Systematic transcriptional profiling of responses to STAT1- and STAT3- activating cytokines in different cancer types

Cytokines orchestrate responses to pathogens and in inflammatory processes but they also play an important role in cancer by shaping the expression levels of cytokine response genes. Here, we conducted a large profiling study comparing miRNome and mRNA transcriptome data generated following different cytokine stimulations. Transcriptomic responses to STAT1- (IFN, IL-27) and STAT3-activating cytokines (IL6, OSM) were systematically compared in nine cancerous and nonneoplastic cell lines of different tissue origins (skin, liver and colon). The largest variation in our datasets was seen between cell lines of the three different tissues rather than stimuli. Notably, the variability in miRNome datasets was a lot more pronounced than in mRNA data. Our data also revealed that cells of skin, liver and colon tissues respond very differently to cytokines and that the cell signaling networks activated or silenced in response to STAT1- or STAT3- activating cytokines are specific to the tissue and the type of cytokine. However, globally, STAT1-activating cytokines had stronger effects than STAT3-inducing cytokines with most significant responses in liver cells, showing more genes up-regulated and with higher fold change. A more detailed analysis of gene regulations upon cytokine stimulation in these cells provided insights into STAT1- versus STAT3-driven processes in hepatocarcinogenesis. Finally, independent component analysis revealed interconnected transcriptional networks distinct between cancer cells and their healthy counterparts

Multispectral image analysis in laparoscopy – A machine learning approach to live perfusion monitoring

Modern visceral surgery is often performed through small incisions. Compared to open surgery, these minimally invasive interventions result in smaller scars, fewer complications and a quicker recovery. While to the patients benefit, it has the drawback of limiting the physician’s perception largely to that of visual feedback through a camera mounted on a rod lens: the laparoscope. Conventional laparoscopes are limited by “imitating” the human eye. Multispectral cameras remove this arbitrary restriction of recording only red, green and blue colors. Instead, they capture many specific bands of light. Although these could help characterize important indications such as ischemia and early stage adenoma, the lack of powerful digital image processing prevents realizing the technique’s full potential. The primary objective of this thesis was to pioneer fluent functional multispectral imaging (MSI) in laparoscopy. The main technical obstacles were: (1) The lack of image analysis concepts that provide both high accuracy and speed. (2) Multispectral image recording is slow, typically ranging from seconds to minutes. (3) Obtaining a quantitative ground truth for the measurements is hard or even impossible. To overcome these hurdles and enable functional laparoscopy, for the first time in this field physical models are combined with powerful machine learning techniques. The physical model is employed to create highly accurate simulations, which in turn teach the algorithm to rapidly relate multispectral pixels to underlying functional changes. To reduce the domain shift introduced by learning from simulations, a novel transfer learning approach automatically adapts generic simulations to match almost arbitrary recordings of visceral tissue. In combination with the only available video-rate capable multispectral sensor, the method pioneers fluent perfusion monitoring with MSI. This system was carefully tested in a multistage process, involving in silico quantitative evaluations, tissue phantoms and a porcine study. Clinical applicability was ensured through in-patient recordings in the context of partial nephrectomy; in these, the novel system characterized ischemia live during the intervention. Verified against a fluorescence reference, the results indicate that fluent, non-invasive ischemia detection and monitoring is now possible. In conclusion, this thesis presents the first multispectral laparoscope capable of videorate functional analysis. The system was successfully evaluated in in-patient trials, and future work should be directed towards evaluation of the system in a larger study. Due to the broad applicability and the large potential clinical benefit of the presented functional estimation approach, I am confident the descendants of this system are an integral part of the next generation OR

New ideas and trends in deep multimodal content understanding: a review

The focus of this survey is on the analysis of two modalities of multimodal deep learning: image and text. Unlike classic reviews of deep learning where monomodal image classifiers such as VGG, ResNet and Inception module are central topics, this paper will examine recent multimodal deep models and structures, including auto-encoders, generative adversarial nets and their variants. These models go beyond the simple image classifiers in which they can do uni-directional (e.g. image captioning, image generation) and bi-directional (e.g. cross-modal retrieval, visual question answering) multimodal tasks. Besides, we analyze two aspects of the challenge in terms of better content understanding in deep multimodal applications. We then introduce current ideas and trends in deep multimodal feature learning, such as feature embedding approaches and objective function design, which are crucial in overcoming the aforementioned challenges. Finally, we include several promising directions for future research.Computer Systems, Imagery and Medi

Podoplanin and the posterior heart field : epicardial-myocardial interaction

This thesis introduces the posterior heart field contributing to the venous pole of the heart by epithelial-mesenchymal-transformation of the coelomic epithelium. Based on studying of podoplanin and Sp3 (novel genes in cardiogenesis) wildtype and knockout mouse embryos between stages 9.5-18.5, we postulate that the posterior heart field contributes through mesenchymal and myocardial cell populations. The mesenchymal population is involved in the formation of the proepicardial organ, epicardium and epicardium-derived cells. The hypoplastic proepicardial organ and impaired epicardial-myocardial interaction result from altered mesenchymal contribution of the posterior heart field by lack of podoplanin and SP3 leading to hypoplasia of the chamber myocardium and coronary arterial vascular wall as well as (atrioventricular) septal defects. Myocardial contribution concerns myocardium of the sinus venosus including the sinoatrial node, venous valves, primary atrial septum and the left atrial dorsal wall as well as the wall of the pulmonary and cardinal veins. Development of smooth-muscle-cells of the wall of the pulmonary vein is also related to the posterior heart field. Moreover, we have reported formation of a transient left-sided sinoatrial node which persists during development in 10% of the cases. Podoplanin mutants show cardiac malformations including a hypoplastic sinoatrial node. This thesis contributes to the understanding of the mechanism underlying the mentioned cardiac malformations and arrhythmias originating in the sinus venosus region.Nederlandse Hartstichting J.E. Jurriaanse Stichting Sanofi-aventis Netherlands B.V. Datascope Bristol-Myers SquibbUBL - phd migration 201

Multiview Learning with Sparse and Unannotated data.

PhD ThesisObtaining annotated training data for supervised learning, is a bottleneck in many contemporary machine learning applications. The increasing prevalence of multi-modal and multi-view data creates both new opportunities for circumventing this issue, and new application challenges. In this thesis we explore several approaches to alleviating annotation issues in multi-view scenarios. We start by studying the problem of zero-shot learning (ZSL) for image recognition, where class-level annotations for image recognition are eliminated by transferring information from text modality instead. We next look at cross-modal matching, where paired instances across views provide the supervised label information for learning. We develop methodology for unsupervised and semi-supervised learning of pairing, thus eliminating the need for annotation requirements. We rst apply these ideas to unsupervised multi-view matching in the context of bilingual dictionary induction (BLI), where instances are words in two languages and nding a correspondence between the words produces a cross-lingual word translation model. We then return to vision and language and look at learning unsupervised pairing between images and text. We will see that this can be seen as a limiting case of ZSL where text-image pairing annotation requirements are completely eliminated. Overall these contributions in multi-view learning provide a suite of methods for reducing annotation requirements: both in conventional classi cation and cross-view matching settings

Image Analysis and Platform Development for Automated Phenotyping in Cytomics

This thesis is dedicated to the empirical study of image analysis in HT/HC screen study. Often a HT/HC screening produces extensive amounts that cannot be manually analyzed. Thus, an automated image analysis solution is prior to an objective understanding of the raw image data. Compared to general application domain, the efficiency of HT/HC image analysis is highly subjected to image quantity and quality. Accordingly, this thesis will address two major procedures, namely image segmentation and object tracking, in the image analysis step of HT/HC screen study. Moreover, this thesis focuses on expending generic computer science and machine learning theorems into the design of dedicated algorithms for HT/HC image analysis. Additionally, this thesis exemplifies a practical implementation of image analysis and data analysis workflow via empirical case studies with different image modalities and experiment settings. However, the data analysis theorem will be generally illustrated without further expansions. Finally, the thesis will briefly address supplementary infrastructures for end-user interaction and data visualization.Netherlands Bioinformatics CentreComputer Systems, Imagery and Medi

Metadata-driven computational (meta)genomics. A practical machine learning approach

Rumming M. Metadata-driven computational (meta)genomics. A practical machine learning approach. Bielefeld: Universität Bielefeld; 2018.A vast amount of bacterial and archaeal genomic sequences have been generated in the past decade through single cell sequencing and in particular binning of metagenomic sequences, but a detailed characterization of the functional features and observable phenotypes of such novel genomes is mostly unknown and thus missing. Machine learning models are trained on previously annotated organisms in relation to the mentioned traits and can be used for the characterization of so far undiscovered novel microbial organisms. The metadata is also used to enrich microbial community profiles with this kind of information, and a client-side webtool has been developed for comparative visualizations of these profiles

Microvascular and proteomic signatures overlap in COVID-19 and bacterial sepsis: the MICROCODE study

Aims Although coronavirus disease 2019 (COVID-19) and bacterial sepsis are distinct conditions, both are known to trigger endothelial dysfunction with corresponding microcirculatory impairment. The purpose of this study was to compare microvascular injury patterns and proteomic signatures in COVID-19 and bacterial sepsis patients.Methods and results This multi-center, observational study included 22 hospitalized adult COVID-19 patients, 43 hospitalized bacterial sepsis patients, and 10 healthy controls from 4 hospitals. Microcirculation and glycocalyx dimensions were quantified via intravital sublingual microscopy. Plasma proteins were measured using targeted proteomics (Olink). Coregulation and cluster analysis of plasma proteins was performed using a training-set and confirmed in a test-set. An independent external cohort of 219 COVID-19 patients was used for validation and outcome analysis. Microcirculation and plasma proteome analysis found substantial overlap between COVID-19 and bacterial sepsis. Severity, but not disease entity explained most data variation. Unsupervised correlation analysis identified two main coregulated plasma protein signatures in both diseases that strictly counteract each other. They were associated with microvascular dysfunction and several established markers of clinical severity. The signatures were used to derive new composite biomarkers of microvascular injury that allow to predict 28-day mortality or/and intubation (area under the curve 0.90, p < 0.0001) in COVID-19.Conclusion Our data imply a common biological host response of microvascular injury in both bacterial sepsis and COVID-19. A distinct plasma signature correlates with endothelial health and improved outcomes, while a counteracting response is associated with glycocalyx breakdown and high mortality. Microvascular health biomarkers are powerful predictors of clinical outcomes.[GRAPHICS]