Data Model Canvas für die IT-Systemübergreifende Integration von Datenmodellen zur Unterstützung von Datenanalyse-Anwendungen im Produktlebenszyklus

Der Data Model Canvas (DMC) unterstützt methodisch und informationstechnisch den Aufbau der für ein durchgängiges und interdisziplinäres Engineering benötigten fachlichen Datengrundlage und deren Abbildung in den betreffenden IT-Systemen. Basierend auf konkreten Analyse-Szenarien erfolgt eine Modellierung der erforderlichen Datenvernetzung, die wiederum die explizit benötigten Datenquellen umfasst. Im Mittelpunkt dieses Ansatzes steht die Entwicklung eines fachlichen Verständnisses über die zur Analyse notwendigen roduktdaten. Unterstützt wird der Ansatz durch ein Softwaretool zur Erstellung der benötigten Datenmodelle

The Crystal Structure of CHIR-AB1: A Primordial Avian Classical Fc Receptor

CHIR-AB1 is a newly identified avian immunoglobulin (Ig) receptor that includes both activating and inhibitory motifs and was therefore classified as a potentially bifunctional receptor. Recently, CHIR-AB1 was shown to bind the Fc region of chicken IgY and to induce calcium mobilization via association with the common γ-chain, a subunit that transmits signals upon ligation of many different immunoreceptors. Here we describe the 1.8-Å-resolution crystal structure of the CHIR-AB1 ectodomain. The receptor ectodomain consists of a single C2-type Ig domain resembling the Ig-like domains found in mammalian Fc receptors such as FcγRs and FcαRI. Unlike these receptors and other monomeric Ig superfamily members, CHIR-AB1 crystallized as a 2-fold symmetrical homodimer that bears no resemblance to variable or constant region dimers in an antibody. Analytical ultracentrifugation demonstrated that CHIR-AB1 exists as a mixture of monomers and dimers in solution, and equilibrium gel filtration revealed a 2:1 receptor/ligand binding stoichiometry. Measurement of the 1:1 CHIR-AB1/IgY interaction affinity indicates a relatively low affinity complex, but a 2:1 CHIR-AB1/IgY interaction allows an increase in apparent affinity due to avidity effects when the receptor is tethered to a surface. Taken together, these results add to the structural understanding of Fc receptors and their functional mechanisms

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Addressing Circularity Strategies by Reconfiguring Smart Products during Their Lifecycle

Considering circularity aspects during the engineering of smart products implies a significantly increased complexity of engineering processes. Especially technical reconfiguration of smart products, offered as services in availability-oriented business models, enables the integration of circular economy aspects in sustainable products and lifecycles, through realizing several aspects of the 9R strategies. This paper introduces an analysis of interdependencies between 9R strategies potential reconfiguration options, technical characteristics of smart products, different maturity levels of smart product and abilities for circularity. Partial engineering models managed in different product lifecycle management systems provide a technical basis for systematization and evaluation of circular abilities of reconfigurable smart products in different lifecycle phases. The approach aims to improve circularity-related decision making in systems engineering processes in the early development phases and during the reconfiguration of smart products during utilization phase. An industrial use case considering a microelectronic-centered smart product used in e-mobility solutions validates the approach

Data Model Canvas für die IT-Systemübergreifende Integration von Datenmodellen zur Unterstützung von Datenanalyse-Anwendungen im Produktlebenszyklus

Der Data Model Canvas (DMC) unterstützt methodisch und informationstechnisch den Aufbau der für ein durchgängiges und interdisziplinäres Engineering benötigten fachlichen Datengrundlage und deren Abbildung in den betreffenden IT-Systemen. Basierend auf konkreten Analyse-Szenarien erfolgt eine Modellierung der erforderlichen Datenvernetzung, die wiederum die explizit benötigten Datenquellen umfasst. Im Mittelpunkt dieses Ansatzes steht die Entwicklung eines fachlichen Verständnisses über die zur Analyse notwendigen roduktdaten. Unterstützt wird der Ansatz durch ein Softwaretool zur Erstellung der benötigten Datenmodelle

Maturity model for determining digitalization levels within different product lifecycle phases

Maintaining pace with ongoing changes due to digitalization is challenging for manufacturing companies. For successful implementation of digitalization, manufacturing companies must consider their existing technical systems, organizational structures, and processes, as well as social aspects. With the support of a maturity model, a company-specific digitalization level can be evaluated to provide manufacturing companies with an initial insight into their particular status quo; this can serve as a starting point for future optimization and digitalization projects. Furthermore, the results of such an analysis allow objective comparison of different areas within the company and with competitors. In this paper, the “Integrierte Arbeitssystemgestaltung in digitalisierten Produktionsunternehmen” (InAsPro) maturity model is presented, which considers the Development, Production, and Assembly product lifecycle phases, as well as Aftersales, and assesses their digitalization level focusing on the four dimensions of Technology, Organization, Social Issues, and Corporate Strategy. The maturity model’s rating scale distinguishes between four maturity levels. The results given by the InAsPro maturity model for an entire company are presented, along with those for each product lifecycle phase. Extensive descriptions for each specific maturity level are also provided

Network for Early Onset Cystic Kidney Diseases—A Comprehensive Multidisciplinary Approach to Hereditary Cystic Kidney Diseases in Childhood

Hereditary cystic kidney diseases comprise a complex group of genetic disorders representing one of the most common causes of end-stage renal failure in childhood. The main representatives are autosomal recessive polycystic kidney disease, nephronophthisis, Bardet–Biedl syndrome, and hepatocyte nuclear factor-1beta nephropathy. Within the last years, genetic efforts have brought tremendous progress for the molecular understanding of hereditary cystic kidney diseases identifying more than 70 genes. Yet, genetic heterogeneity, phenotypic variability, a lack of reliable genotype–phenotype correlations and the absence of disease-specific biomarkers remain major challenges for physicians treating children with cystic kidney diseases. To tackle these challenges comprehensive scientific approaches are urgently needed that match the ongoing “revolution” in genetics and molecular biology with an improved efficacy of clinical data collection. Network for early onset cystic kidney diseases (NEOCYST) is a multidisciplinary, multicenter collaborative combining a detailed collection of clinical data with translational scientific approaches addressing the genetic, molecular, and functional background of hereditary cystic kidney diseases. Consisting of seven work packages, including an international registry as well as a biobank, NEOCYST is not only dedicated to current scientific questions, but also provides a platform for longitudinal clinical surveillance and provides precious sources for high-quality research projects and future clinical trials. Funded by the German Federal Government, the NEOCYST collaborative started in February 2016. Here, we would like to introduce the rationale, design, and objectives of the network followed by a short overview on the current state of progress