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The choroid plexus (CP) is an important structure for the brain. Besides its major role in the production of cerebrospinal fluid (CSF), it conveys signals originating from the brain, and from the circulatory system, shaping brain function in health and in pathology. Previous studies in rodents have revealed altered transcriptome both during aging and in various diseases of the central nervous system, including Alzheimer's disease. In the present study, a high-throughput sequencing of the CP transcriptome was performed in postmortem samples of clinically healthy individuals aged 50's through 80's. The data shows an age-related profile, with the main changes occurring in the transition from the 50's to the 60's, stabilizing thereafter. Specifically, neuronal and membrane functions distinguish the transcriptome between the 50's and the 60's, while neuronal and axon development and extracellular structure organization differentiate the 50's from the 70's. These findings suggest that changes in the CP transcriptome occur early in the aging process. Future studies will unravel whether these relate with processes occurring in late- onset brain diseases.

Obsessive-compulsive disorder (OCD) is a psychiatric disorder characterized by obsessions and/or compulsions. Different striatal subregions belonging to the cortico-striato-thalamic circuitry (CSTC) play an important role in the pathophysiology of OCD. The transcriptomes of 3 separate striatal areas (putamen (PT), caudate nucleus (CN) and accumbens nucleus (NAC)) from postmortem brain tissue were compared between 6 OCD and 8 control cases. In addition to network connectivity deregulation, different biological processes are specific to each striatum region according to the tripartite model of the striatum and contribute in various ways to OCD pathophysiology. Specifically, regulation of neurotransmitter levels and presynaptic processes involved in chemical synaptic transmission were shared between NAC and PT. The Gene Ontology terms cellular response to chemical stimulus, response to external stimulus, response to organic substance, regulation of synaptic plasticity, and modulation of synaptic transmission were shared between CN and PT. Most genes harboring common and/or rare variants previously associated with OCD that were differentially expressed or part of a least preserved coexpression module in our study also suggest striatum subregion specificity. At the transcriptional level, our study supports differences in the 3 circuit CSTC model associated with OCD.

In this paper, we propose a Bayesian approach to estimate the curve of a function f (·) that models the solar power generated at k moments per day for n days and to forecast the curve for the (n + 1) th day by using the history of recorded values. We assume that f (·) is an unknown function and adopt a Bayesian model with a Gaussian-process prior on the vector of values f (t) = f (1) , ... ,   f (k) . An advantage of this approach is that we may estimate the curves of f (·) and f n + 1 (·) as "smooth functions" obtained by interpolating between the points generated from a k-variate normal distribution with appropriate mean vector and covariance matrix. Since the joint posterior distribution for the parameters of interest does not have a known mathematical form, we describe how to implement a Gibbs sampling algorithm to obtain estimates for the parameters. The good performance of the proposed approach is illustrated using two simulation studies and an application to a real dataset. As performance measures, we calculate the absolute percentage error, the mean absolute percentage error (MAPE), and the root-mean-square error (RMSE). In all simulated cases and in the application to real-world data, the MAPE and RMSE values were all near 0, indicating the very good performance of the proposed approach. [ABSTRACT FROM AUTHOR]

The Non-Informative Nuisance Parameter Principle concerns the problem of how inferences about a parameter of interest should be made in the presence of nuisance parameters. The principle is examined in the context of the hypothesis testing problem. We prove that the mixed test obeys the principle for discrete sample spaces. We also show how adherence of the mixed test to the principle can make performance of the test much easier. These findings are illustrated with new solutions to well-known problems of testing hypotheses for count data. [ABSTRACT FROM AUTHOR]

The full Bayesian significance test (FBST) for precise hypotheses is a Bayesian alternative to the traditional significance tests based on p-values. The FBST is characterized by the e-value as an evidence index in favor of the null hypothesis (H). An important practical issue for the implementation of the FBST is to establish how small the evidence against H must be in order to decide for its rejection. In this work, we present a method to find a cutoff value for the e-value in the FBST by minimizing the linear combination of the averaged type-I and type-II error probabilities for a given sample size and also for a given dimensionality of the parameter space. Furthermore, we compare our methodology with the results obtained from the test with adaptive significance level, which presents the capital-P P-value as a decision-making evidence measure. For this purpose, the scenario of linear regression models with unknown variance under the Bayesian approach is considered. [ABSTRACT FROM AUTHOR]

Abstract Background The evaluation of associations between genotypes and diseases in a case-control framework plays an important role in genetic epidemiology. This paper focuses on the evaluation of the homogeneity of both genotypic and allelic frequencies. The traditional test that is used to check allelic homogeneity is known to be valid only under Hardy-Weinberg equilibrium, a property that may not hold in practice. Results We first describe the flaws of the traditional (chi-squared) tests for both allelic and genotypic homogeneity. Besides the known problem of the allelic procedure, we show that whenever these tests are used, an incoherence may arise: sometimes the genotypic homogeneity hypothesis is not rejected, but the allelic hypothesis is. As we argue, this is logically impossible. Some methods that were recently proposed implicitly rely on the idea that this does not happen. In an attempt to correct this incoherence, we describe an alternative frequentist approach that is appropriate even when Hardy-Weinberg equilibrium does not hold. It is then shown that the problem remains and is intrinsic of frequentist procedures. Finally, we introduce the Full Bayesian Significance Test to test both hypotheses and prove that the incoherence cannot happen with these new tests. To illustrate this, all five tests are applied to real and simulated datasets. Using the celebrated power analysis, we show that the Bayesian method is comparable to the frequentist one and has the advantage of being coherent. Conclusions Contrary to more traditional approaches, the Full Bayesian Significance Test for association studies provides a simple, coherent and powerful tool for detecting associations.