17th International Working Conference on Requirements Engineering: Foundation for Software Quality (REFSQ 2011). Proceedings of the REFSQ 2011 Workshops REEW, EPICAL and RePriCo, the REFSQ 2011 Empirical Track (Empirical Live Experiment and Empirical Research Fair), and the REFSQ 2011 Doctoral Symposium

This ICB Research Report constitutes the proceedings of the following events which were held during the Requirements Engineering: Foundation for Software Quality (REFSQ) conference 2011 in Essen, Germany. Requirements Engineering Efficiency Workshop (REEW). Requirements Prioritization for customer-oriented Software-Development (RePriCo). Workshop on Empirical Research in Requirements Engineering: Challenges and Solutions (EPICAL). Empirical Research Fair. Empirical Live Experiment. Doctoral Symposiu

18th International Working Conference on Requirements Engineering: Foundation for Software Quality. Proceedings of the Workshops RE4SuSy, REEW, CreaRE, RePriCo, IWSPM and the Conference Related Empirical Study, Empirical Fair and Doctoral Symposium

This ICB Research Report constitutes the proceedings of the following events which were held during the Requirements Engineering: Foundation for Software Quality (REFSQ) conference 2012 in Essen, Germany. Engineering for Sustainable Systems (RE4SuSy), Requirements Engineering Efficiency Workshop REEW 2012), Creativity in Requirements Engineering (CreaRE 2012), Requirements Prioritization for customer oriented Software Development (RePriCo), International Workshop on Software Product Management (IWSPM), Alive Empirical Study, Online Questionnaires, Empirical Research Fair, Doctoral Symposium

Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality

Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model for predicting the risk of in-hospital mortality post-SARS-CoV-2 infection. Blood samples and clinical data from 1286 COVID-19 patients collected from 2020 to 2023 across four cohorts in Europe and Canada were analyzed, with 2906 long non-coding RNAs profiled using targeted sequencing. From a discovery cohort combining three European cohorts and 804 patients, age and the long non-coding RNA LEF1-AS1 were identified as predictive features, yielding an AUC of 0.83 (95% CI 0.82-0.84) and a balanced accuracy of 0.78 (95% CI 0.77-0.79) with a feedforward neural network classifier. Validation in an independent Canadian cohort of 482 patients showed consistent performance. Cox regression analysis indicated that higher levels of LEF1-AS1 correlated with reduced mortality risk (age-adjusted hazard ratio 0.54, 95% CI 0.40-0.74). Quantitative PCR validated LEF1-AS1's adaptability to be measured in hospital settings. Here, we demonstrate a promising predictive model for enhancing COVID-19 patient management.</p

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Elicitation of Requirements from User Documentation

This paper describes an approach for elicitation of requirements based on existing user documentation. The approach we describe in this paper supports capturing of the information found in user documentation of legacy systems, e.g., user manuals, and the specification of this information in requirements specifications, using, e.g., Use Cases. We propose a conceptual model describing the transition from user documentation to requirements artifacts describing common and variable elements of a product line model or requirements specification. We present heuristics that allow an easy identification of text elements in user documents that are then used to create a significant part of the requirements specification and product line model, respectively

Supporting Decision-Making for Self-Adaptive Systems: From Goal Models to Dynamic Decision Networks

[Context/Motivation] Different modeling techniques have been used to model requirements and decision-making of self-adaptive systems (SASs). Specifically, goal models have been prolific in supporting decision-making depending on partial and total fulfilment of functional (goals) and non-functional requirements (softgoals). Different goalrealization strategies can have different effects on softgoals which are specified with weighted contribution-links. The final decision about what strategy to use is based, among other reasons, on a utility function that takes into account the weighted sum of the different effects on softgoals. [Questions/Problems] One of the main challenges about decisionmaking in self-adaptive systems is to deal with uncertainty during runtime. New techniques are needed to systematically revise the current model when empirical evidence becomes available from the deployment. [Principal ideas/results] In this paper we enrich the decision-making supported by goal models by using Dynamic Decision Networks (DDNs). Goal realization strategies and their impact on softgoals have a correspondence with decision alternatives and conditional probabilities and expected utilities in the DDNs respectively. Our novel approach allows the specification of preferences over the softgoals and supports reasoning about partial satisfaction of softgoals using probabilities. We report results of the application of the approach on two different cases. Our early results suggest the decision-making process of SASs can be improved by using DDNs