Warped Supersymmetric Unification with Non-Unified Superparticle Spectrum

We present a new supersymmetric extension of the standard model. The model is constructed in warped space, with a unified bulk symmetry broken by boundary conditions on both the Planck and TeV branes. In the supersymmetric limit, the massless spectrum contains exotic colored particles along with the particle content of the minimal supersymmetric standard model (MSSM). Nevertheless, the model still reproduces the MSSM prediction for gauge coupling unification and does not suffer from a proton decay problem. The exotic states acquire masses from supersymmetry breaking, making the model completely viable, but there is still the possibility that these states will be detected at the LHC. The lightest of these states is most likely A_5^XY, the fifth component of the gauge field associated with the broken unified symmetry. Because supersymmetry is broken on the SU(5)-violating TeV brane, the gaugino masses generated at the TeV scale are completely independent of one another. We explore some of the unusual features that the superparticle spectrum might have as a consequence.Comment: 21 pages, Latex, version to appear in Phys. Rev.

Proton lifetime bounds from chirally symmetric lattice QCD

We present results for the matrix elements relevant for proton decay in Grand Unified Theories (GUTs). The calculation is performed at a fixed lattice spacing a^{-1}=1.73(3) GeV using 2+1 flavors of domain wall fermions on lattices of size 16^3\times32 and 24^3\times64 with a fifth dimension of length 16. We use the indirect method which relies on an effective field theory description of proton decay, where we need to estimate the low energy constants, \alpha = -0.0112(25) GeV^3 and \beta = 0.0120(26) GeV^3. We relate these low energy constants to the proton decay matrix elements using leading order chiral perturbation theory. These can then be combined with experimental bounds on the proton lifetime to bound parameters of individual GUTs.Comment: 17 pages, 9 Figure

Random billiards with wall temperature and associated Markov chains

By a random billiard we mean a billiard system in which the standard specular reflection rule is replaced with a Markov transition probabilities operator P that, at each collision of the billiard particle with the boundary of the billiard domain, gives the probability distribution of the post-collision velocity for a given pre-collision velocity. A random billiard with microstructure (RBM) is a random billiard for which P is derived from a choice of geometric/mechanical structure on the boundary of the billiard domain. RBMs provide simple and explicit mechanical models of particle-surface interaction that can incorporate thermal effects and permit a detailed study of thermostatic action from the perspective of the standard theory of Markov chains on general state spaces. We focus on the operator P itself and how it relates to the mechanical/geometric features of the microstructure, such as mass ratios, curvatures, and potentials. The main results are as follows: (1) we characterize the stationary probabilities (equilibrium states) of P and show how standard equilibrium distributions studied in classical statistical mechanics, such as the Maxwell-Boltzmann distribution and the Knudsen cosine law, arise naturally as generalized invariant billiard measures; (2) we obtain some basic functional theoretic properties of P. Under very general conditions, we show that P is a self-adjoint operator of norm 1 on an appropriate Hilbert space. In a simple but illustrative example, we show that P is a compact (Hilbert-Schmidt) operator. This leads to the issue of relating the spectrum of eigenvalues of P to the features of the microstructure;(3) we explore the latter issue both analytically and numerically in a few representative examples;(4) we present a general algorithm for simulating these Markov chains based on a geometric description of the invariant volumes of classical statistical mechanics

Systematic analysis of experimental phenotype data reveals gene functions

High-throughput phenotyping projects in model organisms have the potential to improve our understanding of gene functions and their role in living organisms. We have developed a computational, knowledge-based approach to automatically infer gene functions from phenotypic manifestations and applied this approach to yeast (Saccharomyces cerevisiae), nematode worm (Caenorhabditis elegans), zebrafish (Danio rerio), fruitfly (Drosophila melanogaster) and mouse (Mus musculus) phenotypes. Our approach is based on the assumption that, if a mutation in a gene [Image: see text] leads to a phenotypic abnormality in a process [Image: see text], then [Image: see text] must have been involved in [Image: see text], either directly or indirectly. We systematically analyze recorded phenotypes in animal models using the formal definitions created for phenotype ontologies. We evaluate the validity of the inferred functions manually and by demonstrating a significant improvement in predicting genetic interactions and protein-protein interactions based on functional similarity. Our knowledge-based approach is generally applicable to phenotypes recorded in model organism databases, including phenotypes from large-scale, high throughput community projects whose primary mode of dissemination is direct publication on-line rather than in the literature

Biomarker Testing for People With Advanced Lung Cancer in England

Introduction: Optimal management of people with advanced NSCLC depends on accurate identification of predictive markers. Yet, real-world data in this setting are limited. We describe the impact, timeliness, and outcomes of molecular testing for patients with advanced NSCLC and good performance status in England. // Methods: In collaboration with Public Health England, patients with stages IIIB to IV NSCLC, with an Eastern Cooperative Oncology Group performance status of 0 to 2, in England, between June 2017 and December 2017, were identified. All English hospitals were invited to record information. // Results: A total of 60 of 142 invited hospitals in England participated in this study and submitted data on 1157 patients. During the study period, 83% of patients with advanced adenocarcinoma underwent molecular testing for three recommended predictive biomarkers (EGFR, ALK, and programmed death-ligand 1). A total of 80% of patients with nonsquamous carcinomas on whom biomarker testing was performed had adequate tissue for analysis on initial sampling. First-line treatment with a tyrosine kinase inhibitor was received by 71% of patients with adenocarcinoma and a sensitizing EGFR mutation and by 59% of those with an ALK translocation. Of patients with no driver mutation and a programmed death-ligand 1 expression of greater than or equal to 50%, 47% received immunotherapy. // Conclusions: We present a comprehensive data set for molecular testing in England. Although molecular testing is well established in England, timeliness and uptake of targeted therapies should be improved

Visual parameter optimisation for biomedical image processing

Background: Biomedical image processing methods require users to optimise input parameters to ensure high quality output. This presents two challenges. First, it is difficult to optimise multiple input parameters for multiple input images. Second, it is difficult to achieve an understanding of underlying algorithms, in particular, relationships between input and output. Results: We present a visualisation method that transforms users’ ability to understand algorithm behaviour by integrating input and output, and by supporting exploration of their relationships. We discuss its application to a colour deconvolution technique for stained histology images and show how it enabled a domain expert to identify suitable parameter values for the deconvolution of two types of images, and metrics to quantify deconvolution performance. It also enabled a breakthrough in understanding by invalidating an underlying assumption about the algorithm. Conclusions: The visualisation method presented here provides analysis capability for multiple inputs and outputs in biomedical image processing that is not supported by previous analysis software. The analysis supported by our method is not feasible with conventional trial-and-error approaches

Repurposing Immunomodulatory Imide Drugs (IMiDs) in Neuropsychiatric and Neurodegenerative Disorders

Neuroinflammation represents a common trait in the pathology and progression of the major psychiatric and neurodegenerative disorders. Neuropsychiatric disorders have emerged as a global crisis, affecting 1 in 4 people, while neurological disorders are the second leading cause of death in the elderly population worldwide (WHO, 2001; GBD 2016 Neurology Collaborators, 2019). However, there remains an immense deficit in availability of effective drug treatments for most neurological disorders. In fact, for disorders such as depression, placebos and behavioral therapies have equal effectiveness as antidepressants. For neurodegenerative diseases such as Parkinson’s disease and Alzheimer’s disease, drugs that can prevent, slow, or cure the disease have yet to be found. Several non-traditional avenues of drug target identification have emerged with ongoing neurological disease research to meet the need for novel and efficacious treatments. Of these novel avenues is that of neuroinflammation, which has been found to be involved in the progression and pathology of many of the leading neurological disorders. Neuroinflammation is characterized by glial inflammatory factors in certain stages of neurological disorders. Although the meta-analyses have provided evidence of genetic/proteomic upregulation of inflammatory factors in certain stages of neurological disorders. Although the mechanisms underpinning the connections between neuroinflammation and neurological disorders are unclear, and meta-analysis results have shown high sensitivity to factors such as disorder severity and sample type, there is significant evidence of neuroinflammation associations across neurological disorders. In this review, we summarize the role of neuroinflammation in psychiatric disorders such as major depressive disorder, generalized anxiety disorder, post-traumatic stress disorder, and bipolar disorder, as well as in neurodegenerative disorders, such as Parkinson’s disease and Alzheimer’s disease, and introduce current research on the potential of immunomodulatory imide drugs (IMiDs) as a new treatment strategy for these disorders

Localization and chiral symmetry in 2+1 flavor domain wall QCD

We present results for the dependence of the residual mass of domain wall fermions (DWF) on the size of the fifth dimension and its relation to the density and localization properties of low-lying eigenvectors of the corresponding hermitian Wilson Dirac operator relevant to simulations of 2+1 flavor domain wall QCD. Using the DBW2 and Iwasaki gauge actions, we generate ensembles of configurations with a $16^3\times 32$ space-time volume and an extent of 8 in the fifth dimension for the sea quarks. We demonstrate the existence of a regime where the degree of locality, the size of chiral symmetry breaking and the rate of topology change can be acceptable for inverse lattice spacings $a^{-1} \ge 1.6$ GeV.Comment: 59 Pages, 23 figures, 1 MPG linke

2+1 flavor domain wall QCD on a (2 fm)^3 lattice: light meson spectroscopy with Ls = 16

We present results for light meson masses and pseudoscalar decay constants from the first of a series of lattice calculations with 2+1 dynamical flavors of domain wall fermions and the Iwasaki gauge action. The work reported here was done at a fixed lattice spacing of about 0.12 fm on a 16^3\times32 lattice, which amounts to a spatial volume of (2 fm)^3 in physical units. The number of sites in the fifth dimension is 16, which gives m_{res} = 0.00308(4) in these simulations. Three values of input light sea quark masses, m_l^{sea} \approx 0.85 m_s, 0.59 m_s and 0.33 m_s were used to allow for extrapolations to the physical light quark limit, whilst the heavier sea quark mass was fixed to approximately the physical strange quark mass m_s. The exact rational hybrid Monte Carlo algorithm was used to evaluate the fractional powers of the fermion determinants in the ensemble generation. We have found that f_\pi = 127(4) MeV, f_K = 157(5) MeV and f_K/f_\pi = 1.24(2), where the errors are statistical only, which are in good agreement with the experimental values.Comment: RBC and UKQCD Collaborations. 17 pages, 14 figures. Typeset with ReVTEX4. v2: replaced with the version published in PRD with improved introductio