Characterization of Aspergillus species based on fatty acid profiles

Cellular fatty acid (FA) composition was utilized as a taxonomic tool to discriminate between different Aspergillus species. Several of the tested species had the same FA composition and different relative FA concentrations. The most important FAs were palmitic acid (C16:0), estearic acid (C18:0), oleic acid (C18:1) and linoleic acid (C18:2), which represented 95% of Aspergillus FAs. Multivariate data analysis demonstrated that FA analysis is a useful tool for differentiating species belonging to genus Aspergillus. All the species analyzed showed significantly FA acid profiles (p < 0.001). Furthermore, it will be possible to distinguish among Aspergillus spp. in the Flavi Section. FA composition can serve as a useful tool for the identification of filamentous fungi.

Estado da arte de figuras de mérito em calibração multivariada

The validation of an analytical procedure must be certified through the determination of parameters known as figures of merit. For first order data, the acuracy, precision, robustness and bias is similar to the methods of univariate calibration. Linearity, sensitivity, signal to noise ratio, adjustment, selectivity and confidence intervals need different approaches, specific for multivariate data. Selectivity and signal to noise ratio are more critical and they only can be estimated by means of the calculation of the net analyte signal. In second order calibration, some differentes approaches are necessary due to data structure.

REDUÇÃO DE VARIÁVEIS DE ENTRADA DE REDES NEURAIS ARTIFICIAIS A PARTIR DE DADOS DE ANÁLISE DE COMPONENTES PRINCIPAIS NA MODELAGEM DE OXIGÊNIO DISSOLVIDO

The objective of this work is to demonstrate the efficient utilization of the Principal Components Analysis (PCA) as a method to pre-process the original multivariate data, that is rewrite in a new matrix with principal components sorted by it's accumulated variance. The Artificial Neural Network (ANN) with backpropagation algorithm is trained, using this pre-processed data set derived from the PCA method, representing 90.02% of accumulated variance of the original data, as input. The training goal is modeling Dissolved Oxygen using information of other physical and chemical parameters. The water samples used in the experiments are gathered from the Paraíba do Sul River in São Paulo State, Brazil. The smallest Mean Square Errors (MSE) is used to compare the results of the different architectures and choose the best. The utilization of this method allowed the reduction of more than 20% of the input data, which contributed directly for the shorting time and computational effort in the ANN training.

Designing and validating the methodology for the Internet assessment of fish consumption at a university setting

Assessing fish consumption is complex and involves several factors; however, the use of questionnaires in surveys and the use of the Internet as tool to collect data have been considered promising approaches. Therefore, the objective of this research was to design a data collection technique using a questionnaire to assess fish consumption by making it available on a specific home page on the Internet. A bibliographical survey or review was carried out to identify the features of the instrument, and therefore pre-tests were conducted with previous instruments, followed by the Focus Group technique. Specialists then performed an analysis and conducted an online pre-test. Multivariate data analysis was applied using the SmartPLS software. The results indicate that 1.966 participants belonging to the University of São Paulo (USP) community participated in the test, and after the exclusion of some variables, a statistically significant results were obtained. The final constructs comprised consumption, quality, and general characteristics. The instrument consisted of behavioral statements in a 5-point Likert scale and multiple-choice questions. The Cronbach's alpha reliability coefficient was 0.66 for general characteristics, 0.98 for quality, and 0.91 for consumption, which indicate good reliability of the instrument. In conclusion, the results proved that the Internet assessment is efficient. The instrument of analysis allowed us to better understand the process of buying and consuming fish in the country, and it can be used as base for further research.

Multivariate Analysis for Animal Selection in Experimental Research

Background: Several researchers seek methods for the selection of homogeneous groups of animals in experimental studies, a fact justified because homogeneity is an indispensable prerequisite for casualization of treatments. The lack of robust methods that comply with statistical and biological principles is the reason why researchers use empirical or subjective methods, influencing their results. Objective: To develop a multivariate statistical model for the selection of a homogeneous group of animals for experimental research and to elaborate a computational package to use it. Methods: The set of echocardiographic data of 115 male Wistar rats with supravalvular aortic stenosis (AoS) was used as an example of model development. Initially, the data were standardized, and became dimensionless. Then, the variance matrix of the set was submitted to principal components analysis (PCA), aiming at reducing the parametric space and at retaining the relevant variability. That technique established a new Cartesian system into which the animals were allocated, and finally the confidence region (ellipsoid) was built for the profile of the animals’ homogeneous responses. The animals located inside the ellipsoid were considered as belonging to the homogeneous batch; those outside the ellipsoid were considered spurious. Results: The PCA established eight descriptive axes that represented the accumulated variance of the data set in 88.71%. The allocation of the animals in the new system and the construction of the confidence region revealed six spurious animals as compared to the homogeneous batch of 109 animals. Conclusion: The biometric criterion presented proved to be effective, because it considers the animal as a whole, analyzing jointly all parameters measured, in addition to having a small discard rate.

Can Remotely Sensed Meteorological Data Significantly Contribute to Reduce Costs of Tsetse Surveys?

A 0.125 degree raster or grid-based Geographic Information System with data on tsetse, trypanosomosis, animal production, agriculture and land use has recently been developed in Togo. This paper addresses the problem of generating tsetse distribution and abundance maps from remotely sensed data, using a restricted amount of field data. A discriminant analysis model is tested using contemporary tsetse data and remotely sensed, low resolution data acquired from the National Oceanographic and Atmospheric Administration and Meteosat platforms. A split sample technique is adopted where a randomly selected part of the field measured data (training set) serves to predict the other part (predicted set). The obtained results are then compared with field measured data per corresponding grid-square. Depending on the size of the training set the percentage of concording predictions varies from 80 to 95 for distribution figures and from 63 to 74 for abundance. These results confirm the potential of satellite data application and multivariate analysis for the prediction, not only of the tsetse distribution, but more importantly of their abundance. This opens up new avenues because satellite predictions and field data may be combined to strengthen or substitute one another and thus reduce costs of field surveys.

Multivariate analysis and geostatistics of the fertility of a humic rhodic hapludox under coffee cultivation

The spatial variability of soil and plant properties exerts great influence on the yeld of agricultural crops. This study analyzed the spatial variability of the fertility of a Humic Rhodic Hapludox with Arabic coffee, using principal component analysis, cluster analysis and geostatistics in combination. The experiment was carried out in an area under Coffea arabica L., variety Catucai 20/15 - 479. The soil was sampled at a depth 0.20 m, at 50 points of a sampling grid. The following chemical properties were determined: P, K+, Ca2+, Mg2+, Na+, S, Al3+, pH, H + Al, SB, t, T, V, m, OM, Na saturation index (SSI), remaining phosphorus (P-rem), and micronutrients (Zn, Fe, Mn, Cu and B). The data were analyzed with descriptive statistics, followed by principal component and cluster analyses. Geostatistics were used to check and quantify the degree of spatial dependence of properties, represented by principal components. The principal component analysis allowed a dimensional reduction of the problem, providing interpretable components, with little information loss. Despite the characteristic information loss of principal component analysis, the combination of this technique with geostatistical analysis was efficient for the quantification and determination of the structure of spatial dependence of soil fertility. In general, the availability of soil mineral nutrients was low and the levels of acidity and exchangeable Al were high.

Multivariate analysis applied to the study of the relationship between soil and plant properties in a peach orchard

In the State of Rio Grande do Sul, the municipality of Pelotas is responsible for 90 % of peach production due to its suitable climate and soil conditions. However, there is the need for new studies that aim at improved fruit quality and increased yield. The aim of this study was to evaluate the relationship that exists between soil physical properties and properties in the peach plant in the years 2010 and 2011 by the technique of multivariate canonical correlation. The experiment was conducted in a peach orchard located in the municipality of Morro Redondo, RS, Brazil, where an experimental grid of 101 plants was established. In a trench dug beside each one of the 101 plants, soil samples were collected to determine silt, clay, and sand contents, soil density, total porosity, macroporosity, microporosity, and volumetric water content in the 0.00-0.10 and 0.10-0.20 m layers, as well as the depth of the A horizon. In each plant and in each year, the following properties were assessed: trunk diameter, fruit size and number of fruits per plant, average weight of the fruit per plant, fruit pulp firmness, Brix content, and yield from the orchard. Exploratory analysis of the data was undertaken by descriptive statistics, and the relationships between the physical properties of the soil and of the plant were assessed by canonical correlation analysis. The results showed that the clay and microporosity variables were those that exhibited the highest coefficients of canonical cross-loading with the plant properties in the soil layers assessed, and that the variable of mean weight of the fruit per plant was that which had the highest coefficients of canonical loading within the plant group for the two years assessed.

Use of multivariate experimental designs for optimizing the reductive degradation of an azo dye in the presence of redox mediators

The optimization of the anaerobic degradation of the azo dye Remazol golden yellow RNL was performed according to multivariate experimental designs: a 2² full-factorial design and a central composite design (CCD). The CCD revealed that the best incubation conditions (90% color removal) for the degradation of the azo dye (50 mg L- 1) were achieved with 350 mg L- 1 of yeast extract and 45 mL of anaerobic supernatant (free cell extract) produced from the incubation of 650 mg L- 1 of anaerobic microorganisms and 250 mg L- 1 of glucose. A first-order kinetics model best fit the experimental data (k = 0.0837 h- 1, R² = 0.9263).

Determination of sulfur in diesel using ATR/FTIR and multivariate calibration

The aim of this present work was to provide a more fast, simple and less expensive to analyze sulfur content in diesel samples than by the standard methods currently used. Thus, samples of diesel fuel with sulfur concentrations varying from 400 and 2500 mgkg-1 were analyzed by two methodologies: X-ray fluorescence, according to ASTM D4294 and by Fourier transform infrared spectrometry (FTIR). The spectral data obtained from FTIR were used to build multivariate calibration models by partial least squares (PLS). Four models were built in three different ways: 1) a model using the full spectra (665 to 4000 cm-1), 2) two models using some specific spectrum regions and 3) a model with variable selected by classic method of variable selection stepwise. The model obtained by variable selection stepwise and the model built with region spectra between 665 and 856 cm-1 and 1145 and 2717 cm-1 showed better results in the determination of sulfur content.

Simultaneous selection in progenies of yellow passion fruit using selection indices

This study aimed to evaluate the efficiency of simultaneous selection (selection indices) using estimated genetic gains in yellow passion fruit and to make a comparison between the methodologies of Mulamba & Mock and Elston. The study was conducted with 26 sib progenies of yellow passion fruit for intrinsic production characteristics including fruit number, fruit mass, fruit length and diameter, and for the fruit characteristics skin thickness, soluble solids and acidity. Two methodologies were applied: first, in the joint analysis of fruit characteristics and of intrinsic production characteristics in a single phase of selection; and second, in the analysis in two phases, in which priority was given to the intrinsic production characteristics in the first phase, and later, in the second phase, the best fruit characteristics were chosen among the progenies of the first phase. The analysis of variance was applied to the data to detect genetic variability among progenies. The Elston's selection indice was unable to provide distribution of genetic gains consistent with the purposes of the study, as it selected a single progeny of passion fruit. However, the index based on the sum of ranks of Mulamba & Mock was more suitable, as it provided a balanced distribution of gains, selecting a larger number of progenies. The methodology of selection using indices is advantageous in passion fruit, since it contributes to higher genetic gains for all the traits evaluated, and the selection in a single phase was proved efficient for progeny selection.

Gestão do conhecimento usando data mining: estudo de caso na Universidade Federal de Lavras

A gestão do conhecimento abrange toda a forma de gerar, armazenar, distribuir e utilizar o conhecimento, tornando necessária a utilização de tecnologias de informação para facilitar esse processo, devido ao grande aumento no volume de dados. A descoberta de conhecimento em banco de dados é uma metodologia que tenta solucionar esse problema e o data mining é uma técnica que faz parte dessa metodologia. Este artigo desenvolve, aplica e analisa uma ferramenta de data mining, para extrair conhecimento referente à produção científica das pessoas envolvidas com a pesquisa na Universidade Federal de Lavras. A metodologia utilizada envolveu a pesquisa bibliográfica, a pesquisa documental e o método do estudo de caso. As limitações encontradas na análise dos resultados indicam que ainda é preciso padronizar o modo do preenchimento dos currículos Lattes para refinar as análises e, com isso, estabelecer indicadores. A contribuição foi gerar um banco de dados estruturado, que faz parte de um processo maior de desenvolvimento de indicadores de ciência e tecnologia, para auxiliar na elaboração de novas políticas de gestão científica e tecnológica e aperfeiçoamento do sistema de ensino superior brasileiro.

The relationships between selected anthropometric and socio-economic data in schoolchildren from different social strata in Rio de Janeiro, Brazil

The nutritional status according to anthropometric data was assessed in 756 schoolchildren from 5 low-income state schools and in one private school in the same part of Rio de Janeiro, Brazil. The prevalence of stunting and wasting (cut-off point: <90% ht/age and <80% wt/ht) ranged in the public schools from 6.2 to 15.2% and 3.3 to 24.0%, respectively, whereas the figures for the private school were 2.3 and 3.5%, respectively. Much more obesity was found in the private school (18.0%) than in the state schools (0.8 - 6.2%). Nutritional problems seem to develop more severely in accordance with the increasing age of the children. Therefore it appears advisable to assess schoolchildren within the context of a nutritional surveillance system.

Food frequency consumption and lipoproteins serum levels in the population of an urban area, Brazil

OBJECTIVE: To identify the association between food group consumption frequency and serum lipoprotein levels among adults. METHODS: The observations were made during a cross-sectional survey of a representative sample of men and women over 20 years old living in Cotia county, S. Paulo, Brazil. Data on food frequency consumption, serum lipids, and other covariates were available for 1,045 adults. Multivariate analyses adjusted by age, gender, body mass index, waist-to-hip ratio, educational level, family income, physical activity, smoking, and alcohol consumption were performed. RESULTS: Consumption of processed meat, chicken, red meat, eggs and dairy foods were each positively and significantly correlated with LDL-C, whereas the intake of vegetables and fruits showed an inverse correlation. Daily consumption of processed meat, chicken, red meat, eggs, and dairy foods were associated with 16.6 mg/dl, 14.5 mg/dl, 11.1 mg/dl, 5.8 mg/dl, and 4.6 mg/dl increase in blood LDL-C, respectively. Increases of daily consumption of fruit and vegetables were associated with 5.2 mg/dl and 5.5 mg/dl decreases in LDL-C, respectively. Alcohol beverage consumption showed a significant positive correlation with HDL-C. CONCLUSIONS: Dietary habits in the study population seem to contribute substantially to the variation in blood LDL and HDL concentrations. Substantially CHD risk reduction could be achieved with dietary changes.

Blood glucose control in diabetes patients seen in primary health care centers

OBJECTIVE: To identify factors associated to poor glycemic control among diabetic patients seen at primary health care centers. METHODS: A cross-sectional study was carried out in a sample of 372 diabetic patients attending 32 primary health care centers in southern Brazil. Data on three hierarchical levels of health unit infrastructure, medical care and patient characteristics were collected. RESULTS: The frequency of poor glycemic control was 50.5%. Multivariate analysis (multilevel method) showed that patients with body mass indexes below 27 kg/m², patients on oral hypoglycemic agents or insulin, and patients diagnosed as diabetic over five years prior to the interview were more likely to present poor glycemic control when compared to their counterparts. CONCLUSIONS: Given the hierarchical data structuring, all associations found suggest that factors associated to hyperglycemia are related to patient-level characteristics.