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Ciprofloxacin brought on antibiotic resistance within Salmonella Typhimurium mutants as well as genome analysis.
BACKGROUND Exposure to some phthalate diesters has been associated with adverse reproductive health outcomes in both rodents and humans indicative of anti-androgenic effects. Exposure during sensitive periods of development, such as prenatally, is of particular concern. OBJECTIVES We wished to investigate whether phthalate metabolites measured in maternal serum samples from historical birth cohorts can be used to assess prenatal exposure. Further, we aimed to study temporal and geographical trends in phthalate exposure across three different birth cohorts. METHODS We compared phthalate metabolite levels in maternal serum samples from an Australian (1989-91) and a Danish (1997-2001) birth cohort with levels in serum and urine samples from a recent Danish birth cohort (2012-14). Samples were analysed for 32 phthalate metabolites from 15 phthalate diesters by isotope-diluted liquid chromatography-tandem mass spectrometry (LC-MS/MS). Correlations between metabolites were tested by Spearman rank correlation test, tal exposure to phthalates when using serum levels of the monoesters of the low-molecular weight (LMW) phthalates and the secondary metabolites of the high-molecular weight (HMW) phthalates. Serum phthalate measurements are, however, not suitable for human biomonitoring and should only be used to exploit historical samples from cohorts, where urine samples were not collected. Our findings suggest that phthalate exposure may have decreased over time from the early 1990s to the 2010s. To unravel the linkages between ecological ratios (CNP) and the microbial community in rhizosphere soil in response to fertilizer management, soil samples were collected from a proso millet (Panicum miliaceum L.) field under different fertilizer management systems, including nitrogen fertilizer (NF), phosphorus fertilizer (PF), combined N and P (NP) fertilizer, and organic fertilizer (OF); no fertilizer (CK) was used as a control. Furthermore, 16S rRNA and ITS gene sequencing were applied to represent the bacterial and fungal diversity in the soil. Moreover, the elemental properties, including the carbon (C), nitrogen (N), and phosphorus (P) contents, in the microbial biomass and rhizosphere soil were evaluated. The results showed that the C, N, and P contents and microbial biomass (MBC, MBN and MBP, respectively) in the rhizosphere soil were augmented following fertilizer management. Increases in the alpha diversity indices (Shannon and Chao 1) of soil bacteria and fungi were observed in response to the fertic environments. Thus, N status should be an important factor for sustainable agricultural management. Moreover, the synergistic effects were better with the combination of C, N, and P or with organic fertilizer than with C, N and P separately. Organophosphate esters (OPEs) are a group of chemicals used as flame retardants and plasticizers that replaced polybrominated diphenyl ethers in consumer products such as furniture and electronics. To characterize exposure to OPEs during fetal development, we measured urinary OPE metabolite concentrations in women twice during pregnancy (16 and 26 weeks' gestation) and at delivery (n = 357). We also previously quantified house dust OPE parent compound concentrations at 20 weeks' gestation (n = 317). EPZ011989 Diphenyl phosphate (DPHP) had the highest geometric mean urinary concentrations (1.5-2.3 μg/g creatinine), followed by bis(1,3-dichloro-2-propyl) phosphate (BDCIPP; 0.75-0.99 μg/g creatinine), and bis(2-chloroethyl) phosphate (BCEP; 0.72-0.97 μg/g creatinine), while dibutyl phosphate (DNBP) had the lowest concentrations (0.25-0.28 μg/g creatinine). Urinary OPE metabolites were moderately correlated with each other at 26 weeks (rs 0.23-0.38, p less then 0.001) while the correlations at 16 weeks and delivery were slightly weaker. Intra-class correlations for urinary metabolites measured at three time points were poor (0.16-0.34), indicating high variability within individuals. Dust concentrations of OPE parent compounds were associated with BCEP, BDCIPP, and DPHP concentrations in urine at some but not all time points. In linear mixed models of urinary OPE metabolite concentrations, household size was inversely associated with BCEP concentrations, and being non-white was associated with lower BDCIPP and DPHP concentrations. Urine samples collected in the summer had the highest OPE metabolite concentrations. This study highlights the need to collect multiple urine samples during pregnancy to define exposure patterns and investigate potential periods of susceptibility. In the face of multiple habitat alterations originating from both natural and anthropogenic factors, the fast-changing environments pose significant challenges for maintaining ecosystem integrity. Machine learning is a powerful tool for modeling complex non-linear systems through exploratory data analysis. This study aims at exploring a machine learning-based approach to relate environmental factors with fish community for achieving sustainable riverine ecosystem management. A large number of datasets upon a wide variety of eco-environmental variables including river flow, water quality, and species composition were collected at various monitoring stations along the Xindian River of Taiwan during 2005 and 2012. Then the complicated relationship and scientific essences of these heterogonous datasets are extracted using machine learning techniques to have a more holistic consideration in searching a guiding reference useful for maintaining river-ecosystem integrity. We evaluate and select critical environmental variables by the analysis of variance (ANOVA) and the Gamma test (GT), and then we apply the adaptive network-based fuzzy inference system (ANFIS) for an estimation of fish bio-diversity using the Shannon Index (SI). The results show that the correlation between model estimation and the biodiversity index is higher than 0.75. The GT results demonstrate that biochemical oxygen demand (BOD), water temperature, total phosphorus (TP), and nitrate-nitrogen (NO3-N) are important variables for biodiversity modeling. The ANFIS results further indicate lower BOD, higher TP, and larger habitat (flow regimes) would generally provide a more suitable environment for the survival of fish species. The proposed methodology not only possesses a robust estimation capacity but also can explore the impacts of environmental variables on fish biodiversity. This study also demonstrates that machine learning is a promising avenue toward sustainable environmental management in river-ecosystem integrity.
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