University of Texas at Arlington

About: University of Texas at Arlington is a education organization based out in Arlington, Texas, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 11758 authors who have published 28598 publications receiving 801626 citations. The organization is also known as: UT Arlington & University of Texas-Arlington.

Corporate Social Responsibility, Customer Satisfaction, and Market Value

Abstract: Although prior research has addressed the influence of corporate social responsibility (CSR) on perceived customer responses, it is not clear whether CSR affects market value of the firm. This study develops and tests a conceptual framework, which predicts that (1) customer satisfaction partially mediates the relationship between CSR and firm market value (i.e., Tobin's q and stock return), (2) corporate abilities (innovativeness capability and product quality) moderate the financial returns to CSR, and (3) these moderated relationships are mediated by customer satisfaction. Based on a large-scale secondary data set, the results show support for this framework. Notably, the authors find that in firms with low innovativeness capability, CSR actually reduces customer satisfaction levels and, through the lowered satisfaction, harms market value. The uncovered mediated and asymmetrically moderated results offer important implications for marketing theory and practice.

Beyond Relational Demography: Time and the Effects of Surface- and Deep-Level Diversity on Work Group Cohesion

Abstract: We examined the impact of surface-level (demographic) and deep-level (attitudinal) diversity on group social integration. As hypothesized, the length of time group members worked together weakened the effects of surface-level diversity and strengthened the effects of deep-level diversity as group members bad the opportunity to engage in meaningful interactions.

Long Noncoding RNA and Cancer: A New Paradigm.

Abstract: In addition to mutations or aberrant expression in the protein-coding genes, mutations and misregulation of noncoding RNAs, in particular long noncoding RNAs (lncRNA), appear to play major roles in cancer. Genome-wide association studies of tumor samples have identified a large number of lncRNAs associated with various types of cancer. Alterations in lncRNA expression and their mutations promote tumorigenesis and metastasis. LncRNAs may exhibit tumor-suppressive and -promoting (oncogenic) functions. Because of their genome-wide expression patterns in a variety of tissues and their tissue-specific expression characteristics, lncRNAs hold strong promise as novel biomarkers and therapeutic targets for cancer. In this article, we have reviewed the emerging functions and association of lncRNAs in different types of cancer and discussed their potential implications in cancer diagnosis and therapy. Cancer Res; 77(15); 3965-81. ©2017 AACR.

Efficient and Robust Feature Selection via Joint ℓ2,1-Norms Minimization

Abstract: Feature selection is an important component of many machine learning applications. Especially in many bioinformatics tasks, efficient and robust feature selection methods are desired to extract meaningful features and eliminate noisy ones. In this paper, we propose a new robust feature selection method with emphasizing joint l2,1-norm minimization on both loss function and regularization. The l2,1-norm based loss function is robust to outliers in data points and the l2,1-norm regularization selects features across all data points with joint sparsity. An efficient algorithm is introduced with proved convergence. Our regression based objective makes the feature selection process more efficient. Our method has been applied into both genomic and proteomic biomarkers discovery. Extensive empirical studies are performed on six data sets to demonstrate the performance of our feature selection method.

Genome sequencing and analysis of the model grass Brachypodium distachyon

Abstract: Three subfamilies of grasses, the Ehrhartoideae, Panicoideae and Pooideae, provide the bulk of human nutrition and are poised to become major sources of renewable energy. Here we describe the genome sequence of the wild grass Brachypodium distachyon (Brachypodium), which is, to our knowledge, the first member of the Pooideae subfamily to be sequenced. Comparison of the Brachypodium, rice and sorghum genomes shows a precise history of genome evolution across a broad diversity of the grasses, and establishes a template for analysis of the large genomes of economically important pooid grasses such as wheat. The high-quality genome sequence, coupled with ease of cultivation and transformation, small size and rapid life cycle, will help Brachypodium reach its potential as an important model system for developing new energy and food crops.