Secured and Adaptive Load Balancing with Backup Approach for Computational Grids

Load Balancing is one of the big issues in Grid Computing.This work aims to develop a secured load balancing algorithm which reduces the download time, network overhead and improve the packet delivery ratio of the resources. This work enhances the PWSLB algorithm for load balancing, fault tolerant scheduling and security. The experimental results show an average of 0.2 to 8 % increase in Packet delivery Ratio and 0.080 to 0.1 % of network overhead reduction at 0.1324 milliseconds reduction in Download time. Finally this work Reduces, the download time, network overhead of tasks and also increases the packet delivery rati

Recommendation Model-Based 5G Network and Cognitive System of Cloud Data with AI Technique in IOMT Applications

Recommender system provides the significant suggestion towards the effective service offers for the vast range of big data. The Internet of Things (IoT) environment exhibits the value added application services to the customer with the provision of the effective collection and processing of information. In the extension of the IoT, Internet of Medical Things (IoMT) is evolved for the patient healthcare monitoring and processing. The data collected from the IoMT are stored and processed with the cognitive system for the data transmission between the users. However, in the conventional system subjected to challenges of processing big data while transmission with the cognitive radio network. In this paper, developed a effective cognitive 5G communication model with the recommender model for the IoMT big data processing. The proposed model is termed as Ranking Strategy Internet of Medical Things (RSIoMT). The proposed RSIoMT model uses the distance vector estimation between the feature variables with the ranking. The proposed RSIoMT model perform the recommender model with the ranking those are matches with the communication devices for improved wireless communication quality. The proposed system recommender model uses the estimation of direct communication link between the IoMT variables in the cognitive radio system. The proposed RSIoMT model evaluates the collected IoMT model data with the consideration of the four different healthcare datasets for the data transmission through cognitive radio network. Through the developed model the performance of the system is evaluated based on the deep learning model with the consideration of the collaborative features. The simulation analysis is comparatively examined based on the consideration of the wireless performance. Simulation analysis expressed that the proposed RSIoMT model exhibits the superior performance than the conventional classifier. The comparative analysis expressed that the proposed mode exhibits ~3 – 4% performance improvement over the conventional classifiers. The accuracy of the  developed model achieves 99% which is ~3 – 9% higher than the conventional classifier. In terms of the channel performance, the proposed RSIoMT model exhibits the reduced recommender relay selection count of 1 while the other technique achieves the relay value of 13 which implies that proposed model performance is ~4-6% higher than the other techniques

Modular Composition of Gene Transcription Networks

Predicting the dynamic behavior of a large network from that of the composing modules is a central problem in systems and synthetic biology. Yet, this predictive ability is still largely missing because modules display context-dependent behavior. One cause of context-dependence is retroactivity, a phenomenon similar to loading that influences in non-trivial ways the dynamic performance of a module upon connection to other modules. Here, we establish an analysis framework for gene transcription networks that explicitly accounts for retroactivity. Specifically, a module's key properties are encoded by three retroactivity matrices: internal, scaling, and mixing retroactivity. All of them have a physical interpretation and can be computed from macroscopic parameters (dissociation constants and promoter concentrations) and from the modules' topology. The internal retroactivity quantifies the effect of intramodular connections on an isolated module's dynamics. The scaling and mixing retroactivity establish how intermodular connections change the dynamics of connected modules. Based on these matrices and on the dynamics of modules in isolation, we can accurately predict how loading will affect the behavior of an arbitrary interconnection of modules. We illustrate implications of internal, scaling, and mixing retroactivity on the performance of recurrent network motifs, including negative autoregulation, combinatorial regulation, two-gene clocks, the toggle switch, and the single-input motif. We further provide a quantitative metric that determines how robust the dynamic behavior of a module is to interconnection with other modules. This metric can be employed both to evaluate the extent of modularity of natural networks and to establish concrete design guidelines to minimize retroactivity between modules in synthetic systems.United States. Air Force Office of Scientific Research (FA9550-12-1-0129

Immune-mediated competition in rodent malaria is most likely caused by induced changes in innate immune clearance of merozoites

Malarial infections are often genetically diverse, leading to competitive interactions between parasites. A quantitative understanding of the competition between strains is essential to understand a wide range of issues, including the evolution of virulence and drug resistance. In this study, we use dynamical-model based Bayesian inference to investigate the cause of competitive suppression of an avirulent clone of Plasmodium chabaudi (AS) by a virulent clone (AJ) in immuno-deficient and competent mice. We test whether competitive suppression is caused by clone-specific differences in one or more of the following processes: adaptive immune clearance of merozoites and parasitised red blood cells (RBCs), background loss of merozoites and parasitised RBCs, RBC age preference, RBC infection rate, burst size, and within-RBC interference. These processes were parameterised in dynamical mathematical models and fitted to experimental data. We found that just one parameter μ, the ratio of background loss rate of merozoites to invasion rate of mature RBCs, needed to be clone-specific to predict the data. Interestingly, μ was found to be the same for both clones in single-clone infections, but different between the clones in mixed infections. The size of this difference was largest in immuno-competent mice and smallest in immuno-deficient mice. This explains why competitive suppression was alleviated in immuno-deficient mice. We found that competitive suppression acts early in infection, even before the day of peak parasitaemia. These results lead us to argue that the innate immune response clearing merozoites is the most likely, but not necessarily the only, mediator of competitive interactions between virulent and avirulent clones. Moreover, in mixed infections we predict there to be an interaction between the clones and the innate immune response which induces changes in the strength of its clearance of merozoites. What this interaction is unknown, but future refinement of the model, challenged with other datasets, may lead to its discovery

Characterization of Phosphofructokinase Activity in Mycobacterium tuberculosis Reveals That a Functional Glycolytic Carbon Flow Is Necessary to Limit the Accumulation of Toxic Metabolic Intermediates under Hypoxia

10.1371/journal.pone.0056037PLoS ONE82

Methamphetamine Preconditioning Alters Midbrain Transcriptional Responses to Methamphetamine-Induced Injury in the Rat Striatum

Methamphetamine (METH) is an illicit drug which is neurotoxic to the mammalian brain. Numerous studies have revealed significant decreases in dopamine and serotonin levels in the brains of animals exposed to moderate-to-large METH doses given within short intervals of time. In contrast, repeated injections of small nontoxic doses of the drug followed by a challenge with toxic METH doses afford significant protection against monoamine depletion. The present study was undertaken to test the possibility that repeated injections of the drug might be accompanied by transcriptional changes involved in rendering the nigrostriatal dopaminergic system refractory to METH toxicity. Our results confirm that METH preconditioning can provide significant protection against METH-induced striatal dopamine depletion. In addition, the presence and absence of METH preconditioning were associated with substantial differences in the identity of the genes whose expression was affected by a toxic METH challenge. Quantitative PCR confirmed METH-induced changes in genes of interest and identified additional genes that were differentially impacted by the toxic METH challenge in the presence of METH preconditioning. These genes include small heat shock 27 kD 27 protein 2 (HspB2), thyrotropin-releasing hormone (TRH), brain derived neurotrophic factor (BDNF), c-fos, and some encoding antioxidant proteins including CuZn superoxide dismutase (CuZnSOD), glutathione peroxidase (GPx)-1, and heme oxygenase-1 (Hmox-1). These observations are consistent, in part, with the transcriptional alterations reported in models of lethal ischemic injuries which are preceded by ischemic or pharmacological preconditioning. Our findings suggest that multiple molecular pathways might work in tandem to protect the nigrostriatal dopaminergic pathway against the deleterious effects of the toxic psychostimulant. Further analysis of the molecular and cellular pathways regulated by these genes should help to provide some insight into the neuroadaptive potentials of the brain when repeatedly exposed to drugs of abuse

Methamphetamine Causes Differential Alterations in Gene Expression and Patterns of Histone Acetylation/Hypoacetylation in the Rat Nucleus Accumbens

Methamphetamine (METH) addiction is associated with several neuropsychiatric symptoms. Little is known about the effects of METH on gene expression and epigenetic modifications in the rat nucleus accumbens (NAC). Our study investigated the effects of a non-toxic METH injection (20 mg/kg) on gene expression, histone acetylation, and the expression of the histone acetyltransferase (HAT), ATF2, and of the histone deacetylases (HDACs), HDAC1 and HDAC2, in that structure. Microarray analyses done at 1, 8, 16 and 24 hrs after the METH injection identified METH-induced changes in the expression of genes previously implicated in the acute and longterm effects of psychostimulants, including immediate early genes and corticotropin-releasing factor (Crf). In contrast, the METH injection caused time-dependent decreases in the expression of other genes including Npas4 and cholecystokinin (Cck). Pathway analyses showed that genes with altered expression participated in behavioral performance, cell-to-cell signaling, and regulation of gene expression. PCR analyses confirmed the changes in the expression of c-fos, fosB, Crf, Cck, and Npas4 transcripts. To determine if the METH injection caused post-translational changes in histone markers, we used western blot analyses and identified METH-mediated decreases in histone H3 acetylated at lysine 9 (H3K9ac) and lysine 18 (H3K18ac) in nuclear sub-fractions. In contrast, the METH injection caused time-dependent increases in acetylated H4K5 and H4K8. The changes in histone acetylation were accompanied by decreased expression of HDAC1 but increased expression of HDAC2 protein levels. The histone acetyltransferase, ATF2, showed significant METH-induced increased in protein expression. These results suggest that METH-induced alterations in global gene expression seen in rat NAC might be related, in part, to METH-induced changes in histone acetylation secondary to changes in HAT and HDAC expression. The causal role that HATs and HDACs might play in METH-induced gene expression needs to be investigated further

Utilisation of an operative difficulty grading scale for laparoscopic cholecystectomy

Background A reliable system for grading operative difficulty of laparoscopic cholecystectomy would standardise description of findings and reporting of outcomes. The aim of this study was to validate a difficulty grading system (Nassar scale), testing its applicability and consistency in two large prospective datasets. Methods Patient and disease-related variables and 30-day outcomes were identified in two prospective cholecystectomy databases: the multi-centre prospective cohort of 8820 patients from the recent CholeS Study and the single-surgeon series containing 4089 patients. Operative data and patient outcomes were correlated with Nassar operative difficultly scale, using Kendall’s tau for dichotomous variables, or Jonckheere–Terpstra tests for continuous variables. A ROC curve analysis was performed, to quantify the predictive accuracy of the scale for each outcome, with continuous outcomes dichotomised, prior to analysis. Results A higher operative difficulty grade was consistently associated with worse outcomes for the patients in both the reference and CholeS cohorts. The median length of stay increased from 0 to 4 days, and the 30-day complication rate from 7.6 to 24.4% as the difficulty grade increased from 1 to 4/5 (both p < 0.001). In the CholeS cohort, a higher difficulty grade was found to be most strongly associated with conversion to open and 30-day mortality (AUROC = 0.903, 0.822, respectively). On multivariable analysis, the Nassar operative difficultly scale was found to be a significant independent predictor of operative duration, conversion to open surgery, 30-day complications and 30-day reintervention (all p < 0.001). Conclusion We have shown that an operative difficulty scale can standardise the description of operative findings by multiple grades of surgeons to facilitate audit, training assessment and research. It provides a tool for reporting operative findings, disease severity and technical difficulty and can be utilised in future research to reliably compare outcomes according to case mix and intra-operative difficulty