Cluster analysis and artificial neural network on the superovulatory response prediction in mice

Complex biological systems require sophisticated approach for analysis, once there are variables with distinct measure levels to be analyzed at the same time in them. The mouse assisted reproduction, e.g. superovulation and viable embryos production, demand a multidisciplinary control of the environment, endocrinologic and physiologic status of the animals, of the stressing factors and the conditions which are favorable to their copulation and subsequently oocyte fertilization. In the past, analyses with a simplified approach of these variables were not well succeeded to predict the situations that viable embryos were obtained in mice. Thereby, we suggest a more complex approach with association of the Cluster Analysis and the Artificial Neural Network to predict embryo production in superovulated mice. A robust prediction could avoid the useless death of animals and would allow an ethic management of them in experiments requiring mouse embryo.

Multidimensional cluster stability analysis from a Brazilian Bradyrhizobium sp RFLP/PCR data set

The taxonomy of the N(2)-fixing bacteria belonging to the genus Bradyrhizobium is still poorly refined, mainly due to conflicting results obtained by the analysis of the phenotypic and genotypic properties. This paper presents an application of a method aiming at the identification of possible new clusters within a Brazilian collection of 119 Bradryrhizobium strains showing phenotypic characteristics of B. japonicum and B. elkanii. The stability was studied as a function of the number of restriction enzymes used in the RFLP-PCR analysis of three ribosomal regions with three restriction enzymes per region. The method proposed here uses Clustering algorithms with distances calculated by average-linkage clustering. Introducing perturbations using sub-sampling techniques makes the stability analysis. The method showed efficacy in the grouping of the species B. japonicum and B. elkanii. Furthermore, two new clusters were clearly defined, indicating possible new species, and sub-clusters within each detected cluster. (C) 2008 Elsevier B.V. All rights reserved.

Multivariate analysis of fresh-cut carambola slices stored under different temperatures

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Ingredient classification according to the digestible amino acid profile: an exploratory analysis

This study aimed: 1) to classify ingredients according to the digestible amino acid (AA) profile; 2) to determine ingredients with AA profile closer to the ideal for broiler chickens; and 3) to compare digestible AA profiles from simulated diets with the ideal protein profile. The digestible AA levels of 30 ingredients were compiled from the literature and presented as percentages of lysine according to the ideal protein concept. Cluster and principal component analyses (exploratory analyses) were used to compose and describe groups of ingredients according to AA profiles. Four ingredient groups were identified by cluster analysis, and the classification of the ingredients within each of these groups was obtained from a principal component analysis, showing 11 classes of ingredients with similar digestible AA profiles. The ingredients with AA profiles closer to the ideal protein were meat and bone meal 45, fish meal 60 and wheat germ meal, all of them constituting Class 1; the ingredients from the other classes gradually diverged from the ideal protein. Soybean meal, which is the main protein source for poultry, showed good AA balance since it was included in Class 3. on the contrary, corn, which is the main energy source in poultry diets, was classified in Class 8. Dietary AA profiles were improved when corn and/or soybean meal were partially or totally replaced in the simulations by ingredients with better AA balance.

GC Fingerprints Coupled to Pattern-Recognition Multivariate SIMCA Chemometric Analysis for Brazilian Gasoline Quality Studies

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Screening Brazilian commercial gasoline quality by hydrogen nuclear magnetic resonance spectroscopic fingerprintings and pattern-recognition multivariate chemometric analysis

The identification of gasoline adulteration by organic solvents is not an easy task, because compounds that constitute the solvents are already in gasoline composition. In this work, the combination of Hydrogen Nuclear Magnetic Resonance ((1)H NMR) spectroscopic fingerprintings with pattern-recognition multivariate Soft Independent Modeling of Class Analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality in a Monitoring Program for Quality Control of Automotive Fuels. SIMCA was performed on spectroscopic fingerprints to classify the quality of representative commercial gasoline samples selected by Hierarchical Cluster Analysis (HCA) and collected over a 6-month period from different gas stations in the São Paulo state, Brazil. Following optimized the (1)H NMR-SIMCA algorithm, it was possible to correctly classify 92.0% of commercial gasoline samples, which is considered acceptable. The chemometric method is recommended for routine applications in Quality-Control Monitoring Programs, since its measurements are fast and can be easily automated. Also, police laboratories could employ this method for rapid screening analysis to discourage adulteration practices. (C) 2010 Elsevier B.V. All rights reserved.

Analysis of floristic composition and structure as an aid to monitoring protected areas of dense rain forest in southeastern Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Techniques of digital image analysis for histological quantification of melanin

A análise morfométrica da melanina tecidual pode subsidiar quantitativamente a pesquisa em discromias. Os autores demonstram três técnicas de análise de imagem digital que permitem a identificação dos pixels equivalentes à melanina na epiderme pela coloração de Fontana-Masson, possibilitando o cálculo da sua porcentagem nas diferentes camadas da epiderme, e discutem os principais elementos relacionados à análise e a necessidade de rigorosa padronização do processo.

Esterase patterns and phylogenetic relationships of species and strains included in the Drosophila buzzatii cluster

Ten strains of two species in the Drosophila buzzatii cluster (D. serido and D. seriema) were examined as to esterase patterns using polyacrylamide gel electrophoresis. The migration rate of esterases, and their substrate specificity to alpha and beta naphthyl acetates, were analysed. Other esterase features such as inhibition behaviour, presence in males and females and location in the head, thorax or abdomen of flies, were also examined. The present data,together with results obtained by others for eight strains of D. koepferae, D. serido, D. seriema and D. buzzatii, show that 69 bands have been detected in the eighteen strains studied. This total number of bands was used for comparison of strains and species by similarity index, analysis of dependence and cluster analysis. The comparisons confirmed the existence of a high degree of similarity among D. seriema strains and among D. koepferae strains, but indicated differentiation among the D. serido strains. Two strains (D69R2 and D69R5) which differed from the others of the latter species, showed closer affinities with D. buzzatii, which indicates the need for further work on those strains classified as D. serido.

Estimates of genetic parameters, and cluster and principal components analyses of breeding values related to egg production traits in a White Leghorn population

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Analysis of rain quality data from the South African interior

Rain acidity may be ascribed to emissions from power station stacks, as well as emissions from other industry, biomass burning, maritime influences, agricultural influences, etc. Rain quality data are available for 30 sites in the South African interior, some from as early as 1985 for up to 14 rainfall seasons, while others only have relatively short records. The article examines trends over time in the raw and volume weighted concentrations of the parameters measured, separately for each of the sites for which sufficient data are available. The main thrust, however, is to examine the inter-relationship structure between the concentrations within each rain event (unweighted data), separately for each site, and to examine whether these inter-relationships have changed over time. The rain events at individual sites can be characterized by approximately eight combinations of rainfall parameters (or rain composition signatures), and these are common to all sites. Some sites will have more events from one signature than another, but there appear to be no signatures unique to a single site. Analysis via factor and cluster analysis, with a correspondence analysis of the results, also aid interpretation of the patterns. This spatio-temporal analysis, performed by pooling all rain event data, irrespective of site or time period, results in nine combinations of rainfall parameters being sufficient to characterize the rain events. The sites and rainfall seasons show patterns in these combinations of parameters, with some combinations appearing more frequently during certain rainfall seasons. In particular, the presence of the combination of low acetate and formate with high magnesium appears to be increasing in the later rainfall seasons, as does this combination together with calcium, sodium, chloride, potassium and fluoride. As expected, sites close together exhibit similar signatures. Copyright © 2002 John Wiley & Sons, Ltd.

Factors that cause genotype by environment interaction and use of a multiple-trait herd-cluster model for milk yield of Holstein cattle from Brazil and Colombia

Descriptive herd variables (DVHE) were used to explain genotype by environment interactions (G x E) for milk yield (MY) in Brazilian and Colombian production environments and to develop a herd-cluster model to estimate covariance components and genetic parameters for each herd environment group. Data consisted of 180,522 lactation records of 94,558 Holstein cows from 937 Brazilian and 400 Colombian herds. Herds in both countries were jointly grouped in thirds according to 8 DVHE: production level, phenotypic variability, age at first calving, calving interval, percentage of imported semen, lactation length, and herd size. For each DVHE, REML bivariate animal model analyses were used to estimate genetic correlations for MY between upper and lower thirds of the data. Based on estimates of genetic correlations, weights were assigned to each DVHE to group herds in a cluster analysis using the FASTCLUS procedure in SAS. Three clusters were defined, and genetic and residual variance components were heterogeneous among herd clusters. Estimates of heritability in clusters 1 and 3 were 0.28 and 0.29, respectively, but the estimate was larger (0.39) in Cluster 2. The genetic correlations of MY from different clusters ranged from 0.89 to 0.97. The herd-cluster model based on DVHE properly takes into account G x E by grouping similar environments accordingly and seems to be an alternative to simply considering country borders to distinguish between environments.

Comparative studies of the sample decomposition of green and roasted coffee for determination of nutrients and data exploratory analysis

The contents of some nutrients in 35 Brazilian green and roasted coffee samples were determined by flame atomic absorption spectrometry (Ca, Mg, Fe, Cu, Mn, and Zn), flame atomic emission photometry (Na and K) and Kjeldahl (N) after preparing the samples by wet digestion procedures using i) a digester heating block and ii) a conventional microwave oven system with pressure and temperature control. The accuracy of the procedures was checked using three standard reference materials (National Institute of Standards and Technology, SRM 1573a Tomato Leaves, SRM 1547 Peach Leaves, SRM 1570a Trace Elements in Spinach). Analysis of data after application of t-test showed that results obtained by microwave-assisted digestion were more accurate than those obtained by block digester at 95% confidence level. Additionally to better accuracy, other favorable characteristics found were lower analytical blanks, lower reagent consumption, and shorter digestion time. Exploratory analysis of results using Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) showed that Na, K, Ca, Cu, Mg, and Fe were the principal elements to discriminate between green and roasted coffee samples. ©2007 Sociedade Brasileira de Química.

An Integrative Genomic and Transcriptomic Analysis Reveals Potential Targets Associated with Cell Proliferation in Uterine Leiomyomas

Background: Uterine Leiomyomas (ULs) are the most common benign tumours affecting women of reproductive age. ULs represent a major problem in public health, as they are the main indication for hysterectomy. Approximately 40-50% of ULs have non-random cytogenetic abnormalities, and half of ULs may have copy number alterations (CNAs). Gene expression microarrays studies have demonstrated that cell proliferation genes act in response to growth factors and steroids. However, only a few genes mapping to CNAs regions were found to be associated with ULs. Methodology: We applied an integrative analysis using genomic and transcriptomic data to identify the pathways and molecular markers associated with ULs. Fifty-one fresh frozen specimens were evaluated by array CGH (JISTIC) and gene expression microarrays (SAM). The CONEXIC algorithm was applied to integrate the data. Principal Findings: The integrated analysis identified the top 30 significant genes (P<0.01), which comprised genes associated with cancer, whereas the protein-protein interaction analysis indicated a strong association between FANCA and BRCA1. Functional in silico analysis revealed target molecules for drugs involved in cell proliferation, including FGFR1 and IGFBP5. Transcriptional and protein analyses showed that FGFR1 (P = 0.006 and P<0.01, respectively) and IGFBP5 (P = 0.0002 and P = 0.006, respectively) were up-regulated in the tumours when compared with the adjacent normal myometrium. Conclusions: The integrative genomic and transcriptomic approach indicated that FGFR1 and IGFBP5 amplification, as well as the consequent up-regulation of the protein products, plays an important role in the aetiology of ULs and thus provides data for potential drug therapies development to target genes associated with cellular proliferation in ULs. © 2013 Cirilo et al.

Cluster and meta-analyses of genetic parameters for feed intake traits in growing beef cattle

A data set based on 50 studies including feed intake and utilization traits was used to perform a meta-analysis to obtain pooled estimates using the variance between studies of genetic parameters for average daily gain (ADG); residual feed intake (RFI); metabolic body weight (MBW); feed conversion ratio (FCR); and daily dry matter intake (DMI) in beef cattle. The total data set included 128 heritability and 122 genetic correlation estimates published in the literature from 1961 to 2012. The meta-analysis was performed using a random effects model where the restricted maximum likelihood estimator was used to evaluate variances among clusters. Also, a meta-analysis using the method of cluster analysis was used to group the heritability estimates. Two clusters were obtained for each trait by different variables. It was observed, for all traits, that the heterogeneity of variance was significant between clusters and studies for genetic correlation estimates. The pooled estimates, adding the variance between clusters, for direct heritability estimates for ADG, DMI, RFI, MBW and FCR were 0.32 +/- 0.04, 0.39 +/- 0.03, 0.31 +/- 0.02, 0.31 +/- 0.03 and 0.26 +/- 0.03, respectively. Pooled genetic correlation estimates ranged from -0.15 to 0.67 among ADG, DMI, RFI, MBW and FCR. These pooled estimates of genetic parameters could be used to solve genetic prediction equations in populations where data is insufficient for variance component estimation. Cluster analysis is recommended as a statistical procedure to combine results from different studies to account for heterogeneity.