On linear dimension reduction based on diagonalization of scatter matrices for bioinformatics downstream analyses.

Dimension reduction is often a preliminary step in the analysis of data sets with a large number of variables. Most classical, both supervised and unsupervised, dimension reduction methods such as principal component analysis (PCA), independent component analysis (ICA) or sliced inverse regression (SIR) can be formulated using one, two or several different scatter matrix functionals. Scatter matrices can be seen as different measures of multivariate dispersion and might highlight different features of the data and when compared might reveal interesting structures.

Tests for informative cluster size using a novel balanced bootstrap scheme.

Clustered data are often encountered in biomedical studies, and to date, a number of approaches have been proposed to analyze such data. However, the phenomenon of informative cluster size (ICS) is a challenging problem, and its presence has an impact on the choice of a correct analysis methodology. For example, Dutta and Datta (2015, Biometrics) presented a number of marginal distributions that could be tested.

MiRNA Profiles in Lymphoblastoid Cell Lines of Finnish Prostate Cancer Families.

Background: Heritable factors are evidently involved in prostate cancer (PrCa) carcinogenesis, but currently, genetic markers are not routinely used in screening or diagnostics of the disease. More precise information is needed for making treatment decisions to distinguish aggressive cases from indolent disease, for which heritable factors could be a useful tool. The genetic makeup of PrCa has only recently begun to be unravelled through large-scale genome-wide association studies (GWAS).

Inference on the marginal distribution of clustered data with informative cluster size.

In spite of recent contributions to the literature, informative cluster size settings are not well known and understood. In this paper, we give a formal definition of the problem and describe it from different viewpoints. Data generating mechanisms, parametric and nonparametric models are considered in light of examples.

Dynamic logistic regression model and population attributable fraction to investigate the association between adherence, missed visits and mortality: a study of HIV-infected adults surviving the first year of ART.

Background: Adherence is one of the most important determinants of viral suppression and drug resistance in HIV-infected people receiving antiretroviral therapy (ART).

Methods: We examined the association between long-term mortality and poor adherence to ART in DART trial participants in Uganda and Zimbabwe randomly assigned to receive laboratory and clinical monitoring (LCM), or clinically driven monitoring (CDM). Since over 50% of all deaths in the DART trial occurred during the first year on ART, we focussed on participants continuing ART for 12 months to investigate the implications of longer-term adherence to treatment on mortality.

A General Class of Signed Rank Tests for Clustered Data when the Cluster Size is Potentially Informative.

Rank based tests are alternatives to likelihood based tests popularized by their relative robustness and underlying elegant mathematical theory. There has been a serge in research activities in this area in recent years since a number of researchers are working to develop and extend rank based procedures to clustered dependent data which include situations with known correlation structures (e.g.

NMD and microRNA expression profiling of the HPCX1 locus reveal MAGEC1 as a candidate prostate cancer predisposition gene.

Background: Several predisposition loci for hereditary prostate cancer (HPC) have been suggested, including HPCX1 at Xq27-q28, but due to the complex structure of the region, the susceptibility gene has not yet been identified.

Methods: In this study, nonsense-mediated mRNA decay (NMD) inhibition was used for the discovery of truncating mutations. Six prostate cancer (PC) patients and their healthy brothers were selected from a group of HPCX1-linked families.

Clustering based on adherence data.

Adherence to a medical treatment means the extent to which a patient follows the instructions or recommendations by health professionals. There are direct and indirect ways to measure adherence which have been used for clinical management and research. Typically adherence measures are monitored over a long follow-up or treatment period, and some measurements may be missing due to death or other reasons.

Incidence of hospitalised schizophrenia in Finland since 1980: decreasing and increasing again.

Background: It is suggested that the incidence of schizophrenia is decreasing. However, changes in the number of psychiatric beds available and diagnostic practice have not always been taken into account. We studied the annual first-admission rate (per 100,000) for schizophrenia (FARsch) during a rapid deinstitutionalisation period in Finland.

Estimation of the population attributable fraction for mortality in a cohort study using a piecewise constant hazards model.

Quantification of the impact of exposure to modifiable risk factors on a particular outcome at the population level is a fundamental public health issue. In cohort studies, the population attributable fraction (PAF) is used to assess the proportion of the outcome that is attributable to exposure to certain risk factors in a given population during a certain time interval. This is done by combining information about the prevalence of the risk factor in the population with estimates of the strength of the association between the risk factor and the outcome.

Ageing and cardiovascular responses to head-up tilt in healthy subjects.

Objective: Ageing is associated with increased postural blood pressure changes and attenuated cardiovascular reactivity. As the background of these changes remains uncertain, we examined arterial stiffness, cardiac function and vascular resistance in healthy subjects in supine position and during orthostatic challenge.

Methods: Haemodynamics of 179 normotensive subjects (109 female and 70 male, 21-59 years) were examined using continuous radial pulse wave analysis, whole-body impedance cardiography, and plethysmographic finger blood pressures.

Classification based on hybridization of parametric and nonparametric classifiers.

Parametric methods of classification assume specific parametric models for competing population densities (e.g., Gaussian population densities can lead to linear and quadratic discriminant analysis) and they work well when these model assumptions are valid.

Characterisation of human dental stem cells and buccal mucosa fibroblasts.

Human craniofacial stem cells are recently discovered sources of putative mesenchymal stem cells that hold great promise for autogenic or allogenic cell therapy and tissue engineering. Prior to employing these cells in clinical applications, they must be thoroughly investigated and characterized. In this study, the surface marker expression was investigated on dental pulp stem cells (DPSCs), dental follicle cells (DFCs), periodontal ligament stem cells (PDLSCs), and buccal mucosa fibroblasts (BMFs) utilising surface markers for flow cytometry.

Topographic differences in mean computational sleep depth between healthy controls and obstructive sleep apnoea patients.

In this work, topographic differences in computational sleep depth between healthy controls and obstructive sleep apnoea syndrome (OSAS) patients have been examined. Sleep depth estimation was based on continuous monitoring of the mean frequency of the EEG. During the experiments, all-night sleep EEG recordings of carefully age and gender matched sets of 16 healthy controls and 16 OSAS patients were compared on six electrode locations (Fp1-M2, Fp2-M1, C3-M2, C4-M1, O1-M2, and O2-M1).