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Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic datasets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than threefold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed the best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.

Introduction Around 70% to 80% of the 19 million visually disabled children in the world are due to a preventable or curable disease, if detected early enough. Vision screening in childhood is an evidence-based and cost-effective way to detect visual disorders. However, current screening programmes face several limitations: training required to perform them efficiently, lack of accurate screening tools and poor collaboration from young children. Some of these limitations can be overcome by new digital tools. Implementing a system based on artificial intelligence systems avoid the challenge of interpreting visual outcomes. The objective of the TrackAI Project is to develop a system to identify children with visual disorders. The system will have two main components: a novel visual test implemented in a digital device, DIVE (Device for an Integral Visual Examination); and artificial intelligence algorithms that will run on a smartphone to analyse automatically the visual data gathered by DIVE. Methods and analysis This is a multicentre study, with at least five centres located in five geographically diverse study sites participating in the recruitment, covering Europe, USA and Asia. The study will include children aged between 6 months and 14 years, both with normal or abnormal visual development. The project will be divided in two consecutive phases: design and training of an artificial intelligence (AI) algorithm to identify visual problems, and system development and validation. The study protocol will consist of a comprehensive ophthalmological examination, performed by an experienced paediatric ophthalmologist, and an exam of the visual function using a DIVE. For the first part of the study, diagnostic labels will be given to each DIVE exam to train the neural network. For the validation, diagnosis provided by ophthalmologists will be compared with AI system outcomes. Ethics and dissemination The study will be conducted in accordance with the principles of Good Clinical Practice. This protocol was approved by the Clinical Research Ethics Committee of Aragón, CEICA, on January 2019 (Code PI18/346). [ABSTRACT FROM AUTHOR]

Pórtico: la importancia del método (algunas lecciones aprendidas y mucho trabajo pendiente).

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Objective: to determine the contents that must be included in the usual counseling to improve the adherence to antiretroviral therapy (ART) of HIV patients, according to their different levels of alcohol consumption, and to determine the validity of the Counseling Guide in improving the adherence to ART in patients who consume alcohol using Implementation Science. Method: this is an observational study with formative and validation phases. The formative phase defined the content, approach and structure of the counseling. Validation included focus groups with patients and nurses, trial process by an expert and a pilot test. The criteria evaluated based on Implementation Science were: intervention source, evidence strength and quality, relative advantage, and complexity. The following criteria were also evaluated: usefulness, practicality, acceptability, sustainability, effectiveness; content consistency and congruence; procedural compliance and difficulties, and time spent in counseling. Results: the strength of evidence of the counseling is High-IIA, with strong level of recommendation and presenting usefulness, practicality, acceptability, sustainability and effectiveness. Eight in 11 experts argued that the Guide is clear, consistent and congruent. Initial counseling takes around 24 minutes; and follow-up counseling, 21. The instruments of the Guide present reliability levels between good and high (0.65 ≥ alpha ≤ 0.92). Conclusion: the Counseling Guide is valid to improve the adherence to antiretroviral therapy in patients who consume alcohol.