Modeling global net ecosystem exchange is essential to understanding and quantifying the complex interactions between the Earth's terrestrial ecosystems and the atmosphere. Emphasizing the inter-relatedness between the global net ecosystem exchange, global sea surface temperature, and atmospheric levels, intuitively suggests that all three systems may exhibit collective environmental memory. Motivated by this, we explicitly identified a collective memory function and showed a similar non-Markovian stochastic behavior for these systems exhibiting superdiffusive behavior in short time intervals. We obtained the values of the memory parameter, , and the characteristic frequencies, , for global net ecosystem exchange (GNEE) ( ), global sea surface temperature (GSST) ( ), and atmospheric ( ). The values of the memory parameter are within the range, , and thus all three systems are in the superdiffusive regime. We emphasize, further, that these results were consistent with our previous analyses at the ecosystem level (i.e. Great Barrier Reef) suggesting scale invariance for these phenomena. Thus, the observed superdiffusive behavior operating at different scales suggests universality of the collective memory function for these systems.
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http://dx.doi.org/10.1038/s41598-024-73641-z | DOI Listing |
Reprod Health
January 2025
Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium.
Background: Over one-third of the global stillbirth burden occurs in countries affected by conflict or a humanitarian crisis, including Afghanistan. Stillbirth rates in Afghanistan remained high in 2021 at over 26 per 1000 births. Stillbirths have devastating physical, psycho-social and economic impacts on women, families and healthcare providers.
View Article and Find Full Text PDFBMC Neurol
January 2025
Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, School of Medicine, College of Medicine, National Sun Yat-Sen University, No. 123 Ta-Pei Road, Niao-Sung Dist, Kaohsiung, 83305, Taiwan.
Background And Purpose: White matter hyperintensities in brain MRI are key indicators of various neurological conditions, and their accurate segmentation is essential for assessing disease progression. This study aims to evaluate the performance of a 3D convolutional neural network and a 3D Transformer-based model for white matter hyperintensities segmentation, focusing on their efficacy with limited datasets and similar computational resources.
Materials And Methods: We implemented a convolution-based model (3D ResNet-50 U-Net with spatial and channel squeeze & excitation) and a Transformer-based model (3D Swin Transformer with a convolutional stem).
BMC Public Health
January 2025
Department of Urology I, The First Hospital of Jilin University, Changchun, Jilin Province, 130021, China.
Background: Cardiovascular diseases (CVD) remain a significant global health burden, particularly in China, where kidney dysfunction (KD) is a key risk factor. This study analyzed trends in the burden of KD-induced CVD and subtypes among the working-age population (25-64 years) in China over the past 30 years and explored its association with age, period, and birth cohort.
Methods: This study extracted data from the Global Burden of Disease (GBD) 2021, focusing on deaths and disability-adjusted life years (DALYs) caused by KD-induced CVD and subtypes, including ischemic heart disease (IHD), stroke, and lower extremity peripheral artery disease (LEPAD) among 25-64 years globally and in China from 1992 to 2021.
Sci Rep
January 2025
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
Land Surface Temperature (LST) is widely recognized as a sensitive indicator of climate change, and it plays a significant role in ecological research. The ERA5-Land LST dataset, developed and managed by the European Centre for Medium-Range Weather Forecasts (ECMWF), is extensively used for global or regional LST studies. However, its fine-scale application is limited by its low spatial resolution.
View Article and Find Full Text PDFDue to the low contrast of abdominal CT (Computer Tomography) images and the similar color and shape of the liver to other organs such as the spleen, stomach, and kidneys, liver segmentation presents significant challenges. Additionally, 2D CT images obtained from different angles (such as sagittal, coronal, and transverse planes) increase the diversity of liver morphology and the complexity of segmentation. To address these issues, this paper proposes a Detail Enhanced Convolution (DE Conv) to improve liver feature learning and thereby enhance liver segmentation performance.
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