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Impacts of different intensities of commercial Sphagnum moss extraction on CO fluxes in a northern Patagonia peatland.
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Department of Forest Sciences, Faculty of Agricultural and Environmental Sciences, Universidad de La Frontera, Temuco, Chile.
Peatlands are key ecosystems for global climate regulation because they provide the most efficient carbon sink on the planet. Despite this, they have been widely degraded by various anthropogenic disturbances, causing imbalances in their ecological functioning. A more recent type of disturbance corresponds to the commercial extraction of Sphagnum mosses, which has been carried out in temperate peatlands distributed in Australasia and Patagonia.
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Uncontrolled immune responses resulting from overactivated cellular signaling pathways, leading to inflammation and tissue injury, are a major cause of death in pathogen-infected individuals. This phenomenon has been well studied in mammals but is less explored in invertebrates. Bacteria of the genus are among the most harmful pathogens to humans and aquatic animals.
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Patients with Non-Small Cell Lung Cancer (NSCLC) present a variety of clinical symptoms, such as dyspnea and chest pain, complicating accurate diagnosis. NSCLC includes subtypes distinguished by histological characteristics, specifically lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). This study aims to compare and identify abnormal gene expression patterns in LUAD and LUSC samples relative to adjacent healthy tissues using an explainable artificial intelligence (XAI) framework.
View Article and Find Full Text PDFMasking Ability of Subtractively and Additively Manufactured Dental Ceramic Restorations: A Systematic Review.
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Department of Computer Sciences and Mathematics, University of Québec at Chicoutimi, Chicoutimi, QC, Canada.
Artificial Intelligence (AI) has demonstrated exceptional performance in automating critical healthcare tasks, such as diagnostic imaging analysis and predictive modeling, often surpassing human capabilities. The integration of AI in healthcare promises substantial improvements in patient outcomes, including faster diagnosis and personalized treatment plans. However, AI models frequently lack interpretability, leading to significant challenges concerning their performance and generalizability across diverse patient populations.
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