SpectraClassifier 1.0: a user friendly, automated MRS-based classifier-development system

Background: SpectraClassifier (SC) is a Java solution for designing and implementing Magnetic Resonance Spectroscopy (MRS)-based classifiers. The main goal of SC is to allow users with minimum background knowledge of multivariate statistics to perform a fully automated pattern recognition analysis. SC incorporates feature selection (greedy stepwise approach, either forward or backward), and feature extraction (PCA). Fisher Linear Discriminant Analysis is the method of choice for classification. Classifier evaluation is performed through various methods: display of the confusion matrix of the training and testing datasets; K-fold cross-validation, leave-one-out and bootstrapping as well as Receiver Operating Characteristic (ROC) curves. Results: SC is composed of the following modules: Classifier design, Data exploration, Data visualisation, Classifier evaluation, Reports, and Classifier history. It is able to read low resolution in-vivo MRS (single-voxel and multi-voxel) and high resolution tissue MRS (HRMAS), processed with existing tools (jMRUI, INTERPRET, 3DiCSI or TopSpin). In addition, to facilitate exchanging data between applications, a standard format capable of storing all the information needed for a dataset was developed. Each functionality of SC has been specifically validated with real data with the purpose of bug-testing and methods validation. Data from the INTERPRET project was used. Conclusions: SC is a user-friendly software designed to fulfil the needs of potential users in the MRS community. It accepts all kinds of pre-processed MRS data types and classifies them semi-automatically, allowing spectroscopists to concentrate on interpretation of results with the use of its visualisation tools

A variational formulation for GTM through time: Theoretical foundations

Generative Topographic Mapping (GTM) is a latent variable model that, in its standard version, was conceived to provide clustering and visualization of multivariate, real-valued, i.i.d. data. It was also extended to deal with non-i.i.d. data such as multivariate time series in a variant called GTM Through Time (GTMTT), defined as a constrained Hidden Markov Model (HMM). In this technical report, we provide the theoretical foundations of the reformulation of GTM-TT within the Variational Bayesian framework. This approach, in its application, should naturally handle the presence of noise in the time series, helping to avert the problem of data overfitting.Postprint (published version

A variational Bayesian formulation for GTM: Theoretical foundations

Generative Topographic Mapping (GTM) is a non-linear latent variable model of the manifold learning family that provides simultaneous visualization and clustering of high-dimensional data. It was originally formulated as a constrained mixture of Gaussian distributions, for which the adaptive parameters were determined by Maximum Likelihood (ML), using the Expectation-Maximization (EM) algorithm. In this paper, we define an alternative variational formulation of GTM that provides a full Bayesian treatment to a Gaussian Process (GP) - based variation of the model.Postprint (published version

A probabilistic approach to the visual exploration of G protein-coupled receptor sequences

The study of G protein-coupled receptors (GPCRs) is of great interest in pharmaceutical research, but only a few of their 3D structures are known at present. On the contrary, their amino acid sequences are known and accessible. Sequence analysis can provide new insight on GPCR function. Here, we use a kernel-based statistical machine learning model for the visual exploration of GPCR functional groups from their sequences. This is based on the rich information provided by the model regarding the probability of each sequence belonging to a certain receptor group.Postprint (published version

Control de un manipulador antropomórfico por medio de un dispositivo de inmersión

Este trabajo consta de 3 etapas: a) Construcción: dispositivo de inmersión, b) Desarrollo: software de simulación con RV, c) Diseño: sistema electrónico de adquisición y envío de datos. El dispositivo de inmersión captura los movimientos del brazo del operario, por medio de sensores; las señales suministradas por esto, son procesadas por un PIC, y enviadas al PC, para ser leídas por el software, que simula con realidad virtual el movimiento del manipulador UMNG-1. Finalmente, teniendo en cuenta la(s) posición(es) final deseada del efector final, es entregado un código, que enviado al controlador YASKAWA-SMC 2000, posibilita el movimiento real del UMNG-1, para llevar a cabo tareas de difícil ejecución

Embedding MRI information into MRSI data source extraction improves brain tumour delineation in animal models

Glioblastoma is the most frequent malignant intra-cranial tumour. Magnetic resonance imaging is the modality of choice in diagnosis, aggressiveness assessment, and follow-up. However, there are examples where it lacks diagnostic accuracy. Magnetic resonance spectroscopy enables the identification of molecules present in the tissue, providing a precise metabolomic signature. Previous research shows that combining imaging and spectroscopy information results in more accurate outcomes and superior diagnostic value. This study proposes a method to combine them, which builds upon a previous methodology whose main objective is to guide the extraction of sources. To this aim, prior knowledge about class-specific information is integrated into the methodology by setting the metric of a latent variable space where Non-negative Matrix Factorisation is performed. The former methodology, which only used spectroscopy and involved combining spectra from different subjects, was adapted to use selected areas of interest that arise from segmenting the T2-weighted image. Results showed that embedding imaging information into the source extraction (the proposed semi-supervised analysis) improved the quality of the tumour delineation, as compared to those obtained without this information (unsupervised analysis). Both approaches were applied to pre-clinical data, involving thirteen brain tumour-bearing mice, and tested against histopathological data. On results of twenty-eight images, the proposed Semi-Supervised Source Extraction (SSSE) method greatly outperformed the unsupervised one, as well as an alternative semi-supervised approach from the literature, with differences being statistically significant. SSSE has proven successful in the delineation of the tumour, while bringing benefits such as 1) not constricting the metabolomic-based prediction to the image-segmented area, 2) ability to deal with signal-to-noise issues, 3) opportunity to answer specific questions by allowing researchers/radiologists define areas of interest that guide the source extraction, 4) creation of an intra-subject model and avoiding contamination from inter-subject overlaps, and 5) extraction of meaningful, good-quality sources that adds interpretability, conferring validation and better understanding of each case

A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data

BackgroundThe clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyzes single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of tumor type classification from the spectroscopic signal.Methodology/Principal FindingsNon-negative matrix factorization techniques have recently shown their potential for the identification of meaningful sources from brain tissue spectroscopy data. In this study, we use a convex variant of these methods that is capable of handling negatively-valued data and generating sources that can be interpreted as tumor class prototypes. A novel approach to convex non-negative matrix factorization is proposed, in which prior knowledge about class information is utilized in model optimization. Class-specific information is integrated into this semi-supervised process by setting the metric of a latent variable space where the matrix factorization is carried out. The reported experimental study comprises 196 cases from different tumor types drawn from two international, multi-center databases. The results indicate that the proposed approach outperforms a purely unsupervised process by achieving near perfect correlation of the extracted sources with the mean spectra of the tumor types. It also improves tissue type classification.Conclusions/SignificanceWe show that source extraction by unsupervised matrix factorization benefits from the integration of the available class information, so operating in a semi-supervised learning manner, for discriminative source identification and brain tumor labeling from single-voxel spectroscopy data. We are confident that the proposed methodology has wider applicability for biomedical signal processing

Multiproject–multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy

Contains fulltext : 75361.pdf (publisher's version ) (Open Access)14 p

Diseño del Robot Industrial UMNG-I

Este artículo resume el proceso empleado en el diseño del Robot Industrial UMNG-I del programa de Ingeniería Mecatrónica de la Universidad Militar Nueva Granada. Se presenta un conjunto de conceptos fundamentales relacionados con la robótica industrial así como un proceso organizado y secuencial de diseño. Finalmente se muestra los resultados parciales obtenidos para el Robot Industrial UMNG-I

Análisis cinemático

Este artículo comprende el análisis cinemático de un robot industrial de configuración SCARA. El análisis sinemático realizado se divide en dos partes, el análisis cinemático directo y el análisis cinemático inverso. Se muestra cómo ambos ánalisis son importantes para los posteriores análisis dinámico, de trayectorias y de control que en conjunto permitirán el diseño de un robot industrial. Se concluye finalmente que la configuración SCARA ofrece un análisis cinemático sencillo y fácil de implementar por computador en tiempo real