Role of endothelial cells in graft-versus-host disease.

To date, the only curative treatment for high-risk or refractory hematologic malignancies non-responsive to standard chemotherapy is allogeneic hematopoietic transplantation (allo-HCT). Acute graft-versus-host disease (GVHD) is a donor T cell-mediated immunological disorder that is frequently fatal and the leading cause of non-relapse mortality (NRM) in patients post allo-HCT. The pathogenesis of acute GVHD involves recognition of minor and/or major HLA mismatched host antigens by donor T cells followed by expansion, migration and finally end-organ damage due to combination of inflammatory cytokine secretion and direct cytotoxic effects. The endothelium is a thin layer of endothelial cells (EC) that line the innermost portion of the blood vessels and a key regulator in vascular homeostasis and inflammatory responses. Endothelial cells are activated by a wide range of inflammatory mediators including bacterial products, contents released from dying/apoptotic cells and cytokines and respond by secreting cytokines/chemokines that facilitate the recruitment of innate and adaptive immune cells to the site of inflammation. Endothelial cells can also be damaged prior to transplant as well as by alloreactive donor T cells. Prolonged EC activation results in dysfunction that plays a role in multiple post-transplant complications including but not limited to veno-occlusive disease (VOD), transplant associated thrombotic microangiopathy (TA-TMA), and idiopathic pneumonia syndrome. In this mini review, we summarize the biology of endothelial cells, factors regulating EC activation and the role of ECs in inflammation and GVHD pathogenesis.

Corrigendum: Role of endothelial cells in graft-versus-host disease.

[This corrects the article DOI: 10.3389/fimmu.2022.1033490.].

A Systematic Approach for Evaluating Artificial Intelligence Models in Industrial Settings.

Artificial Intelligence (AI) is one of the hottest topics in our society, especially when it comes to solving data-analysis problems. Industry are conducting their digital shifts, and AI is becoming a cornerstone technology for making decisions out of the huge amount of (sensors-based) data available in the production floor. However, such technology may be disappointing when deployed in real conditions. Despite good theoretical performances and high accuracy when trained and tested in isolation, a Machine-Learning (M-L) model may provide degraded performances in real conditions. One reason may be fragility in treating properly unexpected or perturbed data. The objective of the paper is therefore to study the robustness of seven M-L and Deep-Learning (D-L) algorithms, when classifying univariate time-series under perturbations. A systematic approach is proposed for artificially injecting perturbations in the data and for evaluating the robustness of the models. This approach focuses on two perturbations that are likely to happen during data collection. Our experimental study, conducted on twenty sensors' datasets from the public University of California Riverside (UCR) repository, shows a great disparity of the models' robustness under data quality degradation. Those results are used to analyse whether the impact of such robustness can be predictable-thanks to decision trees-which would prevent us from testing all perturbations scenarios. Our study shows that building such a predictor is not straightforward and suggests that such a systematic approach needs to be used for evaluating AI models' robustness.

A Case Driven Study of the Use of Time Series Classification for Flexibility in Industry 4.0.

With the Industry 4.0 paradigm comes the convergence of the Internet Technologies and Operational Technologies, and concepts, such as Industrial Internet of Things (IIoT), cloud manufacturing, Cyber-Physical Systems (CPS), and so on. These concepts bring industries into the big data era and allow for them to have access to potentially useful information in order to optimise the Overall Equipment Effectiveness (OEE); however, most European industries still rely on the Computer-Integrated Manufacturing (CIM) model, where the production systems run as independent systems (i.e., without any communication with the upper levels). Those production systems are controlled by a Programmable Logic Controller, in which a static and rigid program is implemented. This program is static and rigid in a sense that the programmed routines cannot evolve over the time unless a human modifies it. However, to go further in terms of flexibility, we are convinced that it requires moving away from the aforementioned old-fashioned and rigid automation to a ML-based automation, i.e., where the control itself is based on the decisions that were taken by ML algorithms. In order to verify this, we applied a time series classification method on a scale model of a factory using real industrial controllers, and widened the variety of parts the production line has to treat. This study shows that satisfactory results can be obtained only at the expense of the human expertise (i.e., in the industrial process and in the ML process).

Data Exchange Interoperability in IoT Ecosystem for Smart Parking and EV Charging.

Many domains are trying to integrate with the Internet of Things (IoT) ecosystem, such as public administrations starting smart city initiatives all over the world. Cities are becoming smart in many ways: smart mobility, smart buildings, smart environment and so on. However, the problem of non-interoperability in the IoT hinders the seamless communication between all kinds of IoT devices. Different domain specific IoT applications use different interoperability standards. These standards are usually not interoperable with each other. IoT applications and ecosystems therefore tend to use a vertical communication model that does not allow data sharing horizontally across different IoT ecosystems. In 2014, The Open Group published two domain-independent IoT messaging standards, O-MI and O-DF, aiming to solve the interoperability problem. In this article we describe the practical use of O-MI/O-DF standards for reaching interoperability in a mobile application for the smart city context, in particular for the Smart Mobility domain, electric vehicle (EV) charging case study. The proof-of-concept of the smart EV charging ecosystem with mobile application user interface was developed as a part of an EU (Horizon 2020) Project bIoTope.

Open IoT Ecosystem for Enhanced Interoperability in Smart Cities-Example of Métropole De Lyon.

The Internet of Things (IoT) has promised a future where everything gets connected. Unfortunately, building a single global ecosystem of Things that communicate with each other seamlessly is virtually impossible today. The reason is that the IoT is essentially a collection of isolated "Intranets of Things", also referred to as "vertical silos", which cannot easily and efficiently interact with each other. Smart cities are perhaps the most striking examples of this problem since they comprise a wide range of stakeholders and service providers who must work together, including urban planners, financial organisations, public and private service providers, telecommunication providers, industries, citizens, and so forth. Within this context, the contribution of this paper is threefold: (i) discuss business and technological implications as well as challenges of creating successful open innovation ecosystems, (ii) present the technological building blocks underlying an IoT ecosystem developed in the framework of the EU Horizon 2020 programme, (iii) present a smart city pilot (Heat Wave Mitigation in Métropole de Lyon) for which the proposed ecosystem significantly contributes to improving interoperability between a number of system components, and reducing regulatory barriers for joint service co-creation practices.

Stellate Ganglion Block for the Treatment of Posttraumatic Stress Disorder: A Randomized, Double-Blind, Controlled Trial.

OBJECTIVE: In this study, we aimed to determine if stellate ganglion block (SGB) could reduce symptoms of posttraumatic stress disorder (PTSD) in comparison with sham therapy in military service members. METHODS: In a randomized trial in which both participants and assessors were blind, participants with PTSD received either an SGB or a sham procedure. Posttraumatic stress disorder symptoms were measured using the CAPS (Clinician-Administered PTSD Scale) and self-report measures of PTSD, depression, anxiety, and pain. Subjects underwent assessment before the procedure and at 1 week, 1 month, and 3 months after the procedure. Patients receiving sham injections were allowed to cross over to the treatment group, and participants who maintained criteria for PTSD were allowed to receive a second SGB treatment. RESULTS: Posttraumatic stress disorder, anxiety, and depression scores all showed improvement across time, but there was no statistically or clinically relevant difference in outcomes between the active and control groups. Individuals who crossed over from sham treatment to SGB similarly showed no greater improvement with the SGB treatment. Improvement in CAPS was greater with a second SGB treatment than after the first treatment. CONCLUSIONS: Although previous case series have suggested that SGB offers an effective intervention for PTSD, this study did not demonstrate any appreciable difference between SGB and sham treatment on psychological or pain outcomes. Future studies should examine if differences in treatment methods or patient population could allow individuals with PTSD to benefit from SGB, but current evidence does not support widespread or indiscriminant clinical use of the procedure for PTSD.