Conceptual Model Interoperability: a Metamodel-driven Approach

Linking, integrating, or converting conceptual data models represented in different modelling languages is a common aspect in the design and maintenance of complex information systems. While such languages seem similar, they are known to be distinct and no unifying framework exists that respects all of their language features in either model transformations or inter-model assertions to relate them. We aim to address this issue using an approach where the rules are enhanced with a logic-based metamodel. We present the main approach and some essential metamodel-driven rules for the static, structural, components of ER, EER, UML v2.4.1, ORM, and ORM2. The transformations for model elements and patterns are used with the metamodel to verify correctness of inter-model assertions across models in different languages

Altered bile acid profile in mild cognitive impairment and Alzheimer's disease: Relationship to neuroimaging and CSF biomarkers

Introduction: Bile acids (BAs) are the end products of cholesterol metabolism produced by human and gut microbiome co-metabolism. Recent evidence suggests gut microbiota influence pathological features of Alzheimer’s disease (AD) including neuroinflammation and amyloid-b deposition. Method: Serum levels of 20 primary and secondary BA metabolites from the AD Neuroimaging Initiative (n 5 1562) were measured using targeted metabolomic profiling. We assessed the association of BAs with the “A/T/N” (amyloid, tau, and neurodegeneration) biomarkers for AD: cerebrospinal fluid (CSF) biomarkers, atrophy (magnetic resonance imaging), and brain glucose metabolism ([18F]FDG PET). Results: Of 23 BAs and relevant calculated ratios after quality control procedures, three BA signatures were associated with CSF Ab1-42 (“A”) and three with CSF p-tau181 (“T”) (corrected P ,.05). Furthermore, three, twelve, and fourteen BA signatures were associated with CSF t-tau, glucose metabolism, and atrophy (“N”), respectively (corrected P , .05). Discussion: This is the first study to show serum-based BA metabolites are associated with “A/T/N” AD biomarkers, providing further support for a role of BA pathways in AD pathophysiology. Prospective clinical observations and validation in model systems are needed to assess causality and specific mechanisms underlying this association

Metabolic network failures in Alzheimer's disease: A biochemical road map

IntroductionThe Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance.MethodsFasting serum samples from the Alzheimer's Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted.Results Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aβ1–42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease.DiscussionMetabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery.Analytical BioScience

A multi-ancestry genome-wide study incorporating gene-smoking interactions identifies multiple new loci for pulse pressure and mean arterial pressure

Elevated blood pressure (BP), a leading cause of global morbidity and mortality, is influenced by both genetic and lifestyle factors. Cigarette smoking is one such lifestyle factor. Across five ancestries, we performed a genome-wide gene–smoking interaction study of mean arterial pressure (MAP) and pulse pressure (PP) in 129 913 individuals in stage 1 and follow-up analysis in 480 178 additional individuals in stage 2. We report here 136 loci significantly associated with MAP and/or PP. Of these, 61 were previously published through main-effect analysis of BP traits, 37 were recently reported by us for systolic BP and/or diastolic BP through gene–smoking interaction analysis and 38 were newly identified (P < 5 × 10−8, false discovery rate < 0.05). We also identified nine new signals near known loci. Of the 136 loci, 8 showed significant interaction with smoking status. They include CSMD1 previously reported for insulin resistance and BP in the spontaneously hypertensive rats. Many of the 38 new loci show biologic plausibility for a role in BP regulation. SLC26A7 encodes a chloride/bicarbonate exchanger expressed in the renal outer medullary collecting duct. AVPR1A is widely expressed, including in vascular smooth muscle cells, kidney, myocardium and brain. FHAD1 is a long non-coding RNA overexpressed in heart failure. TMEM51 was associated with contractile function in cardiomyocytes. CASP9 plays a central role in cardiomyocyte apoptosis. Identified only in African ancestry were 30 novel loci. Our findings highlight the value of multi-ancestry investigations, particularly in studies of interaction with lifestyle factors, where genomic and lifestyle differences may contribute to novel findings

Low cost hydrogen/novel membrane technology for hydrogen separation from synthesis gas

The production of hydrogen from synthesis gas made by gasification of coal is expensive. The separation of hydrogen from synthesis gas is a major cost element in the total process. In this report we describe the results of a program aimed at the development of membranes and membrane modules for the separation and purification of hydrogen from synthesis gas. The performance properties of the developed membranes were used in an economic evaluation of membrane gas separation systems in the coal gasification process. Membranes tested were polyetherimide and a polyamide copolymer. The work began with an examination of the chemical separations required to produce hydrogen from synthesis gas, identification of three specific separations where membranes might be applicable. A range of membrane fabrication techniques and module configurations were investigated to optimize the separation properties of the membrane materials. Parametric data obtained were used to develop the economic comparison of processes incorporating membranes with a base-case system without membranes. The computer calculations for the economic analysis were designed and executed. Finally, we briefly investigated alternative methods of performing the three separations in the production of hydrogen from synthesis gas. The three potential opportunities for membranes in the production of hydrogen from synthesis gas are: (1) separation of hydrogen from nitrogen as the final separation in a air-blown or oxygen-enriched air-blown gasification process, (2) separation of hydrogen from carbon dioxide and hydrogen sulfide to reduce or eliminate the conventional ethanolamine acid gas removal unit, and (3) separation of hydrogen and/or carbon dioxide form carbon monoxide prior to the shift reactor to influence the shift reaction. 28 refs., 54 figs., 40 tabs

The human pathogenesis of SARS: Gene expression profiling of patients with severe acute respiratory syndrome (SARS)

No abstract availabl