Extremely High Energy Neutrinos and their Detection

We discuss in some detail the production of extremely high energy (EHE) neutrinos with energies above 10^18 eV. The most certain process for producing such neutrinos results from photopion production by EHE cosmic rays in the cosmic background photon field. However, using assumptions for the EHE cosmic ray source evolution which are consistent with results from the deep QSO survey in the radio and X-ray range, the resultant flux of neutrinos from this process is not strong enough for plausible detection. A measurable flux of EHE neutrinos may be present, however, if the highest energy cosmic rays which have recently been detected well beyond 10^20 eV are the result of the annihilation of topological defects which formed in the early universe. Neutrinos resulting from such decays reach energies of the grand unification (GUT) scale, and collisions of superhigh energy neutrinos with the cosmic background neutrinos initiate neutrino cascading which enhances the EHE neutrino flux at Earth. We have calculated the neutrino flux including this cascading effect for either massless or massive neutrinos and we find that these fluxes are conceivably detectable by air fluorescence detectors now in development. The neutrino-induced showers would be recognized by their starting deep in the atmosphere. We evaluate the feasibility of detecting EHE neutrinos this way using air fluorescence air shower detectors and derive the expected event rate. Other processes for producing deeply penetrating air showers constitute a negligible background.Comment: 33 pages, including 12 eps figures, LaTe

Chord-based Resource Identifier-to-Locator Mapping and Searching for the Future Internet

A great many problems, such as scalability, mapping data searching, high frequency update of mapping data, arise in the future network resource mapping system for its vast data processing need. Future Network Chord (FN Chord), an algorithm based on Chord and aims at solving the resources identity mapping and searching problem, is put forward by taking advantage of the qualities of scalability, rapid searching speed, high searching efficiency and flexible naming of chord in order to solve this problem. What’s more, an extra interest node index table for FN Chord is designed to record the hotspot resource mapping location in the paper. So, the resource searching strategy, which is named as Interest Index Table Future Network Chord (IIT-FN Chord) is proposed to search the resource in the paper. The entropy weight method is used to calculate the node interest level according the interest nodes’ resource item online time and visited times and to renew the interest index table. Moreover, probability replacement method is proposed to replace the outdated item on interest index table with new item. Simulation results show that the algorithm can decrease the average searching latency, average searching hops and thus increases the searching efficiency for the resource searching

Longest Path Reroute to Optimize the Optical Multicast Routing in Sparse Splitting WDM Networks

Limited by the sparse light-splitting capability in WDM networks, some nodes need to reroute the optical packet to different destination nodes with the high cost of routing for reducing packet loss possibility. In the paper, the longest path reroute optimization algorithm is put forward to jointly optimize the multicast routing cost and wavelength channel assignment cost for sparse splitting WDM networks. Based on heuristic algorithms, the longest path reroute routing algorithm calls multiple longest paths in existing multicast tree to reroute the path passing from the nodes which are violating the light-splitting constraint to the nodes which are not violating light-splitting constraint with few wavelength channels and low rerouting cost. And a wavelength cost control factor is designed to select the reroute path with the lowest cost by comparing the multicast rerouting path cost increment with the equivalent wavelength channel required cost increment. By adjusting wavelength cost control factor, we can usually get the optimized multicast routing according to the actual network available wavelength conversion cost. Simulation results show that the proposed algorithm can get the low-cost multicast tree and reduce the required number of wavelength channels

Chromosome 20q Amplification Regulates in Vitro Response to Kinesin-5 Inhibitor

We identified gene expression signatures predicting responsiveness to a Kinesin-5 (KIF11) inhibitor (Kinesin-5i) in cultured colon tumor cell lines. Genes predicting resistance to Kinesin-5i were enriched for those from chromosome 20q, a region of frequent amplification in a number of tumor types. siRNAs targeting genes in this chromosomal region identified AURKA, TPX2 and MYBL2 as genes whose disruption enhances response to Kinesin-5i. Taken together, our results show functional interaction between these genes, and suggest that their overexpression is involved in resistance to Kinesin-5i. Furthermore, our results suggest that patients whose tumors overexpress AURKA due to amplification of 20q will more likely resist treatment with Kinesin-5 inhibitor, and that inactivation of AURKA may sensitize these patients to treatment

Inferring causal genomic alterations in breast cancer using gene expression data

<p>Abstract</p> <p>Background</p> <p>One of the primary objectives in cancer research is to identify causal genomic alterations, such as somatic copy number variation (CNV) and somatic mutations, during tumor development. Many valuable studies lack genomic data to detect CNV; therefore, methods that are able to infer CNVs from gene expression data would help maximize the value of these studies.</p> <p>Results</p> <p>We developed a framework for identifying recurrent regions of CNV and distinguishing the cancer driver genes from the passenger genes in the regions. By inferring CNV regions across many datasets we were able to identify 109 recurrent amplified/deleted CNV regions. Many of these regions are enriched for genes involved in many important processes associated with tumorigenesis and cancer progression. Genes in these recurrent CNV regions were then examined in the context of gene regulatory networks to prioritize putative cancer driver genes. The cancer driver genes uncovered by the framework include not only well-known oncogenes but also a number of novel cancer susceptibility genes validated via siRNA experiments.</p> <p>Conclusions</p> <p>To our knowledge, this is the first effort to systematically identify and validate drivers for expression based CNV regions in breast cancer. The framework where the wavelet analysis of copy number alteration based on expression coupled with the gene regulatory network analysis, provides a blueprint for leveraging genomic data to identify key regulatory components and gene targets. This integrative approach can be applied to many other large-scale gene expression studies and other novel types of cancer data such as next-generation sequencing based expression (RNA-Seq) as well as CNV data.</p

Expression profiling predicts outcome in breast cancer

Gruvberger et al. postulate, in their commentary [1] published in this issue of Breast Cancer Research, that our “prognostic gene set may not be broadly applicable to other breast tumor cohorts”, and they suggest that “it may be important to define prognostic expression profiles separately in estrogen receptor (ER) positive and negative tumors”. This is based on two observations derived from our gene expression profiling data in breast cancer [2]: the overlap between reporter genes for prognosis and ER status, and Gruvberger et al.’s inability to confirm the prognosis prediction using a nonoptimal selection of 58 of our 231 prognosis reporter genes. The overlap between our prognosis reporter genes and the ER status genes is certainly very large, mainly because ~10 % of all genes on our microarray contain informatio

PPARα siRNA–Treated Expression Profiles Uncover the Causal Sufficiency Network for Compound-Induced Liver Hypertrophy

Uncovering pathways underlying drug-induced toxicity is a fundamental objective in the field of toxicogenomics. Developing mechanism-based toxicity biomarkers requires the identification of such novel pathways and the order of their sufficiency in causing a phenotypic response. Genome-wide RNA interference (RNAi) phenotypic screening has emerged as an effective tool in unveiling the genes essential for specific cellular functions and biological activities. However, eliciting the relative contribution of and sufficiency relationships among the genes identified remains challenging. In the rodent, the most widely used animal model in preclinical studies, it is unrealistic to exhaustively examine all potential interactions by RNAi screening. Application of existing computational approaches to infer regulatory networks with biological outcomes in the rodent is limited by the requirements for a large number of targeted permutations. Therefore, we developed a two-step relay method that requires only one targeted perturbation for genome-wide de novo pathway discovery. Using expression profiles in response to small interfering RNAs (siRNAs) against the gene for peroxisome proliferator-activated receptor α (Ppara), our method unveiled the potential causal sufficiency order network for liver hypertrophy in the rodent. The validity of the inferred 16 causal transcripts or 15 known genes for PPARα-induced liver hypertrophy is supported by their ability to predict non-PPARα–induced liver hypertrophy with 84% sensitivity and 76% specificity. Simulation shows that the probability of achieving such predictive accuracy without the inferred causal relationship is exceedingly small (p < 0.005). Five of the most sufficient causal genes have been previously disrupted in mouse models; the resulting phenotypic changes in the liver support the inferred causal roles in liver hypertrophy. Our results demonstrate the feasibility of defining pathways mediating drug-induced toxicity from siRNA-treated expression profiles. When combined with phenotypic evaluation, our approach should help to unleash the full potential of siRNAs in systematically unveiling the molecular mechanism of biological events

Predicting prognosis in hepatocellular carcinoma after curative surgery with common clinicopathologic parameters

<p>Abstract</p> <p>Background</p> <p>Surgical resection is one important curative treatment for hepatocellular carcinoma (HCC), but the prognosis following surgery differs substantially and such large variation is mainly unexplained. A review of the literature yields a number of clinicopathologic parameters associated with HCC prognosis. However, the results are not consistent due to lack of systemic approach to establish a prediction model incorporating all these parameters.</p> <p>Methods</p> <p>We conducted a retrospective analysis on the common clinicopathologic parameters from a cohort of 572 ethnic Chinese HCC patients who received curative surgery. The cases were randomly divided into training (n = 272) and validation (n = 300) sets. Each parameter was individually tested and the significant parameters were entered into a linear classifier for model building, and the prediction accuracy was assessed in the validation set</p> <p>Results</p> <p>Our findings based on the training set data reveal 6 common clinicopathologic parameters (tumor size, number of tumor nodules, tumor stage, venous infiltration status, and serum α-fetoprotein and total albumin levels) that were significantly associated with the overall HCC survival and disease-free survival (time to recurrence). We next built a linear classifier model by multivariate Cox regression to predict prognostic outcomes of HCC patients after curative surgery This analysis detected a considerable fraction of variance in HCC prognosis and the area under the ROC curve was about 70%. We further evaluated the model using two other protocols; leave-one-out procedure (n = 264) and independent validation (n = 300). Both were found to have excellent prediction power. The predicted score could separate patients into distinct groups with respect to survival (p-value = 1.8e-12) and disease free survival (p-value = 3.2e-7).</p> <p>Conclusion</p> <p>This described model will provide valuable guidance on prognosis after curative surgery for HCC in clinical practice. The adaptive nature allows easy accommodation for future new biomarker inputs, and it may serve as the foundation for future modeling and prediction for HCC prognosis after surgical treatment.</p