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Ziel des Teilprojekts ist die thematische Erschließung der Korpora, um sowohl themenspezifische virtuelle Subkorpora zusammenstellen zu können als auch aufgrund der Analyse sachgebietsbezogener Häufigkeitsverteilungen z.B. Lesarten disambiguieren zu können. Ausgangspunkt ist die Erstellung einer Taxonomie von Sachgebietsthemen. Dies erfolgt in einem semiautomatischen Verfahren, welches die Anwendung von Textmining (Dokumentclustering) und die manuelle Zuordnung von Clustern in eine externen Ontologie beinhaltet. Es wird argumentiert, dass die so gewonnene Taxonomie sowohl intuitiver als auch objektiver ist als bestehende, rein manuelle Ansätze. Sie eignet sich zudem gleichermaßen für manuelle als auch für maschinelle Klassifikation. Für letzteres wird der Naive Bayes'sche Textklassifikator motiviert und für ein klassifiziertes Korpus von knapp zwei Milliarden Wörtern evaluiert.
Formalin‐fixed, paraffin‐embedded (FFPE ), biobanked tissue samples offer an invaluable resource for clinical and biomarker research. Here, we developed a pressure cycling technology (PCT )‐SWATH mass spectrometry workflow to analyze FFPE tissue proteomes and applied it to the stratification of prostate cancer (PC a) and diffuse large B‐cell lymphoma (DLBCL ) samples. We show that the proteome patterns of FFPE PC a tissue samples and their analogous fresh‐frozen (FF ) counterparts have a high degree of similarity and we confirmed multiple proteins consistently regulated in PC a tissues in an independent sample cohort. We further demonstrate temporal stability of proteome patterns from FFPE samples that were stored between 1 and 15 years in a biobank and show a high degree of the proteome pattern similarity between two types of histological regions in small FFPE samples, that is, punched tissue biopsies and thin tissue sections of micrometer thickness, despite the existence of a certain degree of biological variations. Applying the method to two independent DLBCL cohorts, we identified myeloperoxidase, a peroxidase enzyme, as a novel prognostic marker. In summary, this study presents a robust proteomic method to analyze bulk and biopsy FFPE tissues and reports the first systematic comparison of proteome maps generated from FFPE and FF samples. Our data demonstrate the practicality and superiority of FFPE over FF samples for proteome in biomarker discovery. Promising biomarker candidates for PC a and DLBCL have been discovered.
I. Die Gruppe der Favularien
(1887)
Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.
We have measured the capture cross section of the 155Gd and 157Gd isotopes between 0.025 eV and 1 keV. The capture events were recorded by an array of 4 C6D6 detectors, and the capture yield was deduced exploiting the total energy detection system in combination with the Pulse Height Weighting Techniques. Because of the large cross section around thermal neutron energy, 4 metallic samples of different thickness were used to prevent problems related to self-shielding. The samples were isotopically enriched, with a cross contamination of the other isotope of less than 1.14%. The capture yield was analyzed with an R-Matrix code to describe the cross section in terms of resonance parameters. Near thermal energies, the results are significantly different from evaluations and from previous time-of-flight experiments. The data from the present measurement at n_TOF are publicly available in the experimental nuclear reaction database EXFOR.
New neutron cross section measurements of minor actinides have been performed recently in order to reduce the uncertainties in the evaluated data, which is important for the design of advanced nuclear reactors and, in particular, for determining their performance in the transmutation of nuclear waste. We have measured the 241Am(n,γ) cross section at the n_TOF facility between 0.2 eV and 10 keV with a BaF2 Total Absorption Calorimeter, and the analysis of the measurement has been recently concluded. Our results are in reasonable agreement below 20 eV with the ones published by C. Lampoudis et al. in 2013, who reported a 22% larger capture cross section up to 110 eV compared to experimental and evaluated data published before. Our results also indicate that the 241Am(n,γ) cross section is underestimated in the present evaluated libraries between 20 eV and 2 keV by 25%, on average, and up to 35% for certain evaluations and energy ranges.
The accuracy on neutron capture cross section of fissile isotopes must be improved for the design of future nuclear systems such as Gen-IV reactors and Accelerator Driven Systems. The High Priority Request List of the Nuclear Energy Agency, which lists the most important nuclear data requirements, includes also the neutron capture cross sections of fissile isotopes such as 233,235U and 239,241Pu. A specific experimental setup has been used at the CERN n_TOF facility for the measurement of the neutron capture cross section of 235U by a set of micromegas fission detectors placed inside a segmented BaF2 Total Absorption Calorimeter.