Psychological Self-Other Overlap: Implications for Prosocial Behavior in Close Relationships.

The present dissertation uncovers the processes by which self-other overlap influences prosocial behavior and its consequences across different close relationships. Chapter I reviews extant theory and research on self-other overlap and its role in relationships and prosocial behavior. Chapter II explores whether discrete emotions shift perceptions of self-other overlap, and how shifts influence downstream prosocial tendencies across 4 studies. Study 1 found that reflecting on an angry experience with a close friend led to less self-other overlap and subsequent prosocial tendencies toward that friend, relative to reflecting on a happy or more neutral experience. Furthermore, anger undermined helping through the mediating role of self-other overlap, relative to the other conditions. Study 2 ruled out a general negative valence explanation after finding no significant self-other overlap differences from reflecting on emotions similar in valence (i.e., sad, content, or control) involving a close friend. Study 3 tested emotions that directly implicate others (i.e., gratitude, anger, and control) among best friends. Anger undermined self-other overlap, relative to the control. However, there were no self-other overlap differences between gratitude and control, condition effects on helping, or mediation. Study 4 found null effects of anger on self-other overlap, relative to gratitude and control, suggesting that marital relationships may be one boundary condition. Chapter III explores whether relationship type, a proxy for self-other overlap, moderates the long-term health outcomes of providing support to close others. Giving to emotionally close partners predicted mortality risk, when giving to children. This likely occurred because children activate the caregiving system, which is hypothesized to benefit stress-regulation. Study 5a found that providing support to adult children predicted reduced mortality risk 17 years later among older adult parents, but providing support to other partners (e.g., parents, siblings, other relatives, friends) did not predict mortality risk among either parents (Study 5a) or non-parents (Study 5b), controlling for a number of plausible confounds. Chapter IV concludes by discussing the implications of the research for a variety of research literatures and future directions. Together, the studies begin to illuminate the intricacies by which self-other overlap influences prosocial behavior and its consequences in different close relationships.PHDPsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107282/1/maryyliu_1.pd

Comparison of feature selection and classification for MALDI-MS data

INTRODUCTION: In the classification of Mass Spectrometry (MS) proteomics data, peak detection, feature selection, and learning classifiers are critical to classification accuracy. To better understand which methods are more accurate when classifying data, some publicly available peak detection algorithms for Matrix assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS) data were recently compared; however, the issue of different feature selection methods and different classification models as they relate to classification performance has not been addressed. With the application of intelligent computing, much progress has been made in the development of feature selection methods and learning classifiers for the analysis of high-throughput biological data. The main objective of this paper is to compare the methods of feature selection and different learning classifiers when applied to MALDI-MS data and to provide a subsequent reference for the analysis of MS proteomics data. RESULTS: We compared a well-known method of feature selection, Support Vector Machine Recursive Feature Elimination (SVMRFE), and a recently developed method, Gradient based Leave-one-out Gene Selection (GLGS) that effectively performs microarray data analysis. We also compared several learning classifiers including K-Nearest Neighbor Classifier (KNNC), Naïve Bayes Classifier (NBC), Nearest Mean Scaled Classifier (NMSC), uncorrelated normal based quadratic Bayes Classifier recorded as UDC, Support Vector Machines, and a distance metric learning for Large Margin Nearest Neighbor classifier (LMNN) based on Mahanalobis distance. To compare, we conducted a comprehensive experimental study using three types of MALDI-MS data. CONCLUSION: Regarding feature selection, SVMRFE outperformed GLGS in classification. As for the learning classifiers, when classification models derived from the best training were compared, SVMs performed the best with respect to the expected testing accuracy. However, the distance metric learning LMNN outperformed SVMs and other classifiers on evaluating the best testing. In such cases, the optimum classification model based on LMNN is worth investigating for future study

Comparison of Feature Selection and Classification for MALDI-MS Data

Introduction: In the classification of Mass Spectrometry (MS) proteomics data, peak detection, feature selection, and learning classifiers are critical to classification accuracy. To better understand which methods are more accurate when classifying data, some publicly available peak detection algorithms for Matrix assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS) data were recently compared; however, the issue of different feature selection methods and different classification models as they relate to classification performance has not been addressed. With the application of intelligent computing, much progress has been made in the development of feature selection methods and learning classifiers for the analysis of high-throughput biological data. The main objective of this paper is to compare the methods of feature selection and different learning classifiers when applied to MALDI-MS data and to provide a subsequent reference for the analysis of MS proteomics data. Results: We compared a well-known method of feature selection, Support Vector Machine Recursive Feature Elimination (SVMRFE), and a recently developed method, Gradient based Leave-one-out Gene Selection (GLGS) that effectively performs microarray data analysis. We also compared several learning classifiers including K-Nearest Neighbor Classifier (KNNC), Naïve Bayes Classifier (NBC), Nearest Mean Scaled Classifier (NMSC), uncorrelated normal based quadratic Bayes Classifier recorded as UDC, Support Vector Machines, and a distance metric learning for Large Margin Nearest Neighbor classifier (LMNN) based on Mahanalobis distance. To compare, we conducted a comprehensive experimental study using three types of MALDI-MS data. Conclusion: Regarding feature selection, SVMRFE outperformed GLGS in classification. As for the learning classifiers, when classification models derived from the best training were compared, SVMs performed the best with respect to the expected testing accuracy. However, the distance metric learning LMNN outperformed SVMs and other classifiers on evaluating the best testing. In such cases, the optimum classification model based on LMNN is worth investigating for future study

Teaching Courses Online: A Review of the Research

This literature review summarizes research on online teaching and learning. It is organized into four topics: course environment, learners’ outcomes, learners’ characteristics, and institutional and administrative factors. The authors found little consistency of terminology, discovered some conclusive guidelines, and identified developing lines of inquiry. The conclusions overall suggest that most of the studies reviewed were descriptive and exploratory, that most online students are nontraditional and Anglo American, and that few universities have written policies, guidelines, or technical support for faculty members or students. Asynchronous communication seemed to facilitate in-depth communication (but not more than in traditional classes), students liked to move at their own pace, learning outcomes appeared to be the same as in traditional courses, and students with prior training in computers were more satisfied with online courses. Continued research is needed to inform learner outcomes, learner characteristics, course environment, and institutional factors related to delivery system variables in order to test learning theories and teaching models inherent in course design

High-throughput next-generation sequencing technologies foster new cutting-edge computing techniques in bioinformatics

The advent of high-throughput next generation sequencing technologies have fostered enormous potential applications of supercomputing techniques in genome sequencing, epi-genetics, metagenomics, personalized medicine, discovery of non-coding RNAs and protein-binding sites. To this end, the 2008 International Conference on Bioinformatics and Computational Biology (Biocomp) – 2008 World Congress on Computer Science, Computer Engineering and Applied Computing (Worldcomp) was designed to promote synergistic inter/multidisciplinary research and education in response to the current research trends and advances. The conference attracted more than two thousand scientists, medical doctors, engineers, professors and students gathered at Las Vegas, Nevada, USA during July 14–17 and received great success. Supported by International Society of Intelligent Biological Medicine (ISIBM), International Journal of Computational Biology and Drug Design (IJCBDD), International Journal of Functional Informatics and Personalized Medicine (IJFIPM) and the leading research laboratories from Harvard, M.I.T., Purdue, UIUC, UCLA, Georgia Tech, UT Austin, U. of Minnesota, U. of Iowa etc, the conference received thousands of research papers. Each submitted paper was reviewed by at least three reviewers and accepted papers were required to satisfy reviewers' comments. Finally, the review board and the committee decided to select only 19 high-quality research papers for inclusion in this supplement to BMC Genomics based on the peer reviews only. The conference committee was very grateful for the Plenary Keynote Lectures given by: Dr. Brian D. Athey (University of Michigan Medical School), Dr. Vladimir N. Uversky (Indiana University School of Medicine), Dr. David A. Patterson (Member of United States National Academy of Sciences and National Academy of Engineering, University of California at Berkeley) and Anousheh Ansari (Prodea Systems, Space Ambassador). The theme of the conference to promote synergistic research and education has been achieved successfully

Relative fluorine concentrations in radio frequency/electron cyclotron resonance hybrid glow discharges

The relative concentration of atomic fluorine was measured in a radio frequency (rf) glow discharge and a modified electron cyclotron resonance microwave/rf hybrid discharge in CF4 using an actinometric technique. The dependence of fluorine concentration on rf and microwave power, pressure, flow, and excitation source are presented. Anomalous behavior with rf power at constant microwave power was observed when using the Ar 750‐nm line as the actinometric species.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/70900/2/APPLAB-60-7-818-1.pd

Enhanced mitochondrial superoxide scavenging does not Improve muscle insulin action in the high fat-fed mouse

Improving mitochondrial oxidant scavenging may be a viable strategy for the treatment of insulin resistance and diabetes. Mice overexpressing the mitochondrial matrix isoform of superoxide dismutase (sod2(tg) mice) and/or transgenically expressing catalase within the mitochondrial matrix (mcat(tg) mice) have increased scavenging of O2(˙-) and H2O2, respectively. Furthermore, muscle insulin action is partially preserved in high fat (HF)-fed mcat(tg) mice. The goal of the current study was to test the hypothesis that increased O2(˙-) scavenging alone or in combination with increased H2O2 scavenging (mtAO mice) enhances in vivo muscle insulin action in the HF-fed mouse. Insulin action was examined in conscious, unrestrained and unstressed wild type (WT), sod2(tg), mcat(tg) and mtAO mice using hyperinsulinemic-euglycemic clamps (insulin clamps) combined with radioactive glucose tracers following sixteen weeks of normal chow or HF (60% calories from fat) feeding. Glucose infusion rates, whole body glucose disappearance, and muscle glucose uptake during the insulin clamp were similar in chow- and HF-fed WT and sod2(tg) mice. Consistent with our previous work, HF-fed mcat(tg) mice had improved muscle insulin action, however, an additive effect was not seen in mtAO mice. Insulin-stimulated Akt phosphorylation in muscle from clamped mice was consistent with glucose flux measurements. These results demonstrate that increased O2(˙-) scavenging does not improve muscle insulin action in the HF-fed mouse alone or when coupled to increased H2O2 scavenging

Multiethnic genome-wide meta-analysis of ectopic fat depots identifies loci associated with adipocyte development and differentiation

Variation in body fat distribution contributes to the metabolic sequelae of obesity. The genetic determinants of body fat distribution are poorly understood. The goal of this study was to gain new insights into the underlying genetics of body fat distribution by conducting sample-size weighted fixed-effects genome-wide association meta-analyses in up to 9,594 women and 8,738 men for six ectopic fat traits in European, African, Hispanic, and Chinese ancestry populations, with and without sex stratification. In total, 7 new loci were identified in association with ectopic fat traits (ATXN1, UBE2E2, EBF1, RREB1, GSDMB, GRAMD3 and ENSA; PATXN1 and UBE2E2 in primary mouse adipose progenitor cells impaired adipocyte differentiation, suggesting a physiological role for ATXN1 and UBE2E2 in adipogenesis. Future studies are necessary to further explore the mechanisms by which these genes impact adipocyte biology and how their perturbations contribute to systemic metabolic disease

A new statistical approach to combining p-values using gamma distribution and its application to genome-wide association study

Background: Combining information from different studies is an important and useful practice in bioinformatics, including genome-wide association study, rare variant data analysis and other set-based analyses. Many statistical methods have been proposed to combine p-values from independent studies. However, it is known that there is no uniformly most powerful test under all conditions; therefore, finding a powerful test in specific situation is important and desirable. Results: In this paper, we propose a new statistical approach to combining p-values based on gamma distribution, which uses the inverse of the p-value as the shape parameter in the gamma distribution. Conclusions: Simulation study and real data application demonstrate that the proposed method has good performance under some situations