Joint co-clustering: co-clustering of genomic and clinical bioimaging data

AbstractFor better understanding the genetic mechanisms underlying clinical observations, and better defining a group of potential candidates for protein-family-inhibiting therapy, it is interesting to determine the correlations between genomic, clinical data and data coming from high resolution and fluorescent microscopy. We introduce a computational method, called joint co-clustering, that can find co-clusters or groups of genes, bioimaging parameters and clinical traits that are believed to be closely related to each other based on the given empirical information. As bioimaging parameters, we quantify the expression of growth factor receptor EGFR/erb-B family in non-small cell lung carcinoma (NSCLC) through a fully-automated computer-aided analysis approach. This immunohistochemical analysis is usually performed by pathologists via visual inspection of tissue samples images. Our fully-automated techniques streamlines this error-prone and time-consuming process, thereby facilitating analysis and diagnosis. Experimental results for several real-life datasets demonstrate the high quantitative precision of our approach. The joint co-clustering method was tested with the receptor EGFR/erb-B family data on non-small cell lung carcinoma (NSCLC) tissue and identified statistically significant co-clusters of genes, receptor protein expression and clinical traits. The validation of our results with the literature suggest that the proposed method can provide biologically meaningful co-clusters of genes and traits and that it is a very promising approach to analyse large-scale biological data and to study multi-factorial genetic pathologies through their genetic alterations

Design and synthesis of new molecules based on indolium derivatives for two-photon absorption bioimaging applications

Bioimaging is the visualization of biological processes in real time, interfering as less as possible with these and using non-invasive methods. Among others, fluorescence methods have acquired a very important role in these purposes through the nature of the light. Bioimaging pretends to understand how our organism works identifing subcellular organelles, following cellular processes, quantifying ion or biological species and visualising interactions of molecules with their targets in living cells or tissues. In the last decades, two-photon (TP) microscopy is unseating classical one-photon (OP) microscopy due to its advantages, such as the use of lower energy excitation wavelengths or the possibility of focus in depth, among others. Nowadays, it is an interesting target design and develop of new probes for TP microscopy to biomaging. Fluorophores based on indolenines are a family of compounds with promising properties in this sense. In this work, we present the design, synthesis and characterization of new indolium derivatives with promising properties to be used in bioimaging applications in living cells with different purposes.Real Sociedad Española de Química, Jóvenes Invetigadores Químicos, Facultad de Ciencias Ambientales y Bioquímica (UCLM), Merck, Cortes de Castilla la Mancha, Reaxys (Elsevier), Diputación de Toledo, MestreLab Research, Sección Territorial de Castilla la Mancha (RSEQ), Grupo especializado en Nanociencia y Materiales Moleculares (RESEQ), Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

EDAM-bioimaging : The ontology of bioimage informatics operations, topics, data, and formats

International audienceThe ontology of bioimage informatics operations, topics, data, and formats What? EDAM-bioimaging is an extension of the EDAM ontology, dedicated to bioimage analysis, bioimage informatics, and bioimaging. Why? EDAM-bioimaging enables interoperable descriptions of software, publications, data, and workflows, fostering reliable and transparent science. How? EDAM-bioimaging is developed in a community spirit, in a welcoming collaboration between numerous bioimaging experts and ontology developers. How can I contribute? We need your expertise! You can help by reviewing parts of EDAM-bioimaging, posting comments with suggestions, requirements, or needs for clarification, or participating in a Taggathon or another hackathon. Please see https://github.com/edamontology/edam-bioimaging#contributing. EDAM-bioimaging is developed in an interdisciplinary open collaboration supported by the hosting institutions, participating individuals, and NEUBIAS COST Action (CA15124) and ELIXIR-EXCELERATE (676559) funded by the Horizon 2020 Framework Programme of the European Union. https://github.com/edamontology/edam-bioimaging @edamontology /edamontology/edam-bioimagin

Learning Rigid Image Registration - Utilizing Convolutional Neural Networks for Medical Image Registration

Many traditional computer vision tasks, such as segmentation, have seen large step-changes in accuracy and/or speed with the application of Convolutional Neural Networks (CNNs). Image registration, the alignment of two or more images to a common space, is a fundamental step in many medical imaging workflows. In this paper we investigate whether these techniques can also bring tangible benefits to the registration task. We describe and evaluate the use of convolutional neural networks (CNNs) for both mono- and multi- modality registration and compare their performance to more traditional schemes, namely multi-scale, iterative registration. This paper also investigates incorporating inverse consistency of the learned spatial transformations to impose additional constraints on the network during training and investigate any benefit in accuracy during detection. The approaches are validated with a series of artificial mono-modal registration tasks utilizing T1-weighted MR brain i mages from the Open Access Series of Imaging Studies (OASIS) study and IXI brain development dataset and a series of real multi-modality registration tasks using T1-weighted and T2-weighted MR brain images from the 2015 Ischemia Stroke Lesion segmentation (ISLES) challenge. The results demonstrate that CNNs give excellent performance for both mono- and multi- modality head and neck registration compared to the baseline method with significantly fewer outliers and lower mean errors

Rare earth based nanostructured materials: Synthesis, functionalization, properties and bioimaging and biosensing applications

Rare earth based nanostructures constitute a type of functional materials widely used and studied in the recent literature. The purpose of this review is to provide a general and comprehensive overview of the current state of the art, with special focus on the commonly employed synthesis methods and functionalization strategies of rare earth based nanoparticles and on their different bioimaging and biosensing applications. The luminescent (including downconversion, upconversion and permanent luminescence) and magnetic properties of rare earth based nanoparticles, as well as their ability to absorb X-rays, will also be explained and connected with their luminescent, magnetic resonance and X-ray computed tomography bioimaging applications, respectively. This review is not only restricted to nanoparticles, and recent advances reported for in other nanostructures containing rare earths, such as metal organic frameworks and lanthanide complexes conjugated with biological structures, will also be commented on.European Union 267226Ministerio de Economía y Competitividad MAT2014-54852-

TICAL - a web-tool for multivariate image clustering and data topology preserving visualization

In life science research bioimaging is often used to study two kinds of features in a sample simultaneously: morphology and co-location of molecular components. While bioimaging technology is rapidly proposing and improving new multidimensional imaging platforms, bioimage informatics has to keep pace in order to develop algorithmic approaches to support biology experts in the complex task of data analysis. One particular problem is the availability and applicability of sophisticated image analysis algorithms via the web so different users can apply the same algorithms to their data (sometimes even to the same data to get the same results) and independently from her/his whereabouts and from the technical features of her/his computer. In this paper we describe TICAL, a visual data mining approach to multivariate microscopy analysis which can be applied fully through the web.We describe the algorithmic approach, the software concept and present results obtained for different example images

Bottom-up synthesis of carbon nanoparticles with higher doxorubicin efficacy

open15siembargoed_20180201Bayda, Samer; Hadla, Mohamad; Palazzolo, Stefano; Kumar, Vinit; Caligiuri, Isabella; Ambrosi, EMMANUELE KIZITO; Pontoglio, Enrico; Agostini, Marco; Tuccinardi, Tiziano; Benedetti, Alvise; Riello, Pietro; Canzonieri, Vincenzo; Corona, Giuseppe; Toffoli, Giuseppe; Rizzolio, FlavioBayda, Samer; Hadla, Mohamad; Palazzolo, Stefano; Kumar, Vinit; Caligiuri, Isabella; Ambrosi, EMMANUELE KIZITO; Pontoglio, Enrico; Agostini, Marco; Tuccinardi, Tiziano; Benedetti, Alvise; Riello, Pietro; Canzonieri, Vincenzo; Corona, Giuseppe; Toffoli, Giuseppe; Rizzolio, Flavi