We provide a novel dataset of human capital-weighted population size (HCWP) for 185 countries from 1970 to 2100. HCWP summarizes a population's productive capacity and human capital heterogeneity in a single metric, enabling comparisons across countries and over time. The weights are derived from Mincerian earnings functions applied to multi-country census data on educational attainment. The model used to compute the returns to schooling accounts for the diminishing positive relative relationship between education and wages as the overall education of populations rises. The population weights are adjusted by a skills assessment factor representing differences in education quality across countries and years. HCWP is calculated by applying these adjusted human capital weights to population estimates and projections disaggregated by age, sex and education, spanning the period 1970-2020 and 2020-2100 for five Shared Socioeconomic Pathway scenarios. Validation analyses demonstrate the utility of the new HCWP data in explaining national income trends. As a more comprehensive population measure than basic size and age-sex indicators, HCWP enhances the power of statistical models aimed at the assessment of socioeconomic change impacts and forecasting.
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http://dx.doi.org/10.1038/s41597-024-03466-y | DOI Listing |
Talanta
December 2024
NanoBiosensors and Biodevices Lab, School of Medical Science and Technology, Indian Institute of Technology Kharagpur, West Bengal, 721302, India. Electronic address:
This work presents a robust strategy for quantifying overlapping electrochemical signatures originating from complex mixtures and real human plasma samples using nickel-based electrochemical sensors and machine learning (ML). This strategy enables the detection of a panel of analytes without being limited by the selectivity of the transducer material and leaving accommodation of interference analysis to ML models. Here, we fabricated a non-enzymatic electrochemical sensor for L-lactic acid detection in complex mixtures and human plasma samples using nickel oxide (NiO) nanoparticle-modified glassy carbon electrodes (GCE).
View Article and Find Full Text PDFHum Immunol
January 2025
Department of Urology, Jiaxing Second Hospital, Jiaxing 314000, China. Electronic address:
Previous studies have revealed the essential role of lysosomes in human diseases, including cancer. However, there is a lack of in-depth systematic research on its function in kidney renal clear cell carcinoma (KIRC). In this project, we collected the public dataset of KIRC and selected lysosomal genes tightly linked with survival.
View Article and Find Full Text PDFCancer Genet
December 2024
Department of Obstetrics and Gynecology, Shanghai Tongji Hospital, School of Medicine, Tongji University, 200120, PR China; Department of Obstetrics and Gynecology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200065, PR China. Electronic address:
Background: Mitochondrial dysregulation contributes to the chemoresistance of multiple cancer types. Yet, the functions of mitochondrial dysregulation in Ovarian serous cystadenocarcinoma (OSC) remain largely unknown.
Aim: We sought to investigate the function of mitochondrial dysregulation in OSC from the bioinformatics perspective.
Eur J Cancer
December 2024
Institute for Diagnostic Accuracy, Groningen, the Netherlands; Faculty of Medical Sciences, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands. Electronic address:
Background: Lung cancer screening (LCS) with low-dose CT (LDCT) reduces lung-cancer-related mortality in high-risk individuals. AI can potentially reduce radiologist workload as first-read-filter by ruling-out negative cases. The feasibility of AI as first reader was evaluated in the European 4-IN-THE-LUNG-RUN (4ITLR) trial, comparing its negative-misclassifications (NMs) to those of radiologists and the impact on referral rates.
View Article and Find Full Text PDFGait Posture
December 2024
Engineering Research Center of the Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies, Hebei University of Technology, Tianjin 300401, PR China; Hebei Key Laboratory of Robot Sensing and Human-robot Interaction, Hebei University of Technology, Tianjin 300401, PR China; School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, PR China. Electronic address:
Background: Gait feature recognition is crucial to improve the efficiency and coordination of exoskeleton assistance. The recognition methods based on surface electromyographic (sEMG) signals are popular. However, the recognition accuracy of these methods is poor due to ignoring the correlation of the time series of sEMG signals.
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