A predictive model of the cat cortical connectome based on cytoarchitecture and distance

Information processing in the brain is strongly constrained by anatomical connectivity. However, the principles governing the organization of corticocortical connections remain elusive. Here, we tested three models of relationships between the organization of cortical structure and features of connections linking 49 areas of the cat cerebral cortex. Factors taken into account were relative cytoarchitectonic differentiation (‘structural model’), relative spatial position (‘distance model’), or relative hierarchical position (‘hierarchical model’) of the areas. Cytoarchitectonic differentiation and spatial distance (themselves uncorrelated) correlated strongly with the existence of inter-areal connections, whereas no correlation was found with relative hierarchical position. Moreover, a strong correlation was observed between patterns of laminar projection origin or termination and cytoarchitectonic differentiation. Additionally, cytoarchitectonic differentiation correlated with the absolute number of corticocortical connections formed by areas, and varied characteristically between different cortical subnetworks, including a ‘richclub’ module of hub areas. Thus, connections between areas of the cat cerebral cortex can, to a large part, be explained by the two independent factors of relative cytoarchitectonic differentiation and spatial distance of brain regions. As both the structural and distance model were originally formulated in the macaque monkey, their applicability in another mammalian species suggests a general principle of global cortical organization

Entwicklung eines Frameworks unter Verwendung von Kontextinformationen und kollektiver Intelligenz

Die Bedeutung von Daten, Informationen und Wissen als Faktor für wirtschaftliches und gesellschaftliches Handeln ist enorm und wächst noch weiter an. Ihr Austausch kennzeichnet und bestimmt die Globalisierung und den digitalen Wandel weit mehr als der Austausch von Waren. Grundlage dieser Entwicklung sind in erster Linie die enormen Fortschritte der Informations- und Kommunikationstechnik, die inzwischen insbesondere die Verfügbarkeit von Daten und Informationen nahezu zu jedem Zeitpunkt und an jedem Ort ermöglichen. Allerdings führt die riesige, rasant weiterwachsende verfügbare Menge an Daten und Informationen zu einer Überflutung in dem Sinne, dass es immer schwieriger wird, die jeweils relevanten Daten und Informationen zu finden bzw. zu identifizieren. Insbesondere beim Einsatz von Softwaresystemen ergibt sich aber für die Nutzer der Systeme häufig situations‑/kontextabhängig ein drängender Informationsbedarf, u.a. deshalb, weil die Systeme in immer kürzeren Zyklen verändert bzw. weiterentwickelt werden. Die entsprechende Suche nach Informationen zur Deckung des Informationsbedarfs ist jedoch häufig zeitaufwendig und wird vielfach „suboptimal“ durchgeführt. Michael Beul geht in seiner Arbeit der Frage nach, wie die Suche und Bereitstellung von relevanten Informationen erleichtert bzw. automatisiert durchgeführt werden kann, um eine effektivere Nutzung von Anwendungssystemen zu ermöglichen. Er erarbeitet ein Framework, welches insbesondere mit Hilfe von Konzepten der kollektiven Intelligenz eine kontextabhängige Echtzeit-Informationsbeschaffung für Nutzer softwareintensiver Systeme in den verschiedenen Anwendungsdomänen ermöglicht

Knowledge graph for manufacturing cost estimation of gear shafts - a case study on the availability of product and manufacturing information in practice

Growing cost pressure forces companies to actively manage their product costs to secure profitability. Here, manufacturing cost estimation within product development estimates manufacturing and material costs. As most products are developed in generations, needed product and manufacturing information can origin from reference system elements (RSE), for example similar components of prior product generations. Problematically, this product and manufacturing information as well as the knowledge of its interrelation is often stored in an unstructured way, document based or at least not machine-readable. This makes manufacturing cost estimation an effortful, time consuming and mainly manual activity with low traceability, where a wide manufacturing knowledge is required. Trends in production, like new manufacturing processes and production systems further increase the need for manufacturing information and knowledge. Knowledge graphs as semantic technologies can improve the findability and reusability of reference system elements and enable automatic information processing. Within this research, cost estimation of research and development of a large automotive supplier was used as research environment. Guided by the model of PGE an ontology for the manufacturing cost estimation domain was developed. Then, a knowledge graph was instantiated based on product and manufacturing information from gear shafts of electric axles. A case study was carried out to evaluate process-specific cycle time calculation as exemplary use case of the knowledge graph. Process-specific cycle times are generally effortful estimated based on detailed manufacturing information and then used together with machine hourly rates to estimate manufacturing costs. Here, the structured and machine-readable manufacturing information of identified reference system elements is extracted from the knowledge graph to reduce the effort, increase the traceability and enable future automation. The case study shows exemplary, how a knowledge graph can support manufacturing cost estimation of gear shafts where product and manufacturing information is automatically identified using reference system elements

Enzyme discovery and specificity fingerprints by analysis of correlated positions in CAZy family GH65

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