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The focus of this work is to develop a Bayesian framework to combine information from multiple parts of the response distribution characterized with different quantiles. The goal is to obtain a synthesized estimate of the covariate effects on the response variable as well as to identify the more influential predictors. This framework naturally relates to the traditional quantile regression, which studies the relationship between the covariates and the conditional quantile of the response variable and serves as an attractive alternative to the more widely used mean regression methods. We achieve the objectives through constructing a Bayesian mixture model using quantile regressions as the mixture components.The first stage of the research involves the development of a parametric family of distributions to provide the mixture kernel for the Bayesian quantile mixture regression. We derive a new family of error distributions for model-based quantile regression called generalized asymmetric Laplace distribution, which is constructed through a structured mixture of normal distributions. The construction enables fixing specific percentiles of the distribution while, at the same time, allowing for varying mode, skewness and tail behavior. This family provides a practically important extension of the asymmetric Laplace distribution, which is the standard error distribution for parametric quantile regression. We develop a Bayesian formulation for the proposed quantile regression model, including conditional lasso regularized quantile regression based on a hierarchical Laplace prior for the regression coefficients, and a Tobit quantile regression model.Next, we develop the main framework to model the conditional distribution of the response with a weighted mixture of quantile regression components. We specify a common regression coefficient vector for all components to synthesize information from multiple parts of the response distribution, each modeled with one quantile regression component. The goal is to obtain a combined estimate of the predictive effect of each covariate. We consider the following two choices of kernel densities for the mixture model. When the probability of the quantile in each regression component is known, we model the components with the generalized asymmetric Laplace distribution, as its shape parameter introduces flexibility in shape and skewness to the kernel; else when the quantile probabilities are unknown, we use the asymmetric Laplace distribution as kernel density and view its skewness parameter, which is also the quantile probability of the component, as a random quantity and estimate it from the data. Under each kernel density, we formulate the hierarchical structure of the mixture weights and develop the approach to the posterior inference. We consider both parametric and nonparametric priors for the framework, and explore inferences for the number of components to be included. We demonstrate the performance of the method in identification of influential variables with simulation examples and illustrate the posterior predictive inferences in a realty price data from the Boston metropolitan area.Finally, we extend the framework to apply the methods to specific problems in survival analysis and epidemiology. Both applications involve analyses of two cohorts, which oftentimes exhibit differing responses given the same predictor input. We adapt the proposed framework to model the survival data with right-censoring. For applications in epidemiology, we study the ordering properties of the mixture kernels and incorporate stochastic ordering in the two-cohort mixture framework through structured priors, which conforms with the assumption in certain circumstances of receiver operating characteristic curve estimation. With the adapted models, we carry out cohort-specific identification of influential variables and gain insights into the contribution in estimation and prediction from different parts of the response distribution, which are depicted by the corresponding quantile regression components. We illustrate the applications with a time-to-event data set on length of stay at nursing home and two disease diagnosis data sets, one on adolescent depression and the other on cattle epidemics.

Supplemental Material, sj-pdf-1-jgp-10.1177_08919887221078556 for Prevalence of depressive and anxiety symptoms among Chinese older adults during the COVID-19 pandemic: A systematic review and meta-analysis by Yifei Yan, Xiayu Du, Lizu Lai, Zhihong Ren and Hua Li in Journal of Geriatric Psychiatry and Neurology

In the fields of game design and interactive storytelling, it is of value to consider game mechanics, narrative design, and how they connect and influence each other. Dubbelman’s Narrative game mechanics theory (2016) started an argumentation that certain game mechanics can serve greater narrative power. This research sees a lack of practical considerations and game development guidelines from Dubbelman’s work and tries to conduct a theoretical framework on useful narrative and game design theories and practical considerations focusing on a subset of game mechanics, numerical narrative game mechanics. The framework covered an in-depth explanation of the process from mechanics to narratives, the role of the player during the narration process and related practices one can utilize, an analytical perspective of numerical elements and numerical mechanics, and ways to improve narratability and representation of contexts. Three case studies of existing games are also conducted to present the use of the framework and improve its applicability.

How can the UK government improve its education system? Research by Yifei Yan into the Chinese and Indian education systems finds that carefully managed government intervention in partnership with local stakeholders often yields the best teaching outcomes.

Effective governance that ensures proper management and utilization of various resources is increasingly emphasized as a key to achieving quality and inclusiveness in the basic education sector. This chapter aims to contribute to the understanding of education governance in urban settings of two of the largest – yet relatively under-explored – public education systems in the world, namely China and India, with a particular focus on their arrangement of teachers’ in-service training and career advancement as supportive accountability mechanisms. Hence in most developing countries, including India and China, government schools remain the predominant provider of basic education. Within the emerging literature on governance from bodies such as the World Health Organization, the World Bank, UNDP, the notion of accountability is increasingly highlighted as a key to achieving good governance. To further extend the understanding and theorization of accountability, one part of the literature has come up with several typologies of accountability.

MicroRNAs (miRNAs) are ribonucleic acids (RNAs) of ∼21 nucleotides that interfere with the translation of messenger RNAs (mRNAs) and play significant roles in development and diseases. In bilaterian animals, the specificity of miRNA targeting is determined by sequence complementarity involving the seed. However, the role of the remaining nucleotides (non-seed) is only vaguely defined, impacting negatively on our ability to efficiently use miRNAs exogenously to control gene expression. Here, using reporter assays, we deciphered the role of the base pairs formed between the non-seed region and target mRNA. We used molecular modeling to reveal that this mechanism corresponds to the formation of base pairs mediated by ordered motions of the miRNA-induced silencing complex. Subsequently, we developed an algorithm based on this distinctive recognition to predict from sequence the levels of mRNA downregulation with high accuracy (r2 > 0.5, P-value < 10-12). Overall, our discovery improves the design of miRNA-guide sequences used to simultaneously downregulate the expression of multiple predetermined target genes.