Skew-Laplace and Cell-Size Distribution in Microbial Axenic Cultures: Statistical Assessment and Biological Interpretation

We report a skew-Laplace statistical analysis of both flow cytometry scatters and cell size from microbial strains primarily grown in batch cultures, others in chemostat cultures and bacterial aquatic populations. Cytometry scatters best fit the skew-Laplace distribution while cell size as assessed by an electronic particle analyzer exhibited a moderate fitting. Unlike the cultures, the aquatic bacterial communities clearly do not fit to a skew-Laplace distribution. Due to its versatile nature, the skew-Laplace distribution approach offers an easy, efficient, and powerful tool for distribution of frequency analysis in tandem with the flow cytometric cell sorting

Pattern Recognition Analysis of MR Spectra

The need for multivariate analysis of magnetic resonance spectroscopy (MRS) data was recognized about 20 years ago, when it became evident that spectral patterns were characteristic of some diseases. Despite this, there is no generally accepted methodology for performing pattern recognition (PR) analysis of MRS data sets. Here, the data acquisition and processing requirements for performing successful PR as applied to human MRS studies are introduced, and the main techniques for feature selection, extraction, and classification are described. These include methods of dimensionality reduction such as principal component analysis (PCA), independent component analysis (ICA), non-negative matrix factorization (NMF), and feature selection. Supervised methods such as linear discriminant analysis (LDA), logistic regression (LogR), and nonlinear classification are discussed separately from unsupervised and semisupervised classification techniques, including k –means clustering. Methods for testing and metrics for gauging the performance of PR models (sensitivity and specificity, the ‘Confusion Matrix’, ‘k –fold cross-validation’, ‘Leave One Out’, ‘Bootstrapping’, the ‘Receiver Operating Characteristic curve’, and balanced error and accuracy rates) are briefly described. This article ends with a summary of the main lessons learned from PR applied to MRS to date

Automatic relevance source determination in human brain tumors using Bayesian NMF.

The clinical management of brain tumors is very sensitive; thus, their non-invasive characterization is often preferred. Non-negative Matrix Factorization techniques have been successfully applied in the context of neuro-oncology to extract the underlying source signals that explain different tissue tumor types, for which knowing the number of sources to calculate was always required. In the current study we estimate the number of relevant sources for a set of discrimination problems involving brain tumors and normal brain. For this, we propose to start by calculating a high number of sources using Bayesian NMF and automatically discarding the irrelevant ones during the iterative process of matrices decomposition, hence obtaining a reduced range of interpretable solutions. The real data used in this study come from a widely tested human brain tumor database. Simulated data that resembled the real data was also generated to validate the hypothesis against ground truth. The results obtained suggest that the proposed approach is able to provide a small range of meaningful solutions to the problem of source extraction in human brain tumors

Robust Conditional Independence maps of single-voxel Magnetic Resonance Spectra to elucidate associations between brain tumours and metabolites.

The aim of the paper is two-fold. First, we show that structure finding with the PC algorithm can be inherently unstable and requires further operational constraints in order to consistently obtain models that are faithful to the data. We propose a methodology to stabilise the structure finding process, minimising both false positive and false negative error rates. This is demonstrated with synthetic data. Second, to apply the proposed structure finding methodology to a data set comprising single-voxel Magnetic Resonance Spectra of normal brain and three classes of brain tumours, to elucidate the associations between brain tumour types and a range of observed metabolites that are known to be relevant for their characterisation. The data set is bootstrapped in order to maximise the robustness of feature selection for nominated target variables. Specifically, Conditional Independence maps (CI-maps) built from the data and their derived Bayesian networks have been used. A Directed Acyclic Graph (DAG) is built from CI-maps, being a major challenge the minimization of errors in the graph structure. This work presents empirical evidence on how to reduce false positive errors via the False Discovery Rate, and how to identify appropriate parameter settings to improve the False Negative Reduction. In addition, several node ordering policies are investigated that transform the graph into a DAG. The obtained results show that ordering nodes by strength of mutual information can recover a representative DAG in a reasonable time, although a more accurate graph can be recovered using a random order of samples at the expense of increasing the computation time

Semi-supervised source extraction methodology for the nosological imaging of glioblastoma response to therapy.

Glioblastomas are one the most aggressive brain tumors. Their usual bad prognosis is due to the heterogeneity of their response to treatment and the lack of early and robust biomarkers to decide whether the tumor is responding to therapy. In this work, we propose the use of a semi-supervised methodology for source extraction to identify the sources representing tumor response to therapy, untreated/unresponsive tumor, and normal brain; and create nosological images of the response to therapy based on those sources. Fourteen mice were used to calculate the sources, and an independent test set of eight mice was used to further evaluate the proposed approach. The preliminary results obtained indicate that was possible to discriminate response and untreated/unresponsive areas of the tumor, and that the color-coded images allowed convenient tracking of response, especially throughout the course of therapy

Modulation of the electroluminescence emission from ZnO/Si NCs/p-Si light-emitting devices via pulsed excitation

In this work, the electroluminescence (EL) emission of zinc oxide (ZnO)/Si nanocrystals (NCs)-based light-emitting devices was studied under pulsed electrical excitation. Both Si NCs and deep-level ZnO defects were found to contribute to the observed EL. Symmetric square voltage pulses (50-μs period) were found to notably enhance EL emission by about one order of magnitude. In addition, the control of the pulse parameters (accumulation and inversion times) was found to modify the emission lineshape, long inversion times (i.e., short accumulation times) suppressing ZnO defects contribution. The EL results were discussed in terms of the recombination dynamics taking place within the ZnO/Si NCs heterostructure, suggesting the excitation mechanism of the luminescent centers via a combination of electron impact, bipolar injection, and sequential carrier injection within their respective conduction regimes

A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases

Machine learning has provided, over the last decades, tools for knowledge extraction in complex medical domains. Most of these tools, though, are ad hoc solutions and lack the systematic approach that would be required to become mainstream in medical practice. In this brief paper, we define a machine learning-based analysis pipeline for helping in a difficult problem in the field of neuro-oncology, namely the discrimination of brain glioblastomas from single brain metastases. This pipeline involves source extraction using k-Meansinitialized Convex Non-negative Matrix Factorization and a collection of classifiers, including Logistic Regression, Linear Discriminant Analysis, AdaBoost, and Random Forests

Boron-incorporating silicon nanocrystals embedded in SiO2: absende of free carriers vs. B-induced defects

Boron (B) doping of silicon nanocrystals requires the incorporation of a B-atom on a lattice site of the quantum dot and its ionization at room temperature. In case of successful B-doping the majority carriers (holes) should quench the photoluminescence of Si nanocrystals via non-radiative Auger recombination. In addition, the holes should allow for a non-transient electrical current. However, on the bottom end of the nanoscale, both substitutional incorporation and ionization are subject to significant increase in their respective energies due to confinement and size effects. Nevertheless, successful B-doping of Si nanocrystals was reported for certain structural conditions. Here, we investigate B-doping for small, well-dispersed Si nanocrystals with low and moderate B-concentrations. While small amounts of B-atoms are incorporated into these nanocrystals, they hardly affect their optical or electrical properties. If the B-concentration exceeds ~1 at%, the luminescence quantum yield is significantly quenched, whereas electrical measurements do not reveal free carriers. This observation suggests a photoluminescence quenching mechanism based on B-induced defect states. By means of density functional theory calculations, we prove that B creates multiple states in the bandgap of Si and SiO2. We conclude that non-percolated ultra-small Si nanocrystals cannot be efficiently B-doped