Stochastic series expansion method for quantum Ising models with arbitrary interactions

A quantum Monte Carlo algorithm for the transverse Ising model with arbitrary short- or long-range interactions is presented. The algorithm is based on sampling the diagonal matrix elements of the power series expansion of the density matrix (stochastic series expansion), and avoids the interaction summations necessary in conventional methods. In the case of long-range interactions, the scaling of the computation time with the system size N is therefore reduced from N^2 to Nln(N). The method is tested on a one-dimensional ferromagnet in a transverse field, with interactions decaying as 1/r^2.Comment: 9 pages, 5 figure

On a functional satisfying a weak Palais-Smale condition

In this paper we study a quasilinear elliptic problem whose functional satisfies a weak version of the well known Palais-Smale condition. An existence result is proved under general assumptions on the nonlinearities.Comment: 18 page

CNI-1493 inhibits monocyte/macrophage tumor necrosis factor by suppression of translation efficiency

Tumor necrosis factor (TNF) mediates a wide variety of disease states including septic shock, acute and chronic inflammation, and cachexia. Recently, a multivalent guanylhydrazone (CNI-1493) developed as an inhibitor of macrophage activation was shown to suppress TNF production and protect against tissue inflammation and endotoxin lethality [Bianchi, M., Ulrich, P., Bloom, O., Meistrell, M., Zimmerman, G. A., Schmidtmayerova, H., Bukrinsky, M., Donnelley, T., Bucala, R., Sherry, B., Manogue, K. R., Tortolani, A. J., Cerami, A. & Tracey, K. J. (1995) Mol. Med. 1, 254-266, and Bianchi, M., Bloom, O., Raabe, T., Cohen, P. S., Chesney, J., Sherry, B., Schmidtmayerova, H., Zhang, X., Bukrinsky, M., Ulrich, P., Cerami, A. & Tracey, J. (1996) J. Exp. Med., in press]. We have now elucidated the mechanism by which CNI-1493 inhibits macrophage TNF synthesis and show here that it acts through suppression of TNF translation efficiency. CNI-1493 blocked neither the lipopolysaccharide (LPS)-induced increases in the expression of TNF mRNA nor the translocation of nuclear factor NF-kappa B to the nucleus in macrophages activated by 15 min of LPS stimulation, indicating that CNI-1493 does not interfere with early NF-kappa B-mediated transcriptional regulation of TNF. However, synthesis of the 26-kDa membrane form of TNF was effectively blocked by CNI-1493. Further evidence for the translational suppression of TNF is given by experiments using chloram-phenicol acetyltransferase (CAT) constructs containing elements of the TNF gene that are involved in TNF translational regulation. Both the 5' and 3' untranslated regions of the TNF gene were required to elicit maximal translational suppression by CNI-1493. Identification of the molecular target through which CNI-1493 inhibits TNF translation should provide insight into the regulation of macrophage activation and mechanisms of inflammation

Situation Inference for Mobile Users: a Rule Based Approach

Mobile phones are being increasingly equipped with sensors that ease retrieval of context information about a user. Context data can be aggregated with information centrally available to mobile operators and service providers, to infer higher-level information such as user “situations”, easier to integrate with services. We have been conducting an internal trial monitoring the context of different users in their business life and designing rules to infer high level situations: logical location, activity and social state. In this paper we present the infrastructure and the rulebased reasoning process used for this experiment

Boceprevir is highly effective in treatment-experienced hepatitis C virus-positive genotype-1 menopausal women

AIM: To investigate the safety/efficacy of Boceprevirbased triple therapy in hepatitis C virus (HCV)-G1 menopausal women who were historic relapsers, partial-responders and null-responders. METHODS: In this single-assignment, unblinded study, we treated fifty-six menopausal women with HCV-G1, 46% F3-F4, and previous PEG-α/RBV failure (7% null, 41% non-responder, and 52% relapser) with 4 wk lead-in with PEG-IFNα2b/RBV followed by PEGIFNα2b/RBV+Boceprevir for 32 wk, with an additional 12 wk of PEG-IFN-α-2b/RBV if patients were HCV-RNA-positive by week 8. In previous null-responders, 44 wk of triple therapy was used. The primary objective of retreatment was to verify whether a sustained virological response (SVR) (HCV RNA undetectable at 24 wk of follow-up) rate of at least 20% could be obtained. The secondary objective was the evaluation of the percent of patients with negative HCV RNA at week 4 (RVR), 8 (RVR BOC), 12 (EVR), or at the end-of-treatment (ETR) that reached SVR. To assess the relationship between SVR and clinical and biochemical parameters, multiple logistic regression analysis was used. RESULTS: After lead-in, only two patients had RVR; HCV-RNA was unchanged in all but 62% who had ≤ 1 logio decrease. After Boceprevir, HCV RNA became undetectable at week 8 in 32/56 (57.1%) and at week 12 in 41/56 (73.2%). Of these, 53.8% and 52.0%, respectively, achieved SVR. Overall, SVR was obtained in 25/56 (44.6%). SVR was achieved in 55% previous relapsers vs. 41% non-responders (Ρ = 0.250), in 44% F0-F2 vs 54% F3-F4 (Ρ = 0.488), and in 11/19 (57.9%) of patients with cirrhosis. At univariate analysis for baseline predictors of SVR, only previous response to antiviral therapy (OR = 2.662, 95%CI: 0.957-6.881, Ρ= 0.043), was related with SVR. When considering "on treatment" factors, 1 log10 HCV RNA decline at week 4 (3.733, 95%CI: 1.676-12.658, Ρ= 0.034) and achievement of RVR BOC (7.347, 95%CI: 2.156-25.035, Ρ= 0.001) were significantly related with the SVR, al-though RVR BOC only (6.794, 95%CI: 1.596-21.644, Ρ = 0.010) maintained significance at multivariate logistic regression analysis. Anemia and neutropenia were managed with Erythropoietin and Filgrastim supplementation, respectively. Only six patients discontinued therapy. CONCLUSION: Boceprevir obtained high SVR response independent of previous response, RVR or baseline fibrosis or cirrhosis. RVR BOC was the only independent predictor of SVR

The cBio cancer Genomics portal: An open platform for exploring multidimensional cancer genomics data

Cataloged from PDF version of article.The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource for interactive exploration of multidimensional cancer genomics data sets, currently providing access to data from more than 5,000 tumor samples from 20 cancer studies. The cBio Cancer Genomics Portal significantly lowers the barriers between complex genomic data and cancer researchers who want rapid, intuitive, and high-quality access to molecular profiles and clinical attributes from large-scale cancer genomics projects and empowers researchers to translate these rich data sets into biologic insights and clinical applications. © 2012 American Association for Cancer Research

On dynamic network entropy in cancer

The cellular phenotype is described by a complex network of molecular interactions. Elucidating network properties that distinguish disease from the healthy cellular state is therefore of critical importance for gaining systems-level insights into disease mechanisms and ultimately for developing improved therapies. By integrating gene expression data with a protein interaction network to induce a stochastic dynamics on the network, we here demonstrate that cancer cells are characterised by an increase in the dynamic network entropy, compared to cells of normal physiology. Using a fundamental relation between the macroscopic resilience of a dynamical system and the uncertainty (entropy) in the underlying microscopic processes, we argue that cancer cells will be more robust to random gene perturbations. In addition, we formally demonstrate that gene expression differences between normal and cancer tissue are anticorrelated with local dynamic entropy changes, thus providing a systemic link between gene expression changes at the nodes and their local network dynamics. In particular, we also find that genes which drive cell-proliferation in cancer cells and which often encode oncogenes are associated with reductions in the dynamic network entropy. In summary, our results support the view that the observed increased robustness of cancer cells to perturbation and therapy may be due to an increase in the dynamic network entropy that allows cells to adapt to the new cellular stresses. Conversely, genes that exhibit local flux entropy decreases in cancer may render cancer cells more susceptible to targeted intervention and may therefore represent promising drug targets.Comment: 10 pages, 3 figures, 4 tables. Submitte