The Effects of Chemical Coagulants on the Decolorization of Dyes by Electrocoagulation Using Response Surface Methodology (RSM)

This study assessed the efficiency of electrocoagulation (ECF) coupled with an addition of chemical coagulant to decolorize textile dye. Tests were conducted using Box Behnken methodology to vary six parameters: dye type, weight, coagulant type, dose, initial pH and current density. The combination of electrocoagulation and chemical coagulation was able to decolorize dye up to 99.42 % in 30 min of treatment time which is remarkably shorter in comparison with using conventional chemical coagulation. High color removal was found to be contingent upon the dye type and current density, along with the interactions between the current density and the coagulant dose. The addition of chemical coagulants did enhanced treatment efficiency

Electrocoagulation in Wastewater Treatment

A review of the literature published in from 2008 to 2010 on topics related to electrochemical treatment within wastewater was presented. The review included several sections such as optimization, modeling, various wastewater treatment techniques, analytical and instrumentation, and comparison with other treatment methods

Oxidation Pond for Municipal Wastewater Treatment

This literature review examines process, design, and cost issues related to using oxidation ponds for wastewater treatment. Many of the topics have applications at either full scale or in isolation for laboratory analysis. Oxidation ponds have many advantages. The oxidation pond treatment process is natural, because it uses microorganisms such as bacteria and algae. This makes the method of treatment cost-effective in terms of its construction, maintenance, and energy requirements. Oxidation ponds are also productive, because it generates effluent that can be used for other applications. Finally, oxidation ponds can be considered a sustainable method for treatment of wastewater

FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning

Design flow parameters are of utmost importance to chip design quality and require a painfully long time to evaluate their effects. In reality, flow parameter tuning is usually performed manually based on designers' experience in an ad hoc manner. In this work, we introduce a machine learning-based automatic parameter tuning methodology that aims to find the best design quality with a limited number of trials. Instead of merely plugging in machine learning engines, we develop clustering and approximate sampling techniques for improving tuning efficiency. The feature extraction in this method can reuse knowledge from prior designs. Furthermore, we leverage a state-of-the-art XGBoost model and propose a novel dynamic tree technique to overcome overfitting. Experimental results on benchmark circuits show that our approach achieves 25% improvement in design quality or 37% reduction in sampling cost compared to random forest method, which is the kernel of a highly cited previous work. Our approach is further validated on two industrial designs. By sampling less than 0.02% of possible parameter sets, it reduces area by 1.83% and 1.43% compared to the best solutions hand-tuned by experienced designers

A specific genetic background is required for acquisition and expression of virulence factors

In bacteria, the evolution of pathogenicity seems to be the result of the constant arrival of virulence factors (VFs) into the bacterial genome. However, the integration, retention, and/or expression of these factors may be the result of the interaction between the new arriving genes and the bacterial genomic background. To test this hypothesis, a phylogenetic analysis was done on a collection of 98 Escherichia coli/Shigella strains representing the pathogenic and commensal diversity of the species. The distribution of 17 VFs associated to the different E. coli pathovars was superimposed on the phylogenetic tree. Three major types of VFs can be recognized: (1) VFs that arrive and are expressed in different genetic backgrounds (such as VFs associated with the pathovars of mild chronic diarrhea: enteroaggregative, enteropathogenic, and diffusely-adhering E. coli), (2) VFs that arrive in different genetic backgrounds but are preferentially found, associated with a specific pathology, in only one particular background (such as VFs associated with extraintestinal diseases), and (3) VFs that require a particular genetic background for the arrival and expression of their virulence potential (such as VFs associated with pathovars typical of severe acute diarrhea: enterohemorragic, enterotoxigenic, and enteroinvasive E. coli strains). The possibility of a single arrival of VFs by chance, followed by a vertical transmission, was ruled out by comparing the evolutionary histories of some of these VFs to the strain phylogeny. These evidences suggest that important changes in the genome of E. coli have occurred during the diversification of the species, allowing the virulence factors associated with severe acute diarrhea to arrive in the population. Thus, the E. coli genome seems to be formed by an ''ancestral'' and a ''derived'' background, each one responsible for the acquisition and expression of different virulence factors

Competencies for Global Mental Health: Developing Training Objectives for a Post-Graduate Fellowship for Psychiatrists

Background: Despite an increase in psychiatry trainees’ interest in global mental health (GMH), there is a lack of relevant training competencies developed using educational frameworks that incorporate viewpoints from high- and low-income countries. Objective: The aim of this study was to determine competencies for a two-year post-graduate GMH fellowship for psychiatrists utilizing Kern’s six-step process as a theoretical framework for curriculum development. Methods: We conducted a targeted needs assessment via key informant interviews with a purposive sample of stakeholders (n = 19), including psychiatry trainees, generalist clinicians, medical directors, psychiatrists, researchers, and GMH educators from high- and low-resource settings in the United States and abroad. We analyzed data using a template method of thematic analysis. Findings: We tabulated learning objectives across 20 domains. Broadly, clinical objectives focused on providing supervision for short-term, evidence-based psychotherapies and on identifying red flags and avoiding harmful medication use among vulnerable populations such as children and the elderly. Non-clinical objectives focused on social determinants of health, education, and clinical supervision as part of capacity-building for non-specialists, engagement in a systems-wide project to improve care, and ethical and equitable partnerships that involve reciprocal and bidirectional education. Several competencies were also relevant for global health work in general. Conclusions: A theory-informed framework for curriculum development and a diverse set of key informants can provide educational objectives that meet the priorities of the trainees and the clinical sites in both low- and high-income settings. Limitations of this study include a small sample size and a focus on clinical needs of specific sites, both of which may affect generalizability. Given the focus on training specialists (psychiatrists), the low-resource sites highlighted the importance of educating and supervising their permanent, generalist clinicians, rather than providing direct, independent patient care

Partnerships in mental healthcare service delivery in low-resource settings: developing an innovative network in rural Nepal

Background: Mental illnesses are the largest contributors to the global burden of non-communicable diseases. However, there is extremely limited access to high quality, culturally-sensitive, and contextually-appropriate mental healthcare services. This situation persists despite the availability of interventions with proven efficacy to improve patient outcomes. A partnerships network is necessary for successful program adaptation and implementation. Partnerships network We describe our partnerships network as a case example that addresses challenges in delivering mental healthcare and which can serve as a model for similar settings. Our perspectives are informed from integrating mental healthcare services within a rural public hospital in Nepal. Our approach includes training and supervising generalist health workers by off-site psychiatrists. This is made possible by complementing the strengths and weaknesses of the various groups involved: the public sector, a non-profit organization that provides general healthcare services and one that specializes in mental health, a community advisory board, academic centers in high- and low-income countries, and bicultural professionals from the diaspora community. Conclusions: We propose a partnerships model to assist implementation of promising programs to expand access to mental healthcare in low- resource settings. We describe the success and limitations of our current partners in a mental health program in rural Nepal

HIV Testing and Treatment with the Use of a Community Health Approach in Rural Africa.

BACKGROUND: Universal antiretroviral therapy (ART) with annual population testing and a multidisease, patient-centered strategy could reduce new human immunodeficiency virus (HIV) infections and improve community health. METHODS: We randomly assigned 32 rural communities in Uganda and Kenya to baseline HIV and multidisease testing and national guideline-restricted ART (control group) or to baseline testing plus annual testing, eligibility for universal ART, and patient-centered care (intervention group). The primary end point was the cumulative incidence of HIV infection at 3 years. Secondary end points included viral suppression, death, tuberculosis, hypertension control, and the change in the annual incidence of HIV infection (which was evaluated in the intervention group only). RESULTS: A total of 150,395 persons were included in the analyses. Population-level viral suppression among 15,399 HIV-infected persons was 42% at baseline and was higher in the intervention group than in the control group at 3 years (79% vs. 68%; relative prevalence, 1.15; 95% confidence interval [CI], 1.11 to 1.20). The annual incidence of HIV infection in the intervention group decreased by 32% over 3 years (from 0.43 to 0.31 cases per 100 person-years; relative rate, 0.68; 95% CI, 0.56 to 0.84). However, the 3-year cumulative incidence (704 incident HIV infections) did not differ significantly between the intervention group and the control group (0.77% and 0.81%, respectively; relative risk, 0.95; 95% CI, 0.77 to 1.17). Among HIV-infected persons, the risk of death by year 3 was 3% in the intervention group and 4% in the control group (0.99 vs. 1.29 deaths per 100 person-years; relative risk, 0.77; 95% CI, 0.64 to 0.93). The risk of HIV-associated tuberculosis or death by year 3 among HIV-infected persons was 4% in the intervention group and 5% in the control group (1.19 vs. 1.50 events per 100 person-years; relative risk, 0.79; 95% CI, 0.67 to 0.94). At 3 years, 47% of adults with hypertension in the intervention group and 37% in the control group had hypertension control (relative prevalence, 1.26; 95% CI, 1.15 to 1.39). CONCLUSIONS: Universal HIV treatment did not result in a significantly lower incidence of HIV infection than standard care, probably owing to the availability of comprehensive baseline HIV testing and the rapid expansion of ART eligibility in the control group. (Funded by the National Institutes of Health and others; SEARCH ClinicalTrials.gov number, NCT01864603.)

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery