Team Quotients, Resilience, and Performance of Software Development Projects

Past studies have examined actions and strategies that software project teams can take to reduce the negative impact of uncertainties, such as changing requirements. Software development project teams often have to be flexible to follow the pre-defined plans and strive to meet project goals. Sometimes uncertainty may go extreme to temporarily slow projects down and set project teams into reduced productivity. Project teams should be resilient to recover from the reduce productivity condition and move forward toward predefined goals. This study focuses on understanding the importance of team resilience for software project teams and exploring the antecedents of team resilience. Specifically, we investigate the impacts of intelligence and emotional quotient on team resilience capability, the extent to which project team can recover from the impediment and move forward. This is a research-in-progress work. A future empirical test plan has been discussed at the end

Analysis of Agreement on Traditional Chinese Medical Diagnostics for Many Practitioners

In Traditional Chinese Medicine (TCM) diagnostics, it is an important issue to study the degree of agreement among several distinct practitioners. In order to study the reliability of TCM diagnostics, we have to design an experiment to simultaneously deal with both of the cases when the data is ordinal and when there are many TCM practitioners. In this study, we consider a reliability measure called “Krippendorff's alpha” to investigate the agreement of tongue diagnostics in TCM. Besides, since it is not easy to obtain a large data set with patients rated simultaneously by many TCM practitioners, we use the renowned “bootstrapping” to obtain a 95% confidence interval for the Krippendorff's alpha. The estimated Krippendorff's alpha for the agreement among ten physicians that discerned fifteen randomly chosen patients is 0.7343, and the 95% bootstrapping confidence interval for the true alpha coefficient is [0.6570, 0.7349]. The data was collected and analyzed at the Department of Traditional Chinese Medicine, Changhua Christian Hospital (CCH) in Taiwan

Understanding variation in transcription factor binding by modeling transcription factor genome-epigenome interactions

Despite explosive growth in genomic datasets, the methods for studying epigenomic mechanisms of gene regulation remain primitive. Here we present a model-based approach to systematically analyze the epigenomic functions in modulating transcription factor-DNA binding. Based on the first principles of statistical mechanics, this model considers the interactions between epigenomic modifications and a cis-regulatory module, which contains multiple binding sites arranged in any configurations. We compiled a comprehensive epigenomic dataset in mouse embryonic stem (mES) cells, including DNA methylation (MeDIP-seq and MRE-seq), DNA hydroxymethylation (5-hmC-seq), and histone modifications (ChIP-seq). We discovered correlations of transcription factors (TFs) for specific combinations of epigenomic modifications, which we term epigenomic motifs. Epigenomic motifs explained why some TFs appeared to have different DNA binding motifs derived from in vivo (ChIP-seq) and in vitro experiments. Theoretical analyses suggested that the epigenome can modulate transcriptional noise and boost the cooperativity of weak TF binding sites. ChIP-seq data suggested that epigenomic boost of binding affinities in weak TF binding sites can function in mES cells. We showed in theory that the epigenome should suppress the TF binding differences on SNP-containing binding sites in two people. Using personal data, we identified strong associations between H3K4me2/H3K9ac and the degree of personal differences in NFκB binding in SNP-containing binding sites, which may explain why some SNPs introduce much smaller personal variations on TF binding than other SNPs. In summary, this model presents a powerful approach to analyze the functions of epigenomic modifications. This model was implemented into an open source program APEG (Affinity Prediction by Epigenome and Genome, http://systemsbio.ucsd.edu/apeg)

Understanding Variation in Transcription Factor Binding by Modeling Transcription Factor Genome-Epigenome Interactions

Despite explosive growth in genomic datasets, the methods for studying epigenomic mechanisms of gene regulation remain primitive. Here we present a model-based approach to systematically analyze the epigenomic functions in modulating transcription factor-DNA binding. Based on the first principles of statistical mechanics, this model considers the interactions between epigenomic modifications and a cis-regulatory module, which contains multiple binding sites arranged in any configurations. We compiled a comprehensive epigenomic dataset in mouse embryonic stem (mES) cells, including DNA methylation (MeDIP-seq and MRE-seq), DNA hydroxymethylation (5-hmC-seq), and histone modifications (ChIP-seq). We discovered correlations of transcription factors (TFs) for specific combinations of epigenomic modifications, which we term epigenomic motifs. Epigenomic motifs explained why some TFs appeared to have different DNA binding motifs derived from in vivo (ChIP-seq) and in vitro experiments. Theoretical analyses suggested that the epigenome can modulate transcriptional noise and boost the cooperativity of weak TF binding sites. ChIP-seq data suggested that epigenomic boost of binding affinities in weak TF binding sites can function in mES cells. We showed in theory that the epigenome should suppress the TF binding differences on SNP-containing binding sites in two people. Using personal data, we identified strong associations between H3K4me2/H3K9ac and the degree of personal differences in NFκB binding in SNP-containing binding sites, which may explain why some SNPs introduce much smaller personal variations on TF binding than other SNPs. In summary, this model presents a powerful approach to analyze the functions of epigenomic modifications. This model was implemented into an open source program APEG (Affinity Prediction by Epigenome and Genome, http://systemsbio.ucsd.edu/apeg).</p

Acute necrotizing eosinophilic myocarditis in a young woman

Abstract Eosinophilic myocarditis is recognized by severe heart failure and marked eosinophilia infiltration resulting from different etiologies. Acute necrotizing eosinophilic myocarditis, the initial presentation of the disease, is rare and often fatal, with unique echocardiographic pictures, and followed by endocardial thrombosis and chronic endomyocardial fibrosis. We report a young female with acute lymphoblastic leukemia who presented fever and acute heart failure syndrome. The echocardiography showed severe left ventricle diastolic dysfunction with preserved ejection fraction. Systemic eosinophilia and the unique echocardiographic images made the diagnosis of acute necrotizing eosinophilic myocarditis. The patient survived after intensive cytotoxic chemotherapy including high-dose steroid