Analysed herein are remote results of treatment at terms of 3 and 19 years in two patients with a complicated course of chronic thoracoabdominal aortic dissection. Each of them was subjected to 3 interventions, twice by emergency indications. Surgical corrections: resection of the abdominal aortic aneurysm (2), thoracoabdominal bypass grafting (1). Endovascular interventions: implantation of stent grafts into the descending aorta for a ruptured pseudoaneurysm (1) and in the subrenal segment of the abdominal aorta (1), embolization of the visceral artery for a ruptured aneurysm. The outcomes of treatment were considered good based on clinical and angiographic examinations. Revascularization in the segments of intervention and optimal quality of life of patients were achieved. The scope and choice of the method of correction are discussed with due regard for real clinical possibilities at specific terms of follow up.
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Malar J
January 2025
MIVEGEC, Univ. Montpellier, CNRS, IRD, Montpellier, France.
Background: The increasing availability of electronic health system data and remotely-sensed environmental variables has led to the emergence of statistical models capable of producing malaria forecasts. Many of these models have been operationalized into malaria early warning systems (MEWSs), which provide predictions of malaria dynamics several months in advance at national and regional levels. However, MEWSs rarely produce predictions at the village-level, the operational scale of community health systems and the first point of contact for the majority of rural populations in malaria-endemic countries.
View Article and Find Full Text PDFSci Rep
January 2025
Center Agriculture Food Environment, University of Trento, San Michele all'Adige, Italy.
Various modelling techniques are available to understand the temporal and spatial variations of the phenology of species. Scientists often rely on correlative models, which establish a statistical relationship between a response variable (such as species abundance or presence-absence) and a set of predominantly abiotic covariates. The choice of the modeling approach, i.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Department of Environmental Management, Institute of Environmental Engineering, RUDN University, Miklukho-Maklaya Street, 117198, Moscow, Russia.
Globally, agricultural lands are among the top emitters of greenhouse gases (GHGs), responsible for over 20% of total greenhouse gas (GHG) emissions. Climatic conditions, an acute challenge in sub-Saharan Africa (SSA), where access to mitigation technologies remains limited, have heavily influenced these lands. This study explores GHG contributions from crop production and their devastating and deteriorating impacts on the economy and environment and proposes a sustainable solution.
View Article and Find Full Text PDFFront Plant Sci
January 2025
Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
The Leaf Area Index (LAI) is an essential parameter that affects the exchange of energy and materials between the vegetative canopy and the surrounding environment. Estimating LAI using machine learning models with remote sensing data has become a prevalent method for large-scale LAI estimation. However, existing machine learning models have exhibited various flaws, hindering the accurate estimation of LAI.
View Article and Find Full Text PDFNat Ecol Evol
January 2025
Center for Ecosystem Sentinels, Department of Biology, University of Washington, Seattle, WA, USA.
The emergence of generative artificial intelligence (AI) models specializing in the generation of new data with the statistical patterns and properties of the data upon which the models were trained has profoundly influenced a range of academic disciplines, industry and public discourse. Combined with the vast amounts of diverse data now available to ecologists, from genetic sequences to remotely sensed animal tracks, generative AI presents enormous potential applications within ecology. Here we draw upon a range of fields to discuss unique potential applications in which generative AI could accelerate the field of ecology, including augmenting data-scarce datasets, extending observations of ecological patterns and increasing the accessibility of ecological data.
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