Knowledge-based gene expression classification via matrix factorization

Motivation: Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted features are considered indicative of underlying regulatory processes. They can as well be applied to the classification of gene expression datasets by grouping samples into different categories for diagnostic purposes or group genes into functional categories for further investigation of related metabolic pathways and regulatory networks. Results: In this study we focus on unsupervised matrix factorization techniques and apply ICA and sparse NMF to microarray datasets. The latter monitor the gene expression levels of human peripheral blood cells during differentiation from monocytes to macrophages. We show that these tools are able to identify relevant signatures in the deduced component matrices and extract informative sets of marker genes from these gene expression profiles. The methods rely on the joint discriminative power of a set of marker genes rather than on single marker genes. With these sets of marker genes, corroborated by leave-one-out or random forest cross-validation, the datasets could easily be classified into related diagnostic categories. The latter correspond to either monocytes versus macrophages or healthy vs Niemann Pick C disease patients.Siemens AG, MunichDFG (Graduate College 638)DAAD (PPP Luso - Alem˜a and PPP Hispano - Alemanas

Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning

Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence imaging technology that has the potential to increase intraoperative precision, extend resection, and tailor surgery for malignant invasive brain tumors because of its subcellular dimension resolution. Despite its promising diagnostic potential, interpreting the gray tone fluorescence images can be difficult for untrained users. In this review, we provide a detailed description of bioinformatical analysis methodology of CLE images that begins to assist the neurosurgeon and pathologist to rapidly connect on-the-fly intraoperative imaging, pathology, and surgical observation into a conclusionary system within the concept of theranostics. We present an overview and discuss deep learning models for automatic detection of the diagnostic CLE images and discuss various training regimes and ensemble modeling effect on the power of deep learning predictive models. Two major approaches reviewed in this paper include the models that can automatically classify CLE images into diagnostic/nondiagnostic, glioma/nonglioma, tumor/injury/normal categories and models that can localize histological features on the CLE images using weakly supervised methods. We also briefly review advances in the deep learning approaches used for CLE image analysis in other organs. Significant advances in speed and precision of automated diagnostic frame selection would augment the diagnostic potential of CLE, improve operative workflow and integration into brain tumor surgery. Such technology and bioinformatics analytics lend themselves to improved precision, personalization, and theranostics in brain tumor treatment.Comment: See the final version published in Frontiers in Oncology here: https://www.frontiersin.org/articles/10.3389/fonc.2018.00240/ful

The first joint ESGAR/ ESPR consensus statement on the technical performance of cross-sectional small bowel and colonic imaging

Objectives: To develop guidelines describing a standardised approach to patient preparation and acquisition protocols for magnetic resonance imaging (MRI), computed tomography (CT) and ultrasound (US) of the small bowel and colon, with an emphasis on imaging inflammatory bowel disease. Methods: An expert consensus committee of 13 members from the European Society of Gastrointestinal and Abdominal Radiology (ESGAR) and European Society of Paediatric Radiology (ESPR) undertook a six-stage modified Delphi process, including a detailed literature review, to create a series of consensus statements concerning patient preparation, imaging hardware and image acquisition protocols. Results: One hundred and fifty-seven statements were scored for agreement by the panel of which 129 statements (82 %) achieved immediate consensus with a further 19 (12 %) achieving consensus after appropriate modification. Nine (6 %) statements were rejected as consensus could not be reached. Conclusions: These expert consensus recommendations can be used to help guide cross-sectional radiological practice for imaging the small bowel and colon. Key points: • Cross-sectional imaging is increasingly used to evaluate the bowel • Image quality is paramount to achieving high diagnostic accuracy • Guidelines concerning patient preparation and image acquisition protocols are provided

GOODS-Herschel: Separating High Redshift active galactic Nuclei and star forming galaxies Using Infrared Color Diagnostics

We have compiled a large sample of 151 high redshift (z=0.5-4) galaxies selected at 24 microns (S24>100 uJy) in the GOODS-N and ECDFS fields for which we have deep Spitzer IRS spectroscopy, allowing us to decompose the mid-infrared spectrum into contributions from star formation and activity in the galactic nuclei. In addition, we have a wealth of photometric data from Spitzer IRAC/MIPS and Herschel PACS/SPIRE. We explore how effective different infrared color combinations are at separating our mid-IR spectroscopically determined active galactic nuclei from our star forming galaxies. We look in depth at existing IRAC color diagnostics, and we explore new color-color diagnostics combining mid-IR, far-IR, and near-IR photometry, since these combinations provide the most detail about the shape of a source's IR spectrum. An added benefit of using a color that combines far-IR and mid-IR photometry is that it is indicative of the power source driving the IR luminosity. For our data set, the optimal color selections are S250/S24 vs. S8.0/S3.6 and S100/S24 vs. S8.0/S3.6; both diagnostics have ~10% contamination rate in the regions occupied primarily by star forming galaxies and active galactic nuclei, respectively. Based on the low contamination rate, these two new IR color-color diagnostics are ideal for estimating both the mid-IR power source of a galaxy when spectroscopy is unavailable and the dominant power source contributing to the IR luminosity. In the absence of far-IR data, we present color diagnostics using the WISE mid-IR bands which can efficiently select out high z (z~2) star forming galaxies.Comment: Accepted for publication in ApJ. 13 pages, 8 figure

Development of a recombinase polymerase amplification lateral flow assay for the detection of active Trypanosoma evansi infections

Author summary Neglected tropical diseases (NTDs) affecting humans and/or domestic animals severely impair the socio-economic development of endemic areas. One of these diseases, animal trypanosomosis, affects livestock and is caused by the parasites of the Trypanosoma genus. The most widespread causative agent of animal trypanosomosis is T. evansi, which is found in large parts of the world (Africa, Asia, South America, Middle East, and the Mediterranean). Proper control and treatment of the disease requires the availability of reliable and sensitive diagnostic tools. DNA-based detection techniques are powerful and versatile in the sense that they can be tailored to achieve a high specificity and usually allow the reliable detection of low amounts of parasite genetic material. However, many DNA-based methodologies (such as PCR) require trained staff and well-equipped laboratories, which is why the research community has actively investigated in developing amplification strategies that are simple, fast, cost-effective and are suitable for use in minimally equipped laboratories and field settings. In this paper, we describe the development of a diagnostic test under a dipstick format for the specific detection of T. evansi, based on a DNA amplification principle (Recombinase Polymerase Amplification aka RPA) that meets the above-mentioned criteria. Background Animal trypanosomosis caused by Trypanosoma evansi is known as "surra" and is a widespread neglected tropical disease affecting wild and domestic animals mainly in South America, the Middle East, North Africa and Asia. An essential necessity for T. evansi infection control is the availability of reliable and sensitive diagnostic tools. While DNA-based PCR detection techniques meet these criteria, most of them require well-trained and experienced users as well as a laboratory environment allowing correct protocol execution. As an alternative, we developed a recombinase polymerase amplification (RPA) test for Type A T. evansi. The technology uses an isothermal nucleic acid amplification approach that is simple, fast, cost-effective and is suitable for use in minimally equipped laboratories and even field settings. Methodology/Principle findings An RPA assay targeting the T. evansi RoTat1.2 VSG gene was designed for the DNA-based detection of T. evansi. Comparing post-amplification visualization by agarose gel electrophoresis and a lateral flow (LF) format reveals that the latter displays a higher sensitivity. The RPA-LF assay is specific for RoTat1.2-expressing strains of T. evansi as it does not detect the genomic DNA of other trypanosomatids. Finally, experimental mouse infection trials demonstrate that the T. evansi specific RPA-LF can be employed as a test-of-cure tool