Molekularbiologischer Nachweis von Markergenen für die Virulenz in Escherichia coli

Die Arbeit ermöglicht mit Hilfe spezifischer molekularbiologischer Marker für die Virulenz von Escherichia coli die Differenzierung zwischen EHEC O157:H7 und O157:H-.Anhand von Gendatenbanken wurden 5 spezifische Genabschnitte, sogenannte O-Islands ausgewählt und ihr Vorhandensein in repräsentativen E. coli Stämmen untersucht.Innerhalb der E. coli Population zeigten OI 09 und OI 154 ein fast ausschließliches Auftreten in den Serotypen O55:H7, O157:H7 und O157:H- und fügen sich in das bekannte Evolutionsmodell von EHEC O157. Der Genabschnitt OI 172 konnte vorwiegend in O157:H7 Stämmen gefunden werden. Durch den fehlenden Nachweis in O157:H- Stämmen konnte mit OI 172 eine Unterscheidung zwischen EHEC O157:H7 und den SF EHEC O157: H- Stämmen erfolgen und das Evolutionsmodell erweitert werden. Die Gene der O-Islands sind assoziiert mit schwerem Krankheitsbild und Virulenz.Die OI 172 ermöglicht eine Differenzierung zwischen O157:H7 und dem hochpathogenen SF EHEC O157: H-

NeuralHydrology -- Interpreting LSTMs in Hydrology

Despite the huge success of Long Short-Term Memory networks, their applications in environmental sciences are scarce. We argue that one reason is the difficulty to interpret the internals of trained networks. In this study, we look at the application of LSTMs for rainfall-runoff forecasting, one of the central tasks in the field of hydrology, in which the river discharge has to be predicted from meteorological observations. LSTMs are particularly well-suited for this problem since memory cells can represent dynamic reservoirs and storages, which are essential components in state-space modelling approaches of the hydrological system. On basis of two different catchments, one with snow influence and one without, we demonstrate how the trained model can be analyzed and interpreted. In the process, we show that the network internally learns to represent patterns that are consistent with our qualitative understanding of the hydrological system.Comment: Pre-print of published book chapter. See journal reference and DOI for more inf

HiTSEE KNIME: a visualization tool for hit selection and analysis in high-throughput screening experiments for the KNIME platform

We present HiTSEE (High-Throughput Screening Exploration Environment), a visualization tool for the analysis of large chemical screens used to examine biochemical processes. The tool supports the investigation of structure-activity relationships (SAR analysis) and, through a flexible interaction mechanism, the navigation of large chemical spaces. Our approach is based on the projection of one or a few molecules of interest and the expansion around their neighborhood and allows for the exploration of large chemical libraries without the need to create an all encompassing overview of the whole library. We describe the requirements we collected during our collaboration with biologists and chemists, the design rationale behind the tool, and two case studies on different datasets. The described integration (HiTSEE KNIME) into the KNIME platform allows additional flexibility in adopting our approach to a wide range of different biochemical problems and enables other research groups to use HiTSEE

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

V-awake:a visual analytics approach for correcting sleep predictions from deep learning models

The usage of deep learning models for tagging input data has increased over the past years because of their accuracy and high performance. A successful application is to score sleep stages. In this scenario, models are trained to predict the sleep stages of individuals. Although their predictive accuracy is high, there are still misclassifications that prevent doctors from properly diagnosing sleep-related disorders. This paper presents a system that allows users to explore the output of deep learning models in a real-life scenario to spot and analyze faulty predictions. These can be corrected by users to generate a sequence of sleep stages to be examined by doctors. Our approach addresses a real-life scenario with absence of ground truth. It differs from others in that our goal is not to improve the model itself, but to correct the predictions it provides. We demonstrate that our approach is effective in identifying faulty predictions and helping users to fix them in the proposed use case