An Incremental Learning Method to Support the Annotation of Workflows with Data-to-Data Relations

Workflow formalisations are often focused on the representation of a process with the primary objective to support execution. However, there are scenarios where what needs to be represented is the effect of the process on the data artefacts involved, for example when reasoning over the corresponding data policies. This can be achieved by annotating the workflow with the semantic relations that occur between these data artefacts. However, manually producing such annotations is difficult and time consuming. In this paper we introduce a method based on recommendations to support users in this task. Our approach is centred on an incremental rule association mining technique that allows to compensate the cold start problem due to the lack of a training set of annotated workflows. We discuss the implementation of a tool relying on this approach and how its application on an existing repository of workflows effectively enable the generation of such annotations

Eutrophication problems, causes and potential solutions, and exchange of reusable model building components for the integrated simulation of coastal eutrophication. ISECA Final Report D3.2

This report summarizes the stages of coastal and offshore eutrophication, followed by a description of the European indicators and institutional framework for marine eutrophication assessment. A summary is given of a number of biogeochemical models available to describe the process of eutrophication in the North Sea, and the model for atmospheric inputs which was developed in the ISECA project (see the Action 3 Report – Atmospheric Modelling for more details on this work). Furthermore, the report compares different solutions aimed at reducing the nitrogen inputs from the Scheldt basin, using the nitrogen apportionment model which was developed in the EU-FP6 project SPICOSA (www.spicosa.eu). The report is concluded with a discussion on the principles of component-based modelling and model libraries, using examples for the Scheldt model, and a general discussion on some challenges of modelling marine eutrophication

“Bad Mum Guilt”: The Representation of ‘Work-Life Balance’ in UK Women’s Magazines

The social policy climate, labour market trends and gendered arrangements for paid and family work mean that ‘work-life balance’ remains a key social issue in the UK. Media representations of ‘work-life balance’ are a key source for the construction of gender and working motherhood. Despite evidence of gendered representations in media coverage of other social issues, little attention has been paid to the construction of work-life balance in UK women's magazines. Articles from the highest circulating UK women's magazines are analysed using a discursive approach to explicate constructions of work-life balance and working motherhood. The analysis reveals that multiple roles are constructed as a problematic choice leading to stress and guilt. Problems associated with multiple roles are constructed as individual problems, in a way that decontextualises and depoliticises them and normalises gendered assumptions and a gendered division of labour. Parallels can be drawn between this and wider discourses about women's daily lives and to the UK social policy context

Insulinopathies of the brain? Genetic overlap between somatic insulin-related and neuropsychiatric disorders

The prevalence of somatic insulinopathies, like metabolic syndrome (MetS), obesity, and type 2 diabetes mellitus (T2DM), is higher in Alzheimer’s disease (AD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD). Dysregulation of insulin signalling has been implicated in these neuropsychiatric disorders, and shared genetic factors might partly underlie this observed multimorbidity. We investigated the genetic overlap between AD, ASD, and OCD with MetS, obesity, and T2DM by estimating pairwise global genetic correlations using the summary statistics of the largest available genome-wide association studies for these phenotypes. Having tested these hypotheses, other potential brain “insulinopathies” were also explored by estimating the genetic relationship of six additional neuropsychiatric disorders with nine insulin-related diseases/traits. Stratified covariance analyses were then performed to investigate the contribution of insulin-related gene sets. Significant negative genetic correlations were found between OCD and MetS (rg = −0.315, p = 3.9 × 10−8), OCD and obesity (rg = −0.379, p = 3.4 × 10−5), and OCD and T2DM (rg = −0.172, p = 3 × 10−4). Significant genetic correlations with insulin-related phenotypes were also found for anorexia nervosa (AN), attention-deficit/hyperactivity disorder (ADHD), major depressive disorder, and schizophrenia (p < 6.17 × 10−4). Stratified analyses showed negative genetic covariances between AD, ASD, OCD, ADHD, AN, bipolar disorder, schizophrenia and somatic insulinopathies through gene sets related to insulin signalling and insulin receptor recycling, and positive genetic covariances between AN and T2DM, as well as ADHD and MetS through gene sets related to insulin processing/secretion (p < 2.06 × 10−4). Overall, our findings suggest the existence of two clusters of neuropsychiatric disorders, in which the genetics of insulin-related diseases/traits may exert divergent pleiotropic effects. These results represent a starting point for a new research line on “insulinopathies” of the brain

Whole Exome Sequencing in Multi-Incident Families Identifies Novel Candidate Genes for Multiple Sclerosis

Multiple sclerosis (MS) is a degenerative disease of the central nervous system in which auto-immunity-induced demyelination occurs. MS is thought to be caused by a complex interplay of environmental and genetic risk factors. While most genetic studies have focused on identifying common genetic variants for MS through genome-wide association studies, the objective of the present study was to identify rare genetic variants contributing to MS susceptibility. We used whole exome sequencing (WES) followed by co-segregation analyses in nine multi-incident families with two to four affected individuals. WES was performed in 31 family members with and without MS. After applying a suite of selection criteria, co-segregation analyses for a number of rare variants selected from the WES results were performed, adding 24 family members. This approach resulted in 12 exonic rare variants that showed acceptable co-segregation with MS within the nine families, implicating the genes MBP, PLK1, MECP2, MTMR7, TOX3, CPT1A, SORCS1, TRIM66, ITPR3, TTC28, CACNA1F, and PRAM1. Of these, three genes (MBP, MECP2, and CPT1A) have been previously reported as carrying MS-related rare variants. Six additional genes (MTMR7, TOX3, SORCS1, ITPR3, TTC28, and PRAM1) have also been implicated in MS through common genetic variants. The proteins encoded by all twelve genes containing rare variants interact in a molecular framework that points to biological processes involved in (de-/re-)myelination and auto-immunity. Our approach provides clues to possible molecular mechanisms underlying MS that should be studied further in cellular and/or animal models

Business process variant analysis based on mutual fingerprints of event logs

Comparing business process variants using event logs is a common use case in process mining. Existing techniques for process variant analysis detect statistically-significant differences between variants at the level of individual entities (such as process activities) and their relationships (e.g. directly-follows relations between activities). This may lead to a proliferation of differences due to the low level of granularity in which such differences are captured. This paper presents a novel approach to detect statistically-significant differences between variants at the level of entire process traces (i.e. sequences of directly-follows relations). The cornerstone of this approach is a technique to learn a directly-follows graph called mutual fingerprint from the event logs of the two variants. A mutual fingerprint is a lossless encoding of a set of traces and their duration using discrete wavelet transformation. This structure facilitates the understanding of statistical differences along the control-flow and performance dimensions. The approach has been evaluated using real-life event logs against two baselines. The results show that at a trace level, the baselines cannot always reveal the differences discovered by our approach, or can detect spurious differences.This research is partly funded by the Australian Research Council (DP180102839) and Spanish funds MINECO and FEDER (TIN2017-86727-C2-1-R).Peer ReviewedPostprint (author's final draft

Longitudinal in vivo assessment of host-microbe interactions in a murine model of pulmonary aspergillosis

The fungus Aspergillus fumigatus is ubiquitous in nature and the most common cause of invasive pulmonary aspergillosis (IPA) in patients with a compromised immune system. The development of IPA in patients under immunosuppressive treatment or in patients with primary immunodeficiency demonstrates the importance of the host immune response in controlling aspergillosis. However, study of the host-microbe interaction has been hampered by the lack of tools for their non-invasive assessment. We developed a methodology to study the response of the host's immune system against IPA longitudinally in vivo by using fluorine-19 magnetic resonance imaging (F-19 MRI). We showed the advantage of a perfluorocarbon-based contrast agent for the in vivo labeling of macrophages and dendritic cells, permitting quantification of pulmonary inflammation in different murine IPA models. Our findings reveal the potential of F-19 MRI for the assessment of rapid kinetics of innate immune response against IPA and the permissive niche generated through immunosuppression

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