cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.6870356
title: Additional file 1: of Lower respiratory tract microbial composition was diversified in Pseudomonas aeruginosa ventilator-associated pneumonia patients

description: Figure S1. Rarefaction curve of samples collected from P. aeruginosa VAP patients and control subjects. Control, control group; PA VAP, Pseudomonas aeruginosa VAP group. Figure S2. The LRT microbiota was different between P. aeruginosa VAP patients and control subjects. The unweighted unifrac distance between the LRT microbiota of each sample was calculated and it was shown as a Pcoa two-dimensional map. Each dot represents one sample. Control, control group; PAVAP, Pseudomonas aeruginosa VAP group. Figure S3. The LRT microbiota and hierarchical clustering of P. aeruginosa VAP patients at genus level. Initial samples were analyzed. The Bray curtis distance matrix (in genus level) was used to construct a dendrogram including 36 P. aeruginosa VAP patients. Clusters were separated by black dotted line originating from the top of the dendrogram. The microbial composition at genus level was visualized as bar charts. Patient number is indicated at the left of each bar. Each bar represents 100% of the OTUs detected per patient; OTUs are color-coded according to genus. LRT lower respiratory tract; VAP, ventilator-associated pneumonia; OTUs, operational taxonomic units. Figure S4. The Shannon diversity between different time points was stable in LRT samples from P. aeruginosa VAP patients. Comparison of the Shannon diversity between day1 (initial samples) and day7 (A), day1 and day14 (B) were conducted by paired non-parametric test (both Pâ &gt;â 0.05). Figure S5. The variety of the relative abundance of Pseudomonas, Lactobacillus and Bifidobacterium in acute phase and improvement phase of P. aeruginosa VAP patients. Pseudomonas (A) showed a decreased trend while Lactobacillus (B) and Bifidobacterium (C) showed an increased trend when the P. aeruginosa VAP patients obtained clinical improvement. Figure S6. The LRT microbiota of survivors of P. aeruginosa VAP patients had no significant difference compared to that of the non-survivors. Samples from day1 were collected for analysis. The unweighted unifrac distance between the survivors and non-survivors was calculated and it was shown as a Pcoa two-dimensional map. Each dot represents one sample. Table S1. Baseline characteristics of control subjects and P. aeruginosa VAP patients. Table S2. Conventional culture of endotracheal aspiration samples from P. aeruginosa VAP patients. Table S3. Antibiotic use before one week sampling of P. aeruginosa VAP patients. Table S4. Antibiotics statistics of P. aeruginosa VAP patients before sampling. Table S5. Antibiotic use during the dynamic observation period of P. aeruginosa VAP patients. Table S6. Negative correlation between genus of P. aeruginosa VAP and SOFA score. (ZIP 1314 kb)
DOI: 10.6084/m9.figshare.23647529
title: Additional file 2 of Genome-wide systematic characterization of the NRT2 gene family and its expression profile in wheat (Triticum aestivum L.) during plant growth and in response to nitrate deficiency

description: Additional file 2. The NRT2 genes information in wheat.
DOI: 10.6084/m9.figshare.23647538
title: Additional file 5 of Genome-wide systematic characterization of the NRT2 gene family and its expression profile in wheat (Triticum aestivum L.) during plant growth and in response to nitrate deficiency

description: Additional file 5. The raw data of transcriptome in shoot under nitrogen deficiency condition.
DOI: 10.6084/m9.figshare.20501882
title: Additional file 2 of i-CRISPR: a personalized cancer therapy strategy through cutting cancer-specific mutations

description: Additional file 2.
DOI: 10.6084/m9.figshare.20501882.v1
title: Additional file 2 of i-CRISPR: a personalized cancer therapy strategy through cutting cancer-specific mutations

description: Additional file 2.
DOI: 10.6084/m9.figshare.23648298
title: Optimization of density and durability of pellets using the response surface methodology in ultrasonic vibration-assisted pelleting of corn stover

description: ABSTRACT Top-quality pellets can significantly increase density and durability of agricultural residues, reducing logistic costs. However, these pellets depend on numerous parameters, including feedstock properties and production conditions. To ensure high-quality pellets, a single-factor experiment and the response surface methodology were used to investigate the effects of particle size, moisture content, molding pressure, pelleting time, ultrasonic power, and interaction effects between variables on density and durability of pellets for ultrasonic vibration-assisted pelleting of corn stover. The response surface models between variables and response were established. The results showed that all variables affect the density and durability of pellets. An optimal condition for density and durability was obtained, and a further experiment was conducted to validate the values. The results suggested that desirability (0.999) under optimal conditions confirmed the validation of models. The optimal combination of process parameters included particle size of 1.5 mm, moisture content of 10 %, molding pressure of 379 kPa, pelleting time of 80 s, ultrasonic power of 250 W, with values of 1,381.14 kg m–3 and 97.58 % for density and durability of pellets, respectively.
DOI: 10.6084/m9.figshare.23648298.v1
title: Optimization of density and durability of pellets using the response surface methodology in ultrasonic vibration-assisted pelleting of corn stover

description: ABSTRACT Top-quality pellets can significantly increase density and durability of agricultural residues, reducing logistic costs. However, these pellets depend on numerous parameters, including feedstock properties and production conditions. To ensure high-quality pellets, a single-factor experiment and the response surface methodology were used to investigate the effects of particle size, moisture content, molding pressure, pelleting time, ultrasonic power, and interaction effects between variables on density and durability of pellets for ultrasonic vibration-assisted pelleting of corn stover. The response surface models between variables and response were established. The results showed that all variables affect the density and durability of pellets. An optimal condition for density and durability was obtained, and a further experiment was conducted to validate the values. The results suggested that desirability (0.999) under optimal conditions confirmed the validation of models. The optimal combination of process parameters included particle size of 1.5 mm, moisture content of 10 %, molding pressure of 379 kPa, pelleting time of 80 s, ultrasonic power of 250 W, with values of 1,381.14 kg m–3 and 97.58 % for density and durability of pellets, respectively.
DOI: 10.6084/m9.figshare.20501875
title: Additional file 1 of Single-cell genomic and transcriptomic landscapes of primary and metastatic colorectal cancer tumors

description: Additional file 1: Table S1. Basic information of sample cohort. N, adjacent normal tissue. PT, primary tumor. LyM, lymph node metastasis. LM, liver metastasis. OM, omentummetastasis. LN, adjacent normal tissue collected from liver. Table S2. DEGs between normal epithelial cells and tumor epithelial cells. Related to Fig. 3A Table S3. Cell viability of organoids and DEGs between control and FH534-treated organoids. Related to Fig. 2. Table S4. Mitochondrial mutations of each patient. Only the sites where mutations were detected in at least two cells were listed in the table. WT, wild type. Undet, reads than least than 9X. Heter, heterozygous mutation. Homo, homozygous. T and N represent tumor and normal. Table S5. DEGs between OLFM4+SOX9- and OLFM4+SOX9+ tumor cells. Related to Fig. S10A. Table S6. Single-cell targeted cDNA Sanger sequencing result. The number inside the box represents the number of the cell barcode. Green, orange and red colors represent wild type, heterozygous mutant and homozygous mutant respectively. No color fill represents undetected. Table S7. DEGs between BRAFV600E and BRAF600WT tumor cells. Related to Fig. S10C-D. Table S8. DEGs between KRASWT and KRASmut tumor cells. Related to Fig. S10H-I. Table S9. Somatic mutations of samples. Table S10. Antibody information and sequences of primers.
DOI: 10.6084/m9.figshare.23647579.v1
title: Additional file 1 of Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtyping

description: Additional file 1: Table S1. Dataset Information for PCAWG, TCGA, ICGC, and common tumour subtypes in PCAWG and TCGA datasets. Table S2. Precision, recall, and f1 scores in the PCAWG and TCGA datasets. Table S3. Classification accuracy of the different models benchmarked. Table S4. Coefficients with 95% confidence intervals, p-values and FDR-adjusted q-values of a PCA model associating driver mutation statuses with MuAt features in the PCAWG dataset tumours. Table S5. Coefficients with 95% confidence intervals, p-values and FDR-adjusted q-values of a negative binomial model associating driver mutation statuses with mutation counts in the PCAWG dataset tumours. Table S6. Coefficients with 95% confidence intervals, p-values and FDR-adjusted q-values of a least-squares linear regression model associating MuAt tumour-level features with the fraction of mutated microsatellitesin the PCAWG dataset tumours. Table S7. Coefficients with 95% confidence intervals, p-values and FDR-adjusted q-values of a negative binomial model associating MuAt tumour-level features with mutation counts in the PCAWG dataset tumours. Table S8. Pancreatic neuroendocrine tumours in the PCAWG dataset with germline MUTYH driver mutations. Table S9. Linear association of MuAt factors with mutation counts. Table S10. Linear association of MuAt factors with mutational signatures. Table S11. Attention values for mutation type pairs, averaged over tumour types.
DOI: 10.6084/m9.figshare.23647579
title: Additional file 1 of Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtyping

description: Additional file 1: Table S1. Dataset Information for PCAWG, TCGA, ICGC, and common tumour subtypes in PCAWG and TCGA datasets. Table S2. Precision, recall, and f1 scores in the PCAWG and TCGA datasets. Table S3. Classification accuracy of the different models benchmarked. Table S4. Coefficients with 95% confidence intervals, p-values and FDR-adjusted q-values of a PCA model associating driver mutation statuses with MuAt features in the PCAWG dataset tumours. Table S5. Coefficients with 95% confidence intervals, p-values and FDR-adjusted q-values of a negative binomial model associating driver mutation statuses with mutation counts in the PCAWG dataset tumours. Table S6. Coefficients with 95% confidence intervals, p-values and FDR-adjusted q-values of a least-squares linear regression model associating MuAt tumour-level features with the fraction of mutated microsatellitesin the PCAWG dataset tumours. Table S7. Coefficients with 95% confidence intervals, p-values and FDR-adjusted q-values of a negative binomial model associating MuAt tumour-level features with mutation counts in the PCAWG dataset tumours. Table S8. Pancreatic neuroendocrine tumours in the PCAWG dataset with germline MUTYH driver mutations. Table S9. Linear association of MuAt factors with mutation counts. Table S10. Linear association of MuAt factors with mutational signatures. Table S11. Attention values for mutation type pairs, averaged over tumour types.
DOI: 10.6084/m9.figshare.13161887.v2
title: Sequential Scaled Sparse Factor Regression

description: Large-scale association analysis between multivariate responses and predictors is of great practical importance, as exemplified by modern business applications including social media marketing and crisis management. Despite the rapid methodological advances, how to obtain scalable estimators with free tuning of the regularization parameters remains unclear under general noise covariance structures. In this article, we develop a new methodology called sequential scaled sparse factor regression (SESS) based on a new viewpoint that the problem of recovering a jointly low-rank and sparse regression coefficient matrix can be decomposed into several univariate response sparse regressions through regular eigenvalue decomposition. It combines the strengths of sequential estimation and scaled sparse regression, thus sharing the scalability and the tuning free property for sparsity parameters inherited from the two approaches. The stepwise convex formulation, sequential factor regression framework, and tuning insensitiveness make SESS highly scalable for big data applications. Comprehensive theoretical justifications with new insights into high-dimensional multi-response regressions are also provided. We demonstrate the scalability and effectiveness of the proposed method by simulation studies and stock short interest data analysis.
DOI: 10.6084/m9.figshare.13161887
title: Sequential Scaled Sparse Factor Regression

description: Large-scale association analysis between multivariate responses and predictors is of great practical importance, as exemplified by modern business applications including social media marketing and crisis management. Despite the rapid methodological advances, how to obtain scalable estimators with free tuning of the regularization parameters remains unclear under general noise covariance structures. In this article, we develop a new methodology called sequential scaled sparse factor regression (SESS) based on a new viewpoint that the problem of recovering a jointly low-rank and sparse regression coefficient matrix can be decomposed into several univariate response sparse regressions through regular eigenvalue decomposition. It combines the strengths of sequential estimation and scaled sparse regression, thus sharing the scalability and the tuning free property for sparsity parameters inherited from the two approaches. The stepwise convex formulation, sequential factor regression framework, and tuning insensitiveness make SESS highly scalable for big data applications. Comprehensive theoretical justifications with new insights into high-dimensional multi-response regressions are also provided. We demonstrate the scalability and effectiveness of the proposed method by simulation studies and stock short interest data analysis.
DOI: 10.7925/drs1.duchas_5106883
title: Ned McGuinness the Man of Many Wives

description: Story collected by a student at St Columba's, Cloonagh, Granard school (Cloonagh, Co. Longford) from Rehill.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Peter O'Reilly.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5106885
title: Fairy Story

description: Story collected by Janie Kiernan, a student at Moyne (C.) school (Moyne, Co. Longford) from informant Tom Brady.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Bean Uí Tháibh.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5106888
title: True Story

description: Story collected by a student at Killasona, Granard school (Killasona, Co. Longford) (no informant identified).
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Bean Uí Ghaithbheacháin.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5106894
title: Hidden Treasure

description: Story collected by Catherine Maguire, a student at Moyne (C.) school (Moyne, Co. Longford) from informant Mrs Maguire.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Bean Uí Tháibh.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5106896
title: Old Story

description: Story collected by Patrick Keenan, a student at Killasona, Granard school (Killasona, Co. Longford) from informant Mrs Patrick Gavagan.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Bean Uí Ghaithbheacháin.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5106898
title: A man who lived at Portumna led a very bad life.

description: Story collected by Patrick Hyland, a student at Mostrim (B.) school (Edgeworthstown, Co. Longford) from informant an unidentified informant.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Richard Hyland.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5106903
title: Lady Nailed to the Tree

description: Story collected by Albert Anderson, a student at Mostrim (B.) school (Edgeworthstown, Co. Longford) from informant Patrick Farrell.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Richard Hyland.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5106905
title: One Halloween four men were coming home from rambling...

description: Story collected by Hugh Brady, a student at Mostrim (B.) school (Edgeworthstown, Co. Longford) from informant an unidentified informant.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Richard Hyland.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5106908
title: Folklore

description: Story collected by Micheál Mac Glynn, a student at Mostrim (B.) school (Edgeworthstown, Co. Longford) from informant John Devine.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Richard Hyland.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5106914
title: Folklore - November Night

description: Story collected by Micheál Mac Fhloinn, a student at Mostrim (B.) school (Edgeworthstown, Co. Longford) from informant R. Mc Glynn.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Richard Hyland.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5106916
title: Tommy Concannon

description: Story collected by Thomas Flood, a student at Killasona, Granard school (Killasona, Co. Longford) from informant Tommy Fagan.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Bean Uí Ghaithbheacháin.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5106921
title: Folklore

description: Story collected by a student at Mostrim (B.) school (Edgeworthstown, Co. Longford) from informant Mrs Cunningham.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Richard Hyland.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5106923
title: In the townland of Cloonagh there lived many and many a year ago a quaint old woman called Peggy Hessin.

description: Story collected by a student at St Columba's, Cloonagh, Granard school (Cloonagh, Co. Longford) (no informant identified).
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Peter O'Reilly.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5106924
title: Folklore - Clothes Made Locally

description: Story collected by John O' Rourke, a student at Mostrim (B.) school (Edgeworthstown, Co. Longford) (no informant identified).
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Richard Hyland.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5106927
title: Pishogues

description: Story collected by John O' Rourke, a student at Mostrim (B.) school (Edgeworthstown, Co. Longford) (no informant identified).
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Richard Hyland.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5106931
title: Old Crafts

description: Story collected by James O' Rourke, a student at Mostrim (B.) school (Edgeworthstown, Co. Longford) from informant Mrs Gallagher.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Richard Hyland.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5106935
title: Hidden Treasure

description: Story collected by Lizzie Kane, a student at Moyne (C.) school (Moyne, Co. Longford) from informant Patrick Kane.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Bean Uí Tháibh.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5106941
title: Hidden Treasure

description: Story collected by Martha Masterson, a student at Moyne (C.) school (Moyne, Co. Longford) from informant James Masterson.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Bean Uí Tháibh.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.