On Enabling Data-Aware Compliance Checking of Business Process Models

In the light of an increasing demand on business process compliance, the verication of process models against compliance rules has become essential in enterprise computing. To be broadly applicable compliance checking has to support data-aware compliance rules as well as to consider data conditions within a process model. Independently of the actual technique applied to accomplish compliance checking, dataawareness means that in addition to the control ow dimension, the data dimension has to be explored during compliance checking. However, naive exploration of the data dimension can lead to state explosion. We address this issue by introducing an abstraction approach in this paper. We show how state explosion can be avoided by conducting compliance checking for an abstract process model and abstract compliance rules. Our abstraction approach can serve as preprocessing step to the actual compliance checking and provides the basis for more ecient application of existing compliance checking algorithms

SeaFlows Toolset - Compliance Verification Made Easy for Process-aware Information Systems

In the light of an increasing demand on business process compliance, the verication of process models against compliance rules has become essential in enterprise computing. The SeaFlows Toolset featured in this paper extends process-aware information systems with compliance checking functionality. It provides a user-friendly environment for modeling compliance rules using a graph-based formalism and for enriching process models with these rules. To address a multitude of verification settings, we provide two complementary compliance checking approaches: The structural compliance checking approach derives structural criteria from compliance rules and applies them to detect incompliance. The data-aware behavioral compliance checking approach addresses the state explosion problem that can occur when the data dimension is explored during compliance checking. It performs context-sensitive automatic abstraction to derive an abstract process model which is more compact with regard to the data dimension enabling more efficient compliance checking. Altogether, SeaFlows Toolset constitutes a comprehensive and extensible framework for compliance checking of process models

A Cloud-Based Collaboration Platform for Model-Based Design of Cyber-Physical Systems

Businesses, particularly small and medium-sized enterprises, aiming to start up in Model-Based Design (MBD) face difficult choices from a wide range of methods, notations and tools before making the significant investments in planning, procurement and training necessary to deploy new approaches successfully. In the development of Cyber-Physical Systems (CPSs) this is exacerbated by the diversity of formalisms covering computation, physical and human processes. In this paper, we propose the use of a cloud-enabled and open collaboration platform that allows businesses to offer models, tools and other assets, and permits others to access these on a pay-per-use basis as a means of lowering barriers to the adoption of MBD technology, and to promote experimentation in a sandbox environment

L1CAM protein expression is associated with poor prognosis in non-small cell lung cancer

Background: The L1 cell adhesion molecule (L1CAM) is potentially involved in epithelial-mesenchymal transition (EMT). EMT marker expression is of prognostic significance in non-small cell lung cancer (NSCLC). The relevance of L1CAM for NSCLC is unclear. We investigated the protein expression of L1CAM in a cohort of NSCLC patients. L1CAM protein expression was correlated with clinico-pathological parameters including survival and markers of epithelial-mesenchymal transition. Results: L1CAM protein expression was found in 25% of squamous cell carcinomas and 24% of adenocarcinomas and correlated with blood vessel invasion and metastasis (p < 0.05). L1CAM was an independent predictor of survival in a multivariate analysis including pT, pN, and pM category, and tumor differentiation grade. L1CAM expression positively correlated with vimentin, beta-catenin, and slug, but inversely with E-cadherin (all p-values < 0.05). E-cadherin expression was higher in the tumor center than in the tumor periphery, whereas L1CAM and vimentin were expressed at the tumor-stroma interface. In L1CAM-negative A549 cells the L1CAM expression was upregulated and matrigel invasion was increased after stimulation with TGF-beta1. In L1CAM-positive SK-LU-1 and SK-LC-LL cells matrigel invasion was decreased after L1CAM siRNA knockdown. Conclusions: A subset of NSCLCs with vessel tropism and increased metastasis aberrantly expresses L1CAM. L1CAM is a novel prognostic marker for NSCLCs that is upregulated by EMT induction and appears to be instrumental for enhanced cell invasion

Characterising soundscapes across diverse ecosystems using a universal acoustic feature set

Natural habitats are being impacted by human pressures at an alarming rate. Monitoring these ecosystem-level changes often requires labor-intensive surveys that are unable to detect rapid or unanticipated environmental changes. Here we have developed a generalizable, data-driven solution to this challenge using eco-acoustic data. We exploited a convolutional neural network to embed soundscapes from a variety of ecosystems into a common acoustic space. In both supervised and unsupervised modes, this allowed us to accurately quantify variation in habitat quality across space and in biodiversity through time. On the scale of seconds, we learned a typical soundscape model that allowed automatic identification of anomalous sounds in playback experiments, providing a potential route for real-time automated detection of irregular environmental behavior including illegal logging and hunting. Our highly generalizable approach, and the common set of features, will enable scientists to unlock previously hidden insights from acoustic data and offers promise as a backbone technology for global collaborative autonomous ecosystem monitoring efforts

The Role of lncRNAs TAPIR-1 and -2 as Diagnostic Markers and Potential Therapeutic Targets in Prostate Cancer

In search of new biomarkers suitable for the diagnosis and treatment of prostate cancer, genome-wide transcriptome sequencing was carried out with tissue specimens from 40 prostate cancer (PCa) and 8 benign prostate hyperplasia patients. We identified two intergenic long non-coding transcripts, located in close genomic proximity, which are highly expressed in PCa. Microarray studies on a larger cohort comprising 155 patients showed a profound diagnostic potential of these transcripts (AUC~0.94), which we designated as tumor associated prostate cancer increased lncRNA (TAPIR-1 and -2). To test their therapeutic potential, knockdown experiments with siRNA were carried out. The knockdown caused an increase in the p53/TP53 tumor suppressor protein level followed by downregulation of a large number of cell cycle- and DNA-damage repair key regulators. Furthermore, in radiation therapy resistant tumor cells, the knockdown leads to a renewed sensitization of these cells to radiation treatment. Accordingly, in a preclinical PCa xenograft model in mice, the systemic application of nanoparticles loaded with siRNA targeting TAPIR-1 significantly reduced tumor growth. These findings point to a crucial role of TAPIR-1 and -2 in PCa

Adenosine/A2B receptor signaling ameliorates the effects of ageing and counteracts obesity

The combination of aging populations with the obesity pandemic results in an alarming rise in non-communicable diseases. Here, we show that the enigmatic adenosine A2B receptor (A2B) is abundantly expressed in skeletal muscle (SKM) as well as brown adipose tissue (BAT) and might be targeted to counteract age-related muscle atrophy (sarcopenia) as well as obesity. Mice with SKM-specific deletion of A2B exhibited sarcopenia, diminished muscle strength, and reduced energy expenditure (EE), whereas pharmacological A2B activation counteracted these processes. Adipose tissue-specific ablation of A2B exacerbated age-related processes and reduced BAT EE, whereas A2B stimulation ameliorated obesity. In humans, A2B expression correlated with EE in SKM, BAT activity, and abundance of thermogenic adipocytes in white fat. Moreover, A2B agonist treatment increased EE from human adipocytes, myocytes, and muscle explants. Mechanistically, A2B forms heterodimers required for adenosine signaling. Overall, adenosine/A2B signaling links muscle and BAT and has both anti-aging and anti-obesity potential