Purple phototrophic bacteria (PPB) community, enriched from municipal wastewater, was characterized to assess their growth, tolerance, composition and potential for resource recovery from NH4+-rich medium. Batch experiments were conducted in tissue culture flasks and glass bottles under anaerobic conditions with infra-red lights. PPBs showed remarkable tolerance to high concentrations of NH4+-N and acetate. Below 1.5 g/L, growth was unaffected by NH4+-N with optical density at 590 nm (OD590) reaching 2.6-2.9, while they could tolerate 4.5 g/L NH4+-N. Similarly, PPB growth was unaffected at acetate concentrations below 4 g/L and they could tolerate >20 g/L acetate. https://www.selleckchem.com/products/eribulin-mesylate-e7389.html Taxonomic characterization showed that the community comprised of 37-52% PPBs (with 15-20% proteins) under different conditions, with Rhodobacter sp. over Rhodopseudomonas sp. dominating at higher NH4+-N concentrations. PPBs showed growth and removal rates in anaerobic digestate and accumulated 26% proteins. These results indicated the potential of PPBs in resource recovery from NH4+-rich wastewater. Ventilation-induced tumour motion remains a challenge for the accuracy of proton therapy treatments in lung patients. We investigated the feasibility of using a 4D virtual CT (4D-vCT) approach based on deformable image registration (DIR) and motion-aware 4D CBCT reconstruction (MA-ROOSTER) to enable accurate daily proton dose calculation using a gantry-mounted CBCT scanner tailored to proton therapy. Ventilation correlated data of 10 breathing phases were acquired from a porcine ex-vivo functional lung phantom using CT and CBCT. 4D-vCTs were generated by (1) DIR of the mid-position 4D-CT to the mid-position 4D-CBCT (reconstructed with the MA-ROOSTER) using a diffeomorphic Morphons algorithm and (2) subsequent propagation of the obtained mid-position vCT to the individual 4D-CBCT phases. Proton therapy treatment planning was performed to evaluate dose calculation accuracy of the 4D-vCTs. A robust treatment plan delivering a nominal dose of 60Gy was generated on the average intensity image of the 4D-CT for was 2.3% and D for the phases varied between -5.4% and 5.8%. The gamma pass-rates with 5Gy, 20Gy and 30Gy thresholds for the accumulated doses were 96.7%, 99.6% and 99.9%, respectively. Phase-by-phase comparison yielded pass-rates between 86% and 97%, 88% and 98%, and 94% and 100%. Feasibility of the suggested 4D-vCT workflow using proton therapy specific imaging equipment was shown. Results indicate the potential of the method to be applied for daily 4D proton dose estimation. Feasibility of the suggested 4D-vCT workflow using proton therapy specific imaging equipment was shown. Results indicate the potential of the method to be applied for daily 4D proton dose estimation. To describe the prevalence of suicidal behaviors (ideation, planning, and attempt) and their associated factors in young adolescents in low- and middle-income countries (LMICs). We used the latest data from the Global School-Based Health Survey (GSHS) for adolescents aged 12-15 years during 2009-2015. The weighted prevalence and 95% confidential intervals (CIs) of suicidal behaviors were calculated using a random-effects model. The factors associated with suicidal behaviors were examined using logistic regression analysis. Data from 130,488 adolescents (48.13% boys) in 46 LMICs were included in the study. Across all countries, the pooled 12-month prevalence of suicidal ideation, planning, and attempt were 14.5%, 14.6%, and 12.7%, respectively. The highest prevalence of suicidal ideation, planning, and attempt were all in Africa (16.7%, 19.3% and 17.0%), and the lowest prevalence were all in South-East Asia (8.2%, 10.5% and 7.4%). The overall prevalence of three suicidal behaviors were higher in girls (all P < 0.001). Suicidal ideation and planning were more common in the 14-15 age group than 12-13 age group (both P < 0.001). The factors associated with suicidal behaviors were being female, older age, loneliness, anxiety, a lack of close friends, and having family supportive (all P < 0.001). The GSHS data were obtained from a self-report questionnaire and the participants included in the GSHS were adolescents in school. The prevalence of suicidal behaviors remains high among young adolescents in LMICs, especially in Africa. These countries should be intervention priorities. The prevalence of suicidal behaviors remains high among young adolescents in LMICs, especially in Africa. These countries should be intervention priorities. Depression is a prevalent and disabling condition in youth. Treatment efficacy has been demonstrated for several therapeutic modalities. Acceptability of treatments is also important to explore and was addressed by investigating treatment dropout using meta-analyses. A systematic search was conducted using MEDLINE, CINAHL and PsycARTICLES databases. Peer-reviewed randomised controlled trials investigating psychotherapy treatment of depression in children and youth (aged up to and including 18 years) were included. Proportion meta-analyses were used to calculate estimated dropout rates; odds ratios assessed whether there was greater dropout from intervention or control arms and meta-regressions investigated for associations between dropout, study and treatment characteristics. Thirty-seven studies were included (N=4343). Overall estimate of dropout from active interventions was 14.6% (95% CI 12.0-17.4%). Dropout was equally likely from intervention and control conditions, aside from family/dyadic interveay inform treatment choice and modification of treatments. Mood disorders are characterized by heterogeneity in severity, symptoms and treatment response. The possibility of selecting the correct therapy on the basis of patient-specific biomarker may be a considerable step towards personalized psychiatry. Machine learning methods are gaining increasing popularity in the medical field. Once trained, the possibility to consider single patients in the analyses instead of whole groups makes them particularly appealing to investigate treatment response. Deep learning, a branch of machine learning, lately gained attention, due to its effectiveness in dealing with large neuroimaging data and to integrate them with clinical, molecular or -omics biomarkers. In this mini-review, we summarize studies that use deep learning methods to predict response to treatment in depression. We performed a bibliographic search on PUBMED, Google Scholar and Web of Science using the terms "psychiatry", "mood disorder", "depression", "treatment", "deep learning", "neural networks". Only studies considering patients' datasets are considered.