CNS Recruitment of CD8+ T Lymphocytes Specific for a Peripheral Virus Infection Triggers Neuropathogenesis during Polymicrobial Challenge

Although viruses have been implicated in central nervous system (CNS) diseases of unknown etiology, including multiple sclerosis and amyotrophic lateral sclerosis, the reproducible identification of viral triggers in such diseases has been largely unsuccessful. Here, we explore the hypothesis that viruses need not replicate in the tissue in which they cause disease; specifically, that a peripheral infection might trigger CNS pathology. To test this idea, we utilized a transgenic mouse model in which we found that immune cells responding to a peripheral infection are recruited to the CNS, where they trigger neurological damage. In this model, mice are infected with both CNS-restricted measles virus (MV) and peripherally restricted lymphocytic choriomeningitis virus (LCMV). While infection with either virus alone resulted in no illness, infection with both viruses caused disease in all mice, with ∼50% dying following seizures. Co-infection resulted in a 12-fold increase in the number of CD8+ T cells in the brain as compared to MV infection alone. Tetramer analysis revealed that a substantial proportion (>35%) of these infiltrating CD8+ lymphocytes were LCMV-specific, despite no detectable LCMV in CNS tissues. Mechanistically, CNS disease was due to edema, induced in a CD8-dependent but perforin-independent manner, and brain herniation, similar to that observed in mice challenged intracerebrally with LCMV. These results indicate that T cell trafficking can be influenced by other ongoing immune challenges, and that CD8+ T cell recruitment to the brain can trigger CNS disease in the apparent absence of cognate antigen. By extrapolation, human CNS diseases of unknown etiology need not be associated with infection with any particular agent; rather, a condition that compromises and activates the blood-brain barrier and adjacent brain parenchyma can render the CNS susceptible to pathogen-independent immune attack

Case Studies on Transport Policy

Transport policy is a multidisciplinary field where engineering, economics, sociology and law must come together in well-articulated and effective solutions. Despite being a field of effective intervention, most scientific publications address transport policy with a theoretical and often abstract approach, making its understanding difficult for non-senior academics and even more opaque for practitioners. While the merits of case study methods both for undergraduate and graduate teaching are recognised, academics struggle to find empirical material that provides objective and operational illustration of the theories and approaches lectured. This is a major barrier not only in the teaching context but also for practitioners. Case Studies on Transport Policy covers this gap by providing a repository of relevant material to support teaching and transferability of experiences. Observation of field experience highlighting the details and drawbacks of implementation is invaluable to show how Transport Policy can be applied in the operational field, maintaining consistency with strategic options. Teaching with case studies introduces students to challenges they may face in the real world, and provides a very rich learning method for executive training at every institutional level. For practitioners, and specially governments, case studies are a powerful tool to show the potential benefits from policy measures and packages. Case Studies on Transport Policy and its sister journal Transport Policy provide a valuable reference for the specialised study of transport policy offering in-depth theoretical analysis and detailed case study description and analysis, and in this way providing very complete material for decision makers planners and practitioners to undertake transferability of experiences

Prescriptive Maintenance of Railway Infrastructure: From Data Analytics to Decision Support

One of the main benefits of the railways digital transformation is the possibility of increasing the efficiency of the Asset Management process through the combination of data-driven models and decision support systems, paving the road towards an Intelligent Asset Management System (IAMS). The paper describes the whole IAMS decisional process based on a real railway signaling use case: from field data acquisition to decision support. The process includes data collection, preparation and analytics to extract knowledge on current and future assets' status. Then, the extracted knowledge is used within the decision support system to prioritize asset management interventions in a fully-Automated way, by applying optimization logics and operational constraints.The target is to optimize the scheduling of maintenance activities, to maximize the service reliability and optimize both usage of resources and possession times, avoiding (or minimizing) contractual penalties and delays.In this context, a real use case related to signaling system and, in particular, to track circuits, is presented, applying the proposed methodology to an Italian urban rail network and showing the usefulness of the approach and its possible further developments