Direct Immunofluorescence in Behçet's Disease: A Controlled Study with 108 Cases

Behçet’s disease (BD) is a multisystemic disorder with an unknown etiology. Even though autoimmunity is thought to play a part in the etiopathogenesis of BD, there is no clear evidence to support this hypothesis.1 This multisystemic involvement might be a clue for the autoimmune pathogenesis. The presence of immunoreactans in the skin is either diagnostic or it helps the diagnosis of some autoimmune diseases.2-7 The aim of this study is to evaluate immunoreactans depositions in BD using direct immunofluorescence (DIF), thus implicating the autoimmune theory which we think is important in the etiopatho-genesis of the disease. We compared the data of BD both with systemic lupus erythematosus (SLE), which is an autoimmune disease as well as with healthy controls. A total of 164 skin samples from 108 BD and 36 SLE patients as well as 20 healthy controls were examined for depositions of immunoglobulin (Ig)M, IgG

Indirect Evidence for Molecular Association of Polycyclic Aromatic Hydrocarbons in Aqueous Soap Solutions from Light Absorption and Fluorescence

Aqueous solutions of alkyl sulphates containing small amounts of the free alkanol differ in their dissolving properties for polycyclic aromatic hydrocarbons from aqueous solutions of the pure soap in concentrations near the CMC. The differences are observed only near the CMC of the pure soap, where the soap solutions containing free alkanol have a minimum of the surface tension. The observed alterations are deviations from the Lambert-Beer law, energy transfer, and changes of the fluorescence quantum efficiencies, when the soap concentration is varied. The different results are consistent with the assumption that microcristalline molecular van der Waals associations are brought into solution by the soap solutions which were studied

Multivariate Demand Forecasting for Rental Bike Systems Based on an Unobserved Component Model

Many German cities, municipalities and transport associations are expanding their bike-sharing systems (BSS) to offer citizens a cost-effective and climate-friendly means of transport and an alternative to private motorized transport (PMT). However, operators face the challenge of generating high-quality predictive analyses and time series forecasts. In particular, the prediction of demand is a key component to foster data-driven decisions. To address this problem, an Unobserved Component Model (UCM) has been developed to predict the monthly rentals of a BSS, whereby the station-based BSS VRNnextbike, including over 2000 bikes, 297 stations and 21 municipalities, is employed as an example. The model decomposes the time series into trend, seasonal, cyclical, auto-regressive and irregular components for statistical modeling. Additionally, the model includes exogenous factors such as weather, user behavior (e.g., traveled distance), school holidays and COVID-19 relevant covariates as independent effects to calculate scenario based forecasts. It can be shown that the UCM calculates reasonably accurate forecasts and outperforms classical time series models such as ARIMA(X) or SARIMA(X). Improvements were observed in model quality in terms of AIC/BIC (2.5% to 22%) and a reduction in error metrics from 15% to 45% depending on the considered model