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.

Detection and mapping of a lethal locus in a eucalyptus hybrid population

The objective of this work was to verify the existence of a lethal locus in a eucalyptus hybrid population, and to quantify the segregation distortion in the linkage group 3 of the Eucalyptus genome. A E. grandis x E. urophylla hybrid population, which segregates for rust resistance, was genotyped with 19 microsatellite markers belonging to linkage group 3 of the Eucalyptus genome. To quantify the segregation distortion, maximum likelihood (ML) models, specific to outbreeding populations, were used. These models consider the observed marker genotypes and the lethal locus viability as parameters. The ML solutions were obtained using the expectation‑maximization algorithm. A lethal locus in the linkage group 3 was verified and mapped, with high confidence, between the microssatellites EMBRA 189 e EMBRA 122. This lethal locus causes an intense gametic selection from the male side. Its map position is 25 cM from the locus which controls the rust resistance in this population.

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.

Phylogenetic analysis of Tomato mosaic virus from Hemerocallis sp. and Impatiens hawkeri

The culture and commercialization of ornamental plants have considerably increased in the last years. To supply the commercial demand, several Hemerocallis and Impatiens varieties have been bred for appreciated qualities such as flowers with a diversity of shapes and colors. With the aim of characterizing the tobamovirus isolated from Hemerocallis sp. (tobamo-H) and Impatiens hawkeri (tobamo-I) from the USA and São Paulo, respectively, as well as to establish phylogenetic relationships between them and other Tobamovirus species, the viruses were submitted to RNA extraction, RT-PCR amplification, coat-protein gene sequencing and phylogenetic analyses. Comparison of tobamovirus homologous sequences yielded values superior to 98.5% of identity with Tomato mosaic virus (ToMV) isolates at the nucleotide level. In relation to tobamo-H, 100% of identity with ToMV from tomatoes from Australia and Peru was found. Based on maximum likelihood (ML) analysis it was suggested that tobamo-H and tobamo-I share a common ancestor with ToMV, Tobacco mosaic virus, Odontoglossum ringspot virus and Pepper mild mottle virus. The tree topology reconstructed under ML methodology shows a monophyletic group, supported by 100% of bootstrap, consisting of various ToMV isolates from different hosts, including some ornamentals, from different geographical locations. The results indicate that Hemerocallis sp. and I. hawkeri are infected by ToMV. This is the first report of the occurrence of this virus in ornamental species in Brazil.

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.

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.

Probabilistic structure of an annual extreme rainfall series of a coastal area of the State of São Paulo, Brazil

This study aimed to describe the probabilistic structure of the annual series of extreme daily rainfall (Preabs), available from the weather station of Ubatuba, State of São Paulo, Brazil (1935-2009), by using the general distribution of extreme value (GEV). The autocorrelation function, the Mann-Kendall test, and the wavelet analysis were used in order to evaluate the presence of serial correlations, trends, and periodical components. Considering the results obtained using these three statistical methods, it was possible to assume the hypothesis that this temporal series is free from persistence, trends, and periodicals components. Based on quantitative and qualitative adhesion tests, it was found that the GEV may be used in order to quantify the probabilities of the Preabs data. The best results of GEV were obtained when the parameters of this function were estimated using the method of maximum likelihood. The method of L-moments has also shown satisfactory results.