Rank Reduction of Correlation Matrices by Majorization

A novel algorithm is developed for the problem of finding a low-rank correlation matrix nearest to a given correlation matrix. The algorithm is based on majorization and, therefore, it is globally convergent. The algorithm is computationally efficient, is straightforward to implement, and can handle arbitrary weights on the entries of the correlation matrix. A simulation study suggests that majorization compares favourably with competing approaches in terms of the quality of the solution within a fixed computational time. The problem of rank reduction of correlation matrices occurs when pricing a derivative dependent on a large number of assets, where the asset prices are modelled as correlated log-normal processes. Mainly, such an application concerns interest rates.rank, correlation matrix, majorization, lognormal price processes

More on Multidimensional Scaling and Unfolding in R: smacof Version 2

The smacof package offers a comprehensive implementation of multidimensional scaling (MDS) techniques in R. Since its first publication (De Leeuw and Mair 2009b) the functionality of the package has been enhanced, and several additional methods, features and utilities were added. Major updates include a complete re-implementation of multidimensional unfolding allowing for monotone dissimilarity transformations, including row-conditional, circular, and external unfolding. Additionally, the constrained MDS implementation was extended in terms of optimal scaling of the external variables. Further package additions include various tools and functions for goodness-of-fit assessment, unidimensional scaling, gravity MDS, asymmetric MDS, Procrustes, and MDS biplots. All these new package functionalities are illustrated using a variety of real-life applications

Rapid freeze-quench EPR spectroscopy: improved collection of frozen particles

Biological and Soft Matter Physic

Memento for interprofessional learning

The vast increase of technical, diagnostic, and treatment possibilities and deepened understanding of molecular biology has revolutionized diagnosis and treatment of cancer and thus has great impact on pathology. Different professionals are responsible for proper evaluation of the results and their translating into an accurate diagnosis and appropriate treatment. Next to expertise, a close interaction between clinical molecular biologists, pathologists, and oncologists is required; it is crucial that these professionals speak “the same language.” Key to this is communication skills and creating possibilities for collaboration in a meaningful context. Here, we present an interprofessional, educational workshop model and we describe the parameters that contribute to effective learning by specialists

Alloying effects on the critical layer thickness in InxGa1−xAs/InP heterostructures analyzed by Raman scattering

International audienceRaman scattering has been used to estimate the critical layer thickness and to analyze the alloying effect on strain relaxation in InxGa1−xAs layers grown by molecular beam epitaxy on InP [001]-oriented substrate, for x ranging from 0.0 to 1.0. Measurements of longitudinal optical GaAs-like phonon frequency and Raman linewidth showed that the indium/gallium ratio contents greatly influences the strain relaxation. A comparison between Raman and x-ray diffraction measurements of relaxation ratios as a function of layer thickness is presented. The results can be explained in terms of the combined effect of strain and chemical and structural disorder

第9章 大学コンソーシアムひょうご神戸 社会連携助成事業 : 「平常時・災害時における歴史資料の保全・修復ができる人材の育成事業

In recent years, there has been a considerable amount of research on the use of regularization methods for inference and prediction in quantitative genetics. Such research mostly focuses on selection of markers and shrinkage of their effects. In this review paper, the use of ridge regression for prediction in quantitative genetics using single-nucleotide polymorphism data is discussed. In particular, we consider (i) the theoretical foundations of ridge regression, (ii) its link to commonly used methods in animal breeding, (iii) the computational feasibility, and (iv) the scope for constructing prediction models with nonlinear effects (e.g., dominance and epistasis). Based on a simulation study we gauge the current and future potential of ridge regression for prediction of human traits using genome-wide SNP data. We conclude that, for outcomes with a relatively simple genetic architecture, given current sample sizes in most cohorts (i.e., N<10,000) the predictive accuracy of ridge regression is slightly higher than the classical genome-wide association study approach of repeated simple regression (i.e., one regression per SNP). However, both capture only a small proportion of the heritability. Nevertheless, we find evidence that for large-scale initiatives, such as biobanks, sample sizes can be achieved where ridge regression compared to the classical approach improves predictive accuracy substantially

Regional Regulation of Transcription in the Bovine Genome

Eukaryotic genes are distributed along chromosomes as clusters of highly expressed genes termed RIDGEs (Regions of IncreaseD Gene Expression) and lowly expressed genes termed anti-RIDGEs, interspersed among genes expressed at intermediate levels or not expressed. Previous studies based on this observation suggested a dual mechanism of gene regulation, where, in addition to transcription factors, the chromosomal domain influences the expression level of their embedded genes. The objectives here were to provide evidence for the existence of chromosomal regional regulation of transcription in the bovine genome, to analyse the genomic features of genes located within RIDGEs versus anti-RIDGEs and tissue-specific genes versus housekeeping and to examine the genomic distribution of genes subject to positive selection in bovines. Gene expression analysis of four brain tissues and the anterior pituitary of 28 cows identified 70 RIDGEs and 41 anti-RIDGEs (harbouring 3735 and 1793 bovine genes respectively) across the bovine genome which are significantly higher than expected by chance. Housekeeping genes (defined here as genes expressed in all five tissues) were over-represented within RIDGEs but tissue-specific genes (genes expressed in only one of the five tissues) were not. Housekeeping genes and genes within RIDGEs had, in general, higher expression levels and GC content but shorter gene lengths and intron lengths than tissue-specific genes and genes within anti-RIDGES. Our findings suggest the existence of chromosomal regional regulation of transcription in the bovine genome. The genomic features observed for genes within RIDGEs and housekeeping genes in bovines agree with previous studies in several other species further strengthening the hypothesis of selective pressure to keep the highly and widely expressed genes short and compact for transcriptional efficiency. Further, positively selected genes were found non-randomly distributed on the genome with a preference for RIDGEs and regions of intermediate gene expression compared to anti-RIDGEs