Statistics of intersecting D-brane models on T^6/Z_6

We perform a statistical analysis of supersymmetric intersecting D-brane models on the type II orientifold T^6/Z_6. After providing an analytic proof of the finiteness of the number of possible solutions in this setup we study the frequency distributions of properties of the gauge group and the chiral matter content. In particular we search for models with a standard model gauge group and discuss their statistical suppression. The results are compared with the recent studies on T^6/Z_2xZ_2. The analysis is conducted using a statistical method, based on the choice of random subsets of the full ensemble of solutions. This method allows to calculate the total number of models with high precision to 3x10^28.Comment: 29 pages, 9 figures; v2: typos corrected; v3: comments added, section 6 expande

Clinical Science Unique risks for mortality in patients with end-stage renal disease undergoing nonemergent colorectal surgery

Abstract BACKGROUND: The aim of this study was to identify unique risk factors for mortality in patients with end-stage renal disease undergoing nonemergent colorectal surgery. METHODS: A multivariate logistic regression model predicting 30-day mortality was constructed for patients with end-stage renal disease undergoing nonemergent colorectal procedures. Data were obtained from the National Surgical Quality Improvement Program (2005Program ( -2010. RESULTS: Among the 394 patients analyzed, those with serum creatinine levels .7.5 mg/dL had .07 times the adjusted mortality risk of those with levels ,3.5 mg/dL. For colorectal surgery patients, the average serum creatinine level was 5.52 6 2.6 mg/dL, and mortality was 13% (n 5 50). CONCLUSIONS: High serum creatinine was associated with a lower risk for mortality in patients with end-stage renal disease, even though creatinine is often considered a risk factor for surgery. These results show how variables from a patient-centered subpopulation can differ in meaning from the general population. 4 This database is a tool that records perioperative risk factors for addressing surgical morbidity and mortality. Our objective was to use the NSQIP data to create a multivariate model to identify unique risk factors predicting mortality in patients with ESRD undergoing nonemergent colorectal procedures

Low Complexity Regularization of Linear Inverse Problems

Inverse problems and regularization theory is a central theme in contemporary signal processing, where the goal is to reconstruct an unknown signal from partial indirect, and possibly noisy, measurements of it. A now standard method for recovering the unknown signal is to solve a convex optimization problem that enforces some prior knowledge about its structure. This has proved efficient in many problems routinely encountered in imaging sciences, statistics and machine learning. This chapter delivers a review of recent advances in the field where the regularization prior promotes solutions conforming to some notion of simplicity/low-complexity. These priors encompass as popular examples sparsity and group sparsity (to capture the compressibility of natural signals and images), total variation and analysis sparsity (to promote piecewise regularity), and low-rank (as natural extension of sparsity to matrix-valued data). Our aim is to provide a unified treatment of all these regularizations under a single umbrella, namely the theory of partial smoothness. This framework is very general and accommodates all low-complexity regularizers just mentioned, as well as many others. Partial smoothness turns out to be the canonical way to encode low-dimensional models that can be linear spaces or more general smooth manifolds. This review is intended to serve as a one stop shop toward the understanding of the theoretical properties of the so-regularized solutions. It covers a large spectrum including: (i) recovery guarantees and stability to noise, both in terms of $\ell^2$-stability and model (manifold) identification; (ii) sensitivity analysis to perturbations of the parameters involved (in particular the observations), with applications to unbiased risk estimation ; (iii) convergence properties of the forward-backward proximal splitting scheme, that is particularly well suited to solve the corresponding large-scale regularized optimization problem

The iPlant Collaborative: Cyberinfrastructure for Plant Biology

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanity's projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services

Transitions of cardio-metabolic risk factors in the Americas between 1980 and 2014

Describing the prevalence and trends of cardiometabolic risk factors that are associated with non-communicable diseases (NCDs) is crucial for monitoring progress, planning prevention, and providing evidence to support policy efforts. We aimed to analyse the transition in body-mass index (BMI), obesity, blood pressure, raised blood pressure, and diabetes in the Americas, between 1980 and 2014

Paleobiology of titanosaurs: reproduction, development, histology, pneumaticity, locomotion and neuroanatomy from the South American fossil record

Fil: García, Rodolfo A.. Instituto de Investigación en Paleobiología y Geología. Museo Provincial Carlos Ameghino. Cipolletti; ArgentinaFil: Salgado, Leonardo. Instituto de Investigación en Paleobiología y Geología. General Roca. Río Negro; ArgentinaFil: Fernández, Mariela. Inibioma-Centro Regional Universitario Bariloche. Bariloche. Río Negro; ArgentinaFil: Cerda, Ignacio A.. Instituto de Investigación en Paleobiología y Geología. Museo Provincial Carlos Ameghino. Cipolletti; ArgentinaFil: Carabajal, Ariana Paulina. Museo Carmen Funes. Plaza Huincul. Neuquén; ArgentinaFil: Otero, Alejandro. Museo de La Plata. Universidad Nacional de La Plata; ArgentinaFil: Coria, Rodolfo A.. Instituto de Paleobiología y Geología. Universidad Nacional de Río Negro. Neuquén; ArgentinaFil: Fiorelli, Lucas E.. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica. Anillaco. La Rioja; Argentin

Meta-analysis of type 2 Diabetes in African Americans Consortium

Type 2 diabetes (T2D) is more prevalent in African Americans than in Europeans. However, little is known about the genetic risk in African Americans despite the recent identification of more than 70 T2D loci primarily by genome-wide association studies (GWAS) in individuals of European ancestry. In order to investigate the genetic architecture of T2D in African Americans, the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium examined 17 GWAS on T2D comprising 8,284 cases and 15,543 controls in African Americans in stage 1 analysis. Single nucleotide polymorphisms (SNPs) association analysis was conducted in each study under the additive model after adjustment for age, sex, study site, and principal components. Meta-analysis of approximately 2.6 million genotyped and imputed SNPs in all studies was conducted using an inverse variance-weighted fixed effect model. Replications were performed to follow up 21 loci in up to 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry. We identified three known loci (TCF7L2, HMGA2 and KCNQ1) and two novel loci (HLA-B and INS-IGF2) at genome-wide significance (4.15 × 10(-94)<P<5 × 10(-8), odds ratio (OR)  = 1.09 to 1.36). Fine-mapping revealed that 88 of 158 previously identified T2D or glucose homeostasis loci demonstrated nominal to highly significant association (2.2 × 10(-23) < locus-wide P<0.05). These novel and previously identified loci yielded a sibling relative risk of 1.19, explaining 17.5% of the phenotypic variance of T2D on the liability scale in African Americans. Overall, this study identified two novel susceptibility loci for T2D in African Americans. A substantial number of previously reported loci are transferable to African Americans after accounting for linkage disequilibrium, enabling fine mapping of causal variants in trans-ethnic meta-analysis studies.Peer reviewe