Process algebra for event-driven runtime verification: a case study of wireless network management

Runtime verification is analysis based on information extracted from a running system. Traditionally this involves reasoning about system states, for example using trace predicates. We have been investigating runtime verification for event-driven systems and in that context we propose a higher level of abstraction can be useful, namely reasoning at the level of user-perceived system events. And when considering events, then the natural formalism for verification is a form of process algebra

WormQTL-public archive and analysis web portal for natural variation data in Caenorhabditis spp

Here, we present WormQTL (http://www.wormqtl.org), an easily accessible database enabling search, comparative analysis and meta-analysis of all data on variation in Caenorhabditis spp. Over the past decade, Caenorhabditis elegans has become instrumental for molecular quantitative genetics and the systems biology of natural variation. These efforts have resulted in a valuable amount of phenotypic, high-throughput molecular and genotypic data across different developmental worm stages and environments in hundreds of C. elegans strains. WormQTL provides a workbench of analysis tools for genotype-phenotype linkage and association mapping based on but not limited to R/qtl (http://www.rqtl.org). All data can be uploaded and downloaded using simple delimited text or Excel formats and are accessible via a public web user interface for biologists and R statistic and web service interfaces for bioinformaticians, based on open source MOLGENIS and xQTL workbench software. WormQTL welcomes data submissions from other worm researcher

Psychosocial factors and cancer incidence (PSY-CA): Protocol for individual participant data meta-analyses

Objectives: Psychosocial factors have been hypothesized to increase the risk of cancer. This study aims (1) to test whether psychosocial factors (depression, anxiety, recent loss events, subjective social support, relationship status, general distress, and neuroticism) are associated with the incidence of any cancer (any, breast, lung, prostate, colorectal, smoking-related, and alcohol-related); (2) to test the interaction between psychosocial factors and factors related to cancer risk (smoking, alcohol use, weight, physical activity, sedentary behavior, sleep, age, sex, education, hormone replacement therapy, and menopausal status) with regard to the incidence of cancer; and (3) to test the mediating role of health behaviors (smoking, alcohol use, weight, physical activity, sedentary behavior, and sleep) in the relationship between psychosocial factors and the incidence of cancer. Methods: The psychosocial factors and cancer incidence (PSY-CA) consortium was established involving experts in the field of (psycho-)oncology, methodology, and epidemiology. Using data collected in 18 cohorts (N = 617,355), a preplanned two-stage individual participant data (IPD) meta-analysis is proposed. Standardized analyses will be conducted on harmonized datasets for each cohort (stage 1), and meta-analyses will be performed on the risk estimates (stage 2). Conclusion: PSY-CA aims to elucidate the relationship between psychosocial factors and cancer risk by addressing several shortcomings of prior meta-analyses

Depression, anxiety, and the risk of cancer: An individual participant data meta-analysis

BACKGROUND: Depression and anxiety have long been hypothesized to be related to an increased cancer risk. Despite the great amount of research that has been conducted, findings are inconclusive. To provide a stronger basis for addressing the associations between depression, anxiety, and the incidence of various cancer types (overall, breast, lung, prostate, colorectal, alcohol-related, and smoking-related cancers), individual participant data (IPD) meta-analyses were performed within the Psychosocial Factors and Cancer Incidence (PSY-CA) consortium. METHODS: The PSY-CA consortium includes data from 18 cohorts with measures of depression or anxiety (up to N = 319,613; cancer incidences, 25,803; person-years of follow-up, 3,254,714). Both symptoms and a diagnosis of depression and anxiety were examined as predictors of future cancer risk. Two-stage IPD meta-analyses were run, first by using Cox regression models in each cohort (stage 1), and then by aggregating the results in random-effects meta-analyses (stage 2). RESULTS: No associations were found between depression or anxiety and overall, breast, prostate, colorectal, and alcohol-related cancers. Depression and anxiety (symptoms and diagnoses) were associated with the incidence of lung cancer and smoking-related cancers (hazard ratios [HRs], 1.06–1.60). However, these associations were substantially attenuated when additionally adjusting for known risk factors including smoking, alcohol use, and body mass index (HRs, 1.04–1.23). CONCLUSIONS: Depression and anxiety are not related to increased risk for most cancer outcomes, except for lung and smoking-related cancers. This study shows that key covariates are likely to explain the relationship between depression, anxiety, and lung and smoking-related cancers

Neural correlates of evidence accumulation during value-based decisions revealed via simultaneous EEG-fMRI

Current computational accounts posit that, in simple binary choices, humans accumulate evidence in favour of the different alternatives before committing to a decision. Neural correlates of this accumulating activity have been found during perceptual decisions in parietal and prefrontal cortex; however the source of such activity in value-based choices remains unknown. Here we use simultaneous EEG–fMRI and computational modelling to identify EEG signals reflecting an accumulation process and demonstrate that the within- and across-trial variability in these signals explains fMRI responses in posterior-medial frontal cortex. Consistent with its role in integrating the evidence prior to reaching a decision, this region also exhibits task-dependent coupling with the ventromedial prefrontal cortex and the striatum, brain areas known to encode the subjective value of the decision alternatives. These results further endorse the proposition of an evidence accumulation process during value-based decisions in humans and implicate the posterior-medial frontal cortex in this process

The resilience framework as a strategy to combat stress-related disorders

Consistent failure over the past few decades to reduce the high prevalence of stress-related disorders has motivated a search for alternative research strategies. Resilience refers to the phenomenon of many people maintaining mental health despite exposure to psychological or physical adversity. Instead of aiming to understand the pathophysiology of stress-related disorders, resilience research focuses on protective mechanisms that shield people against the development of such disorders and tries to exploit its insights to improve treatment and, in particular, disease prevention. To fully harness the potential of resilience research, a critical appraisal of the current state of the art — in terms of basic concepts and key methods — is needed. We highlight challenges to resilience research and make concrete conceptual and methodological proposals to improve resilience research. Most importantly, we propose to focus research on the dynamic processes of successful adaptation to stressors in prospective longitudinal studies.In preparing this Perspective, U.B. was supported by the Deutsche Forschungsgemeinschaft (DFG CRC 1193, subproject C06); G.A.B. by the United States-Israel Binational Science Foundation (project 2013067), David and Maureen O’Connor, and the Rockefeller Foundation (2012-RLC 304); A.C. by DFG CRC 1193, subproject C04; E.B. by the European Union’s Horizon 2020 Programme (EU H2020/705217); C.J.F. by DFG CRC 1193, subprojects C03 and C06, DFG FI 848/5-1, and the European Research Council (ERC-CoG 617891); I.G.-L. by the National Institute of Mental Health (K01MH102415); S.G. by DFG CRC 1193, subproject B05; E.J.H. by the ERC (ERCCoG682591); R.K. by DFG CRC 1193, subprojects B01 and C01, and the State of Rhineland- Palatinate (project 1080, MARP); K.L. by DFG CRC 1193, subproject Z03, and the State of Rhineland-Palatinate (project 1080, MARP); B.L. by DFG CRC 1193, subprojects A02, B03, and Z02; M.B.M. by DFG CRC 1193, subprojects A03 and Z02; R.J.M. by the Swiss National Science Foundation (SNF 100014-143398; project no. un 8306); A.R. by DFG CRC 1193, subprojects C07 and Z03, and EU H2020/2014-2020 (643051 (MiND) and 667302 (CoCA)); K.R. by the ERC (ERC_StG2012_313749) and the NWO (NWO VICI no. 453-12-001); B.P.F.R. by the NWO (NWO VENI no. 916-11-086); D.S. by the SNF (SNF 100014-143398, project no. un 8306); O.T. by DFG CRC 1193, subproject C04, and the State of Rhineland-Palatinate (project 1080, MARP); A.-L.v.H. by the Royal Society (DH150176); C.H.V. by the Netherlands Brain Foundation (Fellowship F2013(1)-216) and the NWO (NWO VENI no. 451-13-001); T.D.W. by the National Institute of Health (NIH); M.We. by DFG CRC 1193, subprojects C05 and C07; and M.Wi. by DFG CRC 1193, subproject C04