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ng and promoting well-defined research tracks for both clinicians and academics, investing in grant management, and raising the profile of research within their institutions.

This study highlights the importance of developing research infrastructure alongside capacity-building efforts. International funders should invest in grant management at African universities which would place them at the centre of research initiatives. African universities should prioritise the creation of a research culture by developing and promoting well-defined research tracks for both clinicians and academics, investing in grant management, and raising the profile of research within their institutions.

Peer-led support models have gained increasing popularity in suicide prevention. While previous reviews show positive effects of peer-led support for people with mental health problems and those bereaved by suicide, little is known about the types of lived experience peer support programs in suicide prevention and whether these are effective in improving the health and wellbeing of people at risk of suicide. The aim of this paper is to provide an overview of peer support programs that aim to reduce suicidality and are led by people with lived experience of suicide.

We conducted a systematic scoping review, involving a search of three academic (Medline, PsycINFO, Embase) and selected grey literature databases (Google Scholar, WHO Clinical Trials Registry) for publications between 2000 and 2019. We also contacted suicide prevention experts and relevant internet sites to identify peer support programs that exist but have not been evaluated. The screening of records followed a systematic two-stage process in r comprehensive systematic reviews and meta-analysis in future.

A common mental disorder is characterized by anxiety, depression, and unexplained somatic symptoms that usually encountered in community and primary care settings. Both short and long term bio psychosocial disabilities are inevitable if common mental disorder is not treated. Despite its impact, the prevalence of common mental disorder in the Illu Ababore zone is not well known. Therefore, this study aimed to assess the prevalence and associated factors of common mental disorder among Ilu Ababore zone residents, Southwest Ethiopia.

A community based cross-sectional study was conducted from July 1 to August 30, 2018. A multi-stage sampling technique was applied to recruit participants. Self-Reporting Questionnaire (SRQ-20) was used to assess the presence of common mental disorder. The data were entered into Epidata version 3.1 and analyzed by using SPSS version 23 software. Bivariate and multivariate binary logistic regressions were computed to identify the associated factors. Statistical significance was cs and residing in the rural area are recommended. Strategies that focus on the proper treatment of chronic physical illness can be also helpful to minimize the occurrence of common mental disorder.

The current study showed that the proportion of the common mental disorder was high. Females showed a higher prevalence of the common mental disorder. Having a chronic physical illness, resides in the rural areas and history of lifetime alcohol use were also significantly associated with CMD. Psychological and social interventions with greater emphasis on females who have low educational status and residing in the rural area are recommended. Strategies that focus on the proper treatment of chronic physical illness can be also helpful to minimize the occurrence of common mental disorder.

Optimizing the somatic embryogenesis protocol can be considered as the first and foremost step in successful gene transformation studies. CBR-470-1 solubility dmso However, it is usually difficult to achieve an optimized embryogenesis protocol due to the cost and time-consuming as well as the complexity of this process. Therefore, it is necessary to use a novel computational approach, such as machine learning algorithms for this aim. In the present study, two machine learning algorithms, including Multilayer Perceptron (MLP) as an artificial neural network (ANN) and support vector regression (SVR), were employed to model somatic embryogenesis of chrysanthemum, as a case study, and compare their prediction accuracy.

The results showed that SVR (R

 > 0.92) had better performance accuracy than MLP (R

 > 0.82). Moreover, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was also applied for the optimization of the somatic embryogenesis and the results showed that the highest embryogenesis rate (99.09%) and the maximum number of somatic embryos per explant (56.24) can be obtained from a medium containing 9.10μM 2,4-dichlorophenoxyacetic acid (2,4-D), 4.70μM kinetin (KIN), and 18.73μM sodium nitroprusside (SNP). According to our results, SVR-NSGA-II was able to optimize the chrysanthemum's somatic embryogenesis accurately.

SVR-NSGA-II can be employed as a reliable and applicable computational methodology in future plant tissue culture studies.

SVR-NSGA-II can be employed as a reliable and applicable computational methodology in future plant tissue culture studies.

Sowing time is commonly used as the temporal reference for

(Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. However, individual seeds have different development trajectories even under uniform environmental conditions. This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method. It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. The timeline of each measurement series is shifted to a reference time. The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number.

The DAPD method improved the accuracy of phenotyping measurements by decreasing the statistical dispersion of quantitative traits across a time-series.