The geometrical shape of mesenchymal stromal cells measured by quantitative shape descriptors is determined by the stiffness of the biomaterial and by cyclic tensile forces

Controlling mesenchymal stromal cell (MSC) shape is a novel method for investigating and directing MSC behaviour in vitro. it was hypothesized that specifigc MSC shapes can be generated by using stiffnessâ defined biomaterial surfaces and by applying cyclic tensile forces. Biomaterials used were thin and thick silicone sheets, fibronectin coating, and compacted collagen type I sheets. The MSC morphology was quantified by shape descriptors describing dimensions and membrane protrusions. Nanoscale stiffness was measured by atomic force microscopy and the expression of smooth muscle cell (SMC) marker genes (ACTA2, TAGLN, CNN1) by quantitative reverseâ transcription polymerase chain reaction. Cyclic stretch was applied with 2.5% or 5% amplitudes. Attachment to biomaterials with a higher stiffness yielded more elongated MSCs with fewer membrane protrusions compared with biomaterials with a lower stiffness. For cyclic stretch, compacted collagen sheets were selected, which were associated with the most elongated MSC shape across all investigated biomaterials. As expected, cyclic stretch elongated MSCs during stretch. One hour after cessation of stretch, however, MSC shape was rounder again, suggesting loss of stretchâ induced shape. Different shape descriptor values obtained by different stretch regimes correlated significantly with the expression levels of SMC marker genes. Values of approximately 0.4 for roundness and 3.4 for aspect ratio were critical for the highest expression levels of ACTA2 and CNN1. Thus, specific shape descriptor values, which can be generated using biomaterialâ associated stiffness and tensile forces, can serve as a template for the induction of specific gene expression levels in MSC. Copyright © 2017 John Wiley & Sons, Ltd.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141253/1/term2263.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141253/2/term2263_am.pd

Toward Self-Referential Autonomous Learning of Object and Situation Models

Most current approaches to scene understanding lack the capability to adapt object and situation models to behavioral needs not anticipated by the human system designer. Here, we give a detailed description of a system architecture for self-referential autonomous learning which enables the refinement of object and situation models during operation in order to optimize behavior. This includes structural learning of hierarchical models for situations and behaviors that is triggered by a mismatch between expected and actual action outcome. Besides proposing architectural concepts, we also describe a first implementation of our system within a simulated traffic scenario to demonstrate the feasibility of our approach

A Conceptual Model of Conveyances on the Housing Market

The first important difference between real estate markets and the perfect competition theory is that the real estate market can be divided into a number of sectors: office, retail, industrial, and residential. This chapter outlines how the specific characteristics of the real estate market set the overall rules governing the market. It concludes with a conceptual model of how the conveyance process has different stages. First, a review of different legal systems will be presented. Second, a short exposition of the specific characteristics of the real estate market will be given. Third, and last, the conceptual model of conveyances on the housing market will be outlined and presented. Different legal systems have different prerequisites to confirm it as legally valid. After taking the property in possession and the purchase has been registered, the transaction can still be associated with costs

Digital twins in the smart factory

What does the factory of tomorrow have to offer for companies? This question and its aspects are the focus of many actual articles and publications. According to Gartner digital twins, one of 2017 strategic technology trends will play a big role for the future of manufacturing. At the moment digital twins are gaining more importance for the industrial application. If companies want to be competitive in the future they have to implement the digital twin in the factories of today. Therefore this paper provides a basic overview of the concept of the smart factory and its requirements. In addition, digital twins are identified as a necessary concept for the evolution of the factory of today

Private 5G-Campusnetze für den industriellen Einsatz in der Intralogistik : Untersuchung der Leistungsfähigkeit von privaten 5G-Campusnetzwerken und 5G-Endgeräten im Feldeinsatz

5G-Campusnetze sind vielversprechende Umgebungen für industrielle Anwendungen in Produktion und Intralogistik. Diese erreichen jedoch bisher nicht die versprochenen Leistungen, um intralogistischen Anwendungen das volle Potenzial von 5G bieten zu können. Die im Rahmen des Projekts 5G4KMU erhobenen und in diesem Beitrag vorgestellten Leistungsmessungen dienen zur Evaluierung der derzeitigen Praxistauglichkeit von 5G-Campusnetzen.5G campus networks are promising environments for industrial applications in production and intralogistics. However, campus networks do not yet achieve the promised performance to offer intralogistics applications the full potential of 5G. The performance measurements collected as part of the 5G4KMU project and presented in this article serve to evaluate the current practicality of 5G campus networks

Supporting digital transformation: real-time monitoring of private 5G networks to educate future connectivity experts by means of learning factories

The fifth generation of mobile communication (5G) is a wireless technology developed to provide reliable, fast data transmission for industrial applications, such as autonomous mobile robots and connect cyber-physical systems using Internet of Things (IoT) sensors. In this context, private 5G networks enable the full performance of industrial applications built on dedicated 5G infrastructures. However, emerging wireless communication technologies such as 5G are a complex and challenging topic for training in learning factories, often lacking physical or visual interaction. Therefore, this paper aims to develop a real-time performance monitoring system of private 5G networks and different industrial 5G devices to visualise the performance and impact factors influencing 5G for students and future connectivity experts. Additionally, this paper presents the first long-term measurements of private 5G networks and shows the performance gap between the actual and targeted performance of private 5G networks