cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24138687.v1
title: Supplementary Material for: Incidence and predictors of esophagogastric varices bleeding in patients with hepatocellular carcinoma in lenvatinib

description: Introduction: Lenvatinib is indicated for the forefront treatment of advanced hepatocellular carcinoma (aHCC), but its use maybe limited by the risk of esophagogastric varices (EGV) bleeding. This study assessed the prevalence, predictors, and complications of EGV in aHCC patients treated with lenvatinib. Methods: In this multicenter international retrospective study, cirrhotic patients treated with lenvatinib for aHCC, were enrolled if upper-gastrointestinal endoscopy (UGE) available within 6 months before treatment. Primary endpoint was the incidence of EGV bleeding during lenvatinib therapy; secondary endpoints were predictors for EGV bleeding, prevalence and risk factors for the presence of EGV and high risk EGV at baseline, impact of EGV bleeding on patients’ survival. Results: 535 patients were enrolled in the study [median age 72 years, 78% male, 63% viral aetiology, 89% Child-Pugh A, 16% neoplastic portal vein thrombosis (nPVT), 56% BCLC-C]: 234 had EGV (44%): 70 (30%) at high-risk and 59 on primary prophylaxis. During lenvatinib treatment, 17 patients bled from EGV (3 grade 5), the 12-month cumulative incidence being 3%. The only baseline independent predictor of EGV bleeding was the presence of baseline high risk EGV (HR 6.94, 95% CI 2.23-21.57, p=0.001). In these patients the 12-month risk was 17%. High risk varices were independently associated with Child-Pugh B score (OR 2.12; 95%CI 1.08-4.17, p=0.03), nPVT (OR 2.54; 95%CI 1.40-4.61, p=0.002) and platelets &lt;150.000/uL (OR 2.47; 95%CI 1.35-4.50, p=0.003. Conclusion: In HCC patients treated with lenvatinib, the risk of EGV bleeding was mostly low but significant only in patients with high-risk EGV at baseline.
DOI: 10.6084/m9.figshare.24138693
title: Supplementary Material for: The Worsening Renal Perfusion Index predicts the prognoses of heart failure patients treated with sacubitril/valsartan

description: Introduction: Sacubitril/valsartan (S/V) reduces all-cause mortality in patients with heart failure with reduced ejection fraction (HFrEF), but it may decline their estimated glomerular filtration rates (eGFR). In addition to eGFR, this clinical study aimed to develop a blood urea nitrogen (BUN)-based index to evaluate the status of renal perfusion and then identify predictors of all-cause death or heart transplant in patients with HFrEF receiving S/V. Methods: From the recruited 291 patients with HFrEF who were prescribed S/V from March 2017 to March 2019, we collected demographic, drug history, laboratory, echocardiographic, and clinical data from one year before S/V initiation until December 2020. Regression analysis was conducted by fitting Cox’s models with time-dependent covariates for the survival time and applying the modern stepwise variable selection procedure. The smoothing spline method was used to detect nonlinearity in effect and yield optimal cut-off values for continuous covariates. Results: In the Cox’s model, decreased hemoglobin level, decreased mean left ventricular ejection fraction, declined daily dose of S/V, decreased eGFR within 3 months, and increased BUN levels within 1 month and 9 months over time were significantly associated with an increased risk of all-cause death or heart transplant in patients with HFrEF. Conclusions: Adequate maintenance of renal perfusion is crucial for the continuous use of S/V and to avoid worsening renal function in patients with HFrEF. We defined the maximum increase in BUN levels within a specified period as the Worsening Renal Perfusion Index (WRPSV Index) to capture the prognostic effect of renal hypoperfusion in patients with HFrEF.
DOI: 10.6084/m9.figshare.24138693.v1
title: Supplementary Material for: The Worsening Renal Perfusion Index predicts the prognoses of heart failure patients treated with sacubitril/valsartan

description: Introduction: Sacubitril/valsartan (S/V) reduces all-cause mortality in patients with heart failure with reduced ejection fraction (HFrEF), but it may decline their estimated glomerular filtration rates (eGFR). In addition to eGFR, this clinical study aimed to develop a blood urea nitrogen (BUN)-based index to evaluate the status of renal perfusion and then identify predictors of all-cause death or heart transplant in patients with HFrEF receiving S/V. Methods: From the recruited 291 patients with HFrEF who were prescribed S/V from March 2017 to March 2019, we collected demographic, drug history, laboratory, echocardiographic, and clinical data from one year before S/V initiation until December 2020. Regression analysis was conducted by fitting Cox’s models with time-dependent covariates for the survival time and applying the modern stepwise variable selection procedure. The smoothing spline method was used to detect nonlinearity in effect and yield optimal cut-off values for continuous covariates. Results: In the Cox’s model, decreased hemoglobin level, decreased mean left ventricular ejection fraction, declined daily dose of S/V, decreased eGFR within 3 months, and increased BUN levels within 1 month and 9 months over time were significantly associated with an increased risk of all-cause death or heart transplant in patients with HFrEF. Conclusions: Adequate maintenance of renal perfusion is crucial for the continuous use of S/V and to avoid worsening renal function in patients with HFrEF. We defined the maximum increase in BUN levels within a specified period as the Worsening Renal Perfusion Index (WRPSV Index) to capture the prognostic effect of renal hypoperfusion in patients with HFrEF.
DOI: 10.6084/m9.figshare.24138705
title: Supplementary Material for: Bioinformatics Analysis and Screening of Potential Target Genes Related to the Lung Cancer Prognosis

description: Objective: Several genes have been validated as molecular targets for gene therapy in lung cancer. We screened target genes that affect survival of patients with lung cancer. Methods: Data on gene expression in normal lung tissues/lung adenocarcinoma (LUAD) samples were acquired from Genotype-Tissue Expression (GTEx)/The Cancer Genome Atlas (TCGA) databases and merged to expand the sample size, followed by differential analysis of the merged expression data and acquisition of differentially expressed genes. Survival and simple Cox analyses were used to screen for genes affecting LUAD survival. Protein-protein interaction/multivariable Cox analyses were utilized, and a risk model was established. Candidate genes expression levels in cancer/paracancerous tissues of lung cancer patients, and BEAS-2B/A549/HCC95 cells were measured by RT-qPCR/Western blot. Survival analysis of candidate genes was conducted in LUAD samples collected from TCGA. Results: Among 947 genes differentially expressed in LUAD, 151 were correlated with patient survival, and 116 might act as risk factors for LUAD. The 7 identified candidate genes (TOP2A, TK1, KIF4A, ANLN, KIF2C, ASF1B, CCNB1) were high-risk genes playing possible roles in LUAD. These genes were differentially expressed in lung cancer and were associated with TNM stages (III - IV)/differentiation grade/lymph node metastasis/distant metastasis, which affected lung cancer patient survival. Conclusion: P2A, TK1, KIF4A, ANLN, KIF2C, ASF1B and CCNB1 were highly-expressed in LUAD/lung squamous cell carcinoma (LUSC) and correlated with LUAD patient survival. This study contributes to better understanding of the prognostic regulation mechanism in LUAD and the screening of target genes for clinical treatment.
DOI: 10.5281/zenodo.8254380
title: NewsUnravel Dataset

description: <strong>About the Dataset</strong><br> Media bias is a multifaceted problem, leading to one-sided views and impacting decision-making. A way to address bias in news articles is to automatically detect and indicate it through machine-learning methods. However, such detection is limited due to the difficulty of obtaining reliable training data. To facilitate the data-gathering process, we introduce NewsUnravel, a news-reading web application leveraging an initially tested feedback mechanism to collect reader feedback on machine-generated bias highlights within news articles. Our approach augments dataset quality by significantly increasing inter-annotator agreement by 26.31% and improving classifier performance by 2.49%. As the first human-in-the-loop application for media bias, NewsUnravel shows that a user-centric approach to media bias data collection can return reliable data while being scalable and evaluated as easy to use. NewsUnravel demonstrates that feedback mechanisms are a promising strategy to reduce data collection expenses, fluidly adapt to changes in language, and enhance evaluators' diversity.<br> <br> <strong>Description of the data files</strong><br> This repository contains the datasets for the anonymous NewsUnravel submission. The tables contain following data:<br> <br> <strong>Feedback.csv</strong>: holds the participantID of a single feedback with the sentence ID (contentId), the bias rating, and provided reasons<br> <strong>Content.csv</strong>: holds the participant ID of a rating with the sentence ID (contentId) of a rated sentences and the bias rating, and reason, if given<br> <strong>Article.csv</strong>: holds the article ID, title, source, article meta data, article topic, and bias amount in %<br> <strong>Participant.csv</strong>: holds the participant IDs and data processing consent
DOI: 10.5281/zenodo.8334756
title: NewsUnravel Dataset

description: <strong>About the Dataset</strong><br> Media bias is a multifaceted problem, leading to one-sided views and impacting decision-making. A way to address bias in news articles is to automatically detect and indicate it through machine-learning methods. However, such detection is limited due to the difficulty of obtaining reliable training data. To facilitate the data-gathering process, we introduce NewsUnravel, a news-reading web application leveraging an initially tested feedback mechanism to collect reader feedback on machine-generated bias highlights within news articles. Our approach augments dataset quality by significantly increasing inter-annotator agreement by 26.31% and improving classifier performance by 2.49%. As the first human-in-the-loop application for media bias, NewsUnravel shows that a user-centric approach to media bias data collection can return reliable data while being scalable and evaluated as easy to use. NewsUnravel demonstrates that feedback mechanisms are a promising strategy to reduce data collection expenses, fluidly adapt to changes in language, and enhance evaluators' diversity.<br> <br> <strong>Description of the data files</strong><br> This repository contains the datasets for the anonymous NewsUnravel submission. The tables contain following data: <strong>Statistics.png</strong>: contains all Umami statistics for NewsUnravel's usage data<br> <strong>Feedback.csv</strong>: holds the participantID of a single feedback with the sentence ID (contentId), the bias rating, and provided reasons<br> <strong>Content.csv</strong>: holds the participant ID of a rating with the sentence ID (contentId) of a rated sentences and the bias rating, and reason, if given<br> <strong>Article.csv</strong>: holds the article ID, title, source, article meta data, article topic, and bias amount in %<br> <strong>Participant.csv</strong>: holds the participant IDs and data processing consent
DOI: 10.5281/zenodo.8254379
title: NewsUnravel Dataset

description: <strong>About the Dataset</strong><br> Media bias is a multifaceted problem, leading to one-sided views and impacting decision-making. A way to address bias in news articles is to automatically detect and indicate it through machine-learning methods. However, such detection is limited due to the difficulty of obtaining reliable training data. To facilitate the data-gathering process, we introduce NewsUnravel, a news-reading web application leveraging an initially tested feedback mechanism to collect reader feedback on machine-generated bias highlights within news articles. Our approach augments dataset quality by significantly increasing inter-annotator agreement by 26.31% and improving classifier performance by 2.49%. As the first human-in-the-loop application for media bias, NewsUnravel shows that a user-centric approach to media bias data collection can return reliable data while being scalable and evaluated as easy to use. NewsUnravel demonstrates that feedback mechanisms are a promising strategy to reduce data collection expenses, fluidly adapt to changes in language, and enhance evaluators' diversity.<br> <br> <strong>Description of the data files</strong><br> This repository contains the datasets for the anonymous NewsUnravel submission. The tables contain following data: <strong>NUDAdataset.csv</strong>: the NUDA dataset with 310 new sentences with bias labels<br> <strong>Statistics.png</strong>: contains all Umami statistics for NewsUnravel's usage data<br> <strong>Feedback.csv</strong>: holds the participantID of a single feedback with the sentence ID (contentId), the bias rating, and provided reasons<br> <strong>Content.csv</strong>: holds the participant ID of a rating with the sentence ID (contentId) of a rated sentences and the bias rating, and reason, if given<br> <strong>Article.csv</strong>: holds the article ID, title, source, article meta data, article topic, and bias amount in %<br> <strong>Participant.csv</strong>: holds the participant IDs and data processing consent
DOI: 10.5281/zenodo.8334929
title: NewsUnravel Dataset

description: <strong>About the Dataset</strong><br> Media bias is a multifaceted problem, leading to one-sided views and impacting decision-making. A way to address bias in news articles is to automatically detect and indicate it through machine-learning methods. However, such detection is limited due to the difficulty of obtaining reliable training data. To facilitate the data-gathering process, we introduce NewsUnravel, a news-reading web application leveraging an initially tested feedback mechanism to collect reader feedback on machine-generated bias highlights within news articles. Our approach augments dataset quality by significantly increasing inter-annotator agreement by 26.31% and improving classifier performance by 2.49%. As the first human-in-the-loop application for media bias, NewsUnravel shows that a user-centric approach to media bias data collection can return reliable data while being scalable and evaluated as easy to use. NewsUnravel demonstrates that feedback mechanisms are a promising strategy to reduce data collection expenses, fluidly adapt to changes in language, and enhance evaluators' diversity.<br> <br> <strong>Description of the data files</strong><br> This repository contains the datasets for the anonymous NewsUnravel submission. The tables contain following data: <strong>NUDAdataset.csv</strong>: the NUDA dataset with 310 new sentences with bias labels<br> <strong>Statistics.png</strong>: contains all Umami statistics for NewsUnravel's usage data<br> <strong>Feedback.csv</strong>: holds the participantID of a single feedback with the sentence ID (contentId), the bias rating, and provided reasons<br> <strong>Content.csv</strong>: holds the participant ID of a rating with the sentence ID (contentId) of a rated sentences and the bias rating, and reason, if given<br> <strong>Article.csv</strong>: holds the article ID, title, source, article meta data, article topic, and bias amount in %<br> <strong>Participant.csv</strong>: holds the participant IDs and data processing consent
DOI: 10.5281/zenodo.8344882
title: NewsUnravel Dataset

description: <strong>About the Dataset</strong><br> Media bias is a multifaceted problem, leading to one-sided views and impacting decision-making. A way to address bias in news articles is to automatically detect and indicate it through machine-learning methods. However, such detection is limited due to the difficulty of obtaining reliable training data. To facilitate the data-gathering process, we introduce NewsUnravel, a news-reading web application leveraging an initially tested feedback mechanism to collect reader feedback on machine-generated bias highlights within news articles. Our approach augments dataset quality by significantly increasing inter-annotator agreement by 26.31% and improving classifier performance by 2.49%. As the first human-in-the-loop application for media bias, NewsUnravel shows that a user-centric approach to media bias data collection can return reliable data while being scalable and evaluated as easy to use. NewsUnravel demonstrates that feedback mechanisms are a promising strategy to reduce data collection expenses, fluidly adapt to changes in language, and enhance evaluators' diversity.<br> <br> <strong>Description of the data files</strong><br> This repository contains the datasets for the anonymous NewsUnravel submission. The tables contain following data: <strong>NUDAdataset.csv</strong>: the NUDA dataset with 310 new sentences with bias labels<br> <strong>Statistics.png</strong>: contains all Umami statistics for NewsUnravel's usage data<br> <strong>Feedback.csv</strong>: holds the participantID of a single feedback with the sentence ID (contentId), the bias rating, and provided reasons<br> <strong>Content.csv</strong>: holds the participant ID of a rating with the sentence ID (contentId) of a rated sentences and the bias rating, and reason, if given<br> <strong>Article.csv</strong>: holds the article ID, title, source, article meta data, article topic, and bias amount in %<br> <strong>Participant.csv</strong>: holds the participant IDs and data processing consent
DOI: 10.17882/96479
title: Stable isotope composition of carbon and nitrogen in female porbeagle shark muscle and blood from Western English Channel

description: 17 female porbeagle sharks (Lamna nasus) were sampled for blood and white muscle in the Western English Channel between 2019 and 2021. Bulk tissues were analysed on the with a continuous flow mass spectrometer coupled to an elemental analyser at the Pôle Spectrométrie Océan (Plouzané, France). This dataset contains results from the stable isotopes analysis.
DOI: 10.5281/zenodo.8341765
title: Original data for "Deciphering the Interplay Between Local and Global Dynamics of Anodic Metal Oxidation"

description: This upload includes both original and supplementary data for the publication titled "Deciphering the Interplay Between Local and Global Dynamics of Anodic Metal Oxidation" by A. Makogon, J.-M. Noël, F. Kanoufi, and V. Shkirskiy. The preprint version of the paper can be found on ChemRxiv at https://doi.org/10.26434/chemrxiv-2023-stbm1. The files "DATA_PROCESSING.pdf" and "DATA_PROCESSING.ipynb" contain the developed Python script routine used for data mining and extraction. All figures in the article were generated using this script. For the best experience, we recommend opening the Jupyter Lab files in a Python 3 environment. The "Videos.zip" file houses all original data from the optics experiment, showcasing the evolution of current, averaged normalized reflectivities, and optical movies. The "OriginalData.zip" file contains all raw data from the opto-electrochemical experiments. Refer to the Python script as a guide if you wish to open and process this data. Lastly, "Drafts_of_scripts.zip" includes all the unedited drafts of Python scripts that were developed during the data processing phase.
DOI: 10.5281/zenodo.8341764
title: Original data for "Deciphering the Interplay Between Local and Global Dynamics of Anodic Metal Oxidation"

description: This upload includes both original and supplementary data for the publication titled "Deciphering the Interplay Between Local and Global Dynamics of Anodic Metal Oxidation" by A. Makogon, J.-M. Noël, F. Kanoufi, and V. Shkirskiy. The preprint version of the paper can be found on ChemRxiv at https://doi.org/10.26434/chemrxiv-2023-stbm1. The files "DATA_PROCESSING.pdf" and "DATA_PROCESSING.ipynb" contain the developed Python script routine used for data mining and extraction. All figures in the article were generated using this script. For the best experience, we recommend opening the Jupyter Lab files in a Python 3 environment. The "Videos.zip" file houses all original data from the optics experiment, showcasing the evolution of current, averaged normalized reflectivities, and optical movies. The "OriginalData.zip" file contains all raw data from the opto-electrochemical experiments. Refer to the Python script as a guide if you wish to open and process this data. Lastly, "Drafts_of_scripts.zip" includes all the unedited drafts of Python scripts that were developed during the data processing phase.
DOI: 10.6084/m9.figshare.19448219
title: The China Plant Trait Database Version 2.0

description: Here we present version 2.0 of the China Plant Trait Database, which contains information on morphometric, physical, chemical, photosynthetic and hydraulic traits from 1529 unique species in 140 sites spanning a diversity of vegetation types. Version 2 has five improvements compared to the previous version: (1) new data from a 4-km elevation transect on the edge of Tibetan Plateau, including alpine vegetation types not sampled previously; (2) inclusion of traits related to hydraulic processes, including specific sapwood conductance, the area ratio of sapwood to leaf, wood density and leaf turgor loss point; (3) inclusion of information on soil properties to complement the existing data on climate and vegetation (4) assessments of the reliability of individual trait measurements; and (5) inclusion of standardized checklists and templates for systematical field sampling and measurements. See detailed descriptions here: Wang, H., Harrison, S.P., Li, M. <em>et al.</em> The China plant trait database version 2. <em>Sci Data</em> <strong>9</strong>, 769 (2022). https://doi.org/10.1038/s41597-022-01884-4<br>
DOI: 10.7294/24101649
title: Morphology & Cortisol Data for Repository

description: These data were analyzed for the publication: "Exploring the Domestication Syndrome Hypothesis in Dogs: Pigmentation Does Not Predict Cortisol Levels." <br> Authors: JoAnna M Platzer, Lisa M Gunter, Erica N Feuerbacher File: Morphology & Cortisol Data for Repository Variables include: Dog Name, Study Type, Shelter ID, Dog ID, Cort Value, Phase, Weight (kg), Age (mos), White Spotting, Coat Pattern, Nose Leather
DOI: 10.25824/redu/yxpync
title: Reprodução familiar

description: Postagens (vídeo e texto) de Renato Cardoso encontradas pela busca das palavras cuidado, educação, escola, dívidas, divórcio, velhice, infância, adolescência e trabalho
DOI: 10.25824/redu/rahach
title: Afetividades

description: Postagens de Renato Cardoso coletadas em seu site pela busca das palavras amor, lar, carinho, fidelidade, lealdade, abuso e violência.
DOI: 10.25824/redu/g5sl0z
title: Planejamento

description: Postagens (vídeo e texto) de Renato Cardoso encontradas pela busca das palavras contracepção, sexo, gravidez, aborto, pornografia, pensão, traição e salário
DOI: 10.25824/redu/ldjpe2
title: Núcleo

description: Postagens (vídeo e texto) de Renato Cardoso encontradas pela busca das palavras pai, mãe, marido, esposa, filho, criança, solteiro, noivo, casamento e matrimônio