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Die Arbeit beschreibt die Resultate, welche die “Calorimetrische Analyse” nach den Verfassern fur Fette ergibt. Die erhaltenen Kurven geben das Verhalten der Fette beim Erwarmen quantitativ wieder.

Background The unpredictability of response to biological therapy in ulcerative colitis (UC) patients, including vedolizumab (VDZ), an anti-α4β7 integrin antibody, poses a serious challenge for medical care. This study aims to identify the relevant cellular and molecular signatures that mark response to VDZ by combining single-cell transcriptomics (scRNAseq) and high-dimensional flow cytometry (Cytek). Methods Gut biopsies and peripheral blood mononuclear cells (PBMCs) from patients with active UC were collected 2 weeks prior and 14 weeks after starting VDZ therapy. Response was evaluated at week 14 based on endoscopy and Physician Global Assessment. Transcriptome profiles of 159.188 and 95.057 high-quality single cells were generated from biopsies and PBMCs, respectively. Additional 797.670 CD45+ mucosal cells and 900.000 PBMCs were processed for validating scRNAseq results by Cytek. Results From a total of 25 UC patients (mean age 41±12.7 years, 52% female, 36% anti-TNFα naive), 15 patients (60%) reached response at week 14. Patients with no prior use of anti-TNFα were more likely to achieve remission with VDZ compared to anti-TNFα exposed patients (89% vs 44% response rate, respectively). Analysis of cell abundances revealed a partial recovery of the epithelial and stromal cell mucosal compartments in responders at week 14. Both scRNAseq and Cytek show that in responders VDZ prevented T cells (CD8+IL17+ and CD4+PD1+ subsets) and innate immune cells (NKs, DCs and inflammatory monocytes) from entering mucosa, resulting in their enrichment in PBMCs. In contrast, non-response to VDZ was marked by an expansion of inflammatory fibroblasts and activated endothelial cells, and depletion of most epithelial subsets. VDZ retained CD4 T cells in PBMCs in non-responders, but failed to halt active mucosal inflammation at week 14. Strikingly, involvement of the innate immune cells in UC inflammation prior to VDZ treatment was much more prominent in non-responders who showed higher baseline abundance of NKs, ILCs, γδ-T cells, DCs and inflammatory monocytes in their peripheral blood and mucosa compared to responders. While VDZ inhibited integrin β7 expression on circulating T cells in all patients, only in responders it coincided with the lower mucosal abundance of lymphocytes, suggesting that T cells in non-responders might employ alternative, β7-independent trafficking routes. Conclusion This is the largest prospective scRNAseq study addressing the response to biological therapy in UC. We showed that active, innate immune cell-mediated inflammation marks the primary non-response to VDZ that persists prior to and throughout the treatment, accompanied by β7-independent lymphocyte migration.

The Open Research Knowledge Graph is an infrastructure for the production, curation, publication and use of FAIR scientific information. Its mission is to shape a future scholarly publishing and communication where the contents of scholarly articles are FAIR research data.

We classify embeddings of the finite groups $A_4$ , $S_4$ and $A_5$ in the Lie group $G_2(\mathbb C)$ up to conjugation.

Acute and chronic coronary syndromes (ACS and CCS) are leading causes of mortality. Inflammation is considered to be a key pathogenic driver, but immune states in humans and their clinical implications remain poorly understood. We hypothesized that Multi-Omic blood analysis combined with Multi-Omic Factor Analysis (MOFA) might uncover hidden sources of variance providing pathophysiological insights linked to clinical needs. Here, we compile a single cell longitudinal dataset of the circulating immune states in ACS & CCS (13x103clinical & Multi-Omic variables, n=117 subjects, n=838 analyzed samples) from two independent cohorts. Using MOFA, we identify multilayered factors, characterized by distinct classical monocyte and CD4+& CD8+T cell states that explain a large proportion of inter-patient variance. Three factors either reflect disease course or predict outcome in coronary syndromes. The diagnostic performance of these factors reaches beyond established biomarkers highlighting the potential use of MOFA as a novel tool for multilayered patient risk stratification.

Multiple studies have investigated bibliometric features and uncategorized scholarly documents for the influential scholarly document prediction task. In this paper, we describe our work that attempts to go beyond bibliometric metadata to predict influential scholarly documents. Furthermore, this work also examines the influential scholarly document prediction task over categorized scholarly documents. We also introduce a new approach to enhance the document representation method with domain independent knowledge graph to find the influential scholarly document using categorized scholarly content. As the input collection, we use the WHO corpus with scholarly documents on the theme of COVID-19. This study examines different document representation methods for machine learning, including TF-IDF, BOW, and embedding-based language models (BERT). The TF-IDF document representation method works better than others. From various machine learning methods tested, logistic regression outperformed the other for scholarly document category classification, and the random forest algorithm obtained the best results for influential scholarly document prediction, with the help of a domain-independent knowledge graph, specifically DBpedia, to enhance the document representation method for predicting influential scholarly documents with categorical scholarly content. In this case, our study combines state-of-the-art machine learning methods with the BOW document representation method. We also enhance the BOW document representation with the direct type (rdf type) and unqualified relation from DBpedia. From this experiment, we did not find any impact of the enhanced document representation for the scholarly document category classification. We found an effect in the influential scholarly document prediction with categorical data.

BackgroundThe role of the immune system in the context of acute myocardial infarction (MI), and its response to such event are poorly characterized, but is thought to be an important driver of myocardial remodeling, thromboinflammation, and exacerbation of atherosclerosis - triggering recurrent cardiovascular events. So far, anti-inflammatory approaches drugs have shown promising effects on the prevention of recurrent cardiovascular events or myocardial salvage after myocardial infarction. However, they broadly impair the immune system and some are associated with increased infectious side effects. Therefore, a more detailed understanding of the immune response to myocardial infarction is needed to tailor anti-inflammatory therapeutic approaches in MI patients.MethodsTo gain such detailed longitudinal understanding of the immune response in ST-elevated myocardial infarction (STEMI) patients, we compared peripheral blood mononuclear cell (PBMCs) single-cell RNA-sequencing (scRNA-seq) expression and plasma protein profiles over time and in comparison to age- and sex-balanced controls in 38 STEMI patients at hospital admission, 24 hours (acute phase) and 6-8 weeks (chronic phase) after STEMI.ResultsIn total, 95,995 diseased and 33,878 control PBMCs were analyzed. Compared to controls, we observed a relative increase in the number of classical monocytes and a decrease in the number of CD56dimnatural killer cells in STEMI patients at admission, and these differences persisted until 24 hours after STEMI. The monocytes also showed the largest gene expression changes in STEMI patients compared to controls, and in STEMI patients over time. These were associated with changes in the activity of toll-like receptors, IFN and IL-1 signaling. Subsequent differential cell-cell communication analysis suggested that these monocytes are mainly involved in the outgoing differential communication in the first 24h after a STEMI, whereas in the next 6-8 weeks they become mostly involved in the incoming differential communication. Finally, a targeted protein cardiovascular biomarker panel revealed 33 out of 92 plasma proteins to be changed during the acute and/or chronic phase after STEMI. Interestingly, the plasma levels of three of these proteins were found to be affected by genetic variation, disease status and time after STEMI. Indicating the importance of taking all these aspects into consideration when defining potential future therapies.ConclusionsAltogether, our analyses have revealed the immunological pathways that are disturbed upon MI, and in which cell type and during which stage of the disease. Additionally, we also provide insights in which patients are expected to benefit most from anti-inflammatory treatments, by identifying the genetic variants and disease stage at which these variants affect the outcome of these (drug-targeted) pathways. These findings advance our knowledge of the immune response after MI and provide further guidance for future therapeutic studies.

Due to the growing number of scholarly publications, finding relevant articles becomes increasingly difficult. Scholarly knowledge graphs can be used to organize the scholarly knowledge presented within those publications and represent them in machine-readable formats. Natural language processing (NLP) provides scalable methods to automatically extract knowledge from articles and populate scholarly knowledge graphs. However, NLP extraction is generally not sufficiently accurate and, thus, fails to generate high granularity quality data. In this work, we present TinyGenius, a methodology to validate NLP-extracted scholarly knowledge statements using microtasks performed with crowdsourcing. TinyGenius is employed to populate a paper-centric knowledge graph, using five distinct NLP methods. We extend our previous work of the TinyGenius methodology in various ways. Specifically, we discuss the NLP tasks in more detail and include an explanation of the data model. Moreover, we present a user evaluation where participants validate the generated NLP statements. The results indicate that employing microtasks for statement validation is a promising approach despite the varying participant agreement for different microtasks.

Both gene expression and protein concentrations are regulated by genetic variants. Exploring the regulation of both eQTLs and pQTLs simultaneously in a context- and cell-type dependent manner may help to unravel mechanistic basis for genetic regulation of pQTLs. Here, we performed meta-analysis of Candida albicans-induced pQTLs from two population-based cohorts and intersected the results with Candida-induced cell-type specific expression association data (eQTL). This revealed systematic differences between the pQTLs and eQTL, where only 35% of the pQTLs significantly correlated with mRNA expressions at single cell level, indicating the limitation of eQTLs use as a proxy for pQTLs. By taking advantage of the tightly co-regulated pattern of the proteins, we also identified SNPs affecting protein network upon Candida stimulations. Colocalization of pQTLs and eQTLs signals implicated several genomic loci including MMP-1 and AMZ1. Analysis of Candida-induced single cell gene expression data implicated specific cell types that exhibit significant expression QTLs upon stimulation. By highlighting the role of trans-regulatory networks in determining the abundance of secretory proteins, our study serve as a framework to gain insights into the mechanisms of genetic regulation of protein levels in a context-dependent manner.

Additional file 16. Review history.

Additional file 2: Supplementary Figures. Collection of all Fig. S1-34.

Additional file 3: Supplementary Note. More extensive description of meta-cell evaluation, alternative GRN construction methods and interpretation of additional co-eQTL examples.

Genetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for the disorder as potentially pathogenic variants can reside in genes that are not yet known to be involved in kidney disease. To help identify these genes, we have developed KidneyNetwork, that utilizes tissue-specific expression to predict kidney-specific gene functions.KidneyNetwork is a novel method that we used to enrich a kidney RNA-sequencing co-expression network of 878 samples with a multi-tissue network of 31,499 samples. It then uses expression patterns to predict which genes have a kidney-related function and which (disease) phenotypes might result from variants in these genes, based on established gene-phenotype associations. We applied KidneyNetwork to prioritize rare variants in exome sequencing data from 13 kidney disease patients without a genetic diagnosis.KidneyNetwork can accurately predict kidney-specific gene functions and (kidney disease) phenotypes for disease-associated genes. Applying it to exome sequencing data of kidney disease patients allowed us to highlight a convincing candidate gene for kidney and liver cysts: ALG6.We present KidneyNetwork, a kidney-specific co-expression network that accurately predicts which genes have kidney-specific functions and can result in kidney disease. We show the added value of KidneyNetwork by applying it to kidney disease patients without a molecular diagnosis and consequently, we propose ALG6 as candidate gene in one of these patients. We designed an easy-to-use online interface that allows clinicians and researchers to use gene expression and co-regulation data and gene-phenotype connections to accelerate advances in hereditary kidney disease diagnosis and research.

Heptadien-(3,6)-sauremethylester ist eine einfache „Isolen-Saure” und ist daher als Modellsubstanz geeignet fur Studien zur Frage der Einwirkung von molekularem Sauerstoff auf naturliche ungesattigte ole. Die Darstellung dieses Esters wird beschrieben. Durch Peroxygenierung dieser Verbindung entstand ein Gemisch von Hydroperoxyden. Diese wurden durch folgende Abbaureaktionen nachgewiesen und identifiziert: Hydrierung des peroxygenierten Produktes, Oxydation der gesattigten Hydroxy-onanthsauremethylester zu den entsprechenden Carbonylverbindungen, SCHMIDTscher Abbau der Carbonylverbindungen, Verseifung der gebildeten Amide und papierchromatograpischer Nachweis der Amine und Aminosauren. Die Peroxygenierung verlauft nach dem „α-Methylen-Mechanismus” unter bevorzugter Bildung konjugierter Systeme. Die C=O-Gruppe der veresterten Carboxylgruppe zeigt hierbei den gleichen Einflus auf die Verschiebung von Doppelbindungen wie eine olefinische Doppelbindung. Vier der funf theoretisch moglichen Hydroperoxyde wurden nachgewiesen, die Bildung des funften konnte ausgeschlossen werden.

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