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956 Publications visible to you, out of a total of 956
Automated macrophage counting in DLBCL tissue samples: a ROF filter based approach
MMML Demonstrators - Molecular Mechanisms in Malignant Lymphomas - Demonstrators of Personalized Medicine

Abstract (Expand)

We describe a Rudin-Osher-Fatemi (ROF) filter based segmentation approach for whole tissue samples, combining floating intensity thresholding and rule-based feature detection. Method is validated against … manual counts and compared with two commercial software kits (Tissue Studio 64, Definiens AG, and Halo, Indica Labs) and a straightforward machine-learning approach in a set of 50 test images. Further, the novel method and both commercial packages are applied to a set of 44 whole tissue sections. Outputs are compared with gene expression data available for the same tissue samples. Finally, the ROF based method is applied to 44 expert-specified tumor subregions for testing selection and subsampling strategies. Our method is deterministic, fully automated, externally repeatable, independent on training data and -- in difference to most commercial software kits -- completely documented. Among all tested methods, the novel approach is best correlated with manual count (0.9297). Automated detection of evaluation subregions proved to be fully reliable. Subsampling within tumor subregions is possible with results almost identical to full sampling. Comparison with gene expression data obtained for the same tissue samples reveals only moderate to low correlation levels, thus indicating that image morphometry constitutes an independent source of information about antibody-polarized macrophage occurence and distribution.

Authors: Marcus Wagner, René Hänsel, Sarah Reinke, Julia Richter, Michael Altenbuchinger, Ulf-Dietrich Braumann, Rainer Spang, Markus Löffler, Wolfram Klapper

Date Published: No date defined

Publication Type: Not specified

Human Diseases: diffuse large B-cell lymphoma

On the Cooperation between Epigenetics and Transcription Factor Networks in the Specification of Tissue Stem Cells
LHA - Leipzig Health Atlas

Abstract (Expand)

It is generally accepted that epigenetic modifications, such as DNA and histone methylations, affect transcription and that a gene’s transcription feeds back on its epigenetic profile. Depending on the … epigenetic modification, positive and negative feedback loops have been described. Here, we study whether such interrelation are mandatory and how transcription factor networks affect it. We apply self-organizing map machine learning to a published data set on the specification and differentiation of murine intestinal stem cells in order to provide an integrative view of gene transcription and DNA, as well as histone methylation during this process. We show that, although gain/loss of H3K4me3 at a gene promoter is generally considered to be associated with its increased/decreased transcriptional activity, such an interrelation is not mandatory, i.e., changes of the modification level do not necessarily affect transcription. Similar considerations hold for H3K27me3. In addition, even strong changes in the transcription of a gene do not necessarily affect its H3K4me3 and H3K27me3 modification profile. We provide a mechanistic explanation of these phenomena that is based on a model of epigenetic regulation of transcription. Thereby, the analyzed data suggest a broad variance in gene specific regulation of histone methylation and support the assumption of an independent regulation of transcription by histone methylation and transcription factor networks. The results provide insights into basic principles of the specification of tissue stem cells and highlight open questions about a mechanistic modeling of this process.

Authors: T. Thalheim, Lydia Hopp, Hans Binder, G. Aust, J. Galle

Date Published: No date defined

Publication Type: Not specified

The Influence of Down-Sampling Strategies on SVD Word Embedding Stability.
SMITH - Smart Medical Information Technology for Healthcare

Abstract

Not specified

Authors: B. Kampe, U. Hahn

Date Published: No date defined

Publication Type: Proceedings

Modellierung eines Anwendungssystems zur Unterstützung der Pflegedokumentation unter Berücksichtigung der Integration in ein klinisches Arbeitsplatzsystem
Management of health information systems

Abstract

Not specified

Authors: A. Lagemann, Reinhold Haux, Alfred Winter

Date Published: No date defined

Publication Type: Misc

Multi-omics cartography of glioma heterogeneity
GGN - German Glioma Network

Abstract (Expand)

Molecular mechanisms of lower grade (II- III) diffuse gliomas (LGG) are still poorly understood, mainly because of their heterogeneity. They split into astrocytoma- (IDH-A) and oligodendro-glioma-like … (IDH-O) tumors both carrying mutations(s) at the Isocitrate dehydrogenase (IDH) gene and into IDH wild type (IDH-wt) gliomas of glioblastoma-resemblance. We generated de-tailed maps of the transcriptomes and DNA-methylomes revealing that cell functions divide into three major archeotypic hallmarks: (i) increased proliferation in IDH-wt and, to a less degree, IDH-O, (ii) increased inflammation in IDH-A and IDH-wt, and (iii) the loss of synaptic transmis-sion in all subtypes. Immunogenic properties of IDH-A are diverse partly resembling signatures observed in grade IV mesenchymal glioblastomas or in grade I pilocytic astrocytomas. We ana-lyzed details of coregulation between gene expression and DNA-methylation and of the immu-nogenic micro-environment presumably driving tumor development and treatment resistance. Our transcriptome and methylome maps support personalized, case-by-case views to decipher the heterogeneity of glioma states in terms of data portraits. Thereby molecular cartography provides a graphical coordinate system, which links gene-level information with glioma sub-types, their phenotypes and clinical context.

Authors: Hans Binder, Maria Schmidt, Lydia Hopp, Arsen Arakelyan, Henry Löffler-Wirth

Date Published: No date defined

Publication Type: Journal article

Human Diseases: brain glioma

Skin single-cell transcriptomics reveals a core of sebaceous gland-relevant genes shared by mice and humans
scRNAseq sebaceous gland

Abstract

submitted

Author: Torsten Thalheim

Date Published: No date defined

Publication Type: Journal article

Medical multivariate time series imputation and forecasting based on a recurrent conditional Wasserstein GAN and attention
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

OBJECTIVE: In the fields of medical care and research as well as hospital management, time series are an important part of the overall data basis. To ensure high quality standards and enable suitable … decisions, tools for precise and generic imputations and forecasts that integrate the temporal dynamics are of great importance. Since forecasting and imputation tasks involve an inherent uncertainty, the focus of our work lay on a probabilistic multivariate generative approach that samples infillings or forecasts from an analysable distribution rather than producing deterministic results. MATERIALS AND METHODS: For this task, we developed a system based on generative adversarial networks that consist of recurrent encoders and decoders with attention mechanisms and can learn the distribution of intervals from multivariate time series conditioned on the periods before and, if available, periods after the values that are to be predicted. For training, validation and testing, a data set of jointly measured blood pressure series (ABP) and electrocardiograms (ECG) (length: 1,250=ˆ10s) was generated. For the imputation tasks, one interval of fixed length was masked randomly and independently in both channels of every sample. For the forecasting task, all masks were positioned at the end. RESULTS: The models were trained on around 65,000 bivariate samples and tested against 14,000 series of different persons. For the evaluation, 50 samples were produced for every masked interval to estimate the range of the generated infillings or forecasts. The element-wise arithmetic average of these samples served as an estimator for the mean of the learned conditional distribution. The approach showed better results than a state-of-the-art probabilistic multivariate forecasting mechanism based on Gaussian copula transformation and recurrent neural networks. On the imputation task, the proposed method reached a mean squared error (MSE) of 0.057 on the ECG channel and an MSE of 28.30 on the ABP channel, while the baseline approach reached MSEs of 0.095 (ECG) and 229.1 (ABP). Moreover, on the forecasting task, the presented system achieved MSEs of 0.069 (ECG) and 33.73 (ABP), outperforming the recurrent copula approach, which reached MSEs of 0.082 (ECG) and 196.53 (ABP). CONCLUSION: The presented generative probabilistic system for the imputation and forecasting of (medical) time series features the flexibility to handle masks of different sizes and positions, the ability to quantify uncertainty due to its probabilistic predictions, and an adjustable trade-off between the goals of minimising errors in individual predictions and minimising the distance between the learned and the real conditional distribution of the infillings or forecasts.

Authors: Sven Festag, Cord Spreckelsen

Date Published: 1st Feb 2023

Publication Type: Journal article

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