An ecoregional classification for the state of Roraima, Brazil: the importance of landscape in malaria biology

Understanding the different background landscapes in which malaria transmission occurs is fundamental to understanding malaria epidemiology and to designing effective local malaria control programs. Geology, geomorphology, vegetation, climate, land use, and anopheline distribution were used as a basis for an ecological classification of the state of Roraima, Brazil, in the northern Amazon Basin, focused on the natural history of malaria and transmission. We used unsupervised maximum likelihood classification, principal components analysis, and weighted overlay with equal contribution analyses to fine-scale thematic maps that resulted in clustered regions. We used ecological niche modeling techniques to develop a fine-scale picture of malaria vector distributions in the state. Eight ecoregions were identified and malaria-related aspects are discussed based on this classification, including 5 types of dense tropical rain forest and 3 types of savannah. Ecoregions formed by dense tropical rain forest were named as montane (ecoregion I), submontane (II), plateau (III), lowland (IV), and alluvial (V). Ecoregions formed by savannah were divided into steppe (VI, campos de Roraima), savannah (VII, cerrado), and wetland (VIII, campinarana). Such ecoregional mappings are important tools in integrated malaria control programs that aim to identify specific characteristics of malaria transmission, classify transmission risk, and define priority areas and appropriate interventions. For some areas, extension of these approaches to still-finer resolutions will provide an improved picture of malaria transmission patterns.

Mapping the molecular characteristics of Brazilian human T-cell lymphotropic virus type 1 Env (gp46) and Pol amino acid sequences for vaccine design

This study was carried out to evaluate the molecular pattern of all available Brazilian human T-cell lymphotropic virus type 1 Env (n = 15) and Pol (n = 43) nucleotide sequences via epitope prediction, physico-chemical analysis, and protein potential sites identification, giving support to the Brazilian AIDS vaccine program. In 12 previously described peptides of the Env sequences we found 12 epitopes, while in 4 peptides of the Pol sequences we found 4 epitopes. The total variation on the amino acid composition was 9 and 17% for human leukocyte antigen (HLA) class I and class II Env epitopes, respectively. After analyzing the Pol sequences, results revealed a total amino acid variation of 0.75% for HLA-I and HLA-II epitopes. In 5 of the 12 Env epitopes the physico-chemical analysis demonstrated that the mutations magnified the antigenicity profile. The potential protein domain analysis of Env sequences showed the loss of a CK-2 phosphorylation site caused by D197N mutation in one epitope, and a N-glycosylation site caused by S246Y and V247I mutations in another epitope. Besides, the analysis of selection pressure have found 8 positive selected sites (w = 9.59) using the codon-based substitution models and maximum-likelihood methods. These studies underscore the importance of this Env region for the virus fitness, for the host immune response and, therefore, for the development of vaccine candidates.

Prevalence and genotyping of hepatitis C virus in blood donors in the state of Pará, Northern Brazil

Given the scarcity of epidemiological information on hepatitis C virus (HCV) infection in Northern Brazil, we determined the prevalence and genotypic frequency in blood donors in the state of Pará (PA). Blood samples from all of the blood donors at the Fundação HEMOPA (blood bank of PA) from 2004-2006 were screened for the presence of antibodies to anti-HCV and samples seroreactive to anti-HCV were further tested for HCV RNA using real-time PCR. In total, 116 HCV-RNA samples were genotyped, based on maximum likelihood phylogenetic analyses, using BioEdit, Modelgenerator, PHYML and FigTree software. The population consisted of 242,726 volunteers who donated blood from 2004-2006; the most common subgroup was males between the ages of 18-29 years old (37.30%). Within the whole group, 1,112 blood donors (0.46%) had indeterminate or positive serology; among these, 28.78% were males whose ages ranged from 18-29 years. A diagnosis of chronic HCV infection was confirmed for 304 donors (60.20% males; 66.45% were 30-49 years old), resulting in a prevalence of HCV RNA in 0.13% of the samples (304 of 242,726). HCV genotyping revealed a high frequency of genotype 1 (108/116) followed by genotype 3 (8/116). This study found HCV infection to be relatively infrequent in PA; genotype 1 was most commonly isolated. This information can help guide prevention and control policies aimed at efficient diagnosis and control measures.

Phylogenetic analysis of Biomphalaria tenagophila (Orbigny, 1835) (Mollusca: Gastropoda)

Mitochondrial DNA of Biomphalaria tenagophila, a mollusc intermediate host of Schistosoma mansoni in Brazil, was sequenced and characterised. The genome size found for B. tenagophila was 13,722 bp and contained 13 messenger RNAs, 22 transfer RNAs (tRNA) and two ribosomal RNAs (rRNA). In addition to sequencing, the mitochondrial DNA (mtDNA) genome organization of B. tenagophila was analysed based on its content and localization of both coding and non-coding regions, regions of gene overlap and tRNA nucleotide sequences. Sequences of protein, rRNA 12S and rRNA 16S nucleotides as well as gene organization were compared between B. tenagophila and Biomphalaria glabrata, as the latter is the most important S. mansoni intermediate host in Brazil. Differences between such species were observed regarding rRNA composition. The complete sequence of the B. tenagophila mitochondrial genome was deposited in GenBank (accession EF433576). Furthermore, phylogenetic relationships were estimated among 28 mollusc species, which had their complete mitochondrial genome deposited in GenBank, using the neighbour-joining method, maximum parsimony and maximum likelihood bootstrap. B. tenagophila was positioned at a branch close to B. glabrata and Pulmonata molluscs, collectively comprising a paraphyletic group, contrary to Opistobranchia, which was positioned at a single branch and constituted a monophyletic group.

Evolutionary histories of expanded peptidase families in Schistosoma mansoni

Schistosoma mansoni is one of the three main causative agents of human schistosomiasis, a major health problem with a vast socio-economic impact. Recent advances in the proteomic analysis of schistosomes have revealed that peptidases are the main virulence factors involved in the pathogenesis of this disease. In this context, evolutionary studies can be applied to identify peptidase families that have been expanded in genomes over time in response to different selection pressures. Using a phylogenomic approach, we searched for expanded endopeptidase families in the S. mansoni predicted proteome with the aim of contributing to the knowledge of such enzymes as potential therapeutic targets. We found three endopeptidase families that comprise leishmanolysins (metallopeptidase M8 family), cercarial elastases (serine peptidase S1 family) and cathepsin D proteins (aspartic peptidase A1 family). Our results suggest that the Schistosoma members of these families originated from successive gene duplication events in the parasite lineage after its diversification from other metazoans. Overall, critical residues are conserved among the duplicated genes/proteins. Furthermore, each protein family displays a distinct evolutionary history. Altogether, this work provides an evolutionary view of three S. mansoni peptidase families, which allows for a deeper understanding of the genomic complexity and lineage-specific adaptations potentially related to the parasitic lifestyle.

Diagnostic techniques applied in geostatistics for agricultural data analysis

The structural modeling of spatial dependence, using a geostatistical approach, is an indispensable tool to determine parameters that define this structure, applied on interpolation of values at unsampled points by kriging techniques. However, the estimation of parameters can be greatly affected by the presence of atypical observations in sampled data. The purpose of this study was to use diagnostic techniques in Gaussian spatial linear models in geostatistics to evaluate the sensitivity of maximum likelihood and restrict maximum likelihood estimators to small perturbations in these data. For this purpose, studies with simulated and experimental data were conducted. Results with simulated data showed that the diagnostic techniques were efficient to identify the perturbation in data. The results with real data indicated that atypical values among the sampled data may have a strong influence on thematic maps, thus changing the spatial dependence structure. The application of diagnostic techniques should be part of any geostatistical analysis, to ensure a better quality of the information from thematic maps.

Molecular Identification of Trichoderma spp. in Garlic and Onion Fields and In Vitro Antagonism Trials on Sclerotium cepivorum

ABSTRACT Trichoderma species are non-pathogenic microorganisms that protect against fungal diseases and contribute to increased crop yields. However, not all Trichoderma species have the same effects on crop or a pathogen, whereby the characterization and identification of strains at the species level is the first step in the use of a microorganism. The aim of this study was the identification – at species level – of five strains of Trichoderma isolated from soil samples obtained from garlic and onion fields located in Costa Rica, through the analysis of the ITS1, 5.8S, and ITS2 ribosomal RNA regions; as well as the determination of their individual antagonistic ability over S. cepivorum Berkeley. In order to distinguish the strains, the amplified products were analyzed using MEGA v6.0 software, calculating the genetic distances through the Tamura-Nei model and building the phylogenetic tree using the Maximum Likelihood method. We established that the evaluated strains belonged to the species T. harzianum and T. asperellum; however it was not possible to identify one of the analyzed strains based on the species criterion. To evaluate their antagonistic ability, the dual culture technique, Bell’s scale, and the percentage inhibition of radial growth (PIRG) were used, evidencing that one of the T. asperellum isolates presented the best yields under standard, solid fermentation conditions.

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.

Quantitative genetic analysis of methylxanthines and phenolic compounds in mate progenies

The objective of this work was to determine the contents of methylxanthines, caffeine and theobromine, and phenolic compounds, chlorogenic and caffeic acids, in 51 mate progenies (half-sib families) and estimate the heritability of genetic parameters. Mate progenies were from five Brazilian municipalities: Pinhão, Ivaí, Barão de Cotegipe, Quedas do Iguaçu, and Cascavel. The progenies were grown in the Ivaí locality. The contents of the compounds were obtained by high performance liquid chromatography (HPLC). The estimation of genetic parameters by the restricted maximum likelihood (REML) and the prediction of genotypic values via best linear unbiased prediction (BLUP) were obtained by the Selegen - REML/BLUP software. Caffeine (0.248-1.663%) and theobromine (0.106-0.807%) contents were significantly different (p<0.05) depending on the region of origin, with high individual heritability (ĥ²>0.5). The two different progeny groups determined for chlorogenic (1.365-2.281%) and caffeic (0.027-0.037%) acid contents were not significantly different (p<0.05) depending on the locality of origin. Individual heritability values were low to medium for chlorogenic (ĥ²<0.4) and caffeic acid (ĥ²<0.3). The content of the compounds and the values of genetic parameters could support breeding programs for mate.

Land use/cover classification in the Brazilian Amazon using satellite images

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation‑based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi‑resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Among the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, have the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical‑based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

Parentesco na seleção para produtividade e teores de óleo e proteína em soja via modelos mistos

O objetivo deste trabalho foi avaliar influência da informação de parentesco na seleção de progênies de soja quanto à produtividade e aos teores de óleo e proteína, com base no uso de modelos mistos de predição dos valores genéticos. Novecentas progênies F4:6 e 200 progênies F4:7 de soja foram avaliadas nas safras 2010/2011 e 2011/2012, respectivamente. As progênies foram obtidas de cruzamentos múltiplos a partir de 57 progenitores. Os dados foram analisados por meio de modelos aleatórios (quadrados mínimos) e mistos BLUP/REML ("best linear unbiased prediction/restricted maximum likelihood"). Os maiores valores de ganhos preditos foram obtidos com o BLUP/REML. Os valores genéticos preditos com o método BLUP/REML, sem informação de parentesco, apresentaram alta correlação com aqueles obtidos com o modelo aleatório, além de detectada alta coincidência das progênies selecionadas. A inclusão da matriz de parentesco resultou na seleção de progênies diferentes e em maior acurácia na predição dos valores genéticos.

Estimação de parâmetros genéticos e predição de valor genético aditivo de trigo utilizando modelos mistos

O objetivo deste trabalho foi estimar os parâmetros genéticos e predizer o valor genético de populações e indivíduos oriundos de populações segregantes de trigo, com o uso da metodologia de modelos mistos ("restricted maximum likelihood"/"best linear unbiased prediction", REML/BLUP). Trinta e seis populações segregantes de trigo e quatro controles foram avaliados na geração F3, em delineamento de blocos ao acaso, com informações de indivíduo retiradas de dentro das parcelas. Os caracteres avaliados foram: produção de grãos, índice de colheita, número de perfilhos e altura de planta. Observou-se a existência de variabilidade genética entre populações em todos os caracteres avaliados. A herdabilidade média variou de 39,15 a 92,78%, e a acurácia, de 62,57 a 96,32%, na seleção de populações. A herdabilidade individual no sentido restrito foi baixa dentro das populações, em todos os caracteres. A acurácia na seleção individual apresentou magnitude média, quanto ao caráter altura de plantas, e baixa quanto aos demais caracteres. A herdabilidade individual contribui para maior ganho nos caracteres altura de planta e índice de colheita com o uso do BLUP individual, em comparação ao BLUP de populações. As populações segregantes Embrapa22/BRS207, Embrapa22/VI98053, Embrapa22/IVI01041, BRS254/BRS207, BRS254/VI98053, BRS254/UFVT1Pioneiro e BRS264/BRS207 destacam-se por apresentar valor genético aditivo elevado em dois ou mais caracteres.

Using aster multispectral imagery for mapping woody invasive species in pico da vara natural reserve (Azores Islands, Portugal)

This paper aims to assess the effectiveness of ASTER imagery to support the mapping of Pittosporum undulatum, an invasive woody species, in Pico da Vara Natural Reserve (S. Miguel Island, Archipelago of the Azores, Portugal). This assessment was done by applying K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Maximum Likelihood (MLC) pixel-based supervised classifications to 4 different geographic and remote sensing datasets constituted by the Visible, Near-Infrared (VNIR) and Short Wave Infrared (SWIR) of the ASTER sensor and by digital cartography associated to orography (altitude and "distance to water streams") of which the spatial distribution of Pittosporum undulatum directly depends. Overall, most performed classifications showed a strong agreement and high accuracy. At targeted species level, the two higher classification accuracies were obtained when applying MLC and KNN to the VNIR bands coupled with auxiliary geographic information use. Results improved significantly by including ecology and occurrence information of species (altitude and distance to water streams) in the classification scheme. These results show that the use of ASTER sensor VNIR spectral bands, when coupled to relevant ancillary GIS data, can constitute an effective and low cost approach for the evaluation and continuous assessment of Pittosporum undulatum woodland propagation and distribution within Protected Areas of the Azores Islands.

Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants

This study aimed at identifying different conditions of coffee plants after harvesting period, using data mining and spectral behavior profiles from Hyperion/EO1 sensor. The Hyperion image, with spatial resolution of 30 m, was acquired in August 28th, 2008, at the end of the coffee harvest season in the studied area. For pre-processing imaging, atmospheric and signal/noise effect corrections were carried out using Flaash and MNF (Minimum Noise Fraction Transform) algorithms, respectively. Spectral behavior profiles (38) of different coffee varieties were generated from 150 Hyperion bands. The spectral behavior profiles were analyzed by Expectation-Maximization (EM) algorithm considering 2; 3; 4 and 5 clusters. T-test with 5% of significance was used to verify the similarity among the wavelength cluster means. The results demonstrated that it is possible to separate five different clusters, which were comprised by different coffee crop conditions making possible to improve future intervention actions.

Comparison of maps of spatial variability of soil resistance to penetration constructed with and without covariables using a spatial linear model

A study about the spatial variability of data of soil resistance to penetration (RSP) was conducted at layers 0.0-0.1 m, 0.1-0.2 m and 0.2-0.3 m depth, using the statistical methods in univariate forms, i.e., using traditional geostatistics, forming thematic maps by ordinary kriging for each layer of the study. It was analyzed the RSP in layer 0.2-0.3 m depth through a spatial linear model (SLM), which considered the layers 0.0-0.1 m and 0.1-0.2 m in depth as covariable, obtaining an estimation model and a thematic map by universal kriging. The thematic maps of the RSP at layer 0.2-0.3 m depth, constructed by both methods, were compared using measures of accuracy obtained from the construction of the matrix of errors and confusion matrix. There are similarities between the thematic maps. All maps showed that the RSP is higher in the north region.