Introducing a New Dosimeter for the Assessment and Monitoring of Vocal Risk Situations and Voice Disorders.

PURPOSE: There are many physiological parameters recorded by devices that are becoming more affordable, precise and accurate. However, the lack of development in the recording of voice parameters from the physiological or medical point of view is striking, given that it is a fundamental tool for the work of many people and given the high incidence and prevalence of voice pathologies that affect people's communication. In this paper we perform a complete literature review on the dosimeters used in voice research and to present a prototype dosimeter with a pilot study to show its capabilities. METHOD: We conducted a literature review using the keywords [MONITORING], [PHONATION], [ACCUMULATOR], [PORTABLE], [DOSIMETRY], [VOICE] searching in PubMed, Trip Database, HONcode, and SciELO search engines. From our review of dosimeter designs, we created our own prototype consisting of two main components: a Knowles Electronics BU-7135-0000 accelerometer mounted on a neck brace; and the ultra-low power MSP430FR5994 microcontroller. The selected sampling frequency was 2048 Hz. The device calculates the F0 every 250 ms and the amplitude and phonation activity every 31.25 ms. A pilot study was conducted using 2 subjects: one male during 11 days and one female during 14 days. RESULTS: This work includes devices that have been created during the last 45 years as tools for the diagnosis and monitoring of the treatment of cases of vocal pathology and for the detection of phonatory patterns or risk situations for developing voice disorders or vocal pathologies. We also present recordings with our new device on the pattern of daily talk time, the fundamental frequency and the relative intensity of two subjects on different days. CONCLUSIONS: Interesting work has been done in the development of voice dosimeters with different approaches. In our experience it is not possible to access them for research and they are not yet in clinical use. It is possible that a joint approach with voice and voice disorders professionals and engineers working closely together could take advantage of current technology to develop a fully portable, useful, and efficient system.

Selection of human tissue-specific elementary flux modes using gene expression data.

MOTIVATION: The analysis of high-throughput molecular data in the context of metabolic pathways is essential to uncover their underlying functional structure. Among different metabolic pathway concepts in systems biology, elementary flux modes (EFMs) hold a predominant place, as they naturally capture the complexity and plasticity of cellular metabolism and go beyond predefined metabolic maps. However, their use to interpret high-throughput data has been limited so far, mainly because their computation in genome-scale metabolic networks has been unfeasible. To face this issue, different optimization-based techniques have been recently introduced and their application to human metabolism is promising. RESULTS: In this article, we exploit and generalize the K-shortest EFM algorithm to determine a subset of EFMs in a human genome-scale metabolic network. This subset of EFMs involves a wide number of reported human metabolic pathways, as well as potential novel routes, and constitutes a valuable database where high-throughput data can be mapped and contextualized from a metabolic perspective. To illustrate this, we took expression data of 10 healthy human tissues from a previous study and predicted their characteristic EFMs based on enrichment analysis. We used a multivariate hypergeometric test and showed that it leads to more biologically meaningful results than standard hypergeometric. Finally, a biological discussion on the characteristic EFMs obtained in liver is conducted, finding a high level of agreement when compared with the literature.

Computing the shortest elementary flux modes in genome-scale metabolic networks.

MOTIVATION: Elementary flux modes (EFMs) represent a key concept to analyze metabolic networks from a pathway-oriented perspective. In spite of considerable work in this field, the computation of the full set of elementary flux modes in large-scale metabolic networks still constitutes a challenging issue due to its underlying combinatorial complexity. RESULTS: In this article, we illustrate that the full set of EFMs can be enumerated in increasing order of number of reactions via integer linear programming. In this light, we present a novel procedure to efficiently determine the K-shortest EFMs in large-scale metabolic networks. Our method was applied to find the K-shortest EFMs that produce lysine in the genome-scale metabolic networks of Escherichia coli and Corynebacterium glutamicum. A detailed analysis of the biological significance of the K-shortest EFMs was conducted, finding that glucose catabolism, ammonium assimilation, lysine anabolism and cofactor balancing were correctly predicted. The work presented here represents an important step forward in the analysis and computation of EFMs for large-scale metabolic networks, where traditional methods fail for networks of even moderate size. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of a gene-pathway associated with non-alcoholic steatohepatitis.

BACKGROUND/AIMS: We have integrated gene expression profiling of liver biopsies of NASH patients with liver samples of a mouse model of steatohepatitis (MAT1A-KO) to identify a gene-pathway associated with NASH. METHODS: Affymetrix U133 Plus 2.0 microarrays were used to evaluate nine patients with NASH, six patients with steatosis, and six control subjects; Affymetrix MOE430A microarrays were used to evaluate wild-type and MAT1A-KO mice at 15 days, 1, 3, 5 and 8 months after birth. Transcriptional profiles of patients with NASH and MAT1A-KO mice were compared with those of their proficient controls. RESULTS: We identified a gene-pathway associated with NASH, that accurately distinguishes between patients with early-stage NASH and controls. Patients with steatosis have a gene expression pattern intermediate between that of NASH and controls. Promoter analysis revealed that 34 of the genes associated with NASH contained an Sp1 element. We found that Sp1 binding to these genes is increased in MAT1A-KO mice. Sp1 is also hyperphosphorylated in MAT1A-KO as well as in patients with NASH and steatosis. CONCLUSIONS: A gene-pathway associated with NASH has been identified. We speculate that hyperphosphorylation of Sp1 may be involved in the genesis of steatosis and that other factors, such as oxidative stress, may trigger its progression to NASH.

GARBAN II: an integrative framework for extracting biological information from proteomic and genomic data.

Genomic and proteomic analyses generate a massive amount of data that requires specific bioinformatic tools for its management and interpretation. GARBAN II, developed from the previous GARBAN platform, provides an integrated framework to simultaneously analyse and compare multiple datasets from DNA microarrays and proteomic studies. The general architecture, gene classification and comparison, and graphical representation have been redesigned to ensure a user-friendly feature and to improve the capabilities and efficiency of this system. Additionally, GARBAN II has been extended with new applications to display networks of coexpressed genes and to integrate access to BioRag and MotifScanner so as to facilitate the holistic analysis of users' data.

Correlation between gene expression and GO semantic similarity.

This research analyzes some aspects of the relationship between gene expression, gene function, and gene annotation. Many recent studies are implicitly based on the assumption that gene products that are biologically and functionally related would maintain this similarity both in their expression profiles as well as in their Gene Ontology (GO) annotation. We analyze how accurate this assumption proves to be using real publicly available data. We also aim to validate a measure of semantic similarity for GO annotation. We use the Pearson correlation coefficient and its absolute value as a measure of similarity between expression profiles of gene products. We explore a number of semantic similarity measures (Resnik, Jiang, and Lin) and compute the similarity between gene products annotated using the GO. Finally, we compute correlation coefficients to compare gene expression similarity against GO semantic similarity. Our results suggest that the Resnik similarity measure outperforms the others and seems better suited for use in Gene Ontology. We also deduce that there seems to be correlation between semantic similarity in the GO annotation and gene expression for the three GO ontologies. We show that this correlation is negligible up to a certain semantic similarity value; then, for higher similarity values, the relationship trend becomes almost linear. These results can be used to augment the knowledge provided by clustering algorithms and in the development of bioinformatic tools for finding and characterizing gene products.

GARBAN: genomic analysis and rapid biological annotation of cDNA microarray and proteomic data.

SUMMARY: Genomic Analysis and Rapid Biological ANnotation (GARBAN) is a new tool that provides an integrated framework to analyze simultaneously and compare multiple data sets derived from microarray or proteomic experiments. It carries out automated classifications of genes or proteins according to the criteria of the Gene Ontology Consortium at a level of depth defined by the user. Additionally, it performs clustering analysis of all sets based on functional categories or on differential expression levels. GARBAN also provides graphical representations of the biological pathways in which all the genes/proteins participate. AVAILABILITY: http://garban.tecnun.es.