Palpebral dimensions in Brazilian children: Assessment based on digital images

Purpose: To determine palpebral dimensions and development in Brazilian children using digital images. Methods: An observational study was performed measuring eyelid angles, palpebral fissure area and interpupillary distance in 220 children aged from 4 to 72 months. Digital images were obtained with a Sony Lithium movie camera (Sony DCR-TRV110, Brazil) in frontal view from awake children in primary ocular position; the object of observation was located at pupil height. The images were saved to tape, transferred to a Macintosh G4 (Apple Computer Inc., USA) computer and processed using NIH 1.58 software (NTIS, 5285 Port Royal Rd., Springfield, VA 22161, USA). Data were submitted to statistical analysis. Results: All parameters studied increased with age. The outer palpebral angle was greater than the inner, and palpebral fissure and angles showed greater changes between 4 and 5 months old and at around 24 to 36 months. Conclusion: There are significant variations in palpebral dimensions in children under 72 months old, especially around 24 to 36 months. Copyright © 2006 Informa Healthcare.

Automatic segmentation of digital images applied in cardiac medical images

The digital image processing has been applied in several areas, especially where it is necessary use tools for feature extraction and to get patterns of the studied images. In an initial stage, the segmentation is used to separate the image in parts that represents a interest object, that may be used in a specific study. There are several methods that intends to perform such task, but is difficult to find a method that can easily adapt to different type of images, that often are very complex or specific. To resolve this problem, this project aims to presents a adaptable segmentation method, that can be applied to different type of images, providing an better segmentation. The proposed method is based in a model of automatic multilevel thresholding and considers techniques of group histogram quantization, analysis of the histogram slope percentage and calculation of maximum entropy to define the threshold. The technique was applied to segment the cell core and potential rejection of tissue in myocardial images of biopsies from cardiac transplant. The results are significant in comparison with those provided by one of the best known segmentation methods available in the literature. © 2010 IEEE.

Precipitates segmentation from scanning electron microscope images through machine learning techniques

The presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable and efficient assessments on the materials. In this paper, we introduce four widely used supervised pattern recognition techniques to accomplish metallic precipitates segmentation in scanning electron microscope images from dissimilar welding on a Hastelloy C-276 alloy: Support Vector Machines, Optimum-Path Forest, Self Organizing Maps and a Bayesian classifier. Experimental results demonstrated that all classifiers achieved similar recognition rates with good results validated by an expert in metallographic image analysis. © 2011 Springer-Verlag Berlin Heidelberg.

Hyaluronidase from the venom of the social wasp Polybia paulista (Hymenoptera, Vespidae): Cloning, structural modeling, purification, and immunological analysis

In this study, we describe the cDNA cloning, sequencing, and 3-D structure of the allergen hyaluronidase from Polybia paulista venom (Pp-Hyal). Using a proteomic approach, the native form of Pp-Hyal was purified to homogeneity and used to produce a Pp-specific polyclonal antibody. The results revealed that Pp-Hyal can be classified as a glycosyl hydrolase and that the full-length Pp-Hyal cDNA (1315 bp; GI: 302201582) is similar (80-90%) to hyaluronidase from the venoms of endemic Northern wasp species. The isolated mature protein is comprised of 338 amino acids, with a theoretical pI of 8.77 and a molecular mass of 39,648.8 Da versus a pI of 8.13 and 43,277.0 Da indicated by MS. The Pp-Hyal 3D-structural model revealed a central core (α/β)7 barrel, two sulfide bonds (Cys 19-308 and Cys 185-197), and three putative glycosylation sites (Asn79, Asn187, and Asn325), two of which are also found in the rVes v 2 protein. Based on the model, residues Ser299, Asp107, and Glu109 interact with the substrate and potential epitopes (five conformational and seven linear) located at surface-exposed regions of the structure. Purified native Pp-Hyal showed high similarity (97%) with hyaluronidase from Polistes annularis venom (Q9U6V9). Immunoblotting analysis confirmed the specificity of the Pp-Hyal-specific antibody as it recognized the Pp-Hyal protein in both the purified fraction and P. paulista crude venom. No reaction was observed with the venoms of Apis mellifera, Solenopsis invicta, Agelaia pallipes pallipes, and Polistes lanio lanio, with the exception of immune cross-reactivity with venoms of the genus Polybia (sericea and ignobilis). Our results demonstrate cross-reactivity only between wasp venoms from the genus Polybia. The absence of cross-reactivity between the venoms of wasps and bees observed here is important because it allows identification of the insect responsible for sensitization, or at least of the phylogenetically closest insect, in order to facilitate effective immunotherapy in allergic patients. © 2013 Elsevier Ltd.

Chlorophyll a spatial inference using artificial neural network from multispectral images and in situ measurements

Considering the importance of monitoring the water quality parameters, remote sensing is a practicable alternative to limnological variables detection, which interacts with electromagnetic radiation, called optically active components (OAC). Among these, the phytoplankton pigment chlorophyll a is the most representative pigment of photosynthetic activity in all classes of algae. In this sense, this work aims to develop a method of spatial inference of chlorophyll a concentration using Artificial Neural Networks (ANN). To achieve this purpose, a multispectral image and fluorometric measurements were used as input data. The multispectral image was processed and the net training and validation dataset were carefully chosen. From this, the neural net architecture and its parameters were defined to model the variable of interest. In the end of training phase, the trained network was applied to the image and a qualitative analysis was done. Thus, it was noticed that the integration of fluorometric and multispectral data provided good results in the chlorophyll a inference, when combined in a structure of artificial neural networks.

Generating virtual images from oblique frames

Image acquisition systems based on multi-head arrangement of digital camerasare attractive alternatives enabling a larger imaging area when compared to a single framecamera. The calibration of this kind of system can be performed in several steps or byusing simultaneous bundle adjustment with relative orientation stability constraints. Thepaper will address the details of the steps of the proposed approach for system calibration,image rectification, registration and fusion. Experiments with terrestrial and aerial imagesacquired with two Fuji FinePix S3Pro cameras were performed. The experiments focusedon the assessment of the results of self-calibrating bundle adjustment with and withoutrelative orientation constraints and the effects to the registration and fusion when generatingvirtual images. The experiments have shown that the images can be accurately rectified andregistered with the proposed approach, achieving residuals smaller than one pixel. © 2013 by the authors; licensee MDPI, Basel, Switzerland.

Small-angle X-ray scattering and structural modeling of full-length: Cellobiohydrolase I from Trichoderma harzianum

Cellobiohydrolases hydrolyze cellulose releasing cellobiose units. They are very important for a number of biotechnological applications, such as, for example, production of cellulosic ethanol and cotton fiber processing. The Trichoderma cellobiohydrolase I (CBH1 or Cel7A) is an industrially important exocellulase. It exhibits a typical two domain architecture, with a small C-terminal cellulose-binding domain and a large N-terminal catalytic core domain, connected by an O-glycosylated linker peptide. The mechanism by which the linker mediates the concerted action of the two domains remains a conundrum. Here, we probe the protein shape and domain organization of the CBH1 of Trichoderma harzianum (ThCel7A) by small angle X-ray scattering (SAXS) and structural modeling. Our SAXS data shows that ThCel7A linker is partially-extended in solution. Structural modeling suggests that this linker conformation is stabilized by inter- and intra-molecular interactions involving the linker peptide and its O-glycosylations. © 2013 Springer Science+Business Media Dordrecht.

Isolation, homology modeling and renal effects of a C-type natriuretic peptide from the venom of the Brazilian yellow scorpion (Tityus serrulatus)

Mammalian natriuretic peptides (NPs) have been extensively investigated for use as therapeutic agents in the treatment of cardiovascular diseases. Here, we describe the isolation, sequencing and tridimensional homology modeling of the first C-type natriuretic peptide isolated from scorpion venom. In addition, its effects on the renal function of rats and on the mRNA expression of natriuretic peptide receptors in the kidneys are delineated. Fractionation of Tityusserrulatus venom using chromatographic techniques yielded a peptide with a molecular mass of 2190.64Da, which exhibited the pattern of disulfide bridges that is characteristic of a C-type NP (TsNP, T. serrulatus Natriuretic Peptide). In the isolated perfused rat kidney assay, treatment with two concentrations of TsNP (0.03 and 0.1μg/mL) increased the perfusion pressure, glomerular filtration rate and urinary flow. After 60min of treatment at both concentrations, the percentages of sodium, potassium and chloride transport were decreased, and the urinary cGMP concentration was elevated. Natriuretic peptide receptor-A (NPR-A) mRNA expression was down regulated in the kidneys treated with both concentrations of TsNP, whereas NPR-B, NPR-C and CG-C mRNAs were up regulated at the 0.1μg/mL concentration. In conclusion, this work describes the isolation and modeling of the first natriuretic peptide isolated from scorpion venom. In addition, examinations of the renal actions of TsNP indicate that its effects may be related to the activation of NPR-B, NPR-C and GC-C. © 2013 Elsevier Ltd.

Reconstrução e geração de malhas em estruturas biomecânicas tridimensionais para análise por elemento finitos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Restauração de imagens digitais com texturas utilizando técnicas de decomposição e equações diferenciais parciais

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Cyanobacteria detection in Guarapiranga Reservoir (São Paulo State, Brazil) using Landsat TM and ETM+ images

Algae bloom is one of the major consequences of the eutrophication of aquatic systems, including algae capable of producing toxic substances. Among these are several species of cyanobacteria, also known as blue-green algae, that have the capacity to adapt themselves to changes in the water column. Thus, the horizontal distribution of cyanobacteria harmful algae blooms (CHABs) is essential, not only to the environment, but also for public health. The use of remote sensing techniques for mapping CHABs has been explored by means of bio-optical modeling of phycocyanin (PC), a unique inland waters cyanobacteria pigment. However, due to the small number of sensors with a spectral band of the PC absorption feature, it is difficult to develop semi-analytical models. This study evaluated the use of an empirical model to identify CHABs using TM and ETM+ sensors aboard Landsat 5 and 7 satellites. Five images were acquired for applying the model. Besides the images, data was also collected in the Guarapiranga Reservoir, in São Paulo Metropolitan Region, regarding the cyanobacteria cell count (cells/mL), which was used as an indicator of CHABs biomass. When model values were analyzed excluding calibration factors for temperate lakes, they showed a medium correlation (R²=0.81, p=0.036), while when the factors were included the model showed a high correlation (R²=0.96, p=0.003) to the cyanobacteria cell count. The empirical model analyzed proved useful as an important tool for policy makers, since it provided information regarding the horizontal distribution of CHABs which could not be acquired from traditional monitoring techniques.

Automatic Orientation of Multi-Scale Terrestrial Images for 3D Reconstruction

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Segmentation of Scanning Electron Microscopy Images From Natural Rubber Samples With Gold Nanoparticles Using Starlet Wavelets

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Applying enhancement filters in the pre-processing of images of lymphoma

Lymphoma is a type of cancer that affects the immune system, and is classified as Hodgkin or non-Hodgkin. It is one of the ten types of cancer that are the most common on earth. Among all malignant neoplasms diagnosed in the world, lymphoma ranges from three to four percent of them. Our work presents a study of some filters devoted to enhancing images of lymphoma at the pre-processing step. Here the enhancement is useful for removing noise from the digital images. We have analysed the noise caused by different sources like room vibration, scraps and defocusing, and in the following classes of lymphoma: follicular, mantle cell and B-cell chronic lymphocytic leukemia. The filters Gaussian, Median and Mean-Shift were applied to different colour models (RGB, Lab and HSV). Afterwards, we performed a quantitative analysis of the images by means of the Structural Similarity Index. This was done in order to evaluate the similarity between the images. In all cases we have obtained a certainty of at least 75%, which rises to 99% if one considers only HSV. Namely, we have concluded that HSV is an important choice of colour model at pre-processing histological images of lymphoma, because in this case the resulting image will get the best enhancement.

Automatic method to classify images based on multiscale fractal descriptors and paraconsistent logic

In this study is presented an automatic method to classify images from fractal descriptors as decision rules, such as multiscale fractal dimension and lacunarity. The proposed methodology was divided in three steps: quantification of the regions of interest with fractal dimension and lacunarity, techniques under a multiscale approach; definition of reference patterns, which are the limits of each studied group; and, classification of each group, considering the combination of the reference patterns with signals maximization (an approach commonly considered in paraconsistent logic). The proposed method was used to classify histological prostatic images, aiming the diagnostic of prostate cancer. The accuracy levels were important, overcoming those obtained with Support Vector Machine (SVM) and Bestfirst Decicion Tree (BFTree) classifiers. The proposed approach allows recognize and classify patterns, offering the advantage of giving comprehensive results to the specialists.