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Multiple respiratory viruses can concurrently or sequentially infect the respiratory tract, making their identification crucial for diagnosis, treatment, and disease management. We present a label-free diagnostic platform integrating surface-enhanced Raman scattering (SERS) with deep learning for rapid, quantitative detection of respiratory virus coinfections. Using sensitive silica-coated silver nanorod array substrates, over 1.

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Quantitative fatty acid signature analysis (QFASA) is a common method of estimating the composition of prey species in the diets of consumers from polar and temperate ecosystems in which lipids are an important source of energy. A key characteristic of QFASA is that the large number of fatty acids that typically comprise lipids permits the dietary contributions of a correspondingly large number of prey types to be estimated. Several modifications to the original QFASA methods have been suggested in the literature and a significant extension of the original model published in 2017 allows simultaneous estimation of both diet proportions and calibration coefficients, which are metabolic constants in the model whose values must otherwise be estimated in independent feeding experiments.

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Despite advances in dispersal mechanisms and risk assessment of antibiotic resistance genes (ARGs), how plants influence ARG contamination in agricultural soils remains underexplored. Here, the impacts of plant species and diversity on ARGs and mobile genetic elements (MGEs) in three agricultural soils are comprehensively investigated in a pot experiment. The results indicate that increased plant diversity reduces ARGs and MGEs abundance by 19.

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Katsumada galangal seed ( K. Schum) is an important member of the Zingiberaceae family, with both medicinal value and culinary applications (Park et al. 2020).

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Three bacterial strains, designated FZUC8N2.13, FBOR7N2.3 and FZUR7N2.

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