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The standard screening test for the recognition of autoimmune diseases is the proof of autoantibodies in serum of patients by indirect immunofluorescence (IIF) based on HEp‐2 cells. Manual evaluation of this test is very subjective, slow, and there are no objective parameters as guidelines available. Interlaboratory tests showed occasionally large deviations in the test evaluation resulting in a high variance of results. The aim of this project is fast, objective, safe, and economical automatic analysis of HEp‐2 IIF patterns. Images of IIF patterns were completely and automatically captured using an inverse motorized fluorescence microscope. Thereby, device‐specific parameters were controlled automatically, too. For fast analysis of IIF patterns new algorithms of image processing were developed. Artifacts were recognized and excluded from analysis by the developed software. Analysis of more than 80,000 images clearly demonstrated full automatization and fast processing of IIF patterns. Additionally serum‐specific fluorescence could be easily distinguished from background. Even very weak but positive patterns can be recognized and used for diagnosis. A detailed separation into different basic patterns is possible. Objective, fast, and disease‐related economical analysis of HEp‐2 immunofluorescence patterns is feasible. The implemented software algorithms allowed a mathematical way of describing IIF patterns and can therefore be a useful tool for the needed standardization process.
We present two case studies that demonstrate how a common evaluation methodology can be used to assess the reliability of regional climate model simulations from different fields of research. In Case I, we focused on the agricultural yield loss risk for maize in Northeastern Brazil during a drought linked to an El-Niño event. In Case II, the present-day regional climatic conditions in Europe for a 10-year period are simulated. To comprehensively evaluate the model results for both kinds of investigations, we developed a general methodology. On its basis, we elaborated and implemented modules to assess the quality of model results using both advanced visualization techniques and statistical algorithms. Besides univariate approaches for individual near-surface parameters, we used multivariate statistics to investigate multiple near-surface parameters of interest together. For the latter case, we defined generalized quality measures to quantify the model's accuracy. Furthermore, we elaborated a diagnosis tool applicable for atmospheric variables to assess the model's accuracy in representing the physical processes above the surface under various aspects. By means of this evaluation approach, it could be demonstrated in Case Study I that the accuracy of the applied regional climate model resides at the same level as that we found for another regional model and a global model. Excessive precipitation during the rainy season in coastal regions could be identified as a major contribution leading to this result. In Case Study II, we also identified the accuracy of the investigated mean characteristics for near-surface temperature and precipitation to be comparable to another regional model. In this case, an artificial modulation of the used initial and boundary data during preprocessing could be identified as the major source of error in the simulation. Altogether, the achieved results for the presented investigations indicate the potential of our methodology to be applied as a common test bed to different fields of research in regional climate modeling.
Intestinal colonization is influenced by the ability of the bacterium to inhabit a niche, which is based on the expression of colonization factors. Escherichia coli carries a broad range of virulence-associated genes (VAGs) which contribute to intestinal (inVAGs) and extraintestinal (exVAGs) infection. Moreover, initial evidence indicates that inVAGs and exVAGs support intestinal colonization. We developed new screening tools to genotypically and phenotypically characterize E. coli isolates originating in humans, domestic pigs, and 17 wild mammal and avian species. We analyzed 317 isolates for the occurrence of 44 VAGs using a novel multiplex PCR microbead assay (MPMA) and for adhesion to four epithelial cell lines using a new adhesion assay. We correlated data for the definition of new adhesion genes. inVAGs were identified only sporadically, particularly in roe deer (Capreolus capreolus) and the European hedgehog ( Erinaceus europaeus). The prevalence of exVAGs depended on isolation from a specific host. Human uropathogenic E. coli isolates carried exVAGs with the highest prevalence, followed by badger (Meles meles) and roe deer isolates. Adhesion was found to be very diverse. Adhesion was specific to cells, host, and tissue, though it was also unspecific. Occurrence of the following VAGs was associated with a higher rate of adhesion to one or more cell lines: afa-dra, daaD, tsh, vat, ibeA, fyuA, mat, sfa-foc, malX, pic, irp2, and papC. In summary, we established new screening methods which enabled us to characterize large numbers of E. coli isolates. We defined reservoirs for potential pathogenic E. coli. We also identified a very broad range of colonization strategies and defined potential new adhesion genes.