The effect of Big 5 personality traits in managers and Theory X/Y leadership on employee outcomes

This item is only available electronically.Research shows that managers have a large impact on employees, with studies showing that up to 75% of people report leaving their job because of their manager or something their manager could have changed (Robison, 2008). Increased employee turnover results in negative organisational outcomes, so it is critical to reduce this as much as possible and invest in further research to help prevent it. This study examined the relationship between perceived personality traits, managerial style using McGregor’s (1960) Theory X/Y and managerial likeability. An online questionnaire was administered to participants (N=102) which consisted of the NEO-FFI, and four different measures for Theory X/Y, intrinsic/extrinsic motivation, productivity propensity, and turnover intentions. The results showed that that in terms of personality, 'disliked' managers were described as having a Theory X orientation, higher scores on neuroticism, and lower scores on openness, agreeableness and conscientiousness. Favourably perceived managers were described as having a Theory Y orientation, with higher scores on extraversion. Results also showed that employees who liked their manager were more likely to rate their intrinsic/extrinsic motivation, productivity, job satisfaction, and intention to remain at the workplace more highly compared with those who didn’t like their manager. These results highlight the impact of managerial style and managerial personality on employee outcomes and attitudes towards their managers and their work. The implications of these results are discussed along with ideas for future research.Thesis (B.PsychSc(Hons)) -- University of Adelaide, School of Psychology, 201

Measuring Five Dimensions of Religiosity Across Adolescence

This paper theorizes and tests a latent variable model of adolescent religiosity in which five dimensions of religiosity are interrelated: religious beliefs, religious exclusivity, external religiosity, private practice, and religious salience. Research often theorizes overlapping and independent influences of single items or dimensions of religiosity on outcomes such as adolescent sexual behavior, but rarely operationalizes the dimensions in a measurement model accounting for their associations with each other and across time. We use longitudinal structural equation modeling (SEM) with latent variables to analyze data from two waves of the National Study of Youth and Religion. We test our hypothesized measurement model as compared to four alternate measurement models and find that our proposed model maintains superior fit. We then discuss the associations between the five dimensions of religiosity we measure and how these change over time. Our findings suggest how future research might better operationalize multiple dimensions of religiosity in studies of the influence of religion in adolescence

Pion-Xi correlations in Au-Au collisions at STAR

We present pion-Xi correlation analysis in Au-Au collisions at sqrt(s_NN)= 200 GeV and sqrt(s_NN) = 62.4 GeV, performed using the STAR detector at RHIC. A Xi*(1530) resonance signal is observed for the first time in Au-Au collisions. Experimental data are compared with theoretical predictions. The strength of the Xi* peak is reproduced in the correlation function assuming that pions and Xis emerge from a system in collective expansion.Comment: To appear in the proceedings of 18th Nuclear Physics Division Conference of the EPS (NPDC18),Prague, 23.8.-29.8. 200

Inclusive pi0 spectra at high transverse momentum in d-Au collisions at RHIC

Preliminary results on inclusive neutral pion production in d-Au collisions at sqrt(s_NN) = 200 GeV in the pseudo-rapidity range 0<eta<1 are presented. The measurement is performed using the STAR Barrel Electromagnetic calorimeter (BEMC). In this paper, the analysis of the first BEMC hadron measurement is described and the results are compared with earlier RHIC findings. The pi0 invariant differential cross sections show good agreement with next-to-leading order (NLO) perturbative QCD calculations.Comment: 4 pages, 5 figures, 18th Nuclear Physics Division Conference of the EPS, Prague, submitted to Nucl. Phys.

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

The Canine Oral Microbiome

Determining the bacterial composition of the canine oral microbiome is of interest for two primary reasons. First, while the human oral microbiome has been well studied using molecular techniques, the oral microbiomes of other mammals have not been studied in equal depth using culture independent methods. This study allows a comparison of the number of bacterial taxa, based on 16S rRNA-gene sequence comparison, shared between humans and dogs, two divergent mammalian species. Second, canine oral bacteria are of interest to veterinary and human medical communities for understanding their roles in health and infectious diseases. The bacteria involved are mostly unnamed and not linked by 16S rRNA-gene sequence identity to a taxonomic scheme. This manuscript describes the analysis of 5,958 16S rRNA-gene sequences from 65 clone libraries. Full length 16S rRNA reference sequences have been obtained for 353 canine bacterial taxa, which were placed in 14 bacterial phyla, 23 classes, 37 orders, 66 families, and 148 genera. Eighty percent of the taxa are currently unnamed. The bacterial taxa identified in dogs are markedly different from those of humans with only 16.4% of oral taxa are shared between dogs and humans based on a 98.5% 16S rRNA sequence similarity cutoff. This indicates that there is a large divergence in the bacteria comprising the oral microbiomes of divergent mammalian species. The historic practice of identifying animal associated bacteria based on phenotypic similarities to human bacteria is generally invalid. This report describes the diversity of the canine oral microbiome and provides a provisional 16S rRNA based taxonomic scheme for naming and identifying unnamed canine bacterial taxa