“Care is not just about care anymore”:Micro-level responses to institutional complexity and change in the Dutch home-care sector

Groenewegen, P. [Promotor]Broese Van Groenou, M.I. [Promotor

Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review

A variety of genome-wide profiling techniques are available to probe complementary aspects of genome structure and function. Integrative analysis of heterogeneous data sources can reveal higher-level interactions that cannot be detected based on individual observations. A standard integration task in cancer studies is to identify altered genomic regions that induce changes in the expression of the associated genes based on joint analysis of genome-wide gene expression and copy number profiling measurements. In this review, we provide a comparison among various modeling procedures for integrating genome-wide profiling data of gene copy number and transcriptional alterations and highlight common approaches to genomic data integration. A transparent benchmarking procedure is introduced to quantitatively compare the cancer gene prioritization performance of the alternative methods. The benchmarking algorithms and data sets are available at http://intcomp.r-forge.r-project.orgComment: PDF file including supplementary material. 9 pages. Preprin

Spatial Selectivity in Cochlear Implants: Effects of Asymmetric Waveforms and Development of a Single-Point Measure.

Three experiments studied the extent to which cochlear implant users' spatial selectivity can be manipulated using asymmetric waveforms and tested an efficient method for comparing spatial selectivity produced by different stimuli. Experiment 1 measured forward-masked psychophysical tuning curves (PTCs) for a partial tripolar (pTP) probe. Maskers were presented on bipolar pairs separated by one unused electrode; waveforms were either symmetric biphasic ("SYM") or pseudomonophasic with the short high-amplitude phase being either anodic ("PSA") or cathodic ("PSC") on the more apical electrode. For the SYM masker, several subjects showed PTCs consistent with a bimodal excitation pattern, with discrete excitation peaks on each electrode of the bipolar masker pair. Most subjects showed significant differences between the PSA and PSC maskers consistent with greater masking by the electrode where the high-amplitude phase was anodic, but the pattern differed markedly across subjects. Experiment 2 measured masked excitation patterns for a pTP probe and either a monopolar symmetric biphasic masker ("MP_SYM") or pTP pseudomonophasic maskers where the short high-amplitude phase was either anodic ("TP_PSA") or cathodic ("TP_PSC") on the masker's central electrode. Four of the five subjects showed significant differences between the masker types, but again the pattern varied markedly across subjects. Because the levels of the maskers were chosen to produce the same masking of a probe on the same channel as the masker, it was correctly predicted that maskers that produce broader masking patterns would sound louder. Experiment 3 exploited this finding by using a single-point measure of spread of excitation to reveal significantly better spatial selectivity for TP_PSA compared to TP_PSC maskers

Better prediction by use of co-data: Adaptive group-regularized ridge regression

For many high-dimensional studies, additional information on the variables, like (genomic) annotation or external p-values, is available. In the context of binary and continuous prediction, we develop a method for adaptive group-regularized (logistic) ridge regression, which makes structural use of such 'co-data'. Here, 'groups' refer to a partition of the variables according to the co-data. We derive empirical Bayes estimates of group-specific penalties, which possess several nice properties: i) they are analytical; ii) they adapt to the informativeness of the co-data for the data at hand; iii) only one global penalty parameter requires tuning by cross-validation. In addition, the method allows use of multiple types of co-data at little extra computational effort. We show that the group-specific penalties may lead to a larger distinction between `near-zero' and relatively large regression parameters, which facilitates post-hoc variable selection. The method, termed GRridge, is implemented in an easy-to-use R-package. It is demonstrated on two cancer genomics studies, which both concern the discrimination of precancerous cervical lesions from normal cervix tissues using methylation microarray data. For both examples, GRridge clearly improves the predictive performances of ordinary logistic ridge regression and the group lasso. In addition, we show that for the second study the relatively good predictive performance is maintained when selecting only 42 variables.Comment: 15 pages, 2 figures. Supplementary Information available on first author's web sit

Higher-order functional connectivity analysis of resting-state functional magnetic resonance imaging data using multivariate cumulants

Blood-level oxygenation-dependent (BOLD) functional magnetic resonance imaging (fMRI) is the most common modality to study functional connectivity in the human brain. Most research to date has focused on connectivity between pairs of brain regions. However, attention has recently turned towards connectivity involving more than two regions, that is, higher-order connectivity. It is not yet clear how higher-order connectivity can best be quantified. The measures that are currently in use cannot distinguish between pairwise (i.e., second-order) and higher-order connectivity. We show that genuine higher-order connectivity can be quantified by using multivariate cumulants. We explore the use of multivariate cumulants for quantifying higher-order connectivity and the performance of block bootstrapping for statistical inference. In particular, we formulate a generative model for fMRI signals exhibiting higher-order connectivity and use it to assess bias, standard errors, and detection probabilities. Application to resting-state fMRI data from the Human Connectome Project demonstrates that spontaneous fMRI signals are organized into higher-order networks that are distinct from second-order resting-state networks. Application to a clinical cohort of patients with multiple sclerosis further demonstrates that cumulants can be used to classify disease groups and explain behavioral variability. Hence, we present a novel framework to reliably estimate genuine higher-order connectivity in fMRI data which can be used for constructing hyperedges, and finally, which can readily be applied to fMRI data from populations with neuropsychiatric disease or cognitive neuroscientific experiments.</p

Демографічні фактори розвитку соціального капіталу

У статті проаналізовано тенденції змін основних демографічних показників в Україні й Донецькому регіоні зокрема, показано вплив цих факторів на розвиток соціального капіталу. Акцентовано, що дослідження демографічних факторів формування соціального капіталу на державному і регіональному рівнях дозволяє відповідним державним органам управління отримувати повну інформацію стосовно будь-яких змін демографічного розвитку та вживати заходів з оптимізації параметрів трудового та інтелектуального потенціалу населення.In the article the tendencies of changes of basic demographic indicators are analysed in Ukraine and Donetsk region in particular, influence of these factors is rotined on development of social capital. It is accented, that research of demographic factors of forming of social capital on state and regional levels allows the proper public organs of management to get complete information on any changes of demographic development and take measures from optimization of parameters of labour and intellectual potential of population