Determining and interpreting correlations in lipidomic networks found in glioblastoma cells

Background: Intelligent and multitiered quantitative analysis of biological systems rapidly evolves to a key technique in studying biomolecular cancer aspects. Newly emerging advances in both measurement as well as bio-inspired computational techniques have facilitated the development of lipidomics technologies and offer an excellent opportunity to understand regulation at the molecular level in many diseases. Results: We present computational approaches to study the response of glioblastoma U87 cells to gene- and chemo-therapy. To identify distinct biomarkers and differences in therapeutic outcomes, we develop a novel technique based on graph-clustering. This technique facilitates the exploration and visualization of co-regulations in glioblastoma lipid profiling data. We investigate the changes in the correlation networks for different therapies and study the success of novel gene therapies targeting aggressive glioblastoma. Conclusions: The novel computational paradigm provides unique “fingerprints” by revealing the intricate interactions at the lipidome level in glioblastoma U87 cells with induced apoptosis (programmed cell death) and thus opens a new window to biomedical frontiers

Continuous arterial spin labeling using a local magnetic field gradient coil

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Enhanced catalytic performance of MnxOy-Na2WO4/SiO2 for the oxidative coupling of methane using an ordered mesoporous silica support

The oxidative coupling of methane is a highly promising reaction for its direct conversion. Silica supported MnxOy–Na2WO4 is a suitable catalyst for this reaction. In this study, a variety of different SiO2 materials have been tested as supports. Surprisingly, the application of ordered mesoporous silicas, here exemplarily shown for SBA-15 as support materials, greatly enhances the catalytic performance. The CH4 conversion increased two fold and also the C2 selectivity is strongly increased

Innovation in Creative Industries: From the Quadruple Helix Model to the Systems Theory

Knowledge and creativity have always played a key role in the economy. Since the 2000s, the relevance of the creative industries, a high growth sector, has been pointed out as long as its strong and positive effects on jobs and economic growth. In the current context of rapid globalization and technological development, the innovation system is getting even more complex because it implies a shift in research focus from the supply to the demand side environment (consumption-driven economy). The authors focus on theoretical approaches coming from management and media studies able to explain the current paradigm shift in innovation and knowledge production and use: the Triple Helix model (and its developments) and Systems Theory. As an interesting case study, the Creative Enterprise Australia (CEA) is analyzed according the theoretical approaches shown. The paper tries to shed new light on the evolving role of knowledge pointing out the overlapping relationships between all the actors involved and the interpenetration of systems, and the prominent appointment of the media as an interpretative framework of the convergence of the depicted theories

Silica material variation for the Mn_xO_y-Na₂WO₄/SiO₂

The oxidative coupling of methane (OCM) is one of the best methods for the direct conversion of methane.Among the known OCM catalysts, MnxOy-Na2WO4/SiO2 is a promising candidate for an industrial appli-cation, showing a high methane conversion and C2 selectivity, with a good stability during long-termcatalytic activity tests. In the present study, some results have been already published and discussedbriefly in our previous short communication. However, we herein investigated comprehensively theinfluence of various silica support materials on the performance of the MnxOy-Na2WO4/SiO2 systemin the OCM by means of ex situ and in situ XRD, BET, SEM and TEM characterization methods andshowed new results to reveal possible support effects on the catalyst. The catalytic performance of most MnxOy-Na2WO4/SiO2 catalysts supported by different silica support materials did not differ substan-tially. However, the performance of the SBA-15 supported catalyst was outstanding and the methaneconversion was nearly twofold higher in comparison to the other silica supported catalysts at similar C2 selectivity as shown before in the communication. The reason of this substantial increase in performancecould be the ordered mesoporous structure of the SBA-15 support material, homogeneous dispersion ofactive components and high number of active sites responsible for the OCM

Determining and interpreting correlations in lipidomic networks found in glioblastoma cells

Background: Intelligent and multitiered quantitative analysis of biological systems rapidly evolves to a key technique in studying biomolecular cancer aspects. Newly emerging advances in both measurement as well as bio-inspired computational techniques have facilitated the development of lipidomics technologies and offer an excellent opportunity to understand regulation at the molecular level in many diseases. Results: We present computational approaches to study the response of glioblastoma U87 cells to gene- and chemo-therapy. To identify distinct biomarkers and differences in therapeutic outcomes, we develop a novel technique based on graph-clustering. This technique facilitates the exploration and visualization of co-regulations in glioblastoma lipid profiling data. We investigate the changes in the correlation networks for different therapies and study the success of novel gene therapies targeting aggressive glioblastoma. Conclusions: The novel computational paradigm provides unique “fingerprints” by revealing the intricate interactions at the lipidome level in glioblastoma U87 cells with induced apoptosis (programmed cell death) and thus opens a new window to biomedical frontiers. Background Glioblastoma are highly invasive brain tumors. Th

Identification of plasma lipid biomarkers for prostate cancer by lipidomics and bioinformatics

Background: Lipids have critical functions in cellular energy storage, structure and signaling. Many individual lipid molecules have been associated with the evolution of prostate cancer; however, none of them has been approved to be used as a biomarker. The aim of this study is to identify lipid molecules from hundreds plasma apparent lipid species as biomarkers for diagnosis of prostate cancer. Methodology/Principal Findings: Using lipidomics, lipid profiling of 390 individual apparent lipid species was performed on 141 plasma samples from 105 patients with prostate cancer and 36 male controls. High throughput data generated from lipidomics were analyzed using bioinformatic and statistical methods. From 390 apparent lipid species, 35 species were demonstrated to have potential in differentiation of prostate cancer. Within the 35 species, 12 were identified as individual plasma lipid biomarkers for diagnosis of prostate cancer with a sensitivity above 80%, specificity above 50% and accuracy above 80%. Using top 15 of 35 potential biomarkers together increased predictive power dramatically in diagnosis of prostate cancer with a sensitivity of 93.6%, specificity of 90.1% and accuracy of 97.3%. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) demonstrated that patient and control populations were visually separated by identified lipid biomarkers. RandomForest and 10-fold cross validation analyses demonstrated that the identified lipid biomarkers were able to predict unknown populations accurately, and this was not influenced by patient's age and race. Three out of 13 lipid classes, phosphatidylethanolamine (PE), ether-linked phosphatidylethanolamine (ePE) and ether-linked phosphatidylcholine (ePC) could be considered as biomarkers in diagnosis of prostate cancer. Conclusions/Significance: Using lipidomics and bioinformatic and statistical methods, we have identified a few out of hundreds plasma apparent lipid molecular species as biomarkers for diagnosis of prostate cancer with a high sensitivity, specificity and accuracy

Silicon oxycarbonitride ceramic containing nickel nanoparticles from design to catalytic application

Nickel containing silicon oxycarbonitride ceramic nanocomposites are synthesized from hydrous nickel acetate and poly vinyl silazane Durazane 1800 or perhydropolysilazane NN120 20 A PHPS . A room temperature chemical reaction results in Ni containing polysilazane precursors which are transformed into ceramic nanocomposites with nickel nanoparticles 2 4 nm upon pyrolysis at elevated temperatures 700 1100 C under an argon atmosphere. The ceramic nanocomposites derived from the Durazane 1800 Ni precursor by the thermolysis process at 700 and 900 C manifest a microporous structure with a BET specific surface area of amp; 8764;361 and amp; 8764;232 m2 g amp; 8722;1, respectively. In contrast, all pyrolyzed samples derived from the PHPS Ni precursor exhibit a nonporous structure. The Ni SiOCN ceramic nanocomposites tested in a plug flow fixed bed reactor display significant catalytic activity in dry methane reforming to syngas. The highest CH4 reaction rate of 0.18 mol min amp; 8722;1 gNi amp; 8722;1 is observed at 800 C for the sample derived from the PHPS Ni precursor by pyrolysis at 900 C. All these make the materials developed in this work, i.e. nickel nanoparticles in situ formed in the SiOCN ceramic matrix, as promising candidates for heterogeneous catalysi