Rates of nitrogen transformations support quantitative descriptions and predictive understanding of the complex nitrogen cycle, but measuring these rates is expensive and not readily available to researchers. Here, we compiled a dataset of gross nitrogen transformation rates (GNTR) of mineralization, nitrification, ammonium immobilization, nitrate immobilization, and dissimilatory nitrate reduction to ammonium in terrestrial ecosystems. Data were extracted from 331 studies published from 1984-2022, covering 581 sites. Globally, 1552 observations were appended with standardized soil, vegetation, and climate data (49 variables in total) potentially contributing to the observed variations of GNTR. We used machine learning-based data imputation to fill in partially missing GNTR, which improved statistical relationships between theoretically correlated processes. The dataset is currently the most comprehensive overview of terrestrial ecosystem GNTR and serves as a global synthesis of the extent and variability of GNTR across a wide range of environmental conditions. Future research can utilize the dataset to identify measurement gaps with respect to climate, soil, and ecosystem types, delineate GNTR for certain ecoregions, and help validate process-based models.
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http://dx.doi.org/10.1038/s41597-024-03871-3 | DOI Listing |
Pharmacoeconomics
January 2025
Division of Pulmonology, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, 138 Shengli Road, Tainan, 704, Taiwan.
Background And Objective: Approximately half of lung adenocarcinomas in East Asia harbor epidermal growth factor receptor (EGFR) mutations. EGFR testing followed by tissue-based next-generation sequencing (NGS), upfront tissue-based NGS, and complementary NGS approaches have emerged on the front line to guide personalized therapy. We study the cost effectiveness of exclusionary EGFR testing for Taiwanese patients newly diagnosed with advanced lung adenocarcinoma.
View Article and Find Full Text PDFSci Data
December 2024
University of Turin, Computer Science Department, Turin, 10149, Italy.
Governments procure large amounts of goods and services to help them implement policies and deliver public services; in Italy, this is an essential sector, corresponding to about 12% of the gross domestic product. Data are increasingly recorded in public repositories, although they are often divided into multiple sources and not immediately available for consultation. This paper provides a description and analysis of an effort to collect and arrange a legal public administration database.
View Article and Find Full Text PDFBioData Min
December 2024
Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology (NOVA FCT), Caparica, 2829-516, Portugal.
Gliomas are primary malignant brain tumors with a typically poor prognosis, exhibiting significant heterogeneity across different cancer types. Each glioma type possesses distinct molecular characteristics determining patient prognosis and therapeutic options. This study aims to explore the molecular complexity of gliomas at the transcriptome level, employing a comprehensive approach grounded in network discovery.
View Article and Find Full Text PDFAnn Neurol
December 2024
Department of Neurology with Institute of Translational Neurology, University Hospital Münster, Münster, Germany.
Objective: Cerebrospinal fluid (CSF) provides unique insights into the brain and neurological diseases. However, the potential of CSF flow cytometry applied on a large scale remains unknown.
Methods: We used data-driven approaches to analyze paired CSF and blood flow cytometry measurements from 8,790 patients (discovery cohort) and CSF only data from 3,201 patients (validation cohort) collected across neurological diseases in a real-world setting.
Front Artif Intell
November 2024
Faculty of Management, Royal Roads University, Victoria, BC, Canada.
Introduction: This study investigates the application of machine learning (ML) algorithms, a subset of artificial intelligence (AI), to predict financial distress in companies. Given the critical need for reliable financial health indicators, this research evaluates the predictive capabilities of various ML techniques on firm-level financial data.
Methods: The dataset comprises financial ratios and firm-specific variables from 464 firms listed on the TSX.
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