A Hierarchical Multilevel Markov Chain Monte Carlo Algorithm with Applications to Uncertainty Quantification in Subsurface Flow

In this paper we address the problem of the prohibitively large computational cost of existing Markov chain Monte Carlo methods for large--scale applications with high dimensional parameter spaces, e.g. in uncertainty quantification in porous media flow. We propose a new multilevel Metropolis-Hastings algorithm, and give an abstract, problem dependent theorem on the cost of the new multilevel estimator based on a set of simple, verifiable assumptions. For a typical model problem in subsurface flow, we then provide a detailed analysis of these assumptions and show significant gains over the standard Metropolis-Hastings estimator. Numerical experiments confirm the analysis and demonstrate the effectiveness of the method with consistent reductions of more than an order of magnitude in the cost of the multilevel estimator over the standard Metropolis-Hastings algorithm for tolerances $\varepsilon < 10^{-2}$

Design and Validation of a Bifunctional Ligand Display System for Receptor Targeting

AbstractHere we developed a bacteriophage display particle designed to serve as a bifunctional entity that can target tumors while delivering an agent. We engineered a chimera phage vector containing a pIII-displayed αv integrins-targeting moiety and a pVIII-displayed streptavidin binding adaptor moiety. By using the chimeric phage particle, targeting of αv integrins on cells in culture and tumor-related blood vessels was shown through different applications, including luminescent quantum dots localization, surface plasmon resonance-based binding detection, and an in vivo tumor model. The strategy validated here will accelerate the discovery and characterization of receptor-ligand binding events in high throughput, and cell-specific delivery of diagnostics or therapeutics to organs of choice without the need for chemical conjugation

Методы повышения эффективности работы газотурбинных установок

Объект исследования: газотурбинная установка. Цель работы: аналитический обзор современных направлений повышения эффективности работы ГТУ, сравнительный расчет простой и регенеративной ГТУ при заданных равных условиях. Для достижения поставленной цели рассмотрены следующие задачи: проведение литературного обзора современных направлений повышения эффективности работы газотурбинных установок; сравнительный тепловой расчет простой и регенеративной ГТУ при заданных равных условиях; Определение сметной стоимости работ по монтажу регенератора; анализ вредныхThe object of the study: gas turbine plant. The purpose of the work: analytical review of modern directions of increasing the efficiency of GTP operation, comparative calculation of simple and regenerative GTP under given equal conditions. To achieve this goal, the following tasks are considered: conducting a literature review of modern technologies to increase the efficiency of gas turbine plants; comparative heat calculation of simple and regenerative gas turbine plant (efficiency, specific fuel consumption, heat rate) for given equal conditions; determination of the estimated cost of

Complexity Analysis of Accelerated MCMC Methods for Bayesian Inversion

We study Bayesian inversion for a model elliptic PDE with unknown diffusion coefficient. We provide complexity analyses of several Markov Chain-Monte Carlo (MCMC) methods for the efficient numerical evaluation of expectations under the Bayesian posterior distribution, given data $\delta$. Particular attention is given to bounds on the overall work required to achieve a prescribed error level $\varepsilon$. Specifically, we first bound the computational complexity of "plain" MCMC, based on combining MCMC sampling with linear complexity multilevel solvers for elliptic PDE. Our (new) work versus accuracy bounds show that the complexity of this approach can be quite prohibitive. Two strategies for reducing the computational complexity are then proposed and analyzed: first, a sparse, parametric and deterministic generalized polynomial chaos (gpc) "surrogate" representation of the forward response map of the PDE over the entire parameter space, and, second, a novel Multi-Level Markov Chain Monte Carlo (MLMCMC) strategy which utilizes sampling from a multilevel discretization of the posterior and of the forward PDE. For both of these strategies we derive asymptotic bounds on work versus accuracy, and hence asymptotic bounds on the computational complexity of the algorithms. In particular we provide sufficient conditions on the regularity of the unknown coefficients of the PDE, and on the approximation methods used, in order for the accelerations of MCMC resulting from these strategies to lead to complexity reductions over "plain" MCMC algorithms for Bayesian inversion of PDEs.

A Novel Monoclonal Antibody to Secreted Frizzled-Related Protein 2 Inhibits Tumor Growth

Secreted frizzled related protein 2 (SFRP2) is overexpressed in human angiosarcoma and breast cancer, and stimulates angiogenesis via activation of the calcineurin/ NFATc3 pathway. There are conflicting reports in the literature as to whether SFRP2 is an antagonist or agonist of ß-catenin. The aims of these studies were to assess the effects of SFRP2 antagonism on tumor growth and Wnt-signaling, and to evaluate whether SFRP2 is a viable therapeutic target. The anti-angiogenic and anti-tumor properties of SFRP2 monoclonal antibody (mAb) were assessed using in vitro proliferation, migration, and tube formation assays; and in vivo angiosarcoma and triple negative breast cancer models. Wnt-signaling was assessed in endothelial and tumor cells treated with SFRP2 mAb using Western blotting. Pharmacokinetic (PK) and biodistribution data were generated in tumor-bearing and non-tumor bearing mice. SFRP2 mAb was shown to induce anti-tumor and anti-angiogenic effects in vitro, and inhibit activation of ß-catenin and NFATc3 in endothelial and tumor cells. Treatment of SVR angiosarcoma allografts in nude mice with the SFRP2 mAb decreased tumor volume by 58% compared to control (p=0.004). Treatment of MDA-MB-231 breast carcinoma xenografts with SFRP2 mAb decreased tumor volume by 52% (p=0.03) compared to control, while bevacizumab did not significantly reduce tumor volume. Pharmacokinetic studies show the antibody is long circulating in the blood and preferentially accumulates in SFRP2-positive tumors. In conclusion, antagonizing SFRP2 inhibits activation of ß-catenin and NFATc3 in endothelial and tumor cells, and is a novel therapeutic approach to inhibiting angiosarcoma and triple negative breast cancer

Overview of the PALM model system 6.0

In this paper, we describe the PALM model system 6.0. PALM (formerly an abbreviation for Parallelized Largeeddy Simulation Model and now an independent name) is a Fortran-based code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. This is a follow-up paper to the PALM 4.0 model description in Maronga et al. (2015). During the last years, PALM has been significantly improved and now offers a variety of new components. In particular, much effort was made to enhance the model with components needed for applications in urban environments, like fully interactive land surface and radiation schemes, chemistry, and an indoor model. This paper serves as an overview paper of the PALM 6.0 model system and we describe its current model core. The individual components for urban applications, case studies, validation runs, and issues with suitable input data are presented and discussed in a series of companion papers in this special issue