Die Anfänge der studentischen Vizepräsidentschaft an der Zeppelin Universität

Die Zeppelin-Universität in Friedrichshafen ist eine der ganz wenigen Universitäten, die einen studentischen Vizepräsidenten hat. Der Autor berichtet über die Ausgestaltung des Amtes als erster hauptamtlicher Vizepräsident an dieser Privatuniversität. Seine Aufgabe bestand darin, die Verbindung der Universitätsleitung mit den Studierenden, der zahlenmäßig größten Stakeholdergruppe, zu intensivieren. (DIPF/Orig.

Myelin Oligodendrocyte Glycoprotein–specific T Cell Receptor Transgenic Mice Develop Spontaneous Autoimmune Optic Neuritis

Multiple sclerosis (MS) is considered to be an autoimmune disease of the central nervous system (CNS) that in many patients first presents clinically as optic neuritis. The relationship of optic neuritis to MS is not well understood. We have generated novel T cell receptor (TCR) transgenic mice specific for myelin oligodendrocyte glycoprotein (MOG). MOG-specific transgenic T cells are not deleted nor tolerized and are functionally competent. A large proportion (>30%) of MOG-specific TCR transgenic mice spontaneously develop isolated optic neuritis without any clinical nor histological evidence of experimental autoimmune encephalomyelitis (EAE). Optic neuritis without EAE could also be induced in these mice by sensitization with suboptimal doses of MOG. The predilection of these mice to develop optic neuritis is associated with higher expression of MOG in the optic nerve than in the spinal cord. These results demonstrate that clinical manifestations of CNS autoimmune disease will vary depending on the identity of the target autoantigen and that MOG-specific T cell responses are involved in the genesis of isolated optic neuritis

Spontaneous relapsing-remitting EAE in the SJL/J mouse: MOG-reactive transgenic T cells recruit endogenous MOG-specific B cells

We describe new T cell receptor (TCR) transgenic mice (relapsing-remitting [RR] mice) carrying a TCR specific for myelin oligodendrocyte glycoprotein (MOG) peptide 92–106 in the context of I-As. Backcrossed to the SJL/J background, most RR mice spontaneously develop RR experimental autoimmune encephalomyelitis (EAE) with episodes often altering between different central nervous system tissues like the cerebellum, optic nerve, and spinal cord. Development of spontaneous EAE depends on the presence of an intact B cell compartment and on the expression of MOG autoantigen. There is no spontaneous EAE development in B cell–depleted mice or in transgenic mice lacking MOG. Transgenic T cells seem to expand MOG autoreactive B cells from the endogenous repertoire. The expanded autoreactive B cells produce autoantibodies binding to a conformational epitope on the native MOG protein while ignoring the T cell target peptide. The secreted autoantibodies are pathogenic, enhancing demyelinating EAE episodes. RR mice constitute the first spontaneous animal model for the most common form of multiple sclerosis (MS), RR MS

Do Crash Barriers and Fences Have an Impact on WildlifeVehicle Collisions? : An Artificial Intelligence and GIS-Based Analysis

Wildlifevehicle collisions (WVCs) cause significant road mortality of wildlife and have led to the installation of protective measures along streets. Until now, it has been difficult to determine the impact of roadside infrastructure that might act as a barrier for animals. The main deficits are the lack of geodata for roadside infrastructure and georeferenced accidents recorded for a larger area. We analyzed 113 km of road network of the district Freyung-Grafenau, Germany, and 1571 WVCs, examining correlations between the appearance of WVCs, the presence or absence of roadside infrastructure, particularly crash barriers and fences, and the relevance of the blocking effect for individual species. To receive infrastructure data on a larger scale, we analyzed 5596 road inspection images with a neural network for barrier recognition and a GIS for a complete spatial inventory. This data was combined with the data of WVCs in GIS to evaluate the infrastructures impact on accidents. The results show that crash barriers have an effect on WVCs, as collisions are lower on roads with crash barriers. In particular, smaller animals have a lower collision share. The risk reduction at fenced sections could not be proven as fenced sections are only available at 3% of the analyzed roads. Thus, especially the fence dataset must be validated by a larger sample number. However, these preliminary results indicate that the combination of artificial intelligence and GIS may be used to analyze and better allocate protective barriers or to apply it in alternative measures, such as dynamic WVC risk-warning.(VLID)340689

SPATIOTEMPORAL ANALYSIS FOR WILDLIFE-VEHICLE-COLLISIONS BASED ON ACCIDENT STATISTICS OF THE COUNTY STRAUBING-BOGEN IN LOWER BAVARIA

During the last years the numbers of wildlife-vehicle-collisions (WVC) in Bavaria increased considerably. Despite the statistical registration of WVC and preventive measures at areas of risk along the roads, the number of such accidents could not be contained. Using geospatial analysis on WVC data of the last five years for county Straubing-Bogen, Bavaria, a small-scale methodology was found to analyse the risk of WVC along the roads in the investigated area. Various indicators were examined, which may be related to WVC. The risk depends on the time of the day and year which shows correlations in turn to the traffic density and wildlife population. Additionally the location of the collision depends on the species and on different environmental parameters. Accidents seem to correlate with the land use left and right of the street. Land use data and current vegetation were derived from remote sensing data, providing information of the general land use, also considering the vegetation period. For this a number of hot spots was selected to identify potential dependencies between land use, vegetation and season. First results from these hotspots show, that WVCs do not only depend on land use, but may show a correlation with the vegetation period. With regard to agriculture and seasonal as well as annual changes this indicates that warnings will fail due to their static character in contrast to the dynamic situation of land use and resulting risk for WVCs. This shows that there is a demand for remote sensing data with a high spatial and temporal resolution as well as a methodology to derive WVC warnings considering land use and vegetation. With remote sensing data, it could become possible to classify land use and calculate risk levels for WVC. Additional parameters, derived from remote sensed data that could be considered are relief and crops as well as other parameters such as ponds, natural and infrastructural barriers that could be related to animal behaviour and should be considered by future research

Multi-Dimensionality of Uncertainty in big Geospatial Sensor Data. GI_Forum|GI_Forum 2018, Volume 1 |

Developments in sensor technology have contributed immensely to the growth of big geospatial sensor data. Moreover, advances in telecommunications have made it possible to use in-situ sensors to capture and transfer data about the environment in near real-time. The combination of various sensor types within a predefined geographic space and the possibility of making measurements at high temporal resolution contributes to a better understanding of our environment while also generating big geospatial sensor data. Big data from multi-sensor networks, particularly those that capture dynamic characteristics of the environment, have not been spared the challenges that face other types of big data. Specifically, the quality of data and the propagation of uncertainty through the multi-sensor data processing workflows have remained a major concern in the big geospatial sensor data research community. Attempts to document, quantify and communicate the uncertainty associated with sensor data and related sensor network outputs have been made mainly in the context of individual projects. This paper aims to document the state-of-art in defining uncertainty with regard to multi-sensor geospatial data. In particular, we analyse the current literature to outline different types of uncertainty, and document methods for handling uncertainty in the different stages of multi-sensor geospatial data collection, processing and delivery

PRIVATE GRAPHS – ACCESS RIGHTS ON GRAPHS FOR SEAMLESS NAVIGATION

After the success of GNSS (Global Navigational Satellite Systems) and navigation services for public streets, indoor seems to be the next big development in navigational services, relying on RTLS – Real Time Locating Services (e.g. WIFI) and allowing seamless navigation. In contrast to navigation and routing services on public streets, seamless navigation will cause an additional challenge: how to make routing data accessible to defined users or restrict access rights for defined areas or only to parts of the graph to a defined user group? The paper will present case studies and data from literature, where seamless and especially indoor navigation solutions are presented (hospitals, industrial complexes, building sites), but the problem of restricted access rights was only touched from a real world, but not a technical perspective. The analysis of case studies will show, that the objective of navigation and the different target groups for navigation solutions will demand well defined access rights and require solutions, how to make only parts of a graph to a user or application available to solve a navigational task. The paper will therefore introduce the concept of private graphs, which is defined as a graph for navigational purposes covering the street, road or floor network of an area behind a public street and suggest different approaches how to make graph data for navigational purposes available considering access rights and data protection, privacy and security issues as well

International Journal of Sustainable Transportation / A review of spatial localization methodologies for the electric vehicle charging infrastructure

With view to the high share of the transport sector in total energy consumption, e-mobility should play an important role within the transition of the energy systems. Policymakers in several countries consider electric vehicles (EV) as an alternative to fossil-fueled vehicles. In order to allow for the development of EV, the charging infrastructure has to be set up at locations with high charging potential, identified by means of various criteria such as demand density or trip length. Many methodologies for locating charging stations (CS) have been developed in the last few years. First, this paper presents a broad overview of publications in the domain of CS localization. A classification scheme is proposed regarding modeling theory and empirical application; further on, models are analyzed, distinguishing between users, route or destination centricity of the approaches and outcomes. In a second step, studies in the field of explicit spatial location planning are reviewed in more detail, that is, in terms of their target criteria and the specialization of underlying analytical processes. One divergence of these approaches lies in the varying level of spatial planning, which could be crucial depending on the planning requirements. It is striking that almost all CS locating concepts are proposed for urban areas. Other constraints, such as the lack of extensive empirical EV traffic data for a better understanding of the driving behavior, are identified. This paper provides an overview of the CS models, a classification approach especially considering the problems spatial dimension, and derives perspectives for further research.(VLID)301374

Genetic variation in myelin oligodendrocyte glycoprotein expression and susceptibility to experimental autoimmune encephalomyelitis

Myelin oligodendrocyte glycoprotein (MOG) is encoded within the RT1.M region of the rat MHC a susceptibility locus for MOG-induced experimental autoimmune encephalomyelitis (EAE). We report that the enhanced susceptibility of Brown Norway (BN) rats to MOG-EAE is associated with higher expression of MOG mRNA and protein in the nervous system than in the less susceptible Lewis (LEW) strain. MOG mRNA was also detected in the immune system, but there was no correlation with disease susceptibility. These results suggest that differences in the expression of MOG in the target organ, rather than in the immune system may influence susceptibility to MOG-EAE. (C) 2003 Elsevier Science B.V. All rights reserved

Genetic variation in myelin oligodendrocyte glycoprotein expression and susceptibility to experimental autoimmune encephalomyelitis

Myelin oligodendrocyte glycoprotein (MOG) is encoded within the RT1.M region of the rat MHC a susceptibility locus for MOG-induced experimental autoimmune encephalomyelitis (EAE). We report that the enhanced susceptibility of Brown Norway (BN) rats to MOG-EAE is associated with higher expression of MOG mRNA and protein in the nervous system than in the less susceptible Lewis (LEW) strain. MOG mRNA was also detected in the immune system, but there was no correlation with disease susceptibility. These results suggest that differences in the expression of MOG in the target organ, rather than in the immune system may influence susceptibility to MOG-EAE. (C) 2003 Elsevier Science B.V. All rights reserved