Theory and methodology for estimation and control of errors due to modeling, approximation, and uncertainty

The reliability of computer predictions of physical events depends on several factors: the mathematical model of the event, the numerical approximation of the model, and the random nature of data characterizing the model. This paper addresses the mathematical theories, algorithms, and results aimed at estimating and controlling modeling error, numerical approximation error, and error due to randomness in material coefficients and loads. A posteriori error estimates are derived and applications to problems in solid mechanics are presented. (C) 2004 Elsevier B.V. All rights reserved

Identification of Intracellular and Plasma Membrane Calcium Channel Homologues in Pathogenic Parasites

Ca2+ channels regulate many crucial processes within cells and their abnormal activity can be damaging to cell survival, suggesting that they might represent attractive therapeutic targets in pathogenic organisms. Parasitic diseases such as malaria, leishmaniasis, trypanosomiasis and schistosomiasis are responsible for millions of deaths each year worldwide. The genomes of many pathogenic parasites have recently been sequenced, opening the way for rational design of targeted therapies. We analyzed genomes of pathogenic protozoan parasites as well as the genome of Schistosoma mansoni, and show the existence within them of genes encoding homologues of mammalian intracellular Ca2+ release channels: inositol 1,4,5-trisphosphate receptors (IP3Rs), ryanodine receptors (RyRs), two-pore Ca2+ channels (TPCs) and intracellular transient receptor potential (Trp) channels. The genomes of Trypanosoma, Leishmania and S. mansoni parasites encode IP3R/RyR and Trp channel homologues, and that of S. mansoni additionally encodes a TPC homologue. In contrast, apicomplexan parasites lack genes encoding IP3R/RyR homologues and possess only genes encoding TPC and Trp channel homologues (Toxoplasma gondii) or Trp channel homologues alone. The genomes of parasites also encode homologues of mammalian Ca2+ influx channels, including voltage-gated Ca2+ channels and plasma membrane Trp channels. The genome of S. mansoni also encodes Orai Ca2+ channel and STIM Ca2+ sensor homologues, suggesting that store-operated Ca2+ entry may occur in this parasite. Many anti-parasitic agents alter parasite Ca2+ homeostasis and some are known modulators of mammalian Ca2+ channels, suggesting that parasite Ca2+ channel homologues might be the targets of some current anti-parasitic drugs. Differences between human and parasite Ca2+ channels suggest that pathogen-specific targeting of these channels may be an attractive therapeutic prospect

Comparative Advantages of School and Workplace Environment in Competence Acquisition: Empirical Evidence from a Survey Among Professional Tertiary Education and Training Students in Switzerland

This paper sheds light on the questions how important competences are and which competences can best be learned at school and which competences can be acquired better in the workplace. Exploiting data from a survey among professional tertiary education and training business administration students and their employers in Switzerland, we find that competences related to strategic management, human resource management, organizational design and project management processes are most suitable to be taught in school. However, the results further suggest that soft skills can be acquired more effectively in the workplace than at school. The only exceptions are analytical thinking, joy of learning and organizational competences, for which school and workplace are similarly suitable. Thereby, the paper provides empirical evidence regarding the optimal choice of the learning place for both human resource managers as well as educational decision makers who aim to combine education and training, e.g. in an apprenticeship

Identification of compounds with anti-proliferative activity against Trypanosoma brucei brucei strain 427 by a whole cell viability based HTS campaign

Human African Trypanosomiasis (HAT) is caused by two trypanosome sub-species, Trypanosoma brucei rhodesiense and Trypanosoma brucei gambiense. Drugs available for the treatment of HAT have significant issues related to difficult administration regimes and limited efficacy across species and disease stages. Hence, there is considerable need to find new alternative and less toxic drugs. An approach to identify starting points for new drug candidates is high throughput screening (HTS) of large compound library collections. We describe the application of an Alamar Blue based, 384-well HTS assay to screen a library of 87,296 compounds against the related trypanosome subspecies, Trypanosoma brucei brucei bloodstream form lister 427. Primary hits identified against T.b. brucei were retested and the IC(50) value compounds were estimated for T.b. brucei and a mammalian cell line HEK293, to determine a selectivity index for each compound. The screening campaign identified 205 compounds with greater than 10 times selectivity against T.b. brucei. Cluster analysis of these compounds, taking into account chemical and structural properties required for drug-like compounds, afforded a panel of eight compounds for further biological analysis. These compounds had IC(50) values ranging from 0.22 µM to 4 µM with associated selectivity indices ranging from 19 to greater than 345. Further testing against T.b. rhodesiense led to the selection of 6 compounds from 5 new chemical classes with activity against the causative species of HAT, which can be considered potential candidates for HAT early drug discovery. Structure activity relationship (SAR) mining revealed components of those hit compound structures that may be important for biological activity. Four of these compounds have undergone further testing to 1) determine whether they are cidal or static in vitro at the minimum inhibitory concentration (MIC), and 2) estimate the time to kill

Reliability of Computational Science

Today’s computers allow us to simulate large, complex physical problems. Many times the mathematical models describing such problems are based on a relatively small amount of available information such as experimental measurements. The question arises whether the computed data could be used as the basis for decision in critical engineering, economic, medicine applications. The representative list of engineering accidents occurred in the past years and their reasons illustrates the question. The paper describes a general framework for Verification and Validation which deals with this question. The framework is then applied to an illustrative engineering problem, in which the basis for decision is a specific quantity of interest, namely the probability that the quantity does not exceed a given value. The V&V framework is applied and explained in detail. The result of the analysis is the computation of the failure probability as well as a quantification of the confidence in the computation, depending on the amount of available experimental data.

A systematic approach to model validation based on Bayesian updates and prediction related rejection criteria

This work describes a solution to the validation challenge problem posed at the SANDIA Validation Challenge Workshop, May 21-23, 2006, NM. It presents and applies a general methodology to it. The solution entails several standard steps, namely selecting and fitting several models to the available prior information and then sequentially rejecting those which do not perform satisfactorily in the validation and accreditation experiments. The rejection procedures are based on Bayesian updates, where the prior density is related to the current candidate model and the posterior density is obtained by conditioning on the validation and accreditation experiments. The result of the analysis is the computation of the failure probability as well as a quantification of the confidence in the computation, depending on the amount of available experimental data. © 2007 Elsevier B.V. All rights reserved

A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data

This work proposes and analyzes a stochastic collocation method for solving elliptic partial differential equations with random coefficients and forcing terms. These input data are assumed to depend on a finite number of random variables. The method consists of a Galerkin approximation in space and a collocation in the zeros of suitable tensor product orthogonal polynomials (Gauss points) in the probability space, and naturally leads to the solution of uncoupled deterministic problems as in the Monte Carlo approach. It treats easily a wide range of situations, such as input data that depend nonlinearly on the random variables, diffusivity coefficients with unbounded second moments, and random variables that are correlated or even unbounded. We provide a rigorous convergence analysis and demonstrate exponential convergence of the “probability error” with respect to the number of Gauss points in each direction of the probability space, under some regularity assumptions on the random input data. Numerical examples show the effectiveness of the method. Finally, we include a section with developments posterior to the original publication of this work. There we review sparse grid stochastic collocation methods, which are effective collocation strategies for problems that depend on a moderately large number of random variables