Identification of dominant modes in random dynamical and aeroelastic systems

Identification of dominant modes is an important step in studying linearly vibrating systems, including flow-induced vibrations. In the presence of uncertainty, when some of the system parameters and the external excitation are modeled as random quantities, this step becomes more difficult. This work is aimed at giving a systematic treatment to this end. The ability to capture the time averaged kinetic energy is chosen as the primary criterion for selection of modes. Accordingly, a methodology is proposed based on the overlap of probability density functions (pdf) of the natural and excitation frequencies, proximity of the natural frequencies of the mean or baseline system, modal participation factor, and stochastic variation of mode shapes in terms of the modes of the baseline system - termed here as statistical modal overlapping. The probabilistic descriptors of the natural frequencies and mode shapes are found by solving a random eigenvalue problem. Three distinct vibration scenarios are considered: (i) undamped arid damped free vibrations of a bladed disk assembly, (ii) forced vibration of a building, and (iii) flutter of a bridge model. Through numerical studies, it is observed that the proposed methodology gives an accurate selection of modes. (C) 2015 Elsevier Ltd. All rights reserved.

Definition of a serum marker panel for glioblastoma discrimination and identification of Interleukin 1 beta in the microglial secretome as a novel mediator of endothelial cell survival induced by C-reactive protein

Glioblastoma (GBM) is the most common malignant adult primary brain tumor. We profiled 724 cancer-associated proteins in sera of healthy individuals (n = 27) and GBM (n = 28) using antibody microarray. While 69 proteins exhibited differential abundance in GBM sera, a three-marker panel (LYAM1, BHE40 and CRP) could discriminate GBM sera from that of healthy donors with an accuracy of 89.7% and p < 0.0001. The high abundance of C-reactive protein (CRP) in GBM sera was confirmed in 264 independent samples. High levels of CRP protein was seen in GBM but without a change in transcript levels suggesting a non-tumoral origin. Glioma-secreted Interleukin 6 (IL6) was found to induce hepatocytes to secrete CRP, involving JAK-STAT pathway. The culture supernatant from CRP-treated microglial cells induced endothelial cell survival under nutrient-deprivation condition involving CRP-Fc gamma RIII signaling cascade. Transcript profiling of CRP-treated microglial cells identified Interleukin 1 beta (IL1 beta) present in the microglial secretome as the key mediator of CRP-induced endothelial cell survival. IL1 beta neutralization by antibody-binding or siRNA-mediated silencing in microglial cells reduced the ability of the supernatant from CRP-treated microglial cells to induce endothelial cell survival. Thus our study identifies a serum based three-marker panel for GBM diagnosis and provides leads for developing targeted therapies. Biological significance A complex antibody microarray based serum marker profiling identified a three-marker panel - LYAM1, BHE40 and CRP as an accurate discriminator of glioblastoma sera from that of healthy individuals. CRP protein is seen in high levels without a concomitant increase of CRP transcripts in glioblastoma. Glioma-secreted IL6 induced hepatocytes to produce CRP in a JAK-STAT signaling dependent manner. CRP induced microglial cells to release IL1 beta which in turn promoted endothelial cell survival. This study, besides defining a serum panel for glioblastoma discrimination, identified IL1 beta as a potential candidate for developing targeted therapy. (C) 2015 Elsevier B.V. All rights reserved.

Delineation of homogeneous hydrometeorological regions using wavelet-based global fuzzy cluster analysis

Identification of homogeneous hydrometeorological regions (HMRs) is necessary for various applications. Such regions are delineated by various approaches considering rainfall and temperature as two key variables. In conventional approaches, formation of regions is based on principal components (PCs)/statistics/indices determined from time series of the key variables at monthly and seasonal scales. An issue with use of PCs for regionalization is that they have to be extracted from contemporaneous records of hydrometeorological variables. Therefore, delineated regions may not be effective when the available records are limited over contemporaneous time period. A drawback associated with the use of statistics/indices is that they do not provide effective representation of the key variables when the records exhibit non-stationarity. Consequently, the resulting regions may not be effective for the desired purpose. To address these issues, a new approach is proposed in this article. The approach considers information extracted from wavelet transformations of the observed multivariate hydrometeorological time series as the basis for regionalization by global fuzzy c-means clustering procedure. The approach can account for dynamic variability in the time series and its non-stationarity (if any). Effectiveness of the proposed approach in forming HMRs is demonstrated by application to India, as there are no prior attempts to form such regions over the country. Drought severity-area-frequency (SAF) curves are constructed corresponding to each of the newly formed regions for the use in regional drought analysis, by considering standardized precipitation evapotranspiration index (SPEI) as the drought indicator.