Pre-processing for noise detection in gene expression classification data

Due to the imprecise nature of biological experiments, biological data is often characterized by the presence of redundant and noisy data. This may be due to errors that occurred during data collection, such as contaminations in laboratorial samples. It is the case of gene expression data, where the equipments and tools currently used frequently produce noisy biological data. Machine Learning algorithms have been successfully used in gene expression data analysis. Although many Machine Learning algorithms can deal with noise, detecting and removing noisy instances from the training data set can help the induction of the target hypothesis. This paper evaluates the use of distance-based pre-processing techniques for noise detection in gene expression data classification problems. This evaluation analyzes the effectiveness of the techniques investigated in removing noisy data, measured by the accuracy obtained by different Machine Learning classifiers over the pre-processed data.

Data collapse, scaling functions, and analytical solutions of generalized growth models

We consider a nontrivial one-species population dynamics model with finite and infinite carrying capacities. Time-dependent intrinsic and extrinsic growth rates are considered in these models. Through the model per capita growth rate we obtain a heuristic general procedure to generate scaling functions to collapse data into a simple linear behavior even if an extrinsic growth rate is included. With this data collapse, all the models studied become independent from the parameters and initial condition. Analytical solutions are found when time-dependent coefficients are considered. These solutions allow us to perceive nontrivial transitions between species extinction and survival and to calculate the transition's critical exponents. Considering an extrinsic growth rate as a cancer treatment, we show that the relevant quantity depends not only on the intensity of the treatment, but also on when the cancerous cell growth is maximum.

Gene Expression Noise in Spatial Patterning: hunchback Promoter Structure Affects Noise Amplitude and Distribution in Drosophila Segmentation

Positional information in developing embryos is specified by spatial gradients of transcriptional regulators. One of the classic systems for studying this is the activation of the hunchback (hb) gene in early fruit fly (Drosophila) segmentation by the maternally-derived gradient of the Bicoid (Bcd) protein. Gene regulation is subject to intrinsic noise which can produce variable expression. This variability must be constrained in the highly reproducible and coordinated events of development. We identify means by which noise is controlled during gene expression by characterizing the dependence of hb mRNA and protein output noise on hb promoter structure and transcriptional dynamics. We use a stochastic model of the hb promoter in which the number and strength of Bcd and Hb (self-regulatory) binding sites can be varied. Model parameters are fit to data from WT embryos, the self-regulation mutant hb(14F), and lacZ reporter constructs using different portions of the hb promoter. We have corroborated model noise predictions experimentally. The results indicate that WT (self-regulatory) Hb output noise is predominantly dependent on the transcription and translation dynamics of its own expression, rather than on Bcd fluctuations. The constructs and mutant, which lack self-regulation, indicate that the multiple Bcd binding sites in the hb promoter (and their strengths) also play a role in buffering noise. The model is robust to the variation in Bcd binding site number across a number of fly species. This study identifies particular ways in which promoter structure and regulatory dynamics reduce hb output noise. Insofar as many of these are common features of genes (e. g. multiple regulatory sites, cooperativity, self-feedback), the current results contribute to the general understanding of the reproducibility and determinacy of spatial patterning in early development.

Double generalized linear model for tissue culture proportion data: a Bayesian perspective

Joint generalized linear models and double generalized linear models (DGLMs) were designed to model outcomes for which the variability can be explained using factors and/or covariates. When such factors operate, the usual normal regression models, which inherently exhibit constant variance, will under-represent variation in the data and hence may lead to erroneous inferences. For count and proportion data, such noise factors can generate a so-called overdispersion effect, and the use of binomial and Poisson models underestimates the variability and, consequently, incorrectly indicate significant effects. In this manuscript, we propose a DGLM from a Bayesian perspective, focusing on the case of proportion data, where the overdispersion can be modeled using a random effect that depends on some noise factors. The posterior joint density function was sampled using Monte Carlo Markov Chain algorithms, allowing inferences over the model parameters. An application to a data set on apple tissue culture is presented, for which it is shown that the Bayesian approach is quite feasible, even when limited prior information is available, thereby generating valuable insight for the researcher about its experimental results.

The gaps-in-noise test: Gap detection thresholds in normal-hearing young adults

The aim of this study was to establish parameters for the gaps-in-noise test in normal-hearing young adults. One hundred subjects (50 males and 50 females) received an audiological evaluation to rule out hearing loss and auditory processing disorder. The gaps-in-noise test was then conducted on all subjects. The mean gap detection threshold was 4.19 ms. A psychometric function by gap duration was constructed, revealing that the percentage of correct responses was less than or equal to 5% for a gap duration of 2 ms, 10-30% for a gap duration of 3 ms, 60-70% for a gap duration of 4 ms, and over 96% for gap durations of 5 ms or longer. The results suggest that the data obtained can be applied as reference values for future testing. In the subjects evaluated, the gaps-in-noise test proved to be consistent with low variability.

Effects of hyperbaric oxygen treatment on auditory hair cells after acute noise damage

Acute acoustic trauma (AAT) is a sudden sensorineural hearing loss caused by exposure of the hearing organ to acoustic overstimulation, typically an intense sound impulse, hyperbaric oxygen therapy (HOT), which favors repair of the microcirculation, can be potentially used to treat it. Hence, this study aimed to assess the effects of HOT on guinea pigs exposed to acoustic trauma. Fifteen guinea pigs were exposed to noise in the 4-kHz range with intensity of 110 dB sound level pressure for 72 h. They were assessed by brainstem auditory evoked potential (BAEP) and by distortion product otoacoustic emission (DPOAE) before and after exposure and after HOT at 2.0 absolute atmospheres for 1 h. The cochleae were then analyzed using scanning electron microscopy (SEM). There was a statistically significant difference in the signal-to-noise ratio of the DPOAE amplitudes for the 1- to 4-kHz frequencies and the SEM findings revealed damaged outer hair cells (OHC) after exposure to noise, with recovery after HOT (p = 0.0159), which did not occur on thresholds and amplitudes to BAEP (p = 0.1593). The electrophysiological BAEP data did not demonstrate effectiveness of HOT against AAT damage. However, there was improvement of the anatomical pattern of damage detected by SEM, with a significant reduction of the number of injured cochlear OHC and their functionality detected by DPOAE.

PCA Tomography: how to extract information from data cubes

Astronomy has evolved almost exclusively by the use of spectroscopic and imaging techniques, operated separately. With the development of modern technologies, it is possible to obtain data cubes in which one combines both techniques simultaneously, producing images with spectral resolution. To extract information from them can be quite complex, and hence the development of new methods of data analysis is desirable. We present a method of analysis of data cube (data from single field observations, containing two spatial and one spectral dimension) that uses Principal Component Analysis (PCA) to express the data in the form of reduced dimensionality, facilitating efficient information extraction from very large data sets. PCA transforms the system of correlated coordinates into a system of uncorrelated coordinates ordered by principal components of decreasing variance. The new coordinates are referred to as eigenvectors, and the projections of the data on to these coordinates produce images we will call tomograms. The association of the tomograms (images) to eigenvectors (spectra) is important for the interpretation of both. The eigenvectors are mutually orthogonal, and this information is fundamental for their handling and interpretation. When the data cube shows objects that present uncorrelated physical phenomena, the eigenvector`s orthogonality may be instrumental in separating and identifying them. By handling eigenvectors and tomograms, one can enhance features, extract noise, compress data, extract spectra, etc. We applied the method, for illustration purpose only, to the central region of the low ionization nuclear emission region (LINER) galaxy NGC 4736, and demonstrate that it has a type 1 active nucleus, not known before. Furthermore, we show that it is displaced from the centre of its stellar bulge.

ENHANCING MULTISCALE FRACTAL DESCRIPTORS USING FUNCTIONAL DATA ANALYSIS

This work presents a novel approach in order to increase the recognition power of Multiscale Fractal Dimension (MFD) techniques, when applied to image classification. The proposal uses Functional Data Analysis (FDA) with the aim of enhancing the MFD technique precision achieving a more representative descriptors vector, capable of recognizing and characterizing more precisely objects in an image. FDA is applied to signatures extracted by using the Bouligand-Minkowsky MFD technique in the generation of a descriptors vector from them. For the evaluation of the obtained improvement, an experiment using two datasets of objects was carried out. A dataset was used of characters shapes (26 characters of the Latin alphabet) carrying different levels of controlled noise and a dataset of fish images contours. A comparison with the use of the well-known methods of Fourier and wavelets descriptors was performed with the aim of verifying the performance of FDA method. The descriptor vectors were submitted to Linear Discriminant Analysis (LDA) classification method and we compared the correctness rate in the classification process among the descriptors methods. The results demonstrate that FDA overcomes the literature methods (Fourier and wavelets) in the processing of information extracted from the MFD signature. In this way, the proposed method can be considered as an interesting choice for pattern recognition and image classification using fractal analysis.

Inflammation markers in healthy and periodontitis patients: a preliminary data screening

Advances in diagnostic research are moving towards methods whereby the periodontal risk can be identified and quantified by objective measures using biomarkers. Patients with periodontitis may have elevated circulating levels of specific inflammatory markers that can be correlated to the severity of the disease. The purpose of this study was to evaluate whether differences in the serum levels of inflammatory biomarkers are differentially expressed in healthy and periodontitis patients. Twenty-five patients (8 healthy patients and 17 chronic periodontitis patients) were enrolled in the study. A 15 mL blood sample was used for identification of the inflammatory markers, with a human inflammatory flow cytometry multiplex assay. Among 24 assessed cytokines, only 3 (RANTES, MIG and Eotaxin) were statistically different between groups (p<0.05). In conclusion, some of the selected markers of inflammation are differentially expressed in healthy and periodontitis patients. Cytokine profile analysis may be further explored to distinguish the periodontitis patients from the ones free of disease and also to be used as a measure of risk. The present data, however, are limited and larger sample size studies are required to validate the findings of the specific biomarkers.

Hydrometeor and latent heat profiles of tropical cyclones Conson, Ivan and Catarina using PR/TRMM data

ABSTRACT Microphysical and thermodynamical features of two tropical systems, namely Hurricane Ivan and Typhoon Conson, and one sub-tropical, Catarina, have been analyzed based on space-born radar PR measurements available on the TRMM satellite. The procedure to classify the reflectivity profiles followed the Heymsfield et al (2000) and Steiner et al (1995) methodologies. The water and ice content have been calculated using a relationship obtained with data of the surface SPOL radar and PR in Rondonia State in Brazil. The diabatic heating rate due to latent heat release has been estimated using the methodology developed by Tao et al (1990). A more detailed analysis has been performed for Hurricane Catarina, the first of its kind in South Atlantic. High water content mean value has been found in Conson and Ivan at low levels and close to their centers. Results indicate that hurricane Catarina was shallower than the other two systems, with less water and the water was concentrated closer to its center. The mean ice content in Catarina was about 0.05 g kg-1 while in Conson it was 0.06 g kg-1 and in Ivan 0.08 g kg-1. Conson and Ivan had water content up to 0.3 g kg-1 above the 0ºC layer, while Catarina had less than 0.15 g kg-1. The latent heat released by Catarina showed to be very similar to the other two systems, except in the regions closer to the center.

O uso de sondagens dipolo-dipolo em estudos hidrogeológicos e de depósitos de resíduos

Ensaios de sondagem e caminhamento elétrico têm sido realizados globalmente na avaliação de uma grande variedade de problemas em exploração mineral e hidrogeologia, bem como na caracterização de locais com risco de contaminação. A despeito da utilidade de ensaios geoelétricos em estudos hidrogeológicos e na caracterização de depósitos de resíduos desativados, um problema na utilização de sondagens elétricas ocorre em conseqüência da falta de espaço para aquisição de dados nessas áreas. Em áreas urbanas, isto constitui uma limitação importante devido à existência de construções, o que muitas vezes interrompe o desenvolvimento de ensaios de campo com arranjos de comprimentos adequados. Embora investigações rasas tendam a ser eficazes em caracterização geoambiental, a estimativa de parâmetros-chave pode depender da investigação de porções mais profundas. Entretanto, uma profundidade de investigação ligeiramente maior é obtida pelo arranjo dipolo-dipolo em comparação aos arranjos Schlumberger e Wenner de mesmo comprimento. Essa característica do arranjo dipolo-dipolo pode ser observada em vários estudos, mas provavelmente devido à tradição e à relação sinal-ruído, a grande maioria das sondagens elétricas é feita com os arranjos Schlumberger e Wenner.O arranjo dipolo-dipolo é mais utilizado em ensaios 2D. Até onde se sabe, essa maior capacidade de investigação em profundidade do arranjo dipolo-dipolo 1D não foi explorada em estudos geoambientais e nem em estudos hidrogeológicos em áreas urbanas. Este trabalho apresenta resultados de ensaios de campo que ressaltam a utilidade de sondagens dipolo-dipolo na investigação de tais áreas.

A new genus and two new species of Stevardiinae (Characiformes: Characidae) with a hypothesis on their relationships based on morphological and histological data

Lepidocharax, new genus, and Lepidocharax diamantina and L. burnsi new species from eastern Brazil are described herein. Lepidocharax is considered a monophyletic genus of the Stevardiinae and can be distinguished from the other members of this subfamily except Planaltina, Pseudocorynopoma, and Xenurobrycon by having the dorsal-fin origin vertically aligned with the anal-fin origin, vs. dorsal fin origin anterior or posterior to anal-fin origin. Additionally the new genus can be distinguished from those three genera by not having the scales extending over the ventral caudal-fin lobe modified to form the dorsal border of the pheromone pouch organ or to represent a pouch scale in sexually mature males. In this paper, we describe these two recently discovered species and the ultrastructure of their spermatozoa.

Natural history and population data of fishes in caves of the Serra do Ramalho karst area, Middle São Francisco basin, northeastern Brazil

During the exploration and mapping of new caves in Serra do Ramalho karst area, southern Bahia state, cavers from the Grupo Bambuí de Pesquisas Espeleológicas - GBPE (Belo Horizonte) noticed the presence of troglomorphic catfishes (species with reduced eyes and/or melanic pigmentation), which we intensively investigated with regards to their ecology and behavior since 2005. Non-troglomorphic fishes regularly found in the studied caves were included in this investigation. We present here data on the natural history of two troglobitic (exclusively subterranean troglomorphic species) fishes - Rhamdia enfurnada Bichuette & Trajano, 2005 (Heptapteridae; Gruna do Enfurnado) and Trichomycterus undescribed species (Trichomycteridae; Lapa dos Peixes and Gruna da Água Clara), and non-troglomorphic Hoplias cf. malabaricus, probably a troglophile (able to form populations both in epigean and subterranean habitats) in the Gruna do Enfurnado, and Pimelodella sp., a species with a sink population in the Lapa dos Peixes.

The impact of spatial data redundancy on SOLAP query performance

Geographic Data Warehouses (GDW) are one of the main technologies used in decision-making processes and spatial analysis, and the literature proposes several conceptual and logical data models for GDW. However, little effort has been focused on studying how spatial data redundancy affects SOLAP (Spatial On-Line Analytical Processing) query performance over GDW. In this paper, we investigate this issue. Firstly, we compare redundant and non-redundant GDW schemas and conclude that redundancy is related to high performance losses. We also analyze the issue of indexing, aiming at improving SOLAP query performance on a redundant GDW. Comparisons of the SB-index approach, the star-join aided by R-tree and the star-join aided by GiST indicate that the SB-index significantly improves the elapsed time in query processing from 25% up to 99% with regard to SOLAP queries defined over the spatial predicates of intersection, enclosure and containment and applied to roll-up and drill-down operations. We also investigate the impact of the increase in data volume on the performance. The increase did not impair the performance of the SB-index, which highly improved the elapsed time in query processing. Performance tests also show that the SB-index is far more compact than the star-join, requiring only a small fraction of at most 0.20% of the volume. Moreover, we propose a specific enhancement of the SB-index to deal with spatial data redundancy. This enhancement improved performance from 80 to 91% for redundant GDW schemas.

As técnicas NASVD e MNF e sua aplicação na redução de ruídos em dados gamaespectrométricos

A desintegração radioativa é um processo aleatório e a estimativa de todas as medidas associadas é governada por leis estatísticas. Os perfis de taxas de contagem são sempre "ruidosos" quando utilizados períodos curtos como um segundo para cada medida. Os filtros utilizados e posteriormente as correções feitas no processamento atual de dados gamaespectrométricos não são suficientes para remover ou diminuir, consideravelmente, o ruído oriundo do espectro. Dois métodos estatísticos que atuam diretamente nos dados coletados, isto é, nos espectros, vêm sendo sugeridos na literatura para remover e minimizar estes ruídos remanescentes o Noise-Adjusted Singular Value Decomposition - NASVD e Maximum Noise Fraction - MNF. Estes métodos produzem uma redução no ruído de forma significativa. Neste trabalho eles foram implementados dentro do ambiente de processamento do software Oasis Montaj e aplicados na área compreendida pelos blocos I e II do levantamento aerogeofísico que recobre a porção oeste da Província Mineral do Tapajós, entre os Estados do Pará e Amazonas. Os dados filtrados e não-filtrados com as técnicas de NASVD e MNF foram processados com os parâmetros e constantes fornecidos pela empresa Lasa Engenharia e Prospecções S.A., sendo estes comparados. Os resultados da comparação entre perfis e mapas apresentaram-se de forma promissora, pois houve um ganho na resolução dos produtos.