Bayesian Inference for Mixed Model-Based Genome-Wide Analysis of Expression Quantitative Trait Loci by Gibbs Sampling

The importance of expression quantitative trait locus (eQTL) has been emphasized in understanding the genetic basis of cellular activities and complex phenotypes. Mixed models can be employed to effectively identify eQTLs by explaining polygenic effects. In these mixed models, the polygenic effects are considered as random variables, and their variability is explained by the polygenic variance component. The polygenic and residual variance components are first estimated, and then eQTL effects are estimated depending on the variance component estimates within the frequentist mixed model framework. The Bayesian approach to the mixed model-based genome-wide eQTL analysis can also be applied to estimate the parameters that exhibit various benefits. Bayesian inferences on unknown parameters are based on their marginal posterior distributions, and the marginalization of the joint posterior distribution is a challenging task. This problem can be solved by employing a numerical algorithm of integrals called Gibbs sampling as a Markov chain Monte Carlo. This article reviews the mixed model-based Bayesian eQTL analysis by Gibbs sampling. Theoretical and practical issues of Bayesian inference are discussed using a concise description of Bayesian modeling and the corresponding Gibbs sampling. The strengths of Bayesian inference are also discussed. Posterior probability distribution in the Bayesian inference reflects uncertainty in unknown parameters. This factor is useful in the context of eQTL analysis where a sample size is too small to apply the frequentist approach. Bayesian inference based on the posterior that reflects prior knowledge, will be increasingly preferred with the accumulation of eQTL data. Extensive use of the mixed model-based Bayesian eQTL analysis will accelerate understanding of eQTLs exhibiting various regulatory functions

CSGM Designer: a platform for designing cross-species intron-spanning genic markers linked with genome information of legumes.

BackgroundGenetic markers are tools that can facilitate molecular breeding, even in species lacking genomic resources. An important class of genetic markers is those based on orthologous genes, because they can guide hypotheses about conserved gene function, a situation that is well documented for a number of agronomic traits. For under-studied species a key bottleneck in gene-based marker development is the need to develop molecular tools (e.g., oligonucleotide primers) that reliably access genes with orthology to the genomes of well-characterized reference species.ResultsHere we report an efficient platform for the design of cross-species gene-derived markers in legumes. The automated platform, named CSGM Designer (URL: http://tgil.donga.ac.kr/CSGMdesigner), facilitates rapid and systematic design of cross-species genic markers. The underlying database is composed of genome data from five legume species whose genomes are substantially characterized. Use of CSGM is enhanced by graphical displays of query results, which we describe as "circular viewer" and "search-within-results" functions. CSGM provides a virtual PCR representation (eHT-PCR) that predicts the specificity of each primer pair simultaneously in multiple genomes. CSGM Designer output was experimentally validated for the amplification of orthologous genes using 16 genotypes representing 12 crop and model legume species, distributed among the galegoid and phaseoloid clades. Successful cross-species amplification was obtained for 85.3% of PCR primer combinations.ConclusionCSGM Designer spans the divide between well-characterized crop and model legume species and their less well-characterized relatives. The outcome is PCR primers that target highly conserved genes for polymorphism discovery, enabling functional inferences and ultimately facilitating trait-associated molecular breeding

Seeing Through the Conversation: Audio-Visual Speech Separation based on Diffusion Model

The objective of this work is to extract target speaker's voice from a mixture of voices using visual cues. Existing works on audio-visual speech separation have demonstrated their performance with promising intelligibility, but maintaining naturalness remains a challenge. To address this issue, we propose AVDiffuSS, an audio-visual speech separation model based on a diffusion mechanism known for its capability in generating natural samples. For an effective fusion of the two modalities for diffusion, we also propose a cross-attention-based feature fusion mechanism. This mechanism is specifically tailored for the speech domain to integrate the phonetic information from audio-visual correspondence in speech generation. In this way, the fusion process maintains the high temporal resolution of the features, without excessive computational requirements. We demonstrate that the proposed framework achieves state-of-the-art results on two benchmarks, including VoxCeleb2 and LRS3, producing speech with notably better naturalness.Comment: Project page with demo: https://mm.kaist.ac.kr/projects/avdiffuss

A simple and efficient numerical method for the Allen–Cahn equation on effective symmetric triangular meshes

In this paper, we propose a novel, simple, efficient, and explicit numerical method for the Allen–Cahn (AC) equation on effective symmetric triangular meshes. First, we compute the net vector of all vectors starting from each node point to its one-ring neighbor vertices and virtually adjust the neighbor vertices so that the net vector is zero. Then, we define the values at the virtually adjusted nodes using linear and quadratic interpolations. Finally, we define a discrete Laplace operator on triangular meshes. We perform several computational experiments to demonstrate the performance of the proposed numerical method for the Laplace operator, the diffusion equation, and the AC equation on triangular meshes

CSAI analysis of non-crimp fabric cross-ply laminate manufactured through wet compression molding process

The main purpose of the present work is to demonstrate mechanical performance of a wet-compression-molding (WCM) composite product through conventional compressive-strength-after-impact (CSAI) analysis. Biaxial non-crimp fabric (NCF) is utilized to manufacture laminated composite panels. Specimens are cut from the panels and tested to characterize fundamental mechanical properties of the NCF composite. The volume fractions of fibers and voids are also measured to evaluate the quality of the WCM product. Impact tests are carried out to examine impact resistance of the composite structure. Numerous impact characteristics at various energy levels are quantitatively measured. Internal failure patterns and damage extent are revealed via X-ray CT. Compression tests on the impacted plates are followed to evaluate structural integrity and damage tolerance (SIDT). 3D DIC technique is employed and distinct buckling responses dependent on impact energy levels are successfully visualized. Experimental results are showing a promising potential of the WCM process as one of the alternatives to the conventional autoclave-based fabrication method

Risks of complicated acute appendicitis in patients with psychiatric disorders

Background Acute appendicitis often presents with vague abdominal pain, which fosters diagnostic challenges to clinicians regarding early detection and proper intervention. This is even more problematic with individuals with severe psychiatric disorders who have reduced sensitivity to pain due to long-term or excessive medication use or disturbed bodily sensation perceptions. This study aimed to determine whether psychiatric disorder, psychotropic prescription, and treatment compliance increase the risks of complicated acute appendicitis. Methods The diagnosis records of acute appendicitis from four university hospitals in Korea were investigated from 2002 to 2020. A total of 47,500 acute appendicitis-affected participants were divided into groups with complicated and uncomplicated appendicitis to determine whether any of the groups had more cases of psychiatric disorder diagnoses. Further, the ratio of complicated compared to uncomplicated appendicitis in the mentally ill group was calculated regarding psychotropic dose, prescription duration, and treatment compliance. Results After adjusting for age and sex, presence of psychotic disorder (odds ratio [OR]: 1.951; 95% confidence interval [CI]: 1.218–3.125), and bipolar disorder (OR: 2.323; 95% CI: 1.194–4.520) was associated with a higher risk of having complicated appendicitis compared with absence of psychiatric disorders. Patients who are taking high-daily-dose antipsychotics, regardless of prescription duration, show high complicated appendicitis risks; High-dose antipsychotics for < 1 year (OR: 1.896, 95% CI: 1.077–3.338), high-dose antipsychotics for 1–5 years (OR: 1.930, 95% CI: 1.144–3.256). Poor psychiatric outpatient compliance was associated with a high risk of complicated appendicitis (OR: 1.664, 95% CI: 1.014–2.732). Conclusions This study revealed a close relationship in the possibility of complicated appendicitis in patients with severe psychiatric disorders, including psychotic and bipolar disorders. The effect on complicated appendicitis was more remarkable by the psychiatric disease entity itself than by psychotropic prescription patterns. Good treatment compliance and regular visit may reduce the morbidity of complicated appendicitis in patients with psychiatric disorders.This work was supported by the Technology Innovation Program (or Industrial Strategic Technology Development Program) (20004927, Upgrade of CDM based Distributed Biohealth Data Platform and Development of Verification Technology) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea), and the Bio Industrial Strategic Technology Development Program (20003883) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea), Bio & Medical Technology Development Program of the National Research Foundation of Korea funded by the Ministry of Health and Welfare, Ministry of Science and ICT, Ministry of Trade Industry and Energy (MOTIE, Korea) Disease Control and Prevention Agency (The National Project of Bio Big Data) (NRF‑2020M3E5D7085175), and Bio & Medical Technology Development Program of the National Research Foundation funded by the Ministry of Science & ICT (No. 2021M3A9E408078412)

Identification and functional modelling of plausibly causative cis-regulatory variants in a highly-selected cohort with X-linked intellectual disability.

Identifying causative variants in cis-regulatory elements (CRE) in neurodevelopmental disorders has proven challenging. We have used in vivo functional analyses to categorize rigorously filtered CRE variants in a clinical cohort that is plausibly enriched for causative CRE mutations: 48 unrelated males with a family history consistent with X-linked intellectual disability (XLID) in whom no detectable cause could be identified in the coding regions of the X chromosome (chrX). Targeted sequencing of all chrX CRE identified six rare variants in five affected individuals that altered conserved bases in CRE targeting known XLID genes and segregated appropriately in families. Two of these variants, FMR1CRE and TENM1CRE, showed consistent site- and stage-specific differences of enhancer function in the developing zebrafish brain using dual-color fluorescent reporter assay. Mouse models were created for both variants. In male mice Fmr1CRE induced alterations in neurodevelopmental Fmr1 expression, olfactory behavior and neurophysiological indicators of FMRP function. The absence of another likely causative variant on whole genome sequencing further supported FMR1CRE as the likely basis of the XLID in this family. Tenm1CRE mice showed no phenotypic anomalies. Following the release of gnomAD 2.1, reanalysis showed that TENM1CRE exceeded the maximum plausible population frequency of a XLID causative allele. Assigning causative status to any ultra-rare CRE variant remains problematic and requires disease-relevant in vivo functional data from multiple sources. The sequential and bespoke nature of such analyses renders them time-consuming and challenging to scale for routine clinical use