Comparative analyses of classical phenotypic method and ribosomal RNA gene sequencing for identification of medically relevant Candida species

As the distribution of Candida species and their susceptibility to antifungal agents have changed, a new means of accurately and rapidly identifying these species is necessary for the successful early resolution of infection and the subsequent reduction of morbidity and mortality. The current work aimed to evaluate ribosomal RNA gene sequencing for the identification of medically relevant Candida species in comparison with a standard phenotypic method. Eighteen reference strains (RSs), 69 phenotypically identified isolates and 20 inconclusively identified isolates were examined. Internal transcribed spaces (ITSs) and D1/D2 of the 26S ribosomal RNA gene regions were used as targets for sequencing. Additionally, the sequences of the ITS regions were used to establish evolutionary relationships. The sequencing of the ITS regions was successful for 88% (94/107) of the RS and isolates, whereas 100% of the remaining 12% (13/107) of the samples were successfully analysed by sequencing the D1/D2 region. Similarly, genotypic analysis identified all of the RS and isolates, including the 20 isolates that were not phenotypically identified. Phenotypic analysis, however, misidentified 10% (7/69) of the isolates. Phylogenetic analysis allowed the confirmation of the relationships between evolutionarily close species. Currently, the use of genotypic methods is necessary for the correct identification of Candida species.

Phylogenetic analyses of chikungunya virus among travelers in Rio de Janeiro, Brazil, 2014-2015

Chikungunya virus (CHIKV) is a mosquito-borne pathogen that emerged in Brazil by late 2014. In the country, two CHIKV foci characterized by the East/Central/South Africa and Asian genotypes, were established in North and Northeast regions. We characterized, by phylogenetic analyses of full and partial genomes, CHIKV from Rio de Janeiro state (2014-2015). These CHIKV strains belong to the Asian genotype, which is the determinant of the current Northern Brazilian focus, even though the genome sequence presents particular single nucleotide variations. This study provides the first genetic characterisation of CHIKV in Rio de Janeiro and highlights the potential impact of human mobility in the spread of an arthropod-borne virus.

Phylogenetic relationships among four species of the guarani group of Drosophila (Diptera, Drosophilidae) as inferred by molecular and morphological analyses

Traditionally, the Drosophila guarani species group has been divided into two subgroups: the guarani and the guaramunu subgroups. Two, out of the four species included in this research, are members of the guarani subgroup (D. ornatifrons Duda, 1927 and D. subbadia Paterson & Mainland, 1943) and two are included in the guaramunu subgroup (D. maculifrons Duda, 1927 and D. griseolineata Duda, 1927). However, some authors have suggested that D. maculifrons and D. griseolineata are much closer to some species of the Drosophila tripunctata group than to some of the species of the guarani group. To add new data to the matter under dispute, Polyacrylamide Gel Eletrophoresis (PAGE-SDS) was used for the analysis and comparison of protein composition and Random Amplified Polymorphic DNA (RAPD) analysis to find differences in genomic DNA, in addition to the analysis of quantitative morphological characters previously described. Analysis of PAGE-SDS results in a dendrogram that pointed out D. subbadia as being the most distant within the Drosophila guarani group. However, these results were not supported either by RAPD analysis or by the analysis of continuous morphological characters, which supplied the clustering of D. subbadia with D. ornatifrons. Although our data give strong support to the clustering of D. subbadia and D. ornatifrons, none of the dendrograms provided a clade comprising D. maculifrons and D. griseolineata. Thus, this research does not support the traditional subdivision of the D. guarani group into those two subgroups.

Soil spectral library and its use in soil classification

Soil science has sought to develop better techniques for the classification of soils, one of which is the use of remote sensing applications. The use of ground sensors to obtain soil spectral data has enabled the characterization of these data and the advancement of techniques for the quantification of soil attributes. In order to do this, the creation of a soil spectral library is necessary. A spectral library should be representative of the variability of the soils in a region. The objective of this study was to create a spectral library of distinct soils from several agricultural regions of Brazil. Spectral data were collected (using a Fieldspec sensor, 350-2,500 nm) for the horizons of 223 soil profiles from the regions of Matão, Paraguaçu Paulista, Andradina, Ipaussu, Mirandópolis, Piracicaba, São Carlos, Araraquara, Guararapes, Valparaíso (SP); Naviraí, Maracajú, Rio Brilhante, Três Lagoas (MS); Goianésia (GO); and Uberaba and Lagoa da Prata (MG). A Principal Component Analysis (PCA) of the data was then performed and a graphic representation of the spectral curve was created for each profile. The reflectance intensity of the curves was principally influenced by the levels of Fe2O3, clay, organic matter and the presence of opaque minerals. There was no change in the spectral curves in the horizons of the Latossolos, Nitossolos, and Neossolos Quartzarênicos. Argissolos had superficial horizon curves with the greatest intensity of reflection above 2,200 nm. Cambissolos and Neossolos Litólicos had curves with greater reflectance intensity in poorly developed horizons. Gleisols showed a convex curve in the region of 350-400 nm. The PCA was able to separate different data collection areas according to the region of source material. Principal component one (PC1) was correlated with the intensity of reflectance samples and PC2 with the slope between the visible and infrared samples. The use of the Spectral Library as an indicator of possible soil classes proved to be an important tool in profile classification.

Fertilization response likelihood for the interpretation of leaf analyses

Leaf analysis is the chemical evaluation of the nutritional status where the nutrient concentrations found in the tissue reflect the nutritional status of the plants. Thus, a correct interpretation of the results of leaf analysis is fundamental for an effective use of this tool. The purpose of this study was to propose and compare the method of Fertilization Response Likelihood (FRL) for interpretation of leaf analysis with that of the Diagnosis and Recommendation Integrated System (DRIS). The database consisted of 157 analyses of the N, P, K, Ca, Mg, S, Cu, Fe, Mn, Zn, and B concentrations in coffee leaves, which were divided into two groups: low yield (< 30 bags ha-1) and high yield (> 30 bags ha-1). The DRIS indices were calculated using the method proposed by Jones (1981). The fertilization response likelihood was computed based on the approximation of normal distribution. It was found that the Fertilization Response Likelihood (FRL) allowed an evaluation of the nutritional status of coffee trees, coinciding with the DRIS-based diagnoses in 84.96 % of the crops.

Using numerical classification of profiles based on Vis-NIR spectra to distinguish soils from the Piracicaba Region, Brazil

Considering that information from soil reflectance spectra is underutilized in soil classification, this paper aimed to evaluate the relationship of soil physical, chemical properties and their spectra, to identify spectral patterns for soil classes, evaluate the use of numerical classification of profiles combined with spectral data for soil classification. We studied 20 soil profiles from the municipality of Piracicaba, State of São Paulo, Brazil, which were morphologically described and classified up to the 3rd category level of the Brazilian Soil Classification System (SiBCS). Subsequently, soil samples were collected from pedogenetic horizons and subjected to soil particle size and chemical analyses. Their Vis-NIR spectra were measured, followed by principal component analysis. Pearson's linear correlation coefficients were determined among the four principal components and the following soil properties: pH, organic matter, P, K, Ca, Mg, Al, CEC, base saturation, and Al saturation. We also carried out interpretation of the first three principal components and their relationships with soil classes defined by SiBCS. In addition, numerical classification of the profiles based on the OSACA algorithm was performed using spectral data as a basis. We determined the Normalized Mutual Information (NMI) and Uncertainty Coefficient (U). These coefficients represent the similarity between the numerical classification and the soil classes from SiBCS. Pearson's correlation coefficients were significant for the principal components when compared to sand, clay, Al content and soil color. Visual analysis of the principal component scores showed differences in the spectral behavior of the soil classes, mainly among Argissolos and the others soils. The NMI and U similarity coefficients showed values of 0.74 and 0.64, respectively, suggesting good similarity between the numerical and SiBCS classes. For example, numerical classification correctly distinguished Argissolos from Latossolos and Nitossolos. However, this mathematical technique was not able to distinguish Latossolos from Nitossolos Vermelho férricos, but the Cambissolos were well differentiated from other soil classes. The numerical technique proved to be effective and applicable to the soil classification process.

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Genetical and environmental analyses of yield in six biparental soybean crosses

The yearly genetic progress obtained by breeding for increased soybean yield has been considered acceptable worldwide. It is common sense, however, that this progress can be improved further if refined breeding techniques, developed from the knowledge of the genetic mechanisms controlling soybean yield, are used. In this paper, data from four cultivars and/or lines and their derived sets of F2, F3, F7, F8, F9 and F10 generations assayed in 17 environments were analyzed to allow an insight of the genetic control of soybean yield under different environmental conditions. The general picture was of a complex polygene system controlling yield in soybeans. Additive genetic effects predominated although dominance was often found to be significant. Complications such as epistasis, linkage and macro and micro genotype x environment (G x E) interactions were also commonly detected. The overall heritability was 0.29. The relative magnitude of the additive effects and the complicating factors allowed the inference that the latter are not a serious problem to the breeder. The low heritability values and the considerable magnitude of G x E interactions for yield, however, indicated that careful evaluation through experiments designed to allow for the presence of these effects is necessary for successful selection.

Characterization of mandarin citrus germplasm from Southern Brazil by morphological and molecular analyses

The objective of this work was to characterize mandarin (Citrus spp.) germplasm from Southern Brazil by morphological and molecular analyses. Thirty seven cultivars from 34 distinct mandarin varieties were evaluated by morphological and agronomic traits of leaves, flowers and fruits, and by microsatellite markers. The morphological and agronomic characteristics suggested that almost all varieties can be produced for commercial use, and some, as the Satsuma variety, are recommended for breeding programs. Pooled DNA samples from 1-5 plants belonging to each cultivar were tested. Eight of the nine primers detected polymorphisms. Specific markers were found for some accessions. The dendrogram constructed with the morphological results divided the 37 cultivars into four groups, while that obtained with the microsatellites clustered 35 of the 37 cultivars into three groups only. Generally, intervarietal differences are not high, and this lack of agreement in the two multifactorial analyses indicates that diverse evolutionary factors are acting at these two levels of investigation.

Multivariate analyses of genotype x environment interaction of popcorn

The objectives of this work were to evaluate the genotype x environment (GxE) interaction for popcorn and to compare two multivariate analyses methods. Nine popcorn cultivars were sown on four dates one month apart during each of the agricultural years 1998/1999 and 1999/2000. The experiments were carried out using randomized block designs, with four replicates. The cv. Zélia contributed the least to the GxE interaction. The cv. Viçosa performed similarly to cv. Rosa-claro. Optimization of GxE was obtained for cv. CMS 42 for a favorable mega-environment, and for cv. CMS 43 for an unfavorable environment. Multivariate analysis supported the results from the method of Eberhart & Russell. The graphic analysis of the Additive Main effects and Multiplicative Interaction (AMMI) model was simple, allowing conclusions to be made about stability, genotypic performance, genetic divergence between cultivars, and the environments that optimize cultivar performance. The graphic analysis of the Genotype main effects and Genotype x Environment interaction (GGE) method added to AMMI information on environmental stratification, defining mega-environments and the cultivars that optimized performance in those mega-environments. Both methods are adequate to explain the genotype x environment interactions.

Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil

The objective of this work was to evaluate the application of the spectral-temporal response surface (STRS) classification method on Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) sensor images in order to estimate soybean areas in Mato Grosso state, Brazil. The classification was carried out using the maximum likelihood algorithm (MLA) adapted to the STRS method. Thirty segments of 30x30 km were chosen along the main agricultural regions of Mato Grosso state, using data from the summer season of 2005/2006 (from October to March), and were mapped based on fieldwork data, TM/Landsat-5 and CCD/CBERS-2 images. Five thematic classes were considered: Soybean, Forest, Cerrado, Pasture and Bare Soil. The classification by the STRS method was done over an area intersected with a subset of 30x30-km segments. In regions with soybean predominance, STRS classification overestimated in 21.31% of the reference values. In regions where soybean fields were less prevalent, the classifier overestimated 132.37% in the acreage of the reference. The overall classification accuracy was 80%. MODIS sensor images and the STRS algorithm showed to be promising for the classification of soybean areas in regions with the predominance of large farms. However, the results for fragmented areas and smaller farms were less efficient, overestimating soybean areas.

Incidence of Garlic common latent virus in Argentina, and phylogenetic and recombination analyses of isolates

The objective of this work was to estimate the incidence and prevalence of Garlic common latent virus (GarCLV) in the main production regions of garlic (Allium sativum) in Argentina, and to perform phylogenetic and recombination analyses in isolates from these regions. Leaf samples (3,050) were taken from four garlic commercial types, in 13 departments of the four main garlic-producing provinces of Argentina, in a 1,175-ha sampling area. Virus infection was evaluated with DAS-Elisa test using specific antiserum, and the phylogenetic and recombination analyses were done with capsid protein (CP) nucleotide sequence of seven GarCLV isolates from the provinces. The incidence of GarCLV in the evaluated provinces varied between 6.7 and 22% of the samples, whereas the prevalence varied between 52.6 and 70%. In the analysis of garlic commercial types, Morado showed the highest incidence of the virus, in the province of San Juan, whereas Rosado Paraguayo had the lowest incidence, in the province of Cordoba. Nucleotide identity in the CP sequences ranged between 80.3 and 97.6%. The phylogenetic analysis shows the presence of two main groups of GarCLV and of a possible third group that would include only a German isolate. The recombination analysis between isolates from different parts of the world evidences the presence of recombinant isolates from Poland and Australia.

Triterpenos esterificados com ácidos graxos e ácidos triterpênicos isolados de Byrosonima microphylla

The hexane extract of leaves of B. microphylla afforded a mixture of triterpenes esterified with fatty acids. Analyses of spectral data of the mixture and of the derivatives obtained by a transesterification reaction with NaOMe/MeOH permitted to identify the composition of the mixtures as being 24-hydroxy-urs-12-enyl 3b-eicosanate, estearate and palmitate as well as of the 24-hydroxy-olean-12-enyl 3b-eicosanate, estearate and palmitate. From the choroform and ethyl acetate extracts were isolated the oleanolic and 3b,24-dihydroxy-urs-12-en-28-oic acids, quercetin and methyl galic ester, respectively. The compounds were identified through analysis of their spectral data.

A escolha da faixa espectral no uso combinado de métodos espectroscópicos e quimiométricos

A method is presented for the choice of spectral regions when absorption measurements are coupled to chemometric tools to perform quantitative analyses. The method is based on the spectral distribution of the relative standard deviation of concentration (s c/c). It has been applied to the development of PLS-FTNIR calibration models for the determination of density and MON of gasoline, and ethanol content and density of ethanol fuel. The new method was also compared with the correlation (R²) method and has proved to generate PLS calibration models that present better accuracy and precision than those based on R².

Identification of acetates in elasmopalpulus lignosellus pheromone glands using a newly created mass spectral database and kóvats retention indices

Based on a specially created mass spectral database utilizing 23 tetradecenyl and 22 hexadecenyl acetate standards along with Kóvats retention indices obtained on a very polar stationary phase [poly (biscyanopropyl siloxane)] (SP 2340), (Z)-9-hexadecenyl acetate, (Z)-11-hexadecenyl acetate and (E)-8-hexadecenyl acetate were identified in active pheromone extracts of Elasmopalpus lignosellus. This identification was more efficient than our previous study using gas chromatography/mass spectrometry with a dimethyl disulfide derivative where we could only identify the first two acetates. The acetate composition of the pheromone gland differed from region to region in Brazil and from that from the Tifton (GA, USA) population, suggesting polymorphism or a different sub-species.