Immune stimulation mediated by autoantigen binding sites within small nuclear RNAs involves Toll-like receptors 7 and 8

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by the production of autoantibodies to certain cellular macromolecules, such as the small nuclear ribonucleoprotein particles (snRNPs), which had been considered to be passive targets of the autoimmune response. SLE is also characterized by the increased expression of type I interferon (IFN), which appears to be associated with the development and severity of disease. Here, we show that specific, highly conserved RNA sequences within snRNPs can stimulate Toll-like receptors (TLRs) 7 and 8 as well as activate innate immune cells, such as plasmacytoid dendritic cells (pDCs), which respond by secreting high levels of type I IFN. SLE patient sera containing autoantibodies to snRNPs form immune complexes that are taken up through the Fc receptor γRII and efficiently stimulate pDCs to secrete type I IFNs. These results demonstrate that a prototype autoantigen, the snRNP, can directly stimulate innate immunity and suggest that autoantibodies against snRNP may initiate SLE by stimulating TLR7/8

Modelling of sulphuric acid aerosols in an engine plume: Using one-way-coupled turbulent diffusivity and appropriate microphysical models

This research investigates the formation, growth, and size distribution of H2SO4 aerosols in aircraft engine plumes. It aims to enhance calculations by incorporating spatial variation and turbulence modeling. The study develops a toolchain using AER 3-D software, sets up a CFD model of an engine wake, and couples it with microphysics and advection schemes. The toolchain compares the AER 3-D microphysics model with state-of-the-art box models, showing its effectiveness in handling high H2SO4 concentrations and producing similar size distribution results. Using ANSYS, an axisymmetric engine wake field is calculated, revealing that condensation and nucleation occur in the injection area and wake boundary layer, while coagulation primarily occurs inside the wake. Turbulence influences aerosol diffusion and growth, resulting in size variations with distance from the wake center. The research emphasizes the importance of incorporating spatial variation and turbulence modeling for accurate aerosol predictions.Aerospace Engineerin

Relevance Determination in Reinforcement Learning

Abstract. We propose relevance determination and minimisation schemes in reinforcement learning which are solely based on the Q-matrix and which can thus be applied during training without prior knowledge about the system dynamics. On the one hand, we judge the relevance of separate state space dimensions based on the variance in the Q-matrix. On the other hand, we perform Q-matrix reduction by means of a combination of Qlearning with neighbourhood cooperation of the state values where the neighbourhood is defined based on the Q-values itself. The effectivity of the methods is shown in a (simple though relevant) gridworld example.

Relevance determination in reinforcement learning

Tluk von Toschanowitz K, Hammer B, Ritter H. Relevance determination in reinforcement learning. In: Verleysen M, ed. ESANN'05. d-side publishing; 2005: 369-374

Relevance determination in learning vector quantization

Bojer T, Hammer B, Schunk D, Tluk von Toschanowitz K. Relevance determination in learning vector quantization. In: Verleysen M, ed. ESANN'2001. D-facto publications; 2001: 271-276

Mapping the Design Space of Reinforcement Learning Problems - a Case Study

Tluk von Toschanowitz K, Hammer B, Ritter H. Mapping the Design Space of Reinforcement Learning Problems - a Case Study. In: Gross H-M, Debes K, Böhme H-J, eds. SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior. VDI Verlag; 2004: 251-261

Parkinson's disease staging based of the non-motor symptoms scale

Objective: The Non-Motor Symptoms Scale (NMSS) is a unified instrument for assessment of non-motor symptoms (NMS) in Parkinson’s disease (PD). Present study is aimed at exploring a PD staging based on NMS severity levels determined through NMSS. Methods: International, multicentre, cross-sectional study. Data on patients’ sex, age, disease duration, and treatment were collected. NMSS, Hoehn and Yahr staging (HY), motor examination and motor complications scales, and the PDQ-8 were applied. NMSS scores were broken down by quartiles to establish severity levels. Chi squared, Mann-Whitney, and Kruskal-Wallis tests were applied to compare NMSS severity levels with other variables in the study. Results: The sample was composed by 750 PD patients (58.5% men; mean age: 65.98±10.33 years; disease duration: 7.37±5.64 years; HY median: 2, limits: 1–5). NMSS total score was 56.82±43.62 (range: 0–243, median: 44). NMSS levels were established as follows: level 0 (no NMS); 1 (slight): 1–7 points; 2 (mild): 8–24; 3 (moderate): 25–44; 4 (severe): 45–80; 5 (very severe): ≥81 points. No differences were detected in NMS severity level distribution by gender (p = 0.14) and age (p = 0.09). Disease duration, motor examination, motor complications, and PDQ-8 scores showed significant differences by NMSS severity levels (p < 0.0001). There was also a significant difference between HY and NMS levels (p < 0.0001). Conclusions: Severity levels, based on quartiles, can be extracted from NMSS scores and may be the basis for a staging system based on NMS. A significant difference was found between HY and NMS classifications, showing that motor and non-motor manifestations have a different patter