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Sensitivity of forest mortality to drought in carbon-dense tropical forests remains fraught with uncertainty, while extreme droughts are predicted to be more frequent and intense. Here, the potential of temporal autocorrelation of high-frequency variability in Landsat Enhanced Vegetation Index (EVI), an indicator of ecosystem resilience, to predict spatial and temporal variations of forest biomass mortality is evaluated against in situ census observations for 64 site-year combinations in Costa Rican tropical dry forests during the 2015 ENSO drought. Temporal autocorrelation, within the optimal moving window of 24 months, demonstrated robust predictive power for in situ mortality (leave-one-out cross-validation R = 0.54), which allows for estimates of annual biomass mortality patterns at 30 m resolution. Subsequent spatial analysis showed substantial fine-scale heterogeneity of forest mortality patterns, largely driven by drought intensity and ecosystem properties related to plant water use such as forest deciduousness and topography. Highly deciduous forest patches demonstrated much lower mortality sensitivity to drought stress than less deciduous forest patches after elevation was controlled. Our results highlight the potential of high-resolution remote sensing to "fingerprint" forest mortality and the significant role of ecosystem heterogeneity in forest biomass resistance to drought.
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http://dx.doi.org/10.1111/gcb.16046 | DOI Listing |
Cancer Med
September 2025
Department of Computer Engineering, Social and Biological Network Analysis Laboratory, University of Kurdistan, Sanandaj, Iran.
Background: Ovarian cancer (OC) remains the most lethal gynecological malignancy, largely due to its late-stage diagnosis and nonspecific early symptoms. Advances in biomarker identification and machine learning offer promising avenues for improving early detection and prognosis. This review evaluates the role of biomarker-driven ML models in enhancing the early detection, risk stratification, and treatment planning of OC.
View Article and Find Full Text PDFHeart Lung
September 2025
Department of Nursing, College of Medicine, National Cheng Kung University, No. 1, University Road, East District, Tainan City 70101, Taiwan. Electronic address:
Background: In-hospital mortality in patients with acute myocardial infarction (AMI) following primary percutaneous coronary intervention (pPCI) remains a significant concern. Developing a predictive model of in-hospital mortality is crucial for identifying high-risk patients, guiding clinical decisions, and preventing in-hospital mortality. Machine learning (ML) may analyze patterns in large datasets and provide accurate predictions of in-hospital mortality in AMI patients following pPCI.
View Article and Find Full Text PDFCurr Atheroscler Rep
September 2025
Department of Medicine, Division of Cardiovascular Disease, University of Alabama at Birmingham, 521 19th Street South-GSB 444, Birmingham, AL, 35233, USA.
Purpose Of Review: This review examines cardiovascular disease (CVD) risk prediction models relevant to older adults, a rapidly expanding population with elevated CVD risk. It discusses model characteristics, performance metrics, and clinical implications.
Recent Findings: Some models have been developed specifically for older adults, while several others consider a broader age range, including some older individuals.
J Behav Health Serv Res
September 2025
Center of Practice Transformation, School of Social Work, University of Minnesota-Twin Cities, Saint Paul, MN, USA.
People with mental health and substance use disorders (SUDs) experience worse outcomes, including increased mortality risk, compared to those with SUDs alone. Access to safe, stable housing, in conjunction with treatment, such as intensive outpatient programs (IOP), is vital in early recovery. Nevertheless, those with historically marginalized identities may experience increased disparities in accessing and utilizing services.
View Article and Find Full Text PDFJ Int Med Res
September 2025
Department of Hematology, Guangzhou Eighth People's Hospital, Guangzhou Medical University, China.
ObjectiveAccurate prognostication is crucial for managing human immunodeficiency virus (HIV)-associated cutaneous T-cell lymphoma. In this study, we aimed to develop an improved machine learning-based prognostic model for predicting the 5-year survival rates in HIV-associated cutaneous T-cell lymphoma patients.MethodsWe derived and tested machine learning models using algorithms including Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Random Forest.
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