GliomaPredict: A Clinically Useful Tool for Assigning Glioma Patients to Specific Molecular Subtypes

Background: Advances in generating genome-wide gene expression data have accelerated the development of molecular-based tumor classification systems. Tools that allow the translation of such molecular classification schemas from research into clinical applications are still missing in the emerging era of personalized medicine. Results: We developed GliomaPredict as a computational tool that allows the fast and reliable classification of glioma patients into one of six previously published stratified subtypes based on sets of extensively validated classifiers derived from hundreds of glioma transcriptomic profiles. Our tool utilizes a principle component analysis (PCA)-based approach to generate a visual representation of the analyses, quantifies the confidence of the underlying subtype assessment and presents results as a printable PDF file. GliomaPredict tool is implemented as a plugin application for the widely-used GenePattern framework. Conclusions: GliomaPredict provides a user-friendly, clinically applicable novel platform for instantly assigning gene expression-based subtype in patients with gliomas thereby aiding in clinical trial design and therapeutic decisionmaking. Implemented as a user-friendly diagnostic tool, we expect that in time GliomaPredict, and tools like it, will become routinely used in translational/clinical research and in the clinical care of patients with gliomas

Master Regulators, Regulatory Networks, and Pathways of Glioblastoma Subtypes

Glioblastoma multiforme (GBM) is the most common malignant brain tumor. GBM samples are classified into subtypes based on their transcriptomic and epigenetic profiles. Despite numerous studies to better characterize GBM biology, a comprehensive study to identify GBM subtype-specific master regulators, gene regulatory networks, and pathways is missing. Here, we used FastMEDUSA to compute master regulators and gene regulatory networks for each GBM subtype. We also ran Gene Set Enrichment Analysis and Ingenuity Pathway Analysis on GBM expression dataset from The Cancer Genome Atlas Project to compute GBM- and GBM subtype-specific pathways. Our analysis was able to recover some of the known master regulators and pathways in GBM as well as some putative novel regulators and pathways, which will aide in our understanding of the unique biology of GBM subtypes

Acceleration of Non-Equidiffusive Flames in Channels: Computational Simulations and Analytical Studies

When a premixed flame front spreads in a narrow pipe, wall friction continuously distorts the flame shape. As a result, the flame front acquires a larger surface area, consumes more fuel per unit time and, thereby, propagates faster. While this mechanism of flame acceleration due to wall friction has widely been studied, especially within the last decade, the analytical and computational studies were mostly devoted to equidiffusive flames, where the Lewis number, defined as the thermal to mass diffusivity ratio, is unity, Le = 1. However, in reality thermal and mass diffusion are typically not balanced, especially in rich and lean mixtures. Hence, the micro-scale, diffusional-thermal effects may appear comparable with macro-scale phenomena such as wall friction. The present work sheds the light on the dynamics and morphology of Le ≠ 1 flames in channels. Specifically, it studies, by means of computational and analytical endeavors, how the interplay of finite flame thickness, stretch effect and the thermal-molecular diffusion influence the overall flame acceleration scenario. It is shown that Le \u3e 1 flames accelerate slower, due to an effective thickening of the flame front. In contrast, Le \u3c 1 flames exhibit faster acceleration due to effective flame channeling and other morphological deformations resembling the diffusional-thermal (DT) instability. The analysis also incorporates the internal transport flame properties into the theory of flame acceleration due to wall friction, by means of the Markstein number, Mk, that characterizes the flame response to curvature and stretch. Being a positive or negative function of thermal-chemical combustion parameters, such as the thermal expansion ratio and the Lewis and Zel\u27dovich numbers, the Markstein number either restrains or promotes the flame acceleration. While Mk may substantially facilitate the flame acceleration in narrow channels, this effects diminishes with the increase in the channel width. The analysis is accompanied by extensive numerical simulations of the Navier-Stokes and combustion equations, which clarify the impact of the Lewis number on the flame acceleration. It is obtained that, for Le lower than a certain critical value, at the initial stage of flame acceleration, globally-convex flame fronts split into two or more fingers , accompanied by a drastic increase in the flame surface area and associated enhancement of the flame acceleration. Later, however, the flame fingers meet, promptly consuming the troughs, which rapidly diminishes the flame surface area and moderates the acceleration. Eventually, this results in a single, globally-convex flame front that keeps accelerating. Overall, the thermal-diffusive effects facilitate the flame acceleration scenario, thereby advancing a potential deflagration-to-detonation transition

Stabilization of Linear Systems Over Gaussian Networks

The problem of remotely stabilizing a noisy linear time invariant plant over a Gaussian relay network is addressed. The network is comprised of a sensor node, a group of relay nodes and a remote controller. The sensor and the relay nodes operate subject to an average transmit power constraint and they can cooperate to communicate the observations of the plant's state to the remote controller. The communication links between all nodes are modeled as Gaussian channels. Necessary as well as sufficient conditions for mean-square stabilization over various network topologies are derived. The sufficient conditions are in general obtained using delay-free linear policies and the necessary conditions are obtained using information theoretic tools. Different settings where linear policies are optimal, asymptotically optimal (in certain parameters of the system) and suboptimal have been identified. For the case with noisy multi-dimensional sources controlled over scalar channels, it is shown that linear time varying policies lead to minimum capacity requirements, meeting the fundamental lower bound. For the case with noiseless sources and parallel channels, non-linear policies which meet the lower bound have been identified

Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies

Calculating the sample size in scientific studies is one of the critical issues as regards the scientific contribution of the study. The sample size critically affects the hypothesis and the study design, and there is no straightforward way of calculating the effective sample size for reaching an accurate conclusion. Use of a statistically incorrect sample size may lead to inadequate results in both clinical and laboratory studies as well as resulting in time loss, cost, and ethical problems. This review holds two main aims. The first aim is to explain the importance of sample size and its relationship to effect size (ES) and statistical significance. The second aim is to assist researchers planning to perform sample size estimations by suggesting and elucidating available alternative software, guidelines and references that will serve different scientific purposes

Association of serum ADMA, SDMA and L-NMMA concentrations with disease progression in COVID-19 patients

IntroductionThis study determines and compares the concentrations of arginine and methylated arginine products ((asymmetric dimethylarginine (ADMA), symmetric dimethylarginine (SDMA), n-monomethyl-1-arginine (L-NMMA) and homoarginine (HA)) for assessment of their association with disease severity in serum samples of COVID-19 patients. Materials and methodsSerum arginine and methylated arginine products of 57 mild-moderate and 29 severe (N = 86) COVID-19 patients and 21 controls were determined by tandem mass spectrometry. Moreover, the concentrations of some of the routine clinical laboratory parameters -neutrophil lymphocyte ratio (NLR), C-reactive protein, ferritin, D-dimer, and fibrinogen measured during COVID-19 follow-up were also taken into consideration and compared with the concentrations of arginine and methylated arginine products. ResultsSerum ADMA, SDMA and L-NMMA were found to be significantly higher in severe COVID-19 patients, than in both mild-moderate patients and the control group (P < 0.001 for each). In addition, multiple logistic regression analysis indicated L-NMMA (cut-off =120 nmol/L OR = 34, 95% confidence interval (CI) = 3.5-302.0, P= 0.002), CRP (cut-off = 32 mg/L, OR = 37, 95% CI = 4.8-287.0, P < 0.001), and NLR (cut-off = 7, OR = 22, 95% CI = 1.4-335.0, P = 0.020) as independent risk factors for identification of severe patients. ConclusionsThe concentration of methylated arginine metabolites are significantly altered in COVID-19 disease. The results of this study indicate a significant correlation between the severity of COVID-19 disease and concentrations of CRP, NLR and L-NMMA

Integration and management of Wi-Fi offloading in service provider infrastructures

A. Serdar Tan (MEF Author)##nofulltext##Integration of offloading technologies into mobile network operator's infrastructures that provide heterogeneous access services is a challenging task for mobile operators. A connectivity management platform is a key element for heterogeneous mobile network operators in order to enable optimal offloading. In this study, development and integration of a connectivity management platform that uses a novel multiple attribute decision making algorithms for efficient Wi-Fi Offloading in heterogeneous wireless networks is presented. The proposed platform collects several terminal and network level attributes via infrastructure and client Application Programming Interfaces (APIs) and decides the best network access technology to connect for requested users. Through experimentation, we provide details on the platform integration with service provider's network and sensitivity analysis of the multiple attribute decision making algorithm

HF spectrum occupancy and antennas

This paper deals with the research made during the COST 296 action in the WG2, WP 2.3 in the antennas and HF spectrum management fields, focusing the Mitigation of Ionospheric Effects on Radio Systems as the subject of this COST action.info:eu-repo/semantics/publishedVersio