98%
921
2 minutes
20
Accumulating evidence highlights increased mortality risks for trees during severe drought, particularly under warmer temperatures and increasing vapour pressure deficit (VPD). Resulting forest die-off events have severe consequences for ecosystem services, biophysical and biogeochemical land-atmosphere processes. Despite advances in monitoring, modelling and experimental studies of the causes and consequences of tree death from individual tree to ecosystem and global scale, a general mechanistic understanding and realistic predictions of drought mortality under future climate conditions are still lacking. We update a global tree mortality map and present a roadmap to a more holistic understanding of forest mortality across scales. We highlight priority research frontiers that promote: (1) new avenues for research on key tree ecophysiological responses to drought; (2) scaling from the tree/plot level to the ecosystem and region; (3) improvements of mortality risk predictions based on both empirical and mechanistic insights; and (4) a global monitoring network of forest mortality. In light of recent and anticipated large forest die-off events such a research agenda is timely and needed to achieve scientific understanding for realistic predictions of drought-induced tree mortality. The implementation of a sustainable network will require support by stakeholders and political authorities at the international level.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1111/nph.15048 | 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.
View Article and Find Full Text PDF