Parent formulation at the Lagrangian level

The recently proposed first-order parent formalism at the level of equations of motion is specialized to the case of Lagrangian systems. It is shown that for diffeomorphism-invariant theories the parent formulation takes the form of an AKSZ-type sigma model. The proposed formulation can be also seen as a Lagrangian version of the BV-BRST extension of the Vasiliev unfolded approach. We also discuss its possible interpretation as a multidimensional generalization of the Hamiltonian BFV--BRST formalism. The general construction is illustrated by examples of (parametrized) mechanics, relativistic particle, Yang--Mills theory, and gravity.Comment: 26 pages, discussion of the truncation extended, typos corrected, references adde

First order parent formulation for generic gauge field theories

We show how a generic gauge field theory described by a BRST differential can systematically be reformulated as a first order parent system whose spacetime part is determined by the de Rham differential. In the spirit of Vasiliev's unfolded approach, this is done by extending the original space of fields so as to include their derivatives as new independent fields together with associated form fields. Through the inclusion of the antifield dependent part of the BRST differential, the parent formulation can be used both for on and off-shell formulations. For diffeomorphism invariant models, the parent formulation can be reformulated as an AKSZ-type sigma model. Several examples, such as the relativistic particle, parametrized theories, Yang-Mills theory, general relativity and the two dimensional sigma model are worked out in details.Comment: 36 pages, additional sections and minor correction

Effect of temperature anisotropy on various modes and instabilities for a magnetized non-relativistic bi-Maxwellian plasma

Using kinetic theory for homogeneous collisionless magnetized plasmas, we present an extended review of the plasma waves and instabilities and discuss the anisotropic response of generalized relativistic dielectric tensor and Onsager symmetry properties for arbitrary distribution functions. In general, we observe that for such plasmas only those electromagnetic modes whose magnetic field perturbations are perpendicular to the ambient magneticeld, i.e.,B1 \perp B0, are effected by the anisotropy. However, in oblique propagation all modes do show such anisotropic effects. Considering the non-relativistic bi-Maxwellian distribution and studying the relevant components of the general dielectric tensor under appropriate conditions, we derive the dispersion relations for various modes and instabilities. We show that only the electromagnetic R- and L- waves, those derived from them and the O-mode are affected by thermal anisotropies, since they satisfy the required condition B1\perpB0. By contrast, the perpendicularly propagating X-mode and the modes derived from it (the pure transverse X-mode and Bernstein mode) show no such effect. In general, we note that the thermal anisotropy modifies the parallel propagating modes via the parallel acoustic effect, while it modifies the perpendicular propagating modes via the Larmor-radius effect. In oblique propagation for kinetic Alfven waves, the thermal anisotropy affects the kinetic regime more than it affects the inertial regime. The generalized fast mode exhibits two distinct acoustic effects, one in the direction parallel to the ambient magnetic field and the other in the direction perpendicular to it. In the fast-mode instability, the magneto-sonic wave causes suppression of the firehose instability. We discuss all these propagation characteristics and present graphic illustrations

Patient-centric trials for therapeutic development in precision oncology

An enhanced understanding of the molecular pathology of disease gained from genomic studies is facilitating the development of treatments that target discrete molecular subclasses of tumours. Considerable associated challenges include how to advance and implement targeted drug-development strategies. Precision medicine centres on delivering the most appropriate therapy to a patient on the basis of clinical and molecular features of their disease. The development of therapeutic agents that target molecular mechanisms is driving innovation in clinical-trial strategies. Although progress has been made, modifications to existing core paradigms in oncology drug development will be required to realize fully the promise of precision medicine

FlexOracle: predicting flexible hinges by identification of stable domains

<p>Abstract</p> <p>Background</p> <p>Protein motions play an essential role in catalysis and protein-ligand interactions, but are difficult to observe directly. A substantial fraction of protein motions involve hinge bending. For these proteins, the accurate identification of flexible hinges connecting rigid domains would provide significant insight into motion. Programs such as GNM and FIRST have made global flexibility predictions available at low computational cost, but are not designed specifically for finding hinge points.</p> <p>Results</p> <p>Here we present the novel FlexOracle hinge prediction approach based on the ideas that energetic interactions are stronger <it>within </it>structural domains than <it>between </it>them, and that fragments generated by cleaving the protein at the hinge site are independently stable. We implement this as a tool within the Database of Macromolecular Motions, MolMovDB.org. For a given structure, we generate pairs of fragments based on scanning all possible cleavage points on the protein chain, compute the energy of the fragments compared with the undivided protein, and predict hinges where this quantity is minimal. We present three specific implementations of this approach. In the first, we consider only pairs of fragments generated by cutting at a <it>single </it>location on the protein chain and then use a standard molecular mechanics force field to calculate the enthalpies of the two fragments. In the second, we generate fragments in the same way but instead compute their free energies using a knowledge based force field. In the third, we generate fragment pairs by cutting at <it>two </it>points on the protein chain and then calculate their free energies.</p> <p>Conclusion</p> <p>Quantitative results demonstrate our method's ability to predict known hinges from the Database of Macromolecular Motions.</p

Subclonal diversification of primary breast cancer revealed by multiregion sequencing.

The sequencing of cancer genomes may enable tailoring of therapeutics to the underlying biological abnormalities driving a particular patient's tumor. However, sequencing-based strategies rely heavily on representative sampling of tumors. To understand the subclonal structure of primary breast cancer, we applied whole-genome and targeted sequencing to multiple samples from each of 50 patients' tumors (303 samples in total). The extent of subclonal diversification varied among cases and followed spatial patterns. No strict temporal order was evident, with point mutations and rearrangements affecting the most common breast cancer genes, including PIK3CA, TP53, PTEN, BRCA2 and MYC, occurring early in some tumors and late in others. In 13 out of 50 cancers, potentially targetable mutations were subclonal. Landmarks of disease progression, such as resistance to chemotherapy and the acquisition of invasive or metastatic potential, arose within detectable subclones of antecedent lesions. These findings highlight the importance of including analyses of subclonal structure and tumor evolution in clinical trials of primary breast cancer

Predictors of Chemosensitivity in Triple Negative Breast Cancer: An Integrated Genomic Analysis

Background: Triple negative breast cancer (TNBC) is a highly heterogeneous and aggressive disease, and although no effective targeted therapies are available to date, about one-third of patients with TNBC achieve pathologic complete response (pCR) from standard-of-care anthracycline/taxane (ACT) chemotherapy. The heterogeneity of these tumors, however, has hindered the discovery of effective biomarkers to identify such patients. Methods and Findings: We performed whole exome sequencing on 29 TNBC cases from the MD Anderson Cancer Center (MDACC) selected because they had either pCR (n = 18) or extensive residual disease (n = 11) after neoadjuvant chemotherapy, with cases from The Cancer Genome Atlas (TCGA; n = 144) and METABRIC (n = 278) cohorts serving as validation cohorts. Our analysis revealed that mutations in the AR- and FOXA1-regulated networks, in which BRCA1 plays a key role, are associated with significantly higher sensitivity to ACT chemotherapy in the MDACC cohort (pCR rate of 94.1% compared to 16.6% in tumors without mutations in AR/FOXA1 pathway, adjusted p = 0.02) and significantly better survival outcome in the TCGA TNBC cohort (log-rank test, p = 0.05). Combined analysis of DNA sequencing, DNA methylation, and RNA sequencing identified tumors of a distinct BRCA-deficient (BRCA-D) TNBC subtype characterized by low levels of wild-type BRCA1/2 expression. Patients with functionally BRCA-D tumors had significantly better survival with standard-of-care chemotherapy than patients whose tumors were not BRCA-D (log-rank test, p = 0.021), and they had significantly higher mutation burden (p < 0.001) and presented clonal neoantigens that were associated with increased immune cell activity. A transcriptional signature of BRCA-D TNBC tumors was independently validated to be significantly associated with improved survival in the METABRIC dataset (log-rank test, p = 0.009). As a retrospective study, limitations include the small size and potential selection bias in the discovery cohort. Conclusions: The comprehensive molecular analysis presented in this study directly links BRCA deficiency with increased clonal mutation burden and significantly enhanced chemosensitivity in TNBC and suggests that functional RNA-based BRCA deficiency needs to be further examined in TNBC. © 2016 Jiang et al

Sequencing of prostate cancers identifies new cancer genes, routes of progression and drug targets

Prostate cancer represents a substantial clinical challenge because it is difficult to predict outcome and advanced disease is often fatal. We sequenced the whole genomes of 112 primary and metastatic prostate cancer samples. From joint analysis of these cancers with those from previous studies (930 cancers in total), we found evidence for 22 previously unidentified putative driver genes harboring coding mutations, as well as evidence for NEAT1 and FOXA1 acting as drivers through noncoding mutations. Through the temporal dissection of aberrations, we identified driver mutations specifically associated with steps in the progression of prostate cancer, establishing, for example, loss of CHD1 and BRCA2 as early events in cancer development of ETS fusion-negative cancers. Computational chemogenomic (canSAR) analysis of prostate cancer mutations identified 11 targets of approved drugs, 7 targets of investigational drugs, and 62 targets of compounds that may be active and should be considered candidates for future clinical trials

Triad pattern algorithm for predicting strong promoter candidates in bacterial genomes

Abstract Background Bacterial promoters, which increase the efficiency of gene expression, differ from other promoters by several characteristics. This difference, not yet widely exploited in bioinformatics, looks promising for the development of relevant computational tools to search for strong promoters in bacterial genomes. Results We describe a new triad pattern algorithm that predicts strong promoter candidates in annotated bacterial genomes by matching specific patterns for the group I σ70 factors of Escherichia coli RNA polymerase. It detects promoter-specific motifs by consecutively matching three patterns, consisting of an UP-element, required for interaction with the α subunit, and then optimally-separated patterns of -35 and -10 boxes, required for interaction with the σ70 subunit of RNA polymerase. Analysis of 43 bacterial genomes revealed that the frequency of candidate sequences depends on the A+T content of the DNA under examination. The accuracy of in silico prediction was experimentally validated for the genome of a hyperthermophilic bacterium, Thermotoga maritima, by applying a cell-free expression assay using the predicted strong promoters. In this organism, the strong promoters govern genes for translation, energy metabolism, transport, cell movement, and other as-yet unidentified functions. Conclusion The triad pattern algorithm developed for predicting strong bacterial promoters is well suited for analyzing bacterial genomes with an A+T content of less than 62%. This computational tool opens new prospects for investigating global gene expression, and individual strong promoters in bacteria of medical and/or economic significance.</p