Automated quality control for proton magnetic resonance spectroscopy data using convex non-negative matrix factorization

Proton Magnetic Resonance Spectroscopy (1H MRS) has proven its diagnostic potential in a variety of conditions. However, MRS is not yet widely used in clinical routine because of the lack of experts on its diagnostic interpretation. Although data-based decision support systems exist to aid diagnosis, they often take for granted that the data is of good quality, which is not always the case in a real application context. Systems based on models built with bad quality data are likely to underperform in their decision support tasks. In this study, we propose a system to filter out such bad quality data. It is based on convex Non-Negative Matrix Factorization models, used as a dimensionality reduction procedure, and on the use of several classifiers to discriminate between good and bad quality data.Peer ReviewedPostprint (author's final draft

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

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

Fine mapping of the peach pollen sterility gene (Ps/ps) and detection of markers for marker-assisted selection

In peach, pollen sterility, expressed as absence of pollen in the anthers, segregates as an undesired trait in breeding programs. Pollen fertility screening in progenies is not a common practice mainly because it does not affect fruit set since cross-pollination is frequent. It is also a time-consuming activity that coincides with the busy pollination season. Segregation for this trait could be avoided by using molecular markers to identify appropriate parents or male sterile plants for early culling in progenies expected to segregate, thus increasing breeding efficiency. In peach, pollen sterility is determined by a recessive allele in homozygosis of the major gene, Ps/ps, located on chromosome 6. In this work, using a conventional mapping approach combined with bulked segregant analysis using resequencing data, we fine mapped Ps to a region of almost 160 kb and developed molecular markers for marker-assisted breeding. These markers were validated in plant materials from three peach breeding programs, including progenies, advanced selections, and cultivars, allowing us to determine that the frequency of the ps allele is high (0.23) and also to infer the genotypes of a large collection of cultivars and advanced breeding lines.info:eu-repo/semantics/acceptedVersio

Coassembly and coupling of SK2 channels and mGlu5 receptors

Group I metabotropic glutamate (mGlu) receptors regulate hippocampal CA1 pyramidal neuron excitability via Ca(2+) wave-dependent activation of small-conductance Ca(2+)-activated K(+) (SK) channels. Here, we show that mGlu5 receptors and SK2 channels coassemble in heterologous coexpression systems and in rat brain. Further, in cotransfected cells or rat primary hippocampal neurons, mGlu5 receptor stimulation activated apamin-sensitive SK2-mediated K(+) currents. In addition, coexpression of mGlu5 receptors and SK2 channels promoted plasma membrane targeting of both proteins and correlated with increased mGlu5 receptor function that was unexpectedly blocked by apamin. These results demonstrate a reciprocal functional interaction between mGlu5 receptors and SK2 channels that reflects their molecular coassembly

Going for 2D or 3D? : investigating various machine learning approaches for peach variety identification

Machine learning-based pattern recognition methods are about to revolution-ize the farming sector. For breeding and cultivation purposes, the identifica-tion of plant varieties is a particularly important problem that involves spe-cific challenges for the different crop species. In this contribution, we con-sider the problem of peach variety identification for which alternatives to DNA-based analysis are being sought. While a traditional procedure would suggest using manually designed shape descriptors as the basis for classifica-tion, the technical developments of the last decade have opened up possibili-ties for fully automated approaches, either based on 3D scanning technology or by employing deep learning methods for 2D image classification. In our feasibility study, we investigate the potential of various machine learning ap-proaches with a focus on the comparison of methods based on 2D images and 3D scans. We provide and discuss first results, paving the way for future use of the methods in the field

Pedigree analysis of 220 almond genotypes reveals two world mainstream breeding lines based on only three different cultivars

Loss of genetic variability is an increasing challenge in tree breeding programs due to the repeated use of a reduced number of founder genotypes. However, in almond, little is known about the genetic variability in current breeding stocks, although several cases of inbreeding depression have been reported. To gain insights into the genetic structure in modern breeding programs worldwide, marker-verified pedigree data of 220 almond cultivars and breeding selections were analyzed. Inbreeding coefficients, pairwise relatedness, and genetic contribution were calculated for these genotypes. The results reveal two mainstream breeding lines based on three cultivars: “Tuono”, “Cristomorto”, and “Nonpareil”. Descendants from “Tuono” or “Cristomorto” number 76 (sharing 34 descendants), while “Nonpareil” has 71 descendants. The mean inbreeding coefficient of the analyzed genotypes was 0.041, with 14 genotypes presenting a high inbreeding coefficient, over 0.250. Breeding programs from France, the USA, and Spain showed inbreeding coefficients of 0.075, 0.070, and 0.037, respectively. According to their genetic contribution, modern cultivars from Israel, France, the USA, Spain, and Australia trace back to a maximum of six main founding genotypes. Among the group of 65 genotypes carrying the Sf allele for self-compatibility, the mean relatedness coefficient was 0.125, with “Tuono” as the main founding genotype (24.7% of total genetic contribution). The results broaden our understanding about the tendencies followed in almond breeding over the last 50 years and will have a large impact into breeding decision-making process worldwide. Increasing current genetic variability is required in almond breeding programs to assure genetic gain and continuing breeding progress