Adaptive and powerful microbiome multivariate association analysis via feature selection

ABSTRACT The important role of human microbiome is being increasingly recognized in health and disease conditions. Since microbiome data is typically high dimensional, one popular mode of statistical association analysis for microbiome data is to pool individual microbial features into a group, and then conduct group-based multivariate association analysis. A corresponding challenge within this approach is to achieve adequate power to detect an association signal between a group of microbial features and the outcome of interest across a wide range of scenarios. Recognizing some existing methods’ susceptibility to the adverse effects of noise accumulation, we introduce the Adaptive Microbiome Association Test (AMAT), a novel and powerful tool for multivariate microbiome association analysis, which unifies both blessings of feature selection in high-dimensional inference and robustness of adaptive statistical association testing. AMAT first alleviates the burden of noise accumulation via distance correlation learning, and then conducts a data-adaptive association test under the flexible generalized linear model framework. Extensive simulation studies and real data applications demonstrate that AMAT is highly robust and often more powerful than several existing methods, while preserving the correct type I error rate. A free implementation of AMAT in R computing environment is available at https://github.com/kzb193/AMAT.

The Early Development of Racial Bias in Infants and Childhood

Psychologists are interested in how humans process faces of their own-race and Other-races, and there are plenty of previous research on this topic. This paper will summarize previous paper about Other-race Effect (ORE), and how do ORE develop racial bias in children. Researchers used Implicit Association Test (IAT) to assess implicit racial bias and found that implicit racial bias are different in different cultures and counties.

COVID EPIDEMIC PERCEPTION THROUGH UKRAINIAN-TURKISH СONTEXT (SOCIO-PSYCHOLINGUISTIC APPROACH)

The article depicts the perception of the concept of «coronavirus» in the linguistic consciousness of native speakers of Ukrainian and Turkish concerning the impact of COVID-19 pandemic on students in higher education. Using the methods of free association test, and SPSS handling of data 20 associative fields to the stimuli specifying the concept of coronavirus: «Coronavirus, Covid-19, pandemic, social distance, lockdown quarantine, mask, tests, self-isolation, vaccine» are obtained. The data provides the clear picture with what the coronavirus concept associates in the mentality of Ukrainian and Turkish students. The negative stimuli linked with danger, uncertainty, disaster and illness prevail in both samples.

ТАКСОНОМІЯ АСОЦІАЦІЙ ПРОВІДНИХ КОНЦЕПТІВ РЕЛІГІЙНОГО ДИСКУРСУ

The abstract reveal the problem of the prevailing ideas of young people about the leading religious concepts. The aim of the research is to analyze students" verbal representations of religious discourse concepts. To define the leading concepts, we used a structural approach, which the classic components are: behavioral (prayer, sermon, sacraments), emotional-motivational (faith, sin) and cognitive (religion, church, priest). We used free WAT (word association test) for psycholinguistic analysis. The results of the cluster analysis showed that in the minds of young people religious discourse is represented through two main semantic categories, namely faith and the church - the priest.

MCKAT: a multi-dimensional copy number variant kernel association test

Background Copy number variants (CNVs) are the gain or loss of DNA segments in the genome. Studies have shown that CNVs are linked to various disorders, including autism, intellectual disability, and schizophrenia. Consequently, the interest in studying a possible association of CNVs to specific disease traits is growing. However, due to the specific multi-dimensional characteristics of the CNVs, methods for testing the association between CNVs and the disease-related traits are still underdeveloped. We propose a novel multi-dimensional CNV kernel association test (MCKAT) in this paper. We aim to find significant associations between CNVs and disease-related traits using kernel-based methods. Results We address the multi-dimensionality in CNV characteristics. We first design a single pair CNV kernel, which contains three sub-kernels to summarize the similarity between two CNVs considering all CNV characteristics. Then, aggregate single pair CNV kernel to the whole chromosome CNV kernel, which summarizes the similarity between CNVs in two or more chromosomes. Finally, the association between the CNVs and disease-related traits is evaluated by comparing the similarity in the trait with kernel-based similarity using a score test in a random effect model. We apply MCKAT on genome-wide CNV datasets to examine the association between CNVs and disease-related traits, which demonstrates the potential usefulness the proposed method has for the CNV association tests. We compare the performance of MCKAT with CKAT, a uni-dimensional kernel method. Based on the results, MCKAT indicates stronger evidence, smaller p-value, in detecting significant associations between CNVs and disease-related traits in both rare and common CNV datasets. Conclusion A multi-dimensional copy number variant kernel association test can detect statistically significant associated CNV regions with any disease-related trait. MCKAT can provide biologists with CNV hot spots at the cytogenetic band level that CNVs on them may have a significant association with disease-related traits. Using MCKAT, biologists can narrow their investigation from the whole genome, including many genes and CNVs, to more specific cytogenetic bands that MCKAT identifies. Furthermore, MCKAT can help biologists detect significantly associated CNVs with disease-related traits across a patient group instead of examining each subject’s CNVs case by case.

AgingPLUS: Examining the Malleability of Implicit Views of Aging

AgingPLUS also examines whether the intervention can change participants’ implicit VOA. To that end, participants completed a lexical decision-making task (LDMT) and the Brief Implicit Association Test (BIAT) at baseline and post-intervention. One-way ANCOVAs with baseline scores as covariate were used for these analyses. For LDMT, there was no significant difference between the groups regarding their post-intervention latencies for old-positive words, F(1,181) = 0.01, p = .60, old-negative words, F(1,181) = 0.43, p = .51, young-positive words, F(1,181) = 0.19, p = .67, and young-negative words, F(1,181) = 1.16, p = .28. For BIAT, both groups showed a slight preference for the young at baseline (mean d = 0.39), and post-intervention (mean d = 0.38). There was no significant difference between the groups regarding post-intervention d scores, F(1,181) = 0.002, p = .97. These preliminary findings suggest that in the current subsample, AgingPLUS did not significantly change participants’ implicit VOA.