file 14. DEGs between RAC-1 and RAC-4 from control samples at 4 and 8 dai.
DOI: 10.6084/m9.figshare.11446905
title: MOESM13 of Root system traits impact early fire blight susceptibility in apple (Malus Ã— domestica)

description: Additional file 13. Principal component analysis (PCA) using normalized read counts of apple genes.
DOI: 10.6084/m9.figshare.11446905.v1
title: MOESM13 of Root system traits impact early fire blight susceptibility in apple (Malus Ã— domestica)

description: Additional file 13. Principal component analysis (PCA) using normalized read counts of apple genes.