Evaluation of follicular lymphoid depletion in the Bursa of Fabricius: an alternative methodology using digital image analysis and artificial neural networks

Fifty Bursa of Fabricius (BF) were examined by conventional optical microscopy and digital images were acquired and processed using Matlab® 6.5 software. The Artificial Neuronal Network (ANN) was generated using Neuroshell® Classifier software and the optical and digital data were compared. The ANN was able to make a comparable classification of digital and optical scores. The use of ANN was able to classify correctly the majority of the follicles, reaching sensibility and specificity of 89% and 96%, respectively. When the follicles were scored and grouped in a binary fashion the sensibility increased to 90% and obtained the maximum value for the specificity of 92%. These results demonstrate that the use of digital image analysis and ANN is a useful tool for the pathological classification of the BF lymphoid depletion. In addition it provides objective results that allow measuring the dimension of the error in the diagnosis and classification therefore making comparison between databases feasible.

Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils

ABSTRACT In recent years, geotechnologies as remote and proximal sensing and attributes derived from digital terrain elevation models indicated to be very useful for the description of soil variability. However, these information sources are rarely used together. Therefore, a methodology for assessing and specialize soil classes using the information obtained from remote/proximal sensing, GIS and technical knowledge has been applied and evaluated. Two areas of study, in the State of São Paulo, Brazil, totaling approximately 28.000 ha were used for this work. First, in an area (area 1), conventional pedological mapping was done and from the soil classes found patterns were obtained with the following information: a) spectral information (forms of features and absorption intensity of spectral curves with 350 wavelengths -2,500 nm) of soil samples collected at specific points in the area (according to each soil type); b) obtaining equations for determining chemical and physical properties of the soil from the relationship between the results obtained in the laboratory by the conventional method, the levels of chemical and physical attributes with the spectral data; c) supervised classification of Landsat TM 5 images, in order to detect changes in the size of the soil particles (soil texture); d) relationship between classes relief soils and attributes. Subsequently, the obtained patterns were applied in area 2 obtain pedological classification of soils, but in GIS (ArcGIS). Finally, we developed a conventional pedological mapping in area 2 to which was compared with a digital map, ie the one obtained only with pre certain standards. The proposed methodology had a 79 % accuracy in the first categorical level of Soil Classification System, 60 % accuracy in the second category level and became less useful in the categorical level 3 (37 % accuracy).

Sample size for measurement of root traits on common bean by image analysis

Evaluation of root traits may be facilitated if they are assessed on samples of the root system. The objective of this work was to determine the sample size of the root system in order to estimate root traits of common bean (Phaseolus vulgaris L.) cultivars by digital image analysis. One plant was grown per pot and harvested at pod setting, with 64 and 16 pots corresponding to two and four cultivars in the first and second experiments, respectively. Root samples were scanned up to the completeness of the root system and the root area and length were estimated. Scanning a root sample demanded 21 minutes, and scanning the entire root system demanded 4 hours and 53 minutes. In the first experiment, root area and length estimated with two samples showed, respectively, a correlation of 0.977 and 0.860, with these traits measured in the entire root. In the second experiment, the correlation was 0.889 and 0.915. The increase in the correlation with more than two samples was negligible. The two samples corresponded to 13.4% and 16.9% of total root mass (excluding taproot and nodules) in the first and second experiments. Taproot stands for a high proportion of root mass and must be deducted on root trait estimations. Samples with nearly 15% of total root mass produce reliable root trait estimates.

Variability of root traits in common bean genotypes at different levels of phosphorus supply and ontogenetic stages

Selection of common bean (Phaseolus vulgaris L.) cultivars with enhanced root growth would be a strategy for increasing P uptake and grain yield in tropical soils, but the strong plasticity of root traits may compromise their inclusion in breeding programs. The aim of this study was to evaluate the magnitude of the genotypic variability of root traits in common bean plants at two ontogenetic stages and two soil P levels. Twenty-four common bean genotypes, comprising the four growth habits that exist in the species and two wild genotypes, were grown in 4 kg pots at two levels of applied P (20 and 80 mg kg-1) and harvested at the stages of pod setting and early pod filling. Root area and root length were measured by digital image analysis. Significant genotype × P level and genotype × harvest interactions in analysis of variance indicate that the genotypic variation of root traits depended on soil nutrient availability and the stage at which evaluation was made. Genotypes differed for taproot mass, basal and lateral root mass, root area and root length at both P levels and growth stages; differences in specific root area and length were small. Genotypes with growth habits II (upright indeterminate) and III (prostrate indeterminate) showed better adaptation to limited P supply than genotypes of groups I (determinate) and IV (indeterminate climbing). Between the two harvests, genotypes of groups II and III increased the mass of basal and lateral roots by 40 and 50 %, respectively, whereas genotypes of groups I and IV by only 7 and 19 %. Values of the genotypic coefficient of determination, which estimates the proportion of phenotypic variance resulting from genetic effects, were higher at early pod filling than at pod setting. Correlations between shoot mass and root mass, which could indicate indirect selection of root systems via aboveground biomass, were higher at early pod filling than at pod setting. The results indicate that selection for root traits in common bean genotypes should preferentially be performed at the early pod-filling stage.

Resistance to Fusarium dry root rot disease in cassava accessions

The objective of this work was to identify sources of resistance to dry root rot induced by Fusarium sp. in cassava accessions. A macroconidial suspension (20 µL) of 11 Fusarium sp. isolates was inoculated in cassava roots, from 353 acessions plus seven commercial varieties. Ten days after inoculation, the total area colonized by the pathogen on the root pulp was evaluated by digital image analysis. Cluster analysis revealed the presence of five groups regarding resistance. The root lesion areas ranged from 18.28 to 1,096.07 mm² for the accessions BGM 1518 and BGM 556, respectively. The genotypes BGM 1042, BGM 1552, BGM 1586, BGM 1598, and BGM 1692 present the best agronomical traits.

Root distribution of irrigated grapevine rootstocks in a coarse texture soil of the São Francisco Valley, Brazil

An experiment was carried out to determine the root distribution of four grapevine rootstocks (Salt Creek, Dogridge, Courdec 1613, IAC 572) in a coarse texture soil of a commercial growing area in Petrolina County, São Francisco Valley, Brazil. Rootstocks were grafted to a seedless table grape cv. Festival, and irrigated by microsprinkler. Roots were quantified by the trench wall method aided by digital image analysis. Results indicated that roots reached 1 m depth, but few differences among rootstocks were found. All of them presented at least 90 % of the roots distributed until 0.6 m depth, with a greater root presence in the first 0.4 m. The upper 0.6 m can be taken into account as the effective rooting depth for soil and water management.

Guidelines for irrigation scheduling of banana crop in São Francisco Valley, Brazil. I - Root distribution and activity

In order to establish guidelines for irrigation water management of banana cv. Pacovan (AAB group, Prata sub-group) in Petrolina County, northeastern Brazil, the root distribution and activity were measured on an irrigated plantation, in a medium texture soil, with plants spaced in a 3 x 3 m grid. Root distribution was evaluated by the soil profile method aided by digital image analysis, while root activity was indirectly determined by the changing of soil water content and by the direction of soil water flux. Data were collected since planting in January 1999 to the 3rd harvest in September 2001. Effective rooting depth increased from 0.4 m at 91 days after planting (dap), to 0.6 m at 370, 510, and 903 dap, while water absorption by roots was predominantly in the top 0,6 m.

Análise de imagem em química analítica: empregando metodologias simples e didáticas para entender e prevenir o escurecimento de tecidos vegetais

A simple and didactic experiment was developed for image monitoring of the browning of fruit tissues caused by the enzyme polyphenol oxidase. The procedure, easy and inexpensive, is a valuable tool to teach and demonstrate the redox reaction between the enzyme and the natural polyphenols. To obtain the browning percentage for apple, pear and banana, digital photographs were employed, and the images were analyzed by means of Monte Carlo methods and digital analysis programs. The effects of several experimental conditions were studied, such as pH, light, temperature and the presence of oxygen or anti-oxidants. It was observed that each fruit presented a different condition that better minimized the oxidation process. The absence of oxygen and the application of a bissulphite solution were sufficient to keep the quality of all fruits tested.

Comparison between detailed digital and conventional soil maps of an area with complex geology

Since different pedologists will draw different soil maps of a same area, it is important to compare the differences between mapping by specialists and mapping techniques, as for example currently intensively discussed Digital Soil Mapping. Four detailed soil maps (scale 1:10.000) of a 182-ha sugarcane farm in the county of Rafard, São Paulo State, Brazil, were compared. The area has a large variation of soil formation factors. The maps were drawn independently by four soil scientists and compared with a fifth map obtained by a digital soil mapping technique. All pedologists were given the same set of information. As many field expeditions and soil pits as required by each surveyor were provided to define the mapping units (MUs). For the Digital Soil Map (DSM), spectral data were extracted from Landsat 5 Thematic Mapper (TM) imagery as well as six terrain attributes from the topographic map of the area. These data were summarized by principal component analysis to generate the map designs of groups through Fuzzy K-means clustering. Field observations were made to identify the soils in the MUs and classify them according to the Brazilian Soil Classification System (BSCS). To compare the conventional and digital (DSM) soil maps, they were crossed pairwise to generate confusion matrices that were mapped. The categorical analysis at each classification level of the BSCS showed that the agreement between the maps decreased towards the lower levels of classification and the great influence of the surveyor on both the mapping and definition of MUs in the soil map. The average correspondence between the conventional and DSM maps was similar. Therefore, the method used to obtain the DSM yielded similar results to those obtained by the conventional technique, while providing additional information about the landscape of each soil, useful for applications in future surveys of similar areas.

Spatiotemporal analysis of reported cases of acute Chagas disease in the State of Pernambuco, Brazil, from 2002 to 2013

INTRODUCTION: Control strategies to eliminate the transmission of Chagas disease by insect vectors have significantly decreased the number of reported acute cases in Brazil. However, data regarding the incidence and distribution of acute Chagas disease cases in the State of Pernambuco are unavailable in the literature. METHODS: A geographical information system was used to delineate the spatiotemporal distribution profile of the cases from 2002 to 2013 in 185 municipalities of Pernambuco based on the municipality where notification occurred. The results were presented in digital maps generated by the TerraView software (INPE). RESULTS: A total of 302 cases of acute disease were recorded in 37.8% of the municipalities, for a total of 0.13 cases per 1,000,000 inhabitants per year. Out of the 302 cases, 99.3% were reported between 2002 and 2006. The most affected municipalities were Carnaubeira da Penha, Mirandiba and Terra Nova. The risk maps showed a significant decrease in the number of notifications and a concentration of cases in the Midwest region. CONCLUSIONS: This study highlights a significant decrease in new cases of acute Chagas disease in Pernambuco starting in 2006 when Brazil received an international certification for the interruption of vectorial transmission by Triatoma infestans. However, control strategies should still be encouraged because other triatomine species can also transmit the parasite; moreover, other transmission modes must not be neglected.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

An appropriate data set size for digital soil mapping in Erechim, Rio Grande do Sul, Brazil

Digital information generates the possibility of a high degree of redundancy in the data available for fitting predictive models used for Digital Soil Mapping (DSM). Among these models, the Decision Tree (DT) technique has been increasingly applied due to its capacity of dealing with large datasets. The purpose of this study was to evaluate the impact of the data volume used to generate the DT models on the quality of soil maps. An area of 889.33 km² was chosen in the Northern region of the State of Rio Grande do Sul. The soil-landscape relationship was obtained from reambulation of the studied area and the alignment of the units in the 1:50,000 scale topographic mapping. Six predictive covariates linked to the factors soil formation, relief and organisms, together with data sets of 1, 3, 5, 10, 15, 20 and 25 % of the total data volume, were used to generate the predictive DT models in the data mining program Waikato Environment for Knowledge Analysis (WEKA). In this study, sample densities below 5 % resulted in models with lower power of capturing the complexity of the spatial distribution of the soil in the study area. The relation between the data volume to be handled and the predictive capacity of the models was best for samples between 5 and 15 %. For the models based on these sample densities, the collected field data indicated an accuracy of predictive mapping close to 70 %.

Mapeamento pedológico digital da folha Botucatu (SF-22-Z-B-VI-3): treinamento de dados em mapa tradicional e validação de campo

O mapeamento digital de solos permite prever padrões de ocorrência de solos com base em áreas de referência e no uso de técnicas de mineração de dados para modelar associações solo-paisagem. Os objetivos deste trabalho foram produzir um mapa pedológico digital por meio de técnicas de mineração de dados aplicadas a variáveis geomorfométricas e de geologia, com base em áreas de referência; e testar a confiabilidade desse mapa por meio de validação em campo com diferentes sistemas de amostragem. O mapeamento foi realizado na folha Botucatu (SF-22-Z-B-VI-3), utilizando-se as folhas 1:50.000, Dois Córregos e São Pedro, como áreas de referência. Variáveis descritoras do relevo e de geologia associadas às unidades de mapeamento pedológico das áreas de referência compuseram a matriz de dados de treinamento. A matriz foi analisada pelo algoritmo PART de árvore de decisão, do aplicativo Weka (Waikato Environment for Knowledge Analysis), que cria regras de classificação. Essas regras foram aplicadas aos dados geomorfométricos e geológicos da folha Botucatu, para predição de unidades de mapeamento pedológico. A validação de campo dos mapas digitais deu-se por meio de amostragem por transectos em uma unidade de mapeamento da folha São Pedro e de forma aleatório-estratificada na folha Botucatu. A avaliação da unidade de mapeamento na folha São Pedro verificou confiabilidade, respectivamente, de 83 e 66 %, para os mapas pedológicos digital e tradicional com legenda simplificada. Apesar de terem sido geradas regras para todas as unidades de mapeamento pedológico das áreas de treinamento, nem todas as unidades de mapeamento foram preditas na folha Botucatu, o que resultou das diferenças de relevo e geologia entre as áreas de treinamento e de mapeamento. A validação de campo do mapa digital da folha Botucatu verificou exatidão global de 52 %, compatível com levantamentos em nível de reconhecimento de baixa intensidade, e kappa de 0,41, indicando qualidade Boa. Unidades de mapeamento mais extensas geraram mais regras, resultando melhor reprodução dos padrões solo-relevo na área a ser mapeada. A validação por transectos na folha São Pedro indicou compatibilidade do mapa digital com o nível de reconhecimento de alta intensidade e compatibilidade do mapa tradicional, após simplificação de sua legenda, com o nível de reconhecimento de baixa intensidade. O treinamento do algoritmo em mapas e não em observações pontuais reduziu em 14 % a exatidão do mapa pedológico digital da folha Botucatu. A amostragem aleatório-estratificada pelo hipercubo latino é apropriada a mapeamentos com extensa base de dados, o que permite avaliar o mapa como um todo, tornando os trabalhos de campo mais eficientes. A amostragem em transectos é compatível com a avaliação da pureza de unidades de mapeamento individualmente, não necessitando de base de dados detalhada e permitindo estudos de associações solo-paisagem em pedossequências.

Public-domain software for root image analysis

In the search for high efficiency in root studies, computational systems have been developed to analyze digital images. ImageJ and Safira are public-domain systems that may be used for image analysis of washed roots. However, differences in root properties measured using ImageJ and Safira are supposed. This study compared values of root length and surface area obtained with public-domain systems with values obtained by a reference method. Root samples were collected in a banana plantation in an area of a shallower Typic Carbonatic Haplic Cambisol (CXk), and an area of a deeper Typic Haplic Ta Eutrophic Cambisol (CXve), at six depths in five replications. Root images were digitized and the systems ImageJ and Safira used to determine root length and surface area. The line-intersect method modified by Tennant was used as reference; values of root length and surface area measured with the different systems were analyzed by Pearson's correlation coefficient and compared by the confidence interval and t-test. Both systems ImageJ and Safira had positive correlation coefficients with the reference method for root length and surface area data in CXk and CXve. The correlation coefficient ranged from 0.54 to 0.80, with lowest value observed for ImageJ in the measurement of surface area of roots sampled in CXve. The IC (95 %) revealed that root length measurements with Safira did not differ from that with the reference method in CXk (-77.3 to 244.0 mm). Regarding surface area measurements, Safira did not differ from the reference method for samples collected in CXk (-530.6 to 565.8 mm²) as well as in CXve (-4231 to 612.1 mm²). However, measurements with ImageJ were different from those obtained by the reference method, underestimating length and surface area in samples collected in CXk and CXve. Both ImageJ and Safira allow an identification of increases or decreases in root length and surface area. However, Safira results for root length and surface area are closer to the results obtained with the reference method.

Hardcopy quality parameters to ensure structures detection at digital mammography

Objective To develop procedures to ensure consistency of printing quality of digital images, by means of hardcopy quantitative analysis based on a standard image. Materials and Methods Characteristics of mammography DI-ML and general purpose DI-HL films were studied through the QC-Test utilizing different processing techniques in a FujiFilm®-DryPix4000 printer. A software was developed for sensitometric evaluation, generating a digital image including a gray scale and a bar pattern to evaluate contrast and spatial resolution. Results Mammography films showed maximum optical density of 4.11 and general purpose films, 3.22. The digital image was developed with a 33-step wedge scale and a high-contrast bar pattern (1 to 30 lp/cm) for spatial resolution evaluation. Conclusion Mammographic films presented higher values for maximum optical density and contrast resolution as compared with general purpose films. The utilized digital processing technique could only change the image pixels matrix values and did not affect the printing standard. The proposed digital image standard allows greater control of the relationship between pixels values and optical density obtained in the analysis of films quality and printing systems.