cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.8222771
title: Additional file 4: of Breast cancer PAM50 signature: correlation and concordance between RNA-Seq and digital multiplexed gene expression technologies in a triple negative breast cancer series

description: QC – Fastq. RNA-Seq data Quality Control. FastQC and FastQ Screen reports summarized using MultiQC. All the information is summarized in an interactive .html. Additionally, a table is provided to associate the sample IDs mentioned in the manuscript with the IDs generated during the sequencing process. (ZIP 5962 kb)
DOI: 10.6084/m9.figshare.8222780.v1
title: Additional file 2 of LtrDetector: A tool-suite for detecting long terminal repeat retrotransposons de-novo

description: Script for ground truth generation pipeline. This compressed file (.tar.gz) includes the Python code for the evaluation pipeline. (TAR.GZ 5 kb)
DOI: 10.6084/m9.figshare.8222765
title: Additional file 3: of Breast cancer PAM50 signature: correlation and concordance between RNA-Seq and digital multiplexed gene expression technologies in a triple negative breast cancer series

description: Table S3. PAM50 genes correlation and inter-rate agreement analyses. Correlation analysis using the Spearman’s rho where the gene expression measured in RNA-Seq and NanoString nCounter® are compared. The inter-rater agreement analysis for the PAM50 genes was based on the Cohen’s kappa calculation using two-way mixed effect single measures. The statistics and p-values are provided in both analyses. (XLSX 17 kb)
DOI: 10.6084/m9.figshare.8222777.v1
title: Additional file 1 of LtrDetector: A tool-suite for detecting long terminal repeat retrotransposons de-novo

description: The LtrDetector software and the visualization script. This compressed file (.tar.gz) includes the C++ source code of LtrDetector and the Python script for visualizing putative elements as well as instructions on how to compile and run the programs. (TAR.GZ 145 kb)
DOI: 10.6084/m9.figshare.8222765.v1
title: Additional file 3: of Breast cancer PAM50 signature: correlation and concordance between RNA-Seq and digital multiplexed gene expression technologies in a triple negative breast cancer series

description: Table S3. PAM50 genes correlation and inter-rate agreement analyses. Correlation analysis using the Spearman’s rho where the gene expression measured in RNA-Seq and NanoString nCounter® are compared. The inter-rater agreement analysis for the PAM50 genes was based on the Cohen’s kappa calculation using two-way mixed effect single measures. The statistics and p-values are provided in both analyses. (XLSX 17 kb)
DOI: 10.6084/m9.figshare.8222771.v1
title: Additional file 4: of Breast cancer PAM50 signature: correlation and concordance between RNA-Seq and digital multiplexed gene expression technologies in a triple negative breast cancer series

description: QC – Fastq. RNA-Seq data Quality Control. FastQC and FastQ Screen reports summarized using MultiQC. All the information is summarized in an interactive .html. Additionally, a table is provided to associate the sample IDs mentioned in the manuscript with the IDs generated during the sequencing process. (ZIP 5962 kb)
DOI: 10.6084/m9.figshare.8222759
title: Additional file 3: of Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing

description: Table S5. Recall and precision of SV-calling results with the simulated data (Sim-A, Sim-MEI, Sim-NUMT, Sim-VEI). Table S6. Recall and precision of SV-calling results with the real data (NA12978 data1 or PacBio-data1). Table S7. Recall and precision of SV-calling results with the real data (NA12978 data2 or PacBio-data2). Table S8. Recall and precision of SV-calling results with the real data (NA12978 data3 or PacBio-data3). Table S9. Recall and precision of SV-calling results with the real data (NA12978 data4 or PacBio-HG002). Table S10. SV calling results (recall, precision, Mendelian inheritance error, mean, and standard error) obtained with the four (or three) NA12878 real datasets. (including the numerical data of Fig. 3 and Additional file 1: Figures S3â S5). Table S11. Recall and precision of SV-calling results with the HG00514 real data. Table S14. Root mean squared errors of breakpoints (BPs) and lengths of called SVs for SV detection algorithms. Table S16. Recall and precision of SVs commonly called between a pair of SV detection algorithms with the simulated and the NA12878 real datasets. Table S17. Mean precision and recall of overlapped calls for each algorithm and for each SV category. Table S18. Fold change of precision and recall of overlapped calls between algorithm pair for the four (or three) sets of NA12878 real data. (XLSX 1162 kb)
DOI: 10.6084/m9.figshare.8222756
title: Additional file 2: of Breast cancer PAM50 signature: correlation and concordance between RNA-Seq and digital multiplexed gene expression technologies in a triple negative breast cancer series

description: Table S2. RNA Sample Quality Control. The RNA concentration measured in each sample, absorbance values for A260 and A280, the A260/A280 ratio and the measured RIN. Descriptive statistics are provided for A260/A280 ratio and RIN. (XLSX 16 kb)
DOI: 10.6084/m9.figshare.8222756.v1
title: Additional file 2: of Breast cancer PAM50 signature: correlation and concordance between RNA-Seq and digital multiplexed gene expression technologies in a triple negative breast cancer series

description: Table S2. RNA Sample Quality Control. The RNA concentration measured in each sample, absorbance values for A260 and A280, the A260/A280 ratio and the measured RIN. Descriptive statistics are provided for A260/A280 ratio and RIN. (XLSX 16 kb)
DOI: 10.6084/m9.figshare.8222759.v1
title: Additional file 3: of Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing

description: Table S5. Recall and precision of SV-calling results with the simulated data (Sim-A, Sim-MEI, Sim-NUMT, Sim-VEI). Table S6. Recall and precision of SV-calling results with the real data (NA12978 data1 or PacBio-data1). Table S7. Recall and precision of SV-calling results with the real data (NA12978 data2 or PacBio-data2). Table S8. Recall and precision of SV-calling results with the real data (NA12978 data3 or PacBio-data3). Table S9. Recall and precision of SV-calling results with the real data (NA12978 data4 or PacBio-HG002). Table S10. SV calling results (recall, precision, Mendelian inheritance error, mean, and standard error) obtained with the four (or three) NA12878 real datasets. (including the numerical data of Fig. 3 and Additional file 1: Figures S3â S5). Table S11. Recall and precision of SV-calling results with the HG00514 real data. Table S14. Root mean squared errors of breakpoints (BPs) and lengths of called SVs for SV detection algorithms. Table S16. Recall and precision of SVs commonly called between a pair of SV detection algorithms with the simulated and the NA12878 real datasets. Table S17. Mean precision and recall of overlapped calls for each algorithm and for each SV category. Table S18. Fold change of precision and recall of overlapped calls between algorithm pair for the four (or three) sets of NA12878 real data. (XLSX 1162 kb)
DOI: 10.6084/m9.figshare.8222747
title: Additional file 1: of Breast cancer PAM50 signature: correlation and concordance between RNA-Seq and digital multiplexed gene expression technologies in a triple negative breast cancer series

description: Table S1. PAM50 output for RNA-Seq and NanoString nCounter. PAM50 table of results for RNA-Seq and NanoString nCounter®. It is reported for each sample and platform: the centroid correlation, the subtype call with its confidence, the ROR and ROR + PS values and groups, the Proliferation Score; and the ER/HER2 gene expression. (XLSX 90 kb)
DOI: 10.6084/m9.figshare.8222747.v1
title: Additional file 1: of Breast cancer PAM50 signature: correlation and concordance between RNA-Seq and digital multiplexed gene expression technologies in a triple negative breast cancer series

description: Table S1. PAM50 output for RNA-Seq and NanoString nCounter. PAM50 table of results for RNA-Seq and NanoString nCounter®. It is reported for each sample and platform: the centroid correlation, the subtype call with its confidence, the ROR and ROR + PS values and groups, the Proliferation Score; and the ER/HER2 gene expression. (XLSX 90 kb)
DOI: 10.6084/m9.figshare.8222684
title: Additional file 7: of Transcriptome and proteome profiling of neural stem cells from the human subventricular zone in Parkinsonâ s disease

description: Table S7. Proteins differentially expressed in CD271+ NSCs and SVZ from PD patientsÂ in comparison to control donors. (XLSX 21 kb)
DOI: 10.6084/m9.figshare.8222684.v1
title: Additional file 7: of Transcriptome and proteome profiling of neural stem cells from the human subventricular zone in Parkinsonâ s disease

description: Table S7. Proteins differentially expressed in CD271+ NSCs and SVZ from PD patientsÂ in comparison to control donors. (XLSX 21 kb)
DOI: 10.6084/m9.figshare.8222672
title: Additional file 6: of Transcriptome and proteome profiling of neural stem cells from the human subventricular zone in Parkinsonâ s disease

description: Table S6. Genes differentially expressed in CD271+ NSCs in comparison to CD11b+ microglia cells from PD and control donors pooled together. (XLSX 120 kb)
DOI: 10.6084/m9.figshare.8222672.v1
title: Additional file 6: of Transcriptome and proteome profiling of neural stem cells from the human subventricular zone in Parkinsonâ s disease

description: Table S6. Genes differentially expressed in CD271+ NSCs in comparison to CD11b+ microglia cells from PD and control donors pooled together. (XLSX 120 kb)
DOI: 10.6084/m9.figshare.8222666
title: Additional file 5: of Transcriptome and proteome profiling of neural stem cells from the human subventricular zone in Parkinsonâ s disease

description: Table S5. Genes differentially expressed in CD271+ cells in comparison to SVZ homogenate from control donors and differentially expressed genes in CD271+ NSCs from PD patients compared to CD271+ cellsÂ from control donors. (XLSX 189 kb)
DOI: 10.6084/m9.figshare.8222666.v1
title: Additional file 5: of Transcriptome and proteome profiling of neural stem cells from the human subventricular zone in Parkinsonâ s disease

description: Table S5. Genes differentially expressed in CD271+ cells in comparison to SVZ homogenate from control donors and differentially expressed genes in CD271+ NSCs from PD patients compared to CD271+ cellsÂ from control donors. (XLSX 189 kb)
DOI: 10.6084/m9.figshare.8222654
title: Additional file 2: of Single-cell transcriptomics unveils gene regulatory network plasticity

description: MSigDB enrichments for gene sets with altered centrality. (XLSX 14300 kb)
DOI: 10.6084/m9.figshare.8222654.v1
title: Additional file 2: of Single-cell transcriptomics unveils gene regulatory network plasticity

description: MSigDB enrichments for gene sets with altered centrality. (XLSX 14300 kb)
DOI: 10.6084/m9.figshare.8222636
title: mSWEEP_reference_v1-0-0.tgz

description: Reference data (sequence assemblies and group indicators) used in the mSWEEP pre-print (https://doi.org/10.1101/332544). Contains sequence assemblies from C. jejuni, E. coli, K. pneumoniae, S. aureus, and S. epidermidis alongside groupings into sequence type complexes.
DOI: 10.6084/m9.figshare.8222639
title: Additional file 1: of Transcriptome and proteome profiling of neural stem cells from the human subventricular zone in Parkinson’s disease

description: Table S1. Characteristics, diagnosis and pathological evaluation of PD patients and control donors. CNTR = control; PD = Parkinson’s disease; PDD = Parkinson’s disease and dementia; CAA = cerebral amyloid angiopathy; AG = argyrophilic grain; PMD = post-mortem delay; UNK = unknown; ND = not determined; NA = not applicable. (XLSX 13 kb)
DOI: 10.6084/m9.figshare.8222639.v1
title: Additional file 1: of Transcriptome and proteome profiling of neural stem cells from the human subventricular zone in Parkinson’s disease

description: Table S1. Characteristics, diagnosis and pathological evaluation of PD patients and control donors. CNTR = control; PD = Parkinson’s disease; PDD = Parkinson’s disease and dementia; CAA = cerebral amyloid angiopathy; AG = argyrophilic grain; PMD = post-mortem delay; UNK = unknown; ND = not determined; NA = not applicable. (XLSX 13 kb)
DOI: 10.6084/m9.figshare.8222576
title: A new <i>in vitro</i> muscle contraction model and its application for analysis of mTORC1 signaling in combination with contraction and beta-hydroxy-beta-methylbutyrate administration

description: Several food constituents augment exercise-induced muscle strength improvement; however, the detailed mechanism underlying these combined effects is unknown because of the lack of a cultured cell model for evaluating the contraction-induced muscle protein synthesis level. Here, we aimed to establish a new <i>in vitro</i> muscle contraction model for analyzing the activation of mammalian target of rapamycin complex 1 (mTORC1) signaling. We adopted the tetanic electric stimulation of 50 V at 100 Hz for 10 min in L6.C11 myotubes. Akt, ERK1/2, and p70S6K phosphorylation increased significantly after electrical pulse stimulation (EPS), compared to untreated cells. Next, we used this model to analyze mTORC1 signaling in combination with exercise and beta-hydroxy-beta-methylbutyrate (HMB), an l-leucine metabolite. p70S6K phosphorylation increased significantly in the EPS+HMB group compared to that in the EPS-alone group. These findings show that our model could be used to analyze mTORC1 signaling and that HMB enhances muscle contraction-activated mTORC1 signaling. Experimental set-up of a new <i>in vitro</i> muscle contraction model for analysis of the activation of mTORC1 signaling pathway in cultured L6.C11 myotubes.
DOI: 10.6084/m9.figshare.8222576.v2
title: A new <i>in vitro</i> muscle contraction model and its application for analysis of mTORC1 signaling in combination with contraction and beta-hydroxy-beta-methylbutyrate administration

description: Several food constituents augment exercise-induced muscle strength improvement; however, the detailed mechanism underlying these combined effects is unknown because of the lack of a cultured cell model for evaluating the contraction-induced muscle protein synthesis level. Here, we aimed to establish a new <i>in vitro</i> muscle contraction model for analyzing the activation of mammalian target of rapamycin complex 1 (mTORC1) signaling. We adopted the tetanic electric stimulation of 50 V at 100 Hz for 10 min in L6.C11 myotubes. Akt, ERK1/2, and p70S6K phosphorylation increased significantly after electrical pulse stimulation (EPS), compared to untreated cells. Next, we used this model to analyze mTORC1 signaling in combination with exercise and beta-hydroxy-beta-methylbutyrate (HMB), an l-leucine metabolite. p70S6K phosphorylation increased significantly in the EPS+HMB group compared to that in the EPS-alone group. These findings show that our model could be used to analyze mTORC1 signaling and that HMB enhances muscle contraction-activated mTORC1 signaling. Experimental set-up of a new <i>in vitro</i> muscle contraction model for analysis of the activation of mTORC1 signaling pathway in cultured L6.C11 myotubes.
DOI: 10.6084/m9.figshare.8222495
title: Supplementary Material for: Endoscopic Palliation of Hilar Cholangiocarcinoma: Usefulness of Selective Occlusion of One Intrahepatic Duct

description: Medicine
DOI: 10.6084/m9.figshare.8222576.v1
title: A new <i>in vitro</i> muscle contraction model and its application for analysis of mTORC1 signaling in combination with contraction and beta-hydroxy-beta-methylbutyrate administration

description: Several food constituents augment exercise-induced muscle strength improvement; however, the detailed mechanism underlying these combined effects is unknown because of the lack of a cultured cell model for evaluating the contraction-induced muscle protein synthesis level. Here, we aimed to establish a new <i>in vitro</i> muscle contraction model for analyzing the activation of mammalian target of rapamycin complex 1 (mTORC1) signaling. We adopted the tetanic electric stimulation of 50 V at 100 Hz for 10 min in L6.C11 myotubes. Akt, ERK1/2, and p70S6K phosphorylation increased significantly after electrical pulse stimulation (EPS), compared to untreated cells. Next, we used this model to analyze mTORC1 signaling in combination with exercise and beta-hydroxy-beta-methylbutyrate (HMB), an l-leucine metabolite. p70S6K phosphorylation increased significantly in the EPS+HMB group compared to that in the EPS-alone group. These findings show that our model could be used to analyze mTORC1 signaling and that HMB enhances muscle contraction-activated mTORC1 signaling. Experimental set-up of a new <i>in vitro</i> muscle contraction model for analysis of the activation of mTORC1 signaling pathway in cultured L6.C11 myotubes.
DOI: 10.6084/m9.figshare.8222477
title: Results of using robotic-assisted navigational system in pedicle screw placement

description: Results of using robotic-assisted navigational system in pedicle screw placement
DOI: 10.6084/m9.figshare.8222477.v2
title: Results of using robotic-assisted navigational system in pedicle screw placement

description: Results of using robotic-assisted navigational system in pedicle screw placement
DOI: 10.6084/m9.figshare.8222450
title: Unveiling early cortical and subcortical neuronal degeneration in ALS mice by ultra-high field diffusion MRI

description: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease primarily characterized by the progressive impairment of motor functions. However, a significant portion of affected patients develops severe cognitive dysfunction, developing a widespread white (WM) and gray matter (GM) microstructural impairment. The objective of this study is to determine if Gaussian and non-Gaussian diffusion models gathered by ultra-high field diffusion MRI (UHFD-MRI) are an appropriate tool to detect early structural changes in brain white and gray matter in a preclinical model of ALS. ALS brains (G93A-SOD1mice) were scanned in a 16.7 T magnet. Diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) have shown presymptomatic decrease in axonal organization by Fractional Anisotropy (FA) and neurite content by Intracellular Volume Fraction (ICVF) across deep WM (corpus callosum) as well as superficial (cortex) and deep (hippocampus) GM. Additional diffusion kurtosis imaging (DKI) analysis demonstrated broader and earlier GM reductions in mean kurtosis (MK), possibly related to the decrease in neuronal complexity. Histological validation was obtained by an ALS fluorescent mice reporter (YFP, G93A-SOD1 mice). The combination of DTI, NODDI, and DKI models have proved to provide a more complete assessment of the early microstructural changes in the ALS brain, particularly in areas associated with high cognitive functions. This comprehensive approach should be considered as a valuable tool for the early detection of neuroimaging markers.
DOI: 10.6084/m9.figshare.8222477.v1
title: Results of using robotic-assisted navigational system in pedicle screw placement

description: Results of using robotic-assisted navigational system in pedicle screw placement
DOI: 10.6084/m9.figshare.8222450.v2
title: Unveiling early cortical and subcortical neuronal degeneration in ALS mice by ultra-high field diffusion MRI

description: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease primarily characterized by the progressive impairment of motor functions. However, a significant portion of affected patients develops severe cognitive dysfunction, developing a widespread white (WM) and gray matter (GM) microstructural impairment. The objective of this study is to determine if Gaussian and non-Gaussian diffusion models gathered by ultra-high field diffusion MRI (UHFD-MRI) are an appropriate tool to detect early structural changes in brain white and gray matter in a preclinical model of ALS. ALS brains (G93A-SOD1mice) were scanned in a 16.7 T magnet. Diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) have shown presymptomatic decrease in axonal organization by Fractional Anisotropy (FA) and neurite content by Intracellular Volume Fraction (ICVF) across deep WM (corpus callosum) as well as superficial (cortex) and deep (hippocampus) GM. Additional diffusion kurtosis imaging (DKI) analysis demonstrated broader and earlier GM reductions in mean kurtosis (MK), possibly related to the decrease in neuronal complexity. Histological validation was obtained by an ALS fluorescent mice reporter (YFP, G93A-SOD1 mice). The combination of DTI, NODDI, and DKI models have proved to provide a more complete assessment of the early microstructural changes in the ALS brain, particularly in areas associated with high cognitive functions. This comprehensive approach should be considered as a valuable tool for the early detection of neuroimaging markers.
DOI: 10.6084/m9.figshare.8222450.v1
title: Unveiling early cortical and subcortical neuronal degeneration in ALS mice by ultra-high field diffusion MRI

description: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease primarily characterized by the progressive impairment of motor functions. However, a significant portion of affected patients develops severe cognitive dysfunction, developing a widespread white (WM) and gray matter (GM) microstructural impairment. The objective of this study is to determine if Gaussian and non-Gaussian diffusion models gathered by ultra-high field diffusion MRI (UHFD-MRI) are an appropriate tool to detect early structural changes in brain white and gray matter in a preclinical model of ALS. ALS brains (G93A-SOD1mice) were scanned in a 16.7 T magnet. Diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) have shown presymptomatic decrease in axonal organization by Fractional Anisotropy (FA) and neurite content by Intracellular Volume Fraction (ICVF) across deep WM (corpus callosum) as well as superficial (cortex) and deep (hippocampus) GM. Additional diffusion kurtosis imaging (DKI) analysis demonstrated broader and earlier GM reductions in mean kurtosis (MK), possibly related to the decrease in neuronal complexity. Histological validation was obtained by an ALS fluorescent mice reporter (YFP, G93A-SOD1 mice). The combination of DTI, NODDI, and DKI models have proved to provide a more complete assessment of the early microstructural changes in the ALS brain, particularly in areas associated with high cognitive functions. This comprehensive approach should be considered as a valuable tool for the early detection of neuroimaging markers.
DOI: 10.6084/m9.figshare.8222372
title: Additional file 6: of Analysis of hereditary cancer syndromes by using a panel of genes: novel and multiple pathogenic mutations

description: Table S5. List of Variants of Uncertain Significance (VUS). (XLSX 87 kb)
DOI: 10.6084/m9.figshare.8222372.v1
title: Additional file 6: of Analysis of hereditary cancer syndromes by using a panel of genes: novel and multiple pathogenic mutations

description: Table S5. List of Variants of Uncertain Significance (VUS). (XLSX 87 kb)
DOI: 10.6084/m9.figshare.8222354
title: Additional file 3: of Analysis of hereditary cancer syndromes by using a panel of genes: novel and multiple pathogenic mutations

description: Table S2. List of Pathogenic/ Likely Pathogenic variants. (XLSX 51 kb)
DOI: 10.6084/m9.figshare.8222354.v1
title: Additional file 3: of Analysis of hereditary cancer syndromes by using a panel of genes: novel and multiple pathogenic mutations

description: Table S2. List of Pathogenic/ Likely Pathogenic variants. (XLSX 51 kb)