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Longitudinal changes in gene expression, splicing patterns and adaptive immune repertoire in murine models of colitis are examined and associated with histologic inflammation in mice and in patients with inflammatory bowel disease.

SMEs and Enterprise companies are looking for workplace-ready graduates that have already gained a relevant range of skills and knowledge as part of their studies. These include having specific proficiencies as well as a broad understanding of industry, including transferable skills such as self-awareness, critical thinking, teamwork, listening, time management, and leadership [1]. This demand entails a reciprocal relationship between industry and academia, which is one of many aspects that drives the need for solid collaborations between the two sectors [2]. When facing the recruitment process, however, SMEs and Enterprise companies often struggle to match their requirements to the learning outcomes of new graduates applying for positions. Companies are faced with an overwhelming array of degree programmes to engage with, most of which consist of multiple modules and options. Even within the same institute and school, students graduate with the same qualification, but have gone through vastly different pathways and gained a varied experience based on the optional modules they may have taken. Without enough academic knowledge and familiarity and no means to distinguish between these courses and the graduates, the recruitment process for companies must rely heavily on lengthy interview procedures to search for the right graduate with the right experience and transversal skills, a process that can be resource intensive in terms of time and financial cost. Given that learning trajectories across programmes and curricula are often not visible from an employer perspective some form of mapping of academic curriculum to industry graduate requirements would seem an essential step to help relieve employers, at least partially, from burdensome recruitment procedure [3]. The broad goal of the HubLinked Knowledge Alliance is to strengthen Europe’s software innovation capacity by learning from regions of proven Information Computing Technology (ICT) strength in Europe and Asia and sharing that knowledge with all regions. A key goal of the Alliance was to conduct research on the effectiveness of University-Industry (U-I) collaborations between Computer Science faculties and Companies (including non-ICT companies) as U-I collaborations are understood as a core driver of innovation capacity. In recognising that SMEs and Enterprise companies often struggle to match their graduate requirements to the learning outcomes of new graduates, two key challenges (presented here as fundamental questions) emerged: How can SME requirements for graduate recruitment be captured in a way that facilitates matching their requirements to academic programmes? How do you match university programmes from different institution to the industry requirement? In this paper we present a Curriculum Mapping Framework (CMF) and a Curriculum Mapping Tool (CMT) to address these issues. The CMF encodes the companies graduate attributes into a virtual curriculum after which the CMT maps the virtual curriculum onto specific educational pathway within an academic programme to determine the level of match between the two. The CMF and the CMT were both designed within the HubLinked Knowledge Alliance [4], a partnership of seven large industry-focused Computer Science Faculties and four Industry partners representing large multinationals, SMEs and start-up companies. Section two will explore the context that led to the development of the CMF and the CMT. In order to map learning outcomes across different programmes and courses, across different academic award levels and across different institutions, it is necessary to understand the general structure of a programme and how curricula are constructed. Our approach has been strongly inspired by the reports of the Association for Computing Machinery (ACM) [1] and Bloom’s Taxonomy [5] and by the assumption that multiple pathways are possible within each academic programme, meaning individuals undertaking the same programme gain varied skills depending on the optional modules for which they have opted. Section three describes in the development of the CMF which provides a mechanism for encoding industry requirements into a curriculum. Qualitative data was collected over a three-year period in the form of interviews with 40 Industry professionals and through organised focus groups with academic partners and stakeholders. Data collection was a central theme at each of the quarterly meetings hosted by each of the project partners who also facilitated the contribution of additional academic staff from outside of the project. Section four presents the CMT and demonstrates how the mapping process between ICT programmes and the Hublinked curriculum is achieved. The CMT is available on the HubLinked website for download[2]. Observations on the CMF and the CMT including recommendations on its future use are presented in the last sections of this paper.

Molecular Medicine: Personalized expression profiles elucidate disease heterogeneity A comparison of gene expression profiles of different patients with the same disease reveals a combinatorial model for disease heterogeneity. A framework is developed to generalize group-wise differential expression profiles to personalized perturbation profiles (PEEPs) for individual subjects. It is shown that similarities among patients with the same disease cannot be attributed to a few widely shared genes, but arise from more complex patterns of pairwise overlaps that often correspond to perturbations within the same biological pathway. This points to the existence of a disease module, i.e. a broader group of functionally related genes whose perturbations are associated with the specific disease. The overlap of an individual’s PEEP with the disease module accurately predicts disease status, suggesting their potential for developing combinatorial biomarker signatures or identifying most widely effective drug targets.

To date, no large scale, systematic description of the blood serum proteome has been performed in inflammatory bowel disease (IBD) patients. By using microarray technology, a more complete description of the blood proteome of IBD patients is feasible. It may help to achieve a better understanding of the disease. We analyzed blood serum profiles of 1128 proteins in IBD patients of European descent (84 Crohn's Disease (CD) subjects and 88 Ulcerative Colitis (UC) subjects) as well as 15 healthy control subjects, and linked protein variability to patient age (all cohorts) and genetic components (genotype data generated from CD patients). We discovered new, previously unreported aging-associated proteomic traits (such as serum Albumin level), confirmed previously reported results from different tissues (i.e., upregulation of APOE with aging), and found loss of regulation of MMP7 in CD patients. In carrying out a genome wide genotype-protein association study (proteomic Quantitative Trait Loci, pQTL) within the CD patients, we identified 41 distinct proteomic traits influenced by cis pQTLs (underlying SNPs are referred to as pSNPs). Significant overlaps between pQTLs and cis eQTLs corresponding to the same gene were observed and in some cases the QTL were related to inflammatory disease susceptibility. Importantly, we discovered that serum protein levels of MST1 (Macrophage Stimulating 1) were regulated by SNP rs3197999 (p = 5.96E-10, FDR

Nowadays, industrial networks are often used for safety-critical applications with real-time requirements. Such applications usually have a time-triggered nature with message scheduling as a core property. Scheduling requires nodes to share the same notion of time, that is, to be synchronized. Therefore, clock synchronization is a fundamental asset in real-time networks. However, since typical standards for clock synchronization, for example, IEEE 1588, do not provide the required level of security, it raises the question of clock synchronization protection. In this paper, we identify a way to break synchronization based on the IEEE 1588 standard, by conducting a man-in-the-middle (MIM) attack followed by a delay attack. A MIM attack can be accomplished through, for example, Address Resolution Protocol (ARP) poisoning. Using the AVISPA tool, we evaluate the potential to perform a delay attack using ARP poisoning and analyze its consequences showing both that the attack can, indeed, break clock synchronization and that some design choices, such as a relaxed synchronization condition mode, delay bounding, and using knowledge of environmental conditions, can make the network more robust/resilient against these kinds of attacks. Lastly, a Configuration Agent is proposed to monitor and detect anomalies introduced by an adversary performing attacks targeting clock synchronization.

Complex diseases such as obesity and type II diabetes can result from a failure in multiple organ systems including the central nervous system and tissues involved in partitioning and disposal of nutrients. Studying the genetics of gene expression in tissues that are involved in the development of these diseases can provide insights into how these tissues interact within the context of disease. Expression quantitative trait locus (eQTL) studies identify mRNA expression changes linked to proximal genetic signals (cis eQTLs) that have been shown to affect disease. Given the high impact of recent eQTL studies, it is important to understand what role sample size and environment plays in identification of cis eQTLs. Here we show in a genotyped obese human population that the number of cis eQTLs obey precise scaling laws as a function of sample size in three profiled tissues, i.e. omental adipose, subcutaneous adipose and liver. Also, we show that genes (or transcripts) with cis eQTL associations detected in a small population are detected at approximately 90% rate in the largest population available for our study, indicating that genes with strong cis acting regulatory elements can be identified with relatively high confidence in smaller populations. However, by increasing the sample size we allow for better detection of weaker and more distantly located cis-regulatory elements. Yet, we determined that the number of tissue specific cis eQTLs saturates in a modestly sized cohort while the number of cis eQTLs common to all tissues fails to reach a maximum value. Understanding the power laws that govern the number and specificity of eQTLs detected in different tissues, will allow a better utilization of genetics of gene expression to inform the molecular mechanism underlying complex disease traits.

Genome-wide association studies (GWAS) have demonstrated the ability to identify the strongest causal common variants in complex human diseases. However, to date, the massive data generated from GWAS have not been maximally explored to identify true associations that fail to meet the stringent level of association required to achieve genome-wide significance. Genetics of gene expression (GGE) studies have shown promise towards identifying DNA variations associated with disease and providing a path to functionally characterize findings from GWAS. Here, we present the first empiric study to systematically characterize the set of single nucleotide polymorphisms associated with expression (eSNPs) in liver, subcutaneous fat, and omental fat tissues, demonstrating these eSNPs are significantly more enriched for SNPs that associate with type 2 diabetes (T2D) in three large-scale GWAS than a matched set of randomly selected SNPs. This enrichment for T2D association increases as we restrict to eSNPs that correspond to genes comprising gene networks constructed from adipose gene expression data isolated from a mouse population segregating a T2D phenotype. Finally, by restricting to eSNPs corresponding to genes comprising an adipose subnetwork strongly predicted as causal for T2D, we dramatically increased the enrichment for SNPs associated with T2D and were able to identify a functionally related set of diabetes susceptibility genes. We identified and validated malic enzyme 1 (Me1) as a key regulator of this T2D subnetwork in mouse and provided support for the association of this gene to T2D in humans. This integration of eSNPs and networks provides a novel approach to identify disease susceptibility networks rather than the single SNPs or genes traditionally identified through GWAS, thereby extracting additional value from the wealth of data currently being generated by GWAS.

Aging involves a progressive physiological remodeling that is controlled by both genetic and environmental factors. Many of these factors impact also on white adipose tissue (WAT), which has been shown to be a determinant of lifespan. Interrogating a transcriptional network for predicted causal regulatory interactions in a collection of mouse WAT from F2 crosses with a seed set of 60 known longevity genes, we identified a novel transcriptional subnetwork of 742 genes which represent thus-far-unknown longevity genes. Within this subnetwork, one gene was Pparg (Nr1c3), an adipose-enriched nuclear receptor previously not associated with longevity. In silico, both the PPAR signaling pathway and the transcriptional signature of Ppargamma agonist rosiglitazone overlapped with the longevity subnetwork, while in vivo, lowered expression of Pparg reduced lifespan in both the lipodystrophic Pparg1/2-hypomorphic and the Pparg2-deficient mice. These results establish Ppargamma2 as one of the determinants of longevity and suggest that lifespan may be rather determined by a purposeful genetic program than a random process.