Computed tomography image of the mediastinal and axillary lymph nodes in clinically sound Rottweilers

Trough computed tomography (CT), it is possible to evaluate lymph nodes in detail and to detect changes in these structures earlier than with radiographs and ultrasound. Lack of information in the veterinary literature directed the focus of this report to normal aspects of the axillary and mediastinal lymph nodes of adult dogs on CT imaging. A CT scan of 15 normal adult male and female Rottweilers was done. To define them as clinically sound, anamnesis, physical examination, complete blood count, renal and hepatic biochemistry, ECG, and thoracic radiographs were performed. After the intravenous injection of hydrosoluble ionic iodine contrast medium contiguous 10mm in thickness thoracic transverse images were obtained with an axial scanner. In the obtained images mediastinal and axillary lymph nodes were sought and when found measured in their smallest diameter and their attenuation was compared to musculature. Mean and standard deviation of: age, weight, body length and the smallest diameter of the axillary and mediastinal lymph nodes were determined. Mean and standard deviation of parameters: age 3.87±2.03 years, weight 41.13±5.12, and body length 89.61±2.63cm. Axillary lymph nodes were seen in 60% of the animals, mean of the smallest diameter was 3.58mm with a standard deviation of 2.02 and a minimum value of 1mm and a maximum value of 7mm. From 13 observed lymph nodes 61.53% were hypopodense when compared with musculature, and 30.77% were isodense. Mediastinal lymph nodes were identified in 73.33% of the dogs; mean measure of the smallest diameter was 4.71mm with a standard deviation of 2.61mm and a minimum value of 1mm, and a maximum value of 8mm. From 14 observed lymph nodes 85.71% were isodense when compared with musculature and 14.28% were hypodense. The results show that it is possible to visualize axillary and mediastinal lymph nodes in adult clinically sound Rottweilers with CT using a slice thickness and interval of 10mm. The smallest diameter of the axillary and mediastinal lymph nodes not surpassed 7mm and 8mm respectively. Their attenuations were equal or smaller than that of musculature in the post contrast scan.

Effect of image resolution manipulation in rearfoot angle measurements obtained with photogrammetry

The aim of this study was to investigate the influence of image resolution manipulation on the photogrammetric measurement of the rearfoot static angle. The study design was that of a reliability study. We evaluated 19 healthy young adults (11 females and 8 males). The photographs were taken at 1536 pixels in the greatest dimension, resized into four different resolutions (1200, 768, 600, 384 pixels) and analyzed by three equally trained examiners on a 96-pixels per inch (ppi) screen. An experienced physiotherapist marked the anatomic landmarks of rearfoot static angles on two occasions within a 1-week interval. Three different examiners had marked angles on digital pictures. The systematic error and the smallest detectable difference were calculated from the angle values between the image resolutions and times of evaluation. Different resolutions were compared by analysis of variance. Inter- and intra-examiner reliability was calculated by intra-class correlation coefficients (ICC). The rearfoot static angles obtained by the examiners in each resolution were not different (P > 0.05); however, the higher the image resolution the better the inter-examiner reliability. The intra-examiner reliability (within a 1-week interval) was considered to be unacceptable for all image resolutions (ICC range: 0.08-0.52). The whole body image of an adult with a minimum size of 768 pixels analyzed on a 96-ppi screen can provide very good inter-examiner reliability for photogrammetric measurements of rearfoot static angles (ICC range: 0.85-0.92), although the intra-examiner reliability within each resolution was not acceptable. Therefore, this method is not a proper tool for follow-up evaluations of patients within a therapeutic protocol.

Patterns of antenal sensilla of Panstrongylus megistus from three Brazilian states

The objective of the present study was to analyze and describe the phenotype of the antennal sensilla of Panstrongylus megistus, one of the epidemiologically most important species of triatomines in Brazil. Specimens from the Brazilian states of Goiás (GO), Minas Gerais (MG), and Rio Grande do Sul (RS) were compared, based on studies of four types of sensilla on three antennal segments: thick-walled trichoid (TK), thin-walled trichoid (TH), bristles (BR), and basiconica (BA). Discriminant analysis allowed the separation of the RS specimens from those of GO and MG. Multivariate discriminant analysis demonstrated that the sensilla of males differed from those of females, the variables with greatest weight being the BA of all three segments and the TK of flagellum 1. The basiconica sensilla were significantly more abundant in females, on all three segments. Antennal sensilla patterns also demonstrated significant differences among P. megistus specimens.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.