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As dairy industries has been emerged as one of the most rapidly developing industry in both small as well as large scale, the volume of effluent generated is also very high. In the present study, aerated electrocoagulation combined with phytoremediation treatment was conducted in dairy industry wastewater. Electrocoagulation was performed with aluminium and iron electrodes and effect of various operating parameters such as electrode combination, pH, and voltage were tested. Electrocoagulation was found effective at neutral pH and its efficiency increased with increase in applied voltage. The maximum COD removal efficiency of 86.4% was obtained in case of Al-Fe electrode combination with aeration at 120 min reaction time, initial pH 7, voltage 5 V. Significant growth of Canna indica was observed in electrocoagulation treated wastewater compared to raw dairy wastewater. COD removal of 97% was achieved when combined electrocoagulation and phytoremediation process was used. Thus, it proves to be a proficient method for the treatment of dairy industry wastewater. In addition to the above, bacterial toxicity tests were performed to investigate the toxic nature of wastewater and the results showed that both treated and untreated wastewater favoured bacterial growth. Although the use of ferrate (VI), an emerging green oxidant, has been widely investigated to remove organic pollutants in water, its ability to remediate contaminated soils has been scarcely evaluated. Here, we explore the use of ferrate (VI) to degrade a polychlorinated persistent compound, the pentachlorophenol (PCP), in aqueous solution and in an aged contaminated soil under batch, water-saturated and water-unsaturated flow conditions. The first results showed the prominent efficiency of ferrate (VI) over conventional oxidants (e.g. H2O2 and persulfate) in both matrices and at different oxidant doses. In aqueous solution, more than 80% of PCP was degraded by ferrate (VI) while complete removal was observed in soil under batch conditions. In column experiments, PCP removal by ferrate (VI) remained efficient but dependent on the flow rate and water saturation. Maximum PCP removal (95%) in columns was observed under water saturated conditions when ferrate (VI) (0.2 g g-1 of soil) was injected at a low flow rate (i.e. 0.025 mL min-1). This study has strong implications in the development of new sustainable processes based on ferrate (VI) for the remediation of different environmental compartments. The osteometric methods are the most reliable way to estimate the sex of skeletons when DNA analysis is not used. However, as osteometric studies usually ignore the overlap in male and female skeletal dimensions, they rarely achieve accuracy sufficient for forensic application. buy Selitrectinib To resolve this issue, recent studies suggest sex estimation only when posterior probability (pp) is greater than 0.95, but that approach does not always provide sufficient accuracy and creates a large proportion of unsexed skeleton. Thus, our study aimed to explore whether it is possible to adjust pp on skeletal measurements with pronounced sexual dimorphism to meet 95% accuracy and to enable sex estimation on a reasonable proportion of individuals. From 207 skeletons, we included 65 postcranial measurements and selected 10% of variables with the highest sexual dimorphism. We computed univariate and bivariate discriminant functions using pp threshold of 0.5, 0.95, and the threshold required to achieve accuracy of ≥ 95%. Discriminant functions with pp=0.5 obtained accuracy of 85%-93%, while those with pp≥0.95 and adjusted posterior probabilities obtained 94%-99%. However, we showed that by selecting a particular threshold, sex could be estimated on a greater proportion of individuals than for pp≥0.95 42%-86% vs. 24%-62% for univariate and 69%-95% vs. 49%-78% for bivariate functions. Therefore, when developing sex estimation models, we suggest not to use fixed pp level, but to adjust pp to achieve 95% accuracy and to minimize the percentage of unsexed skeletons. Elucidating how the racialized social system of whiteness affects the health of white Americans is critically important given current trends. Mirroring the nation, whites in rural North Carolina are currently experiencing increases in early mortality at greater levels than any other racial group in the state. Health focused oral history interviews conducted with whites (n = 13) in an economically distressed, rural community provide insight into potential determinants of this trend. Interview transcripts were coded and analyzed using a modified grounded theory approach. Analysis was guided by the whiteness and health framework, which considers how structural racism is both health promoting and health harmful for whites. Analysis highlighted three key themes color-blind ideology, belief in the American Creed, and resistance to change. Whiteness influenced these rural whites' beliefs such that they were blind to the experiences of people of color in their community. Their explicit and figurative nostalgia for The Andy Griffith Show's idyllic town of Mayberry (a fictional Southern community devoid of Black American characters and racial tension during the 1960s) exposed color-blind expectations of what it means to have a healthy community. Additionally, interviewees attributed health status to individual effort and health behaviors, rather than considering how macro-level social determinants impact health. Individual level blame and resistance to change influenced interviewees' political views and suspicion of some social programs that could benefit the health of their community. These meritocratic beliefs about hard-work and self-sufficiency have implications for individual and community level health outcomes. Sex and gender influence health differently. Associations between sex and health have been extensively studied, but gender (i.e. psychosocial sex) has been largely neglected, partly due to the absence of gender measures in cohort studies. Therefore, our objective was to test the unique associations of gender and sex with common somatic symptoms and chronic diseases, using a gender index created from existing cohort data. We applied LASSO logistic regression to identify, out of 153 unique variables, psychosocial variables that were predictive of sex (i.e. gender-related) in the Dutch LifeLines Cohort Study. These psychosocial variables covered gender roles and institutionalized gender. Using the estimated coefficients, gender indexes were calculated for each adult participant in the study (n = 152,728; 58.5% female; mean age 44.6 (13.1) years). We applied multiple ordinal and logistic regression to test the unique associations of the gender index and sex, and their interactions, with common somatic symptoms assessed by the SCL-90 SOM and self-reported lifetime prevalence of chronic diseases, respectively.
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