Despite recent large-scale profiling efforts, the best prognostic predictor of glioblastoma multiforme (GBM) remains the patient's age at diagnosis. We describe a global pattern of tumor-exclusive co-occurring copy-number alterations (CNAs) that is correlated, possibly coordinated with GBM patients' survival and response to chemotherapy. The pattern is revealed by GSVD comparison of patient-matched but probe-independent GBM and normal aCGH datasets from The Cancer Genome Atlas (TCGA). We find that, first, the GSVD, formulated as a framework for comparatively modeling two composite datasets, removes from the pattern copy-number variations (CNVs) that occur in the normal human genome (e.g., female-specific X chromosome amplification) and experimental variations (e.g., in tissue batch, genomic center, hybridization date and scanner), without a-priori knowledge of these variations. Second, the pattern includes most known GBM-associated changes in chromosome numbers and focal CNAs, as well as several previously unreported CNAs in 3% of the patients. These include the biochemically putative drug target, cell cycle-regulated serine/threonine kinase-encoding TLK2, the cyclin E1-encoding CCNE1, and the Rb-binding histone demethylase-encoding KDM5A. Third, the pattern provides a better prognostic predictor than the chromosome numbers or any one focal CNA that it identifies, suggesting that the GBM survival phenotype is an outcome of its global genotype. The pattern is independent of age, and combined with age, makes a better predictor than age alone. GSVD comparison of matched profiles of a larger set of TCGA patients, inclusive of the initial set, confirms the global pattern. GSVD classification of the GBM profiles of an independent set of patients validates the prognostic contribution of the pattern.

The number of large-scale high-dimensional datasets recording different aspects of a single disease is growing, accompanied by a need for frameworks that can create one coherent model from multiple tensors of matched columns, e.g., patients and platforms, but independent rows, e.g., probes. We define and prove the mathematical properties of a novel tensor generalized singular value decomposition (GSVD), which can simultaneously find the similarities and dissimilarities, i.e., patterns of varying relative significance, between any two such tensors. We demonstrate the tensor GSVD in comparative modeling of patient- and platform-matched but probe-independent ovarian serous cystadenocarcinoma (OV) tumor, mostly high-grade, and normal DNA copy-number profiles, across each chromosome arm, and combination of two arms, separately. The modeling uncovers previously unrecognized patterns of tumor-exclusive platform-consistent co-occurring copy-number alterations (CNAs). We find, first, and validate that each of the patterns across only 7p and Xq, and the combination of 6p+12p, is correlated with a patient’s prognosis, is independent of the tumor’s stage, the best predictor of OV survival to date, and together with stage makes a better predictor than stage alone. Second, these patterns include most known OV-associated CNAs that map to these chromosome arms, as well as several previously unreported, yet frequent focal CNAs. Third, differential mRNA, microRNA, and protein expression consistently map to the DNA CNAs. A coherent picture emerges for each pattern, suggesting roles for the CNAs in OV pathogenesis and personalized therapy. In 6p+12p, deletion of the p21-encoding CDKN1A and p38-encoding MAPK14 and amplification of RAD51AP1 and KRAS encode for human cell transformation, and are correlated with a cell’s immortality, and a patient’s shorter survival time. In 7p, RPA3 deletion and POLD2 amplification are correlated with DNA stability, and a longer survival. In Xq, PABPC5 deletion and BCAP31 amplification are correlated with a cellular immune response, and a longer survival.

Abstract. With the advent of advanced imaging techniques, genotyping, and methods to assess clinical and biological progression, there is a growing need for a unified framework that could exploit information available from multiple sources to aid diagnosis and the identification of early signs of Alzheimer's disease (AD). We propose a modeling strategy using supervised feature extraction to optimally combine highdimensional imaging modalities with several other low-dimensional disease risk factors. The motivation is to discover new imaging biomarkers and use them in conjunction with other known biomarkers for prognosis of individuals at high risk of developing AD. Our framework also has the ability to assess the relative importance of imaging modalities for predicting AD conversion. We evaluate the proposed methodology on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to predict conversion of individuals with Mild Cognitive Impairment (MCI) to AD, only using information available at baseline.

<p class="PaperAbstract">A simple, disposable and low - cost voltammetric sensor based on the anodized pencil graphite electrode (APGE) for the simultaneous determination of dopamine (DA) and uric acid (UA) is demonstrated. The physico-chemical properties of the pencil graphite electrode (PGE) before and after anodization were analyzed using FT-IR, FT-Raman, SEM and EIS characterization techniques. In comparison to PGE, APGE exhibited excellent electrochemical activity towards the simultaneous detection of DA and UA with peak-to-peak separation of about 0.18 V even in the presence of high concentration (2 mM) of ascorbic acid (AA). The discrimination of APGE towards AA was rationalized through the absence of favorable surface interactions between oxygen rich functional groups on the surface of APGE and AA. Using DPV without any pre-concentration step and under optimized conditions, APGE displayed a linear range of 1 – 80 μM with an estimated limit of detection (LOD, 3σ/m) of 0.008 μM and 0.014 μM for DA and UA, respectively. Moreover, a higher sensitivity in comparison to other previously reported pretreated pencil graphite electrodes was observed for DA (34.32 μA/μM) and UA (12.33 μA/μM). The practical applicability of APGE was demonstrated through the estimation of DA in human blood serum and UA in urine samples.</p>

Abstract:The purpose of this study was to analyse the impact of yoga and physical exercise on resting heart rate among diabetes patients. Thirty type-2 female diabetic patients (n = 30) from Rajah Muthiah Medical College Hospital, Annamalai University, Tamil Nadu were randomly selected as subjects. The age of the subjects ranged from 35 to 45 years. The subjects divided into three equal groups of ten subjects each (n = 10). In which, group I underwent yogic exercises (YEG), group II underwent physical activities (PAG) for six days per week for sixteen weeks and group III acted as control (CG) who did not undergo any special training programme apart from their regular activities. Resting heart rate was selected as a test variable and assessed before and after the training period. The collected data were statistically analysed by using Analysis of Covariance (ANCOVA) and Scheffe"s test was applied as post hoc test to determine the paired mean difference. From the results of the study, it was found that there was a significant reduction (p ≤ 0.05) in resting heart rate of training groups when compared to control group.