covRNA: discovering covariate associations in large-scale gene expression data

OBJECTIVE: The biological interpretation of gene expression measurements is a challenging task. While ordination methods are routinely used to identify clusters of samples or co-expressed genes, these methods do not take sample or gene annotations into account. We aim to provide a tool that allows users of all backgrounds to assess and visualize the intrinsic correlation structure of complex annotated gene expression data and discover the covariates that jointly affect expression patterns. RESULTS: The Bioconductor package covRNA provides a convenient and fast interface for testing and visualizing complex relationships between sample and gene covariates mediated by gene expression data in an entirely unsupervised setting. The relationships between sample and gene covariates are tested by statistical permutation tests and visualized by ordination. The methods are inspired by the fourthcorner and RLQ analyses used in ecological research for the analysis of species abundance data, that we modified to make them suitable for the distributional characteristics of both, RNA-Seq read counts and microarray intensities, and to provide a high-performance parallelized implementation for the analysis of large-scale gene expression data on multi-core computational systems. CovRNA provides additional modules for unsupervised gene filtering and plotting functions to ensure a smooth and coherent analysis workflow

Evolution of planar defects during homoepitaxial growth of β-Ga2O3 layers on (100) substrates—A quantitative model

We study the homoepitaxial growth of β-Ga2O3 (100) grown by metal-organic vapour phase as dependent on miscut-angle vs. the c direction. Atomic force microscopy of layers grown on substrates with miscut-angles smaller than 2° reveals the growth proceeding through nucleation and growth of two-dimensional islands. With increasing miscut-angle, step meandering and finally step flow growth take place. While step-flow growth results in layers with high crystalline perfection, independent nucleation of two-dimensional islands causes double positioning on the (100) plane, resulting in twin lamellae and stacking mismatch boundaries. Applying nucleation theory in the mean field approach for vicinal surfaces, we can fit experimentally found values for the density of twin lamellae in epitaxial layers as dependent on the miscut-angle. The model yields a diffusion coefficient for Ga adatoms of D = 7 × 10−9 cm2 s−1 at a growth temperature of 850 °C, two orders of magnitude lower than the values published for GaAs

Electron microscopic and optical investigations of the indium distribution GaAs capped InxGa1-xAs islands

Results from a structural and optical analysis of buried InxGa1-xAs islands carried out after the process of GaAs overgrowth are presented. It is found that during the growth process, the indium concentration profile changes and the thickness of the wetting layer emanating from a Stranski-Krastanow growth mode grows significantly. Quantum dots are formed due to strong gradients in the indium concentration, which is demonstrated by photoluminescence and excitation spectroscopy of the buried InxGa1-xAs islands. (C) 1997 American Institute of Physics

Embracing trustworthiness and authenticity in the validation of learning analytics systems

Learning analytics sits in the middle space between learning theory and data analytics. The inherent diversity of learning analytics manifests itself in an epistemology that strikes a balance between positivism and interpretivism, and knowledge that is sourced from theory and practice. In this paper, we argue that validation approaches for learning analytics systems should be cognisant of these diverse foundations. Through a systematic review of learning analytics validation research, we find that there is currently an over-reliance on positivistic validity criteria. Researchers tend to ignore interpretivistic criteria such as trustworthiness and authenticity. In the 38 papers we analysed, researchers covered positivistic validity criteria 221 times, whereas interpretivistic criteria were mentioned 37 times. We motivate that learning analytics can only move forward with holistic validation strategies that incorporate “thick descriptions” of educational experiences. We conclude by outlining a planned validation study using argument-based validation, which we believe will yield meaningful insights by considering a diverse spectrum of validity criteria.Horizon 2020(H2020)883588Algorithms and the Foundations of Software technolog

The Role of Receptor for Advanced Glycation End Products in Airway Inflammation in CF and CF related Diabetes

Cystic Fibrosis (CF) is often accompanied by diabetes leading to worsening lung function, the reason for which is unclear. The receptor for advanced-glycation-end-products (RAGE) regulates immune responses and inflammation and has been linked to diabetes and possibly CF. We performed a pilot study to determine if CF and CF-related diabetes (CFRD) are associated with enhanced RAGE expression. Full length (fl)RAGE, soluble (s)RAGE, endogenous soluble (es)RAGE, S100A12 (enRAGE) and advanced-glycation-end-products (AGE) expression was assessed in serum, white blood cells and sputum of patients with CF; diabetes; CFRD and healthy subjects. Sputum enRAGE/sRAGE ratios were high in CF but particularly in CFRD which negatively correlated with % predicted FEV1. Serum AGE and AGE/sRAGE ratios were high in diabetics but not in CF. A complex, multifaceted approach was used to assess the role of RAGE and its ligands which is fundamental to determining their impact on airway inflammation. There is a clear association between RAGE activity in the airways of CF and CFRD patients that is not evident in the vascular compartment and correlates with lung function, in contrast to diabetes. This strongly suggests a role for RAGE in contributing to the inflammatory overdrive seen in CF and to a greater extent in CFRD

Multivariate analysis of oestrogen receptor alpha, pS2, metallothionein and CD24 expression in invasive breast cancers

Determination of oestrogen receptor alpha (ER) represents at present the most important predictive factor in breast cancers. Data of ours and of other authors suggest that promising predictive/prognostic factors may also include pS2, metallothionein (MT) and CD24. Present study aimed at determining prognostic and predictive value of immunohistochemical determination of ER, pS2, MT, and CD24 expression in sections originating from 104 patients with breast cancer. An univariate and multivariate analysis was performed. Both univariate and multivariate analyses demonstrated that cytoplasmic-membranous expression of CD24 (CD24c-m) represents a strong unfavourable prognostic factor in the entire group and in most of the subgroups of patients. In several subgroups of the patients also a prognostic value was demonstrated of elevated expression of pS2 and of membranous expression of CD24. Our studies demonstrated that all patients with good prognostic factors (higher ER and pS2 expressions, lower MT expression, CD24c-m negativity) survived total period of observation (103 months). The study documented that cytoplasmic-membranous expression of CD24 represented an extremely strong unfavourable prognostic factor in breast cancer. Examination of the entire panel of the studied proteins permitted to select a group of patients of an exceptionally good prognosis