A stochastic model updating method for parameter variability quantification based on response surface models and Monte Carlo simulation

Stochastic model updating must be considered for quantifying uncertainties inherently existing in real-world engineering structures. By this means the statistical properties,instead of deterministic values, of structural parameters can be sought indicating the parameter variability. However, the implementation of stochastic model updating is much more complicated than that of deterministic methods particularly in the aspects of theoretical complexity and low computational efficiency. This study attempts to propose a simple and cost-efficient method by decomposing a stochastic updating process into a series of deterministic ones with the aid of response surface models and Monte Carlo simulation. The response surface models are used as surrogates for original FE models in the interest of programming simplification, fast response computation and easy inverse optimization. Monte Carlo simulation is adopted for generating samples from the assumed or measured probability distributions of responses. Each sample corresponds to an individual deterministic inverse process predicting the deterministic values of parameters. Then the parameter means and variances can be statistically estimated based on all the parameter predictions by running all the samples. Meanwhile, the analysis of variance approach is employed for the evaluation of parameter variability significance. The proposed method has been demonstrated firstly on a numerical beam and then a set of nominally identical steel plates tested in the laboratory. It is found that compared with the existing stochastic model updating methods, the proposed method presents similar accuracy while its primary merits consist in its simple implementation and cost efficiency in response computation and inverse optimization.

Analysis of the suitability of existing medical ontologies for building a scalable semantic interoperability solution supporting multi-site collaboration in oncology

Semantic interoperability is essential to facilitate efficient collaboration in heterogeneous multi-site healthcare environments. The deployment of a semantic interoperability solution has the potential to enable a wide range of informatics supported applications in clinical care and research both within as ingle healthcare organization and in a network of organizations. At the same time, building and deploying a semantic interoperability solution may require significant effort to carryout data transformation and to harmonize the semantics of the information in the different systems. Our approach to semantic interoperability leverages existing healthcare standards and ontologies, focusing first on specific clinical domains and key applications, and gradually expanding the solution when needed. An important objective of this work is to create a semantic link between clinical research and care environments to enable applications such as streamlining the execution of multi-centric clinical trials, including the identification of eligible patients for the trials. This paper presents an analysis of the suitability of several widely-used medical ontologies in the clinical domain: SNOMED-CT, LOINC, MedDRA, to capture the semantics of the clinical trial eligibility criteria, of the clinical trial data (e.g., Clinical Report Forms), and of the corresponding patient record data that would enable the automatic identification of eligible patients. Next to the coverage provided by the ontologies we evaluate and compare the sizes of the sets of relevant concepts and their relative frequency to estimate the cost of data transformation, of building the necessary semantic mappings, and of extending the solution to new domains. This analysis shows that our approach is both feasible and scalable.

Deletion of the loop region of Bcl-2 completely blocks paclitaxel-induced apoptosis

At high concentrations, the tubule poison paclitaxel is able to kill cancer cells that express Bcl-2; it inhibits the antiapoptotic activity of Bcl-2 by inducing its phosphorylation. To localize the site on Bcl-2 regulated by phosphorylation, mutant forms of Bcl-2 were constructed. Mutant forms of Bcl-2 with an alteration in serine at amino acid 70 (S70A) or with deletion of a 60-aa loop region between the α1 and α2 helices (Δloop Bcl-2, which also deletes amino acid 70) were unable to be phosphorylated by paclitaxel treatment of MDA-MB-231 cells into which the genes for the mutant proteins were transfected. The Δloop mutant completely inhibited paclitaxel-induced apoptosis. In cells expressing the S70A mutant, paclitaxel induced about one-third the level of apoptosis seen with wild-type Bcl-2. To evaluate the role of mitogen-activated protein kinases (MAPKs) in Bcl-2 phosphorylation, the activation of c-Jun N-terminal kinase (JNK), extracellular signal-regulated kinase (ERK), and p38 was examined. Paclitaxel-induced apoptosis was associated with phosphorylation of Bcl-2 and activation of ERK and JNK MAPKs. If JNK activation was blocked by transfections with either a stress-activated protein kinase kinase dominant-negative (K→R) gene (which prevents the activation of a kinase upstream of JNK) or MAPK phosphatase-1 gene (which dephosphorylates and inactivates JNK), Bcl-2 phosphorylation did not occur, and the cells were not killed by paclitaxel. By contrast, neither an ERK inhibitor (PD098059) nor p38 inhibitors (SB203580 and SB202190) had an effect on Bcl-2 phosphorylation. Thus, our data show that the antiapoptotic effects of Bcl-2 can be overcome by phosphorylation of Ser-70; forms of Bcl-2 lacking the loop region are much more effective at preventing apoptosis than wild-type Bcl-2 because they cannot be phosphorylated. JNK, but not ERK or p38 MAPK, appear to be involved in the phosphorylation of Bcl-2 induced by paclitaxel.