Christina Kratsch

The availability of structured legal data is important for advancing Natural Language Processing (NLP) techniques for the German legal system. One of the most widely used datasets, Open Legal Data, provides a large-scale collection of German court decisions. While the metadata in this raw dataset is consistently structured, the decision texts themselves are inconsistently formatted and often lack clearly marked sections. Reliable separation of these sections is important not only for rhetorical role classification but also for downstream tasks such as retrieval and citation analysis. In this work, we introduce a cleaned and sectioned dataset of 251,038 German court decisions derived from the official Open Legal Data dataset. We systematically separated three important sections in German court decisions, namely Tenor (operative part of the decision), Tatbestand (facts of the case), and Entscheidungsgründe (judicial reasoning), which are often inconsistently represented in the original dataset. To ensure the reliability of our extraction process, we used Cochran’s formula with a 95% confidence level and a 5% margin of error to draw a statistically representative random sample of 384 cases, and manually verified that all three sections were correctly identified. We also extracted the Rechtsmittelbelehrung (appeal notice) as a separate field, since it is a procedural instruction and not part of the decision itself. The resulting corpus is publicly available in the JSONL format, making it an accessible resource for further research on the German legal system.

Zusammenfassung: Die individuelle Zelle stellt die fundamentale Einheit des Lebens dar. Ihre Funktionsweise ist seit Jahrhunderten Gegenstand der biomedizinischen Forschung. Seit Kurzem erlaubt die sogenannte Einzelzellsequenzierung revolutionäre neue Einblicke in die Komposition von Geweben, die Interaktion zwischen Zellen und den Ablauf dynamischer Prozesse. Die damit verbundenen Daten, z. B. aus der Transkriptomik, stellen Analysten jedoch vor neue Herausforderungen. Die Daten sind typischerweise sehr groß, verrauscht und stark mit anderen Informationen vernetzt, sodass herkömmliche Verfahren ausscheiden. Neue, speziell adaptierte Algorithmen aus dem Feld der künstlichen Intelligenz können hier Abhilfe schaffen. In Verbindung stellen Einzelzellsequenzierung und künstliche Intelligenz mächtige neue Werkzeuge für die biomedizinische Forschung dar und erlauben Einblicke in höchster Auflösung. Wir geben einen kurzen Überblick über beide Technologien und liefern Beispiele für ihre Anwendung im medizinischen Bereich. Wir demonstrieren anschließend beispielhaft, wie Methoden der künstlichen Intelligenz erfolgreich für die Analyse von Einzelzelltranskriptomdaten eingesetzt werden können. Da der korrekte und gewinnbringende Einsatz dieser Methoden weiterhin detailliertes Hintergrundwissen verlangt, ist ein intensivierter Austausch zwischen den Disziplinen unumgänglich. Wir gehen deshalb abschließend auf den Einsatz neuer Plattformen zur Kollaboration und zum Wissensaustausch ein.

Recent technological advances enable genomics of individual cells, the building blocks of all living organisms. Single cell data characteristics differ from those of bulk data, which led to a plethora of new analytical strategies. However, solutions are only useful for experts and currently, there are no widely accepted gold standards for single cell data analysis. To meet the requirements of analytical flexibility, ease of use and data security, we developed FASTGenomics ( https://fastgenomics.org ) as a powerful, efficient, versatile, robust, safe and intuitive analytical ecosystem for single-cell transcriptomics.

Human influenza viruses are rapidly evolving RNA viruses that cause short-term respiratory infections with substantial morbidity and mortality in annual epidemics. Uncovering the general principles of viral coevolution with human hosts is important for pathogen surveillance and vaccine design. Protein regions are an appropriate model for the interactions between two macromolecules, but the currently used epitope definition for the major antigen of influenza viruses, namely hemagglutinin, is very broad. Here, we combined genetic, evolutionary, antigenic, and structural information to determine the most relevant regions of the hemagglutinin of human influenza A/H3N2 viruses for interaction with human immunoglobulins. We estimated the antigenic weights of amino acid changes at individual sites from hemagglutination inhibition data using antigenic tree inference followed by spatial clustering of antigenicity-altering protein sites on the protein structure. This approach determined six relevant areas (patches) for antigenic variation that had a key role in the past antigenic evolution of the viruses. Previous transitions between successive predominating antigenic types of H3N2 viruses always included amino acid changes in either the first or second antigenic patch. Interestingly, there was only partial overlap between the antigenic patches and the patches under strong positive selection. Therefore, besides alterations of antigenicity, other interactions with the host may shape the evolution of human influenza A/H3N2 viruses.

Jean-Pierre Bourquin, Martin Stanulla and colleagues report whole genome, whole exome and transcriptome sequencing of TCF3-HLF fusion–positive acute lymphoblastic leukemia. Drug response profiling in patient-derived xenografts identified sensitivity to the BCL2-specific inhibitor ABT-199 (venetoclax) as a new option for treating this fatal disease. TCF3-HLF−positive acute lymphoblastic leukemia (ALL) is currently incurable. Using an integrated approach, we uncovered distinct mutation, gene expression and drug response profiles in TCF3-HLF−positive and treatment-responsive TCF3-PBX1−positive ALL. We identified recurrent intragenic deletions of PAX5 or VPREB1 in constellation with the fusion of TCF3 and HLF. Moreover somatic mutations in the non-translocated allele of TCF3 and a reduction of PAX5 gene dosage in TCF3-HLF ALL suggest cooperation within a restricted genetic context. The enrichment for stem cell and myeloid features in the TCF3-HLF signature may reflect reprogramming by TCF3-HLF of a lymphoid-committed cell of origin toward a hybrid, drug-resistant hematopoietic state. Drug response profiling of matched patient-derived xenografts revealed a distinct profile for TCF3-HLF ALL with resistance to conventional chemotherapeutics but sensitivity to glucocorticoids, anthracyclines and agents in clinical development. Striking on-target sensitivity was achieved with the BCL2-specific inhibitor venetoclax (ABT-199). This integrated approach thus provides alternative treatment options for this deadly disease.

The Thaumarchaeota is an abundant and ubiquitous phylum of archaea that plays a major role in the global nitrogen cycle. Previous analyses of the ammonia monooxygenase gene amoA suggest that pH is an important driver of niche specialization in these organisms. Although the ecological distribution and ecophysiology of extant Thaumarchaeota have been studied extensively, the evolutionary rise of these prokaryotes to ecological dominance in many habitats remains poorly understood. To characterize processes leading to their diversification, we investigated coevolutionary relationships between amoA, a conserved marker gene for Thaumarchaeota, and soil characteristics, by using deep sequencing and comprehensive environmental data in Bayesian comparative phylogenetics. These analyses reveal a large and rapid increase in diversification rates during early thaumarchaeotal evolution; this finding was verified by independent analyses of 16S rRNA. Our findings suggest that the entire Thaumarchaeota diversification regime was strikingly coupled to pH adaptation but less clearly correlated with several other tested environmental factors. Interestingly, the early radiation event coincided with a period of pH adaptation that enabled the terrestrial Thaumarchaeota ancestor to initially move from neutral to more acidic and alkaline conditions. In contrast to classic evolutionary models, whereby niches become rapidly filled after adaptive radiation, global diversification rates have remained stably high in Thaumarchaeota during the past 400–700 million years, suggesting an ongoing high rate of niche formation or switching for these microbes. Our study highlights the enduring importance of environmental adaptation during thaumarchaeotal evolution and, to our knowledge, is the first to link evolutionary diversification to environmental adaptation in a prokaryotic phylum. Significance The link between species diversification and adaptation has long interested biologists working on multicellular eukaryotes, but remains poorly understood in prokaryotes, in which diversity is much greater. We tested the hypothesis that diversification is associated with environmental adaptation in Thaumarchaeota, an ancient and abundant microbial group and key player in the global nitrogen cycle. We provide evidence that the Thaumarchaeota underwent a major radiation event hundreds of millions of years ago that coincided with a major period of pH adaptation. Subsequently, these microbes have maintained high rates of diversification, potentially because of the high rate at which new terrestrial niches arise. This study provides a framework for comparing dynamics of evolutionary processes across the tree of life.

Motivation: Ancestral character state reconstruction describes a set of techniques for estimating phenotypic or genetic features of species or related individuals that are the predecessors of those present today. Such reconstructions can reach into the distant past and can provide insights into the history of a population or a set of species when fossil data are not available, or they can be used to test evolutionary hypotheses, e.g. on the co-evolution of traits. Typical methods for ancestral character state reconstruction of continuous characters consider the phylogeny of the underlying data and estimate the ancestral process along the branches of the tree. They usually assume a Brownian motion model of character evolution or extensions thereof, requiring specific assumptions on the rate of phenotypic evolution. Results: We suggest using ridge regression to infer rates for each branch of the tree and the ancestral values at each inner node. We performed extensive simulations to evaluate the performance of this method and have shown that the accuracy of its reconstructed ancestral values is competitive to reconstructions using other state-of-the-art software. Using a hierarchical clustering of gene mutation profiles from an ovarian cancer dataset, we demonstrate the use of the method as a feature selection tool. Availability and implementation: The algorithm described here is implemented in C++ as a stand-alone program, and the source code is freely available at http://algbio.cs.uni-duesseldorf.de/software/RidgeRace.tar.gz. Contact:mchardy@hhu.de Supplementary information: Supplementary data are available at Bioinformatics online.

MiRNAs are discussed as diagnostic and therapeutic molecules. However, effective miRNA drug treatments with miRNAs are, so far, hampered by the complexity of the miRNA networks. To identify potential miRNA drugs in colorectal cancer, we profiled miRNA and mRNA expression in matching normal, tumor and metastasis tissues of eight patients by Illumina sequencing. We validated six miRNAs in a large tissue screen containing 16 additional tumor entities and identified miRNA-1, miRNA-129, miRNA-497 and miRNA-215 as constantly de-regulated within the majority of cancers. Of these, we investigated miRNA-1 as representative in a systems-biology simulation of cellular cancer models implemented in PyBioS and assessed the effects of depletion as well as overexpression in terms of miRNA-1 as a potential treatment option. In this system, miRNA-1 treatment reverted the disease phenotype with different effectiveness among the patients. Scoring the gene expression changes obtained through mRNA-Seq from the same patients we show that the combination of deep sequencing and systems biological modeling can help to identify patient-specific responses to miRNA treatments. We present this data as guideline for future pre-clinical assessments of new and personalized therapeutic options.