Improved Fair-Zone technique using Mobility Prediction in WSN

The self-organizational ability of ad-hoc Wireless Sensor Networks (WSNs) has led them to be the most popular choice in ubiquitous computing. Clustering sensor nodes organizing them hierarchically have proven to be an effective method to provide better data aggregation and scalability for the sensor network while conserving limited energy. It has some limitation in energy and mobility of nodes. In this paper we propose a mobility prediction technique which tries overcoming above mentioned problems and improves the life time of the network. The technique used here is Exponential Moving Average for online updates of nodal contact probability in cluster based network.Comment: 10 pages, 7 figures, Published in International Journal Of Advanced Smart Sensor Network Systems (IJASSN

A comparative study of clusterhead selection algorithms in wireless sensor networks

In Wireless Sensor Network, sensor nodes life time is the most critical parameter. Many researches on these lifetime extension are motivated by LEACH scheme, which by allowing rotation of cluster head role among the sensor nodes tries to distribute the energy consumption over all nodes in the network. Selection of clusterhead for such rotation greatly affects the energy efficiency of the network. Different communication protocols and algorithms are investigated to find ways to reduce power consumption. In this paper brief survey is taken from many proposals, which suggests different clusterhead selection strategies and a global view is presented. Comparison of their costs of clusterhead selection in different rounds, transmission method and other effects like cluster formation, distribution of clusterheads and creation of clusters shows a need of a combined strategy for better results.Comment: 12 pages, 3 figures, 5 tables, Int JournaL, International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.4, November 201

CLOUD COMPUTING SECURITY THROUGH SYMMETRIC CIPHER MODEL

ABSTRACT Cloud computing can be defined as an application and services which runs on distributed network using virtualized and it is accessed through interne

Information Technology and Computer Science

Abstract-Imaging in the presence of subject motion has been an ongoing challenge for magnetic resonance imaging (MRI). In this paper some o f the important issues regarding the acquisition and reconstruction of anatomical and DTI imag ing of moving subjects are addressed; methods to achieve high resolution and high Signal to Noise Rat io (SNR) volu me data. Excellent fetal brain 3D Apparent Diffusion Coefficient maps in high resolution have been achieved for the first time as well as pro mising Fractional Anisotropy maps. Growth curves for the normally developing fetal brain have been devised by the quantificat ion of cerebral and cerebellar volu mes as well as someone dimensional measurements. A Verhulst model is to describe these growth curves, and this approach has achieved a correlation over 0.99 between the fitted model and actual data

A Ten-microRNA Expression Signature Predicts Survival in Glioblastoma

Glioblastoma (GBM) is the most common and aggressive primary brain tumor with very poor patient median survival. To identify a microRNA (miRNA) expression signature that can predict GBM patient survival, we analyzed the miRNA expression data of GBM patients (n = 222) derived from The Cancer Genome Atlas (TCGA) dataset. We divided the patients randomly into training and testing sets with equal number in each group. We identified 10 significant miRNAs using Cox regression analysis on the training set and formulated a risk score based on the expression signature of these miRNAs that segregated the patients into high and low risk groups with significantly different survival times (hazard ratio [HR] = 2.4; 95% CI = 1.4–3.8; p<0.0001). Of these 10 miRNAs, 7 were found to be risky miRNAs and 3 were found to be protective. This signature was independently validated in the testing set (HR = 1.7; 95% CI = 1.1–2.8; p = 0.002). GBM patients with high risk scores had overall poor survival compared to the patients with low risk scores. Overall survival among the entire patient set was 35.0% at 2 years, 21.5% at 3 years, 18.5% at 4 years and 11.8% at 5 years in the low risk group, versus 11.0%, 5.5%, 0.0 and 0.0% respectively in the high risk group (HR = 2.0; 95% CI = 1.4–2.8; p<0.0001). Cox multivariate analysis with patient age as a covariate on the entire patient set identified risk score based on the 10 miRNA expression signature to be an independent predictor of patient survival (HR = 1.120; 95% CI = 1.04–1.20; p = 0.003). Thus we have identified a miRNA expression signature that can predict GBM patient survival. These findings may have implications in the understanding of gliomagenesis, development of targeted therapy and selection of high risk cancer patients for adjuvant therapy

A Pilot Study of IL-2Rα Blockade during Lymphopenia Depletes Regulatory T-cells and Correlates with Enhanced Immunity in Patients with Glioblastoma

Preclinical studies in mice have demonstrated that the prophylactic depletion of immunosuppressive regulatory T-cells (T(Regs)) through targeting the high affinity interleukin-2 (IL-2) receptor (IL-2Rα/CD25) can enhance anti-tumor immunotherapy. However, therapeutic approaches are complicated by the inadvertent inhibition of IL-2Rα expressing anti-tumor effector T-cells.To determine if changes in the cytokine milieu during lymphopenia may engender differential signaling requirements that would enable unarmed anti-IL-2Rα monoclonal antibody (MAbs) to selectively deplete T(Regs) while permitting vaccine-stimulated immune responses.A randomized placebo-controlled pilot study was undertaken to examine the ability of the anti-IL-2Rα MAb daclizumab, given at the time of epidermal growth factor receptor variant III (EGFRvIII) targeted peptide vaccination, to safely and selectively deplete T(Regs) in patients with glioblastoma (GBM) treated with lymphodepleting temozolomide (TMZ).Daclizumab treatment (n = 3) was well-tolerated with no symptoms of autoimmune toxicity and resulted in a significant reduction in the frequency of circulating CD4+Foxp3+ TRegs in comparison to saline controls (n = 3)( p = 0.0464). A significant (p<0.0001) inverse correlation between the frequency of TRegs and the level of EGFRvIII specific humoral responses suggests the depletion of TRegs may be linked to increased vaccine-stimulated humoral immunity. These data suggest this approach deserves further study.ClinicalTrials.gov NCT00626015

A Model to Simulate Sweet Potato Growth

Abstract: A new paper a process model (SPOTCOMS) for simulating growth of sweet potato is proposed by modifying the earlier model MADHURAM. Crop phenology was predicted as a function of growing degree days (GDD). Crop growth was divided into three phases i.e. first phase from planting to tuber initiation, middle phase from tuber initiation to the beginning of tuber bulking and the final phase from the beginning of tuber bulking to harvest. Vine growth rate and tuber growth rate in terms of GDD were worked out. Branching event also was predicted as a function of number of leaves. For computing solar radiation, photosynthesis and partitioning of dry matter, methods used in MADHURAM model was followed. Sensitivity analysis confirmed the importance of leaf area and the length of middle phase on the tuber yield. The model was tested under three different environments. The yields predicted in this model are in good agreement with the corresponding observed values in most of the cases. Prediction number of tubers was also reasonably accurate. However prediction of number of leaves and branching deviated considerably from the observed value. However, predictions made by this model are in better agreement with the observed values in the case of number of tubers and tuber yield than MADHURAM model