Bjerregaard Kondrup (beltdish27)

Future studies are planned to validate and follow up the immune biomarkers (and combinations/interactions thereof) associated with clinical responses identified with this computational pipeline.The field of cyber-physical systems is a growing IT research area that addresses the deep integration of computing, communication and process control, possibly with humans in the loop. The goal of such area is to define modelling, controlling and programming methodologies for designing and managing complex mechatronics systems, also called industrial agents. Our research topic mainly focuses on the area of data mining and analysis by means of multi-agent orchestration of intelligent sensor nodes using internet protocols, providing also web-based HMI visualizations for data interpretability and analysis. Thanks to the rapid spreading of IoT systems, supported by modern and efficient telecommunication infrastructures and new decentralized control paradigms, the field of service-oriented programming finds new application in wireless sensor networks and microservices paradigm we adopted such paradigm in the implementation of two different industrial use cases. Indeed, we expect a concrete and deep use of such technologies with 5G spreading. In the article, we describe the common software architectural pattern in IoT applications we used for the distributed smart sensors, providing also design and implementation details. In the use case section, the prototypes developed as proof of concept and the KPIs used for the system validation are described to provide a concrete solution overview.In order to ensure the production quality of high-speed laser welding, it is necessary to simultaneously monitor multiple state properties. Monitoring methods combining vision sensing and deep learning models are popular but most models used can only make predictions on single welding state property. In this contribution, we propose a multi-output model based on a lightweight convolutional neural network (CNN) architecture and introduce the particle swarm optimization (PSO) technique to optimize the loss function of the model, to simultaneously monitor multiple state properties of high-speed laser welding of AISI 304 austenitic stainless steel. High-speed imaging is performed to capture images of the melt pool and the dataset is built. Test results of different models show that the proposed model can achieve monitoring of multiple welding state properties accurately and efficiently. In addition, we make an interpretation and discussion on the prediction of the model through a visualization method, which can help to deepen our understanding of the relationship between the melt pool appearance and welding state. The proposed method can not only be applied to the monitoring of high-speed laser welding but also has the potential to be used in other procedures of welding state monitoring.Several studies have demonstrated a relevant role of intestinal epithelial cells in the immune response and in chronic inflammatory conditions, including ulcers, colitis, and Crohn's disease. Baicalin (BA), extracted from the root of Scutellaria baicalensis, has various beneficial healthy effects, including anti-inflammatory activity. However, few studies have evaluated BA effects on autophagic signaling in epithelial cell response to inflammatory stimuli. To explore possible beneficial effects of BA, HT-29 cells were exposed to lipopolysaccharide (LPS), in presence or absence of BA, for 4 h. We evaluated mRNA levels of autophagy-related genes and cytokines, triggering inflammatory response. Furthermore, the expression of claudin 1, involved in the regulation of paracellular permeability was analyzed. BA treatment repressed LPS-induced expression of TNF-α and IL-1β. The down-regulation of autophagy-related genes induced by LPS was counteracted by cell pretreatment with BA. Under these conditions, BA reduced the NF-κB activation caused by LPS. Also, BA restored mRNA and protein le