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The impact of anthropogenic activity on ecosystems has highlighted the need to move beyond the biogeographical delineation of species richness patterns to understanding the vulnerability of species assemblages, including the functional components that are linked to the processes they support. We developed a decision theory framework to quantitatively assess the global taxonomic and functional vulnerability of fish assemblages on tropical reefs using a combination of sensitivity to species loss, exposure to threats and extent of protection. Fish assemblages with high taxonomic and functional sensitivity are often exposed to threats but are largely missed by the global network of marine protected areas. We found that areas of high species richness spatially mismatch areas of high taxonomic and functional vulnerability. Nevertheless, there is strong spatial match between taxonomic and functional vulnerabilities suggesting a potential win-win conservation-ecosystem service strategy if more protection is set in these locations.
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http://dx.doi.org/10.1111/ele.12316 | DOI Listing |
Cell Mol Biol (Noisy-le-grand)
September 2025
Medical Microbiology Department, College of Medicine, Ibn Sina University of Medical and Pharmaceutical Sciences, Baghdad, Iraq.
Pseudomonas aeruginosa is a prominent opportunistic pathogen, especially in burn wound infections, and is often associated with high morbidity and mortality due to its multidrug resistance (MDR) characteristics.This study aimed to evaluate the multidrug resistance profile and perform a molecular phylogenetic analysis of P. aeruginosa isolates recovered from human burn infection sample .
View Article and Find Full Text PDFHead Neck Pathol
September 2025
Department of Laboratory Medicine and Pathology, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL, 32224, USA.
Myoepithelial carcinoma (MECA) is a malignant neoplasm composed exclusively of myoepithelial cells and accounts for less than 1% of all salivary gland tumors. Its diagnosis is often challenging due to histologic overlaps with benign lesions and its variable morphologic presentation. Although molecular profiling has emerged as a valuable tool in salivary gland tumor classification, the genetic landscape of MECA remains incompletely defined.
View Article and Find Full Text PDFFunct Integr Genomics
September 2025
Zhengzhou Research Base, State Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Zhengzhou University/Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Zhengzhou, China.
In this study, a comprehensive genome-wide identification and analysis of the aldo-keto reductase (AKR) gene family was performed to explore the role of Gossypium hirsutumAKR40 under salt stress in cotton. A total of 249 AKR genes were identified with uneven distribution on the chromosomes in four cotton species. The diversity and evolutionary relationship of the cotton AKR gene family was identified using physio-chemical analysis, phylogenetic tree construction, conserved motif analysis, chromosomal localization, prediction of cis-acting elements, and calculation of evolutionary selection pressure under 300 mM NaCl stress.
View Article and Find Full Text PDFArch Microbiol
September 2025
College of Agriculture and Biology, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, People's Republic of China.
Cystofilobasidium infirmominiatum, biotechnologically significant yeast, is increasingly garnering attention due to its superior ability to produce valuable carotenoids and lipids. Nonetheless, until now, the reference genome that governs the biosynthesis of carotenoids and lipids in C. infirmominiatum remains unreported.
View Article and Find Full Text PDFBrief Bioinform
August 2025
Department of Respiratory Medicine, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157, Xiwu Road, Xincheng District, Xi'an 710004, China.
Accurate tumor mutation burden (TMB) quantification is critical for immunotherapy stratification, yet remains challenging due to variability across sequencing platforms, tumor heterogeneity, and variant calling pipelines. Here, we introduce TMBquant, an explainable AI-powered caller designed to optimize TMB estimation through dynamic feature selection, ensemble learning, and automated strategy adaptation. Built upon the H2O AutoML framework, TMBquant integrates variant features, minimizes classification errors, and enhances both accuracy and stability across diverse datasets.
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