University of Colorado Colorado Springs

About: University of Colorado Colorado Springs is a education organization based out in Colorado Springs, Colorado, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 6664 authors who have published 10872 publications receiving 323416 citations. The organization is also known as: UCCS & University of Colorado at Colorado Springs.

Evidence for terror management theory: I. The effects of mortality salience on reactions to those who violate or uphold cultural values.

Abstract: On the basis of terror management theory, it was hypothesized that when mortality is made salient, Ss would respond especially positively toward those who uphold cultural values and especially negatively toward those who violate cultural values. In Experiment 1, judges recommended especially harsh bonds for a prostitute when mortality was made salient. Experiment 2 replicated this finding with student Ss and demonstrated that it occurs only among Ss with relatively negative attitudes toward prostitution. Experiment 3 demonstrated that mortality salience also leads to larger reward recommendations for a hero who upheld cultural values. Experiments 4 and 5 showed that the mortality salience effect does not result from heightened self-awareness or physiological arousal. Experiment 6 replicated the punishment effect with a different mortality salience manipulation. Implications for the role of fear of death in social behavior are discussed.

Why do people need self-esteem? A theoretical and empirical review.

Abstract: Terror management theory (TMT; J. Greenberg, T. Pyszczynski, & S. Solomon, 1986) posits that people are motivated to pursue positive self-evaluations because self-esteem provides a buffer against the omnipresent potential for anxiety engendered by the uniquely human awareness of mortality. Empirical evidence relevant to the theory is reviewed showing that high levels of self-esteem reduce anxiety and anxiety-related defensive behavior, reminders of one's mortality increase self-esteem striving and defense of self-esteem against threats in a variety of domains, high levels of self-esteem eliminate the effect of reminders of mortality on both self-esteem striving and the accessibility of death-related thoughts, and convincing people of the existence of an afterlife eliminates the effect of mortality salience on self-esteem striving. TMT is compared with other explanations for why people need self-esteem, and a critique of the most prominent of these, sociometer theory, is provided.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

Research Methodology In Management: Current Practices, Trends, And Implications For Future Research

Abstract: This study is a comparison of the strategies employed in management research in two periods, 1995–97 and 1985–87. Through a content analysis of articles from the Academy of Management Journal, Admi...