Intrathecal antibody production against Epstein-Barr and other neurotropic viruses in pediatric and adult onset multiple sclerosis

Epstein-Barr virus (EBV) has been implicated in the pathogenesis of multiple sclerosis (MS). Recent reports proposed an increased EBV-targeted humoral immune response in MS, which appears to be more pronounced in pediatric patients. However, little is known about the CNS-derived antibody production against EBV in patients with MS. The objective of this study was to assess the frequency and intensity of intrathecal antibody production against EBV as compared to other neurotropic viruses in pediatric and adult onset MS. In cohorts of 43 childhood, 50 adult onset MS patients, 20 children and 12 adults with other CNS disorders, paired CSF and serum samples were studied. Frequency and intensity of intrathecal antibody production against EBV as compared to measles, rubella, varicella zoster (VZV) and herpes simplex virus (HSV) were analyzed by determination of virus-specific CSF-to-serum Antibody Indices (AI). Intrathecally synthesized EBV antibodies were detectable in 26% pediatric and 10% adult onset MS patients, compared to frequencies ranging in both groups from 10 to 60% for the other viruses. Median AIs for EBV were lower than those for all other viruses, with more than twofold higher median AI for measles, rubella and VZV. The EBV-targeted humoral immune response in the CNS is only part of the intrathecal polyspecific antibody production in MS, directed against various neurotropic viruses. Our results do not rule out the possibility that EBV is involved in the pathogenesis of MS by triggering diverse cellular immune mechanisms, but they argue against a direct pathogenic role of EBV-targeted humoral immune response within the CNS

Roll Maneuver Control of UCAV Wing Using Anisotropic Piezoelectric Actuators

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76333/1/AIAA-2002-1720-656.pd

The role of natural science collections in the biomonitoring of environmental contaminants in apex predators in support of the EU's zero pollution ambition

The chemical industry is the leading sector in the EU in terms of added value. However, contaminants pose a major threat and significant costs to the environment and human health. While EU legislation and international conventions aim to reduce this threat, regulators struggle to assess and manage chemical risks, given the vast number of substances involved and the lack of data on exposure and hazards. The European Green Deal sets a 'zero pollution ambition for a toxic free environment' by 2050 and the EU Chemicals Strategy calls for increased monitoring of chemicals in the environment. Monitoring of contaminants in biota can, inter alia: provide regulators with early warning of bioaccumulation problems with chemicals of emerging concern; trigger risk assessment of persistent, bioaccumulative and toxic substances; enable risk assessment of chemical mixtures in biota; enable risk assessment of mixtures; and enable assessment of the effectiveness of risk management measures and of chemicals regulations overall. A number of these purposes are to be addressed under the recently launched European Partnership for Risk Assessment of Chemicals (PARC). Apex predators are of particular value to biomonitoring. Securing sufficient data at European scale implies large-scale, long-term monitoring and a steady supply of large numbers of fresh apex predator tissue samples from across Europe. Natural science collections are very well-placed to supply these. Pan-European monitoring requires effective coordination among field organisations, collections and analytical laboratories for the flow of required specimens, processing and storage of specimens and tissue samples, contaminant analyses delivering pan-European data sets, and provision of specimen and population contextual data. Collections are well-placed to coordinate this. The COST Action European Raptor Biomonitoring Facility provides a well-developed model showing how this can work, integrating a European Raptor Biomonitoring Scheme, Specimen Bank and Sampling Programme. Simultaneously, the EU-funded LIFE APEX has demonstrated a range of regulatory applications using cutting-edge analytical techniques. PARC plans to make best use of such sampling and biomonitoring programmes. Collections are poised to play a critical role in supporting PARC objectives and thereby contribute to delivery of the EU's zero-pollution ambition.Non peer reviewe

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving.Comment: 93 page