The multi-resistance gene is widely distributed among various gram-positive and gram-negative species in livestock in China. To better understand the epidemiology of among spp. and isolates, 254 spp. and 398 strains collected from six swine farms in China were subjected to prevalence and genetic analysis. Forty (15.7%) spp. isolates, including 38 strains, one strain, and one strain, and two (0.5%) isolates were found to contain the gene. Most of the 38 strains were clonally unrelated; however, clonal dissemination of -positive was detected at the same farm. In eight randomly selected -positive staphylococci, a -harboring module (ISΔ) was detected in six isolates; was bracketed by two copies of IS or IS in the remaining two isolates. In the two isolates, EP25 and EP28, was flanked by two IS elements in the same or opposite orientation, respectively. Complete sequence analysis of the novel F43:A-:B- plasmid pHNEP28 revealed that it contains two multi-resistance regions: together with , interspersed with IS, ΔIS and IS, and together with (M) interspersed with IS, IS, ΔTn, and ΔIS. The coexistence of with other resistance genes on a conjugative plasmid may contribute to the dissemination of these genes by co-selection. Thus, rational drug use and continued surveillance of in swine farms are warranted.
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http://dx.doi.org/10.3389/fmicb.2017.00329 | DOI Listing |
Mol Plant
January 2025
College of Plant Protection, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, China. Electronic address:
Plants possess remarkably durable resistance against non-adapted pathogens in nature. However, the molecular mechanisms underlying this resistance remain poorly understood, and it is unclear how the resistance is maintained without coevolution between hosts and the non-adapted pathogens. In this study, we used Phytophthora sojae (Ps), a non-adapted pathogen of N.
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January 2025
College of Physical Education and Health Sciences, Zhejiang Normal University, Jinhua, 321004, China.
Athlete engagement is influenced by several factors, including cohesion, passion and mental toughness. Machine learning methods are frequently employed to construct predictive models as a result of their high efficiency. In order to comprehend the effects of cohesion, passion and mental toughness on athlete engagement, this study utilizes the relevant methods of machine learning to construct a prediction model, so as to find the intrinsic connection between them.
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January 2025
Sichuan Academy of Eco-Environmental Sciences, Chengdu, 610041, China.
The widespread application of swine-farming wastewater to soil and water is increasingly contributing to heavy metal contamination, posing significant environmental risks. This study investigated the concentrations of eight heavy metals in swine-farming wastewater following different treatment processes, and assessed their ecological risks in Sichuan Province, China. The findings revealed that zinc, copper and nickel exhibited the highest concentrations, potentially causing heavy or strong contamination levels and leading to heavy or slight ecological risks.
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January 2025
Department of Biochemistry, College of Science, King Saud University, P.O.Box 2455, Riyadh, 11451, Saudi Arabia.
Nano-biochar considers a versatile and valuable sorbent to enhance plant productivity by improving soil environment and emerged as a novel solution for environmental remediation and sustainable agriculture in modern era. In this study, roles of foliar applied nanobiochar colloidal solution (NBS) on salt stressed tomato plants were investigated. For this purpose, NBS was applied (0%, 1% 3% and 5%) on two groups of plants (control 0 mM and salt stress 60 mM).
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January 2025
College of Ecology and Environment, Hainan University, Haikou, 570228, China.
Agroforestry systems are known to enhance soil health and climate resilience, but their impact on greenhouse gas (GHG) emissions in rubber-based agroforestry systems across diverse configurations is not fully understood. Here, six representative rubber-based agroforestry systems (encompassing rubber trees intercropped with arboreal, shrub, and herbaceous species) were selected based on a preliminary investigation, including Hevea brasiliensis intercropping with Alpinia oxyphylla (AOM), Alpinia katsumadai (AKH), Coffea arabica (CAA), Theobroma cacao (TCA), Cinnamomum cassia (CCA), and Pandanus amaryllifolius (PAR), and a rubber monoculture as control (RM). Soil physicochemical properties, enzyme activities, and GHG emission characteristics were determined at 0-20 cm soil depth.
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