Prediction of crack growth based on a hierarchical diffusion model

A general Bayesian approach for stochastic versions of deterministic growth models is presented to provide predictions for crack propagation in an early stage of the growth process. To improve the prediction, the information of other crack growth processes is used in a hierarchical (mixed-effects) model. Two stochastic versions of a deterministic growth model are considered. One is a nonlinear regression setup where the trajectory is assumed to be the solution of an ordinary differential equation with additive errors. The other is a diffusion model defined by a stochastic differential equation (SDE) where increments have additive errors. Six growth models in the two versions are compared with respect to their ability to predict the crack propagation in a large data example. Two of them are based on the classical Paris-Erdogan law for crack growth, and four are other widely used growth models. It turned out that the three-parameter Paris-Erdogan model and the Weibull model provide the best results followed by the logistic model. Suprisingly, the SDE approach has no advantage for the prediction compared with the nonlinear regression setup

Bayesian prediction for a jump diffusion process with application to crack growth in fatigue experiments

In many felds of technological developments, understanding and controlling material fatigue is an important point of interest. This article is concerned with statistical modeling of the damage process of prestressed concrete under low cyclic load. A crack width process is observed which exhibits jumps with increasing frequency. Firstly, these jumps are modeled using a Poisson process where two intensity functions are presented and compared. Secondly, based on the modeled jump process, a stochastic process for the crack width is considered through a stochastic differential equation (SDE). It turns out that this SDE has an explicit solution. For both modeling steps, a Bayesian estimation and prediction procedure is presented

Stochastic modeling and statistical analysis of fatigue tests on prestressed concrete beams under cyclic loadings

To evaluate the fatigue behavior of prestressed concrete, bridges for instance, it is necessary to determine the built in tendons’ fatigue strength. Therefore, prestressing steel samples (strands), obtained from an existing bridge built in 1957, were examined and tested by TU Dortmund University, see [1, 2]. Additionally, similar prestressing steels were tested in comparable experiments. As large experiments on prestressed concrete beams under cyclic load with small stress range are very time-consuming and expensive, an early prediction of failure trend in the experiment is desirable. Here, it is shown that a crack width function can be evolved dependent on the process of single wire failures. This process will differ for each experiment because of the randomness of single wire failure. Description of this uncertainty is the first step and is achieved by a predictive distribution for the counting process of wire failure. The second step is to include these results into the model for the crack width process for which a nonlinear regression model based on a physically evolved function depending on the counting process is suitable. For both modeling steps, we present a Bayesian estimation and prediction procedure

Empirical Bayes analysis of single nucleotide polymorphisms

<p>Abstract</p> <p>Background</p> <p>An important goal of whole-genome studies concerned with single nucleotide polymorphisms (SNPs) is the identification of SNPs associated with a covariate of interest such as the case-control status or the type of cancer. Since these studies often comprise the genotypes of hundreds of thousands of SNPs, methods are required that can cope with the corresponding multiple testing problem. For the analysis of gene expression data, approaches such as the empirical Bayes analysis of microarrays have been developed particularly for the detection of genes associated with the response. However, the empirical Bayes analysis of microarrays has only been suggested for binary responses when considering expression values, i.e. continuous predictors.</p> <p>Results</p> <p>In this paper, we propose a modification of this empirical Bayes analysis that can be used to analyze high-dimensional categorical SNP data. This approach along with a generalized version of the original empirical Bayes method are available in the R package siggenes version 1.10.0 and later that can be downloaded from <url>http://www.bioconductor.org</url>.</p> <p>Conclusion</p> <p>As applications to two subsets of the HapMap data show, the empirical Bayes analysis of microarrays cannot only be used to analyze continuous gene expression data, but also be applied to categorical SNP data, where the response is not restricted to be binary. In association studies in which typically several ten to a few hundred SNPs are considered, our approach can furthermore be employed to test interactions of SNPs. Moreover, the posterior probabilities resulting from the empirical Bayes analysis of (prespecified) interactions/genotypes can also be used to quantify the importance of these interactions.</p

Covid-19 as a breakdown in the texture of social practices

A lot of things need to be repaired and a lot of relationships are in need of a knowledgeable mending. Can we start to talk/write about them? This invitation - sent by one of the authors to the others - led us, as feminist women in academia, to join together in an experimental writing about the effects of COVID-19 on daily social practices and on potential (and innovative) ways for repairing work in different fields of social organization. By diffractively intertwining our embodied experiences of becoming together-with Others, we foreground a multiplicity of repair (care) practices COVID-19 is making visible. Echoing one another, we take a stand and say that we need to prevent the future from becoming the past. We are not going back to the past; our society has already changed and there is a need to cope with innovation and repairing practices that do not reproduce the past.Funding Agencies|European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programmeEuropean Research Council (ERC) [715950]</p

Polymorphisms in genes of melatonin biosynthesis and signaling support the light-at-night hypothesis for breast cancer

Light-at-night triggers the decline of pineal gland melatonin biosynthesis and secretion and is an IARC-classified probable breast-cancer risk factor. We applied a large-scale molecular epidemiology approach to shed light on the putative role of melatonin in breast cancer. We investigated associations between breast-cancer risk and polymorphisms at genes of melatonin biosynthesis/signaling using a study population of 44,405 women from the Breast Cancer Association Consortium (22,992 cases, 21,413 population-based controls). Genotype data of 97 candidate single nucleotide polymorphisms (SNPs) at 18 defined gene regions were investigated for breast-cancer risk effects. We calculated adjusted odds ratios (ORs) and 95% confidence intervals (CI) by logistic regression for the main-effect analysis as well as stratified analyses by estrogen- and progesterone-receptor (ER, PR) status. SNP-SNP interactions were analyzed via a two-step procedure based on logic regression. The Bayesian false-discovery probability (BFDP) was used for all analyses to account for multiple testing. Noteworthy associations (BFDP < 0.8) included 10 linked SNPs in tryptophan hydroxylase 2 (TPH2) (e.g. rs1386492: OR = 1.07, 95% CI 1.02–1.12), and a SNP in the mitogen-activated protein kinase 8 (MAPK8) (rs10857561: OR = 1.11, 95% CI 1.04–1.18). The SNP-SNP interaction analysis revealed noteworthy interaction terms with TPH2- and MAPK-related SNPs (e.g. rs1386483R ∧ rs1473473D ∧ rs3729931D: OR = 1.20, 95% CI 1.09–1.32). In line with the light-at-night hypothesis that links shift work with elevated breast-cancer risks our results point to SNPs in TPH2 and MAPK-genes that may impact the intricate network of circadian regulation

Identification and replication of the interplay of four genetic high risk variants for urinary bladder cancer

Little is known whether genetic variants identified in genome-wide association studies interact to increase bladder cancer risk. Recently, we identified two- and three-variant combinations associated with a particular increase of bladder cancer risk in a urinary bladder cancer case-control series (IfADo, 1501 cases, 1565 controls). In an independent case-control series (Nijmegen Bladder Cancer Study, NBCS, 1468 cases, 1720 controls) we confirmed these two- and three-variant combinations. Pooled analysis of the two studies as discovery group (IfADo-NBCS) resulted in sufficient statistical power to test up to four-variant combinations by a logistic regression approach. The New England and Spanish Bladder Cancer Studies (2080 cases and 2167 controls) were used as a replication series. Twelve previously identified risk variants were considered.The strongest four-variant combination was obtained in never smokers. The combination of rs1014971[AA] near APOBEC3A and CBX6, SLC14A1 exon SNP rs1058396[AG,GG], UGT1A intron SNP rs11892031[AA], and rs8102137[CC,CT] near CCNE resulted in an unadjusted odds ratio of 2.59 (95% CI = 1.93-3.47; P = 1.87x10-10), while the individual variant odds ratios ranged only between 1.11-1.30. The combination replicated in the New England and Spanish bladder Cancer Studies (ORunadjusted=1.60, 95% CI = 1.10-2.33; P = 0.013). The four-variant combination is relatively frequent, with 25% in never smoking cases and 11% in never smoking controls (total study group: 19% cases, 14% controls). In conclusion, we show that four high risk variants can statistically interact to confer increased bladder cancer risk particularly in never smokers