Towards a business process model warehouse framework

This dissertation focuses on the re-use of business process reference models, available in a business process model warehouse, to enable the definition of more comprehensive business requirements. It proposes a business process model warehouse framework to promote the re-use of multiple business process reference models and the flexible visualisation of business process models. The critical success factor for such a framework is that it should contribute to minimise to some extent the causes of inadequate business requirements. The proposed framework is based on an analogy with a data warehouse framework, consisting of the following components: usage of multiple business process reference models as source models, the conceptual design of a process to extract, load and transform multiple business process reference models into a repository, a description of repository functionality for managing enterprise architecture artefacts, and motivation of flexible visualisation of business process models to ensure more comprehensive business requirements.Computer Science (School of Computing)M.Sc. (Information Systems

Psychiatric Diagnosis Revisited:Towards a System of Staging and Profiling Combining Nomothetic and Idiographic Parameters of Momentary Mental States

BACKGROUND: Mental disorders may be reducible to sets of symptoms, connected through systems of causal relations. A clinical staging model predicts that in earlier stages of illness, symptom expression is both non-specific and diffuse. With illness progression, more specific syndromes emerge. This paper addressed the hypothesis that connection strength and connection variability between mental states differ in the hypothesized direction across different stages of psychopathology. METHODS: In a general population sample of female siblings (mostly twins), the Experience Sampling Method was used to collect repeated measures of three momentary mental states (positive affect, negative affect and paranoia). Staging was operationalized across four levels of increasing severity of psychopathology, based on the total score of the Symptom Check List. Multilevel random regression was used to calculate inter- and intra-mental state connection strength and connection variability over time by modelling each momentary mental state at t as a function of the three momentary states at t-1, and by examining moderation by SCL-severity. RESULTS: Mental states impacted dynamically on each other over time, in interaction with SCL-severity groups. Thus, SCL-90 severity groups were characterized by progressively greater inter- and intra-mental state connection strength, and greater inter- and intra-mental state connection variability. CONCLUSION: Diagnosis in psychiatry can be described as stages of growing dynamic causal impact of mental states over time. This system achieves a mode of psychiatric diagnosis that combines nomothetic (group-based classification across stages) and idiographic (individual-specific psychopathological profiles) components of psychopathology at the level of momentary mental states impacting on each other over time

Unraveling the Role of Loneliness in Depression:The Relationship Between Daily Life Experience and Behavior

Objective: Focusing on temporal associations between momentary (or state) loneliness, appraisal of social company, and being alone in daily life may help elucidate mechanisms that contribute to the development of prolonged (or trait) loneliness and major depressive disorder (MDD). We aim to examine if (a) a self-reinforcing loop between loneliness, negative appraisals of social company, and being alone in daily life may contribute to trait loneliness; (b) this possible self-reinforcing loop may also contribute to the development of MDD, by testing differences in temporal relationships between these social elements in participants who did or did not develop MDD during follow-up; and (c) any of these social elements at baseline predicted a MDD at follow-up. Methods: A female general population sample (n = 417) participated in an experience sampling method (ESM) study. Time-lagged analyses between loneliness, appraisal of social company, and being alone were examined at baseline, and their associations with the development of MDD during 20 months follow-up were investigated. Results: State loneliness was followed by an increase in negative appraisals of social company and a higher frequency of being alone. Further, negative appraisals of social company were associated with a higher frequency of being alone afterward. Only the latter was significant in the transition to MDD group. Trait loneliness predicted MDD during follow-up. Conclusions: Avoiding social contact after appraising company more negatively may contribute to the development of MDD.</p

Prediction of the Effect of Adaptation and Active HB Mechanics on Prestin-Based Amplification Using a Macroscopic Model of the Cochlea

Introduction: Negative social evaluation is associated with psychopathology. Given the frequency of evaluation through increasingly prevalent virtual social networks, increased understanding of the effects of this social evaluation is urgently required. Methods: A new digital social peer evaluation experiment (digi-SPEE) was developed to mimic everyday online social interactions between peers. Participants received mildly negative feedback on their appearance, intelligence, and congeniality. Two hundred and forty-one young people [58.9% female, aged 18.9 years (15 to 34)] from an ongoing novel general population twin study participated in this study. Positive affect (PA), negative affect (NA), implicit self-esteem, and cortisol were assessed before and after exposure to the social evaluation experiment. Results: The social evaluation experiment decreased PA (B=-5.25, p Conclusion: The digi-SPEE represents a social evaluation stressor that elicits biological and implicit and explicit mental changes that are relevant to mechanisms of psychopathology

Deglacierization of a marginal basin and implications for outburst floods

This article was submitted to Cryospheric Sciences, a section of the journal Frontiers in Earth ScienceSuicide Basin is a partly glacierized marginal basin of Mendenhall Glacier, Alaska, that has released glacier lake outburst floods (GLOFs) annually since 2011. The floods cause inundation and erosion in the Mendenhall Valley, impacting homes and other infrastructure. Here, we utilize in-situ and remote sensing data to assess the recent evolution and current state of Suicide Basin. We focus on the 2018 and 2019 melt seasons, during which we collected most of our data, partly using unmanned aerial vehicles (UAVs). To provide longer-term context, we analyze DEMs collected since 2006 and model glacier surface mass balance over the 2006–2019 period. During the 2018 and 2019 outburst flood events, Suicide Basin released ∼ 30 Å~ 106 m3 of water within approximately 4–5 days. Since lake drainage was partial in both years, these ∼ 30 Å~ 106 m3 represent only a fraction (∼ 60%) of the basin’s total storage capacity. In contrast to previous years, subglacial drainage was preceded by supraglacial outflow over the ice dam, which lasted ∼ 1 day in 2018 and 6 days in 2019. Two large calving events occurred in 2018 and 2019, with submerged ice breaking off the main glacier during lake filling, thereby increasing the basin’s storage capacity. In 2018, the floating ice in the basin was 36 m thick on average. In 2019, ice thickness was 29 m, suggesting rapid decay of the ice tongue despite increasing ice inflow from Mendenhall Glacier. The ice dam at the basin entrance thinned by more than 5 m a–1 from 2018 to 2019, which is approximately double the rate of the reference period 2006–2018. While ice-dam thinning reduces water storage capacity in the basin, that capacity is increased by declining ice volume in the basin and longitudinal lake expansion, with the latter process challenging to predict. The potential for premature drainage onset (i.e., drainage before the lake’s storage capacity is reached), intermittent drainage decelerations, and early drainage termination further complicates prediction of future GLOF events.This work was funded by the Alaska Climate Adaptation Science Center (AK CASC). UAVs and other surveying equipment were partly funded through the U.S. National Science Foundation (NSF) award EAR-1921598. EH and SH were partially supported by the NSF award OIA-1753748 and the State of Alaska. Streamflow monitoring of the Mendenhall River and real-time imagery of Suicide Basin were funded by the U.S. Geological Survey Groundwater and Streamflow Information Program. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.Ye

Network Approach to Understanding Emotion Dynamics in Relation to Childhood Trauma and Genetic Liability to Psychopathology:Replication of a Prospective Experience Sampling Analysis

Background: The network analysis of intensive time series data collected using the Experience Sampling Method (ESM) may provide vital information in gaining insight into the link between emotion regulation and vulnerability to psychopathology. The aim of this study was to apply the network approach to investigate whether genetic liability (GL) to psychopathology and childhood trauma (CT) are associated with the network structure of the emotions "cheerful,""insecure,""relaxed,""anxious,""irritated,"and "down"-collected using the ESM method. Methods: Using data from a population-based sample of twin pairs and siblings (704 individuals), we examined whether momentary emotion network structures differed across strata of CT and GL. GL was determined empirically using the level of psychopathology in monozygotic and dizygotic co-twins. Network models were generated using multilevel time-lagged regression analysis and were compared across three strata (low, medium, and high) of CT and GL, respectively. Permutations were utilized to calculate p values and compare regressions coefficients, density, and centrality indices. Regression coefficients were presented as connections, while variables represented the nodes in the network. Results: In comparison to the low GL stratum, the high GL stratum had significantly denser overall (p = 0.018) and negative affect network density (p < 0.001). The medium GL stratum also showed a directionally similar (in-between high and low GL strata) statistically inconclusive association with network density. In contrast to GL, the results of the CT analysis were less conclusive, with increased positive affect density (p = 0.021) and overall density (p = 0.042) in the high CT stratum compared to the medium CT stratum but not to the low CT stratum. The individual node comparisons across strata of GL and CT yielded only very few significant results, after adjusting for multiple testing. Conclusions: The present findings demonstrate that the network approach may have some value in understanding the relation between established risk factors for mental disorders (particularly GL) and the dynamic interplay between emotions. The present finding partially replicates an earlier analysis, suggesting it may be instructive to model negative emotional dynamics as a function of genetic influence

Genome-wide Association Study Identifies Two Susceptibility Loci for Osteosarcoma

Osteosarcoma is the most common primary bone malignancy of adolescents and young adults. In order to better understand the genetic etiology of osteosarcoma, we performed a multi-stage genome-wide association study (GWAS) consisting of 941 cases and 3,291 cancer-free adult controls of European ancestry. Two loci achieved genome-wide significance: rs1906953 at 6p21.3, in the glutamate receptor metabotropic 4 [GRM4] gene (P = 8.1 ×10-9), and rs7591996 and rs10208273 in a gene desert on 2p25.2 (P = 1.0 ×10-8 and 2.9 ×10-7). These two susceptibility loci warrant further exploration to uncover the biological mechanisms underlying susceptibility to osteosarcoma

Gene–environment interaction study on the polygenic risk score for neuroticism, childhood adversity, and parental bonding

The present study examines whether neuroticism is predicted by genetic vulnerability, summarized as polygenic risk score for neuroticism (PRSN), in interaction with bullying, parental bonding, and childhood adversity. Data were derived from a general population adolescent and young adult twin cohort. The final sample consisted of 202 monozygotic and 436 dizygotic twins and 319 twin pairs. The Short Eysenck Personality questionnaire was used to measure neuroticism. PRSN was trained on the results from the Genetics of Personality Consortium (GPC) and United Kingdom Biobank (UKB) cohorts, yielding two different PRSN. Multilevel mixed-effects models were used to analyze the main and interacting associations of PRSN, childhood adversity, bullying, and parental bonding style with neuroticism. We found no evidence of gene–environment correlation. PRSN thresholds of .005 and .2 were chosen, based on GPC and UKB datasets, respectively. After correction for confounders, all the individual variables were associated with the expression of neuroticism: both PRSN from GPC and UKB, childhood adversity, maternal bonding, paternal bonding, and bullying in primary school and secondary school. However, the results indicated no evidence for gene–environment interaction in this cohort. These results suggest that genetic vulnerability on the one hand and negative life events (childhood adversity and bullying) and positive life events (optimal parental bonding) on the other represent noninteracting pathways to neuroticism

Common variants near MC4R are associated with fat mass, weight and risk of obesity.

To identify common variants influencing body mass index (BMI), we analyzed genome-wide association data from 16,876 individuals of European descent. After previously reported variants in FTO, the strongest association signal (rs17782313, P = 2.9 x 10(-6)) mapped 188 kb downstream of MC4R (melanocortin-4 receptor), mutations of which are the leading cause of monogenic severe childhood-onset obesity. We confirmed the BMI association in 60,352 adults (per-allele effect = 0.05 Z-score units; P = 2.8 x 10(-15)) and 5,988 children aged 7-11 (0.13 Z-score units; P = 1.5 x 10(-8)). In case-control analyses (n = 10,583), the odds for severe childhood obesity reached 1.30 (P = 8.0 x 10(-11)). Furthermore, we observed overtransmission of the risk allele to obese offspring in 660 families (P (pedigree disequilibrium test average; PDT-avg) = 2.4 x 10(-4)). The SNP location and patterns of phenotypic associations are consistent with effects mediated through altered MC4R function. Our findings establish that common variants near MC4R influence fat mass, weight and obesity risk at the population level and reinforce the need for large-scale data integration to identify variants influencing continuous biomedical traits

Results of the COVID-19 mental health international for the general population (COMET-G) study.

INTRODUCTION: There are few published empirical data on the effects of COVID-19 on mental health, and until now, there is no large international study. MATERIAL AND METHODS: During the COVID-19 pandemic, an online questionnaire gathered data from 55,589 participants from 40 countries (64.85% females aged 35.80 ± 13.61; 34.05% males aged 34.90±13.29 and 1.10% other aged 31.64±13.15). Distress and probable depression were identified with the use of a previously developed cut-off and algorithm respectively. STATISTICAL ANALYSIS: Descriptive statistics were calculated. Chi-square tests, multiple forward stepwise linear regression analyses and Factorial Analysis of Variance (ANOVA) tested relations among variables. RESULTS: Probable depression was detected in 17.80% and distress in 16.71%. A significant percentage reported a deterioration in mental state, family dynamics and everyday lifestyle. Persons with a history of mental disorders had higher rates of current depression (31.82% vs. 13.07%). At least half of participants were accepting (at least to a moderate degree) a non-bizarre conspiracy. The highest Relative Risk (RR) to develop depression was associated with history of Bipolar disorder and self-harm/attempts (RR = 5.88). Suicidality was not increased in persons without a history of any mental disorder. Based on these results a model was developed. CONCLUSIONS: The final model revealed multiple vulnerabilities and an interplay leading from simple anxiety to probable depression and suicidality through distress. This could be of practical utility since many of these factors are modifiable. Future research and interventions should specifically focus on them