Loss of RKIP expression during the carcinogenic evolution of endometrial cancer

Aims Endometrial cancer is one of the most common cancers in women worldwide, but there is a lack of diagnostic markers for early detection of these tumours. The raf kinase inhibitory protein (RKIP) negatively regulates the Raf/MEK/ERK pathway, and the downregulation of RKIP is associated with tumour progression and metastasis in several human neoplasms. The aim of this study was to assess the expression levels of RKIP in endometrial cancer and determine whether this expression correlates with clinical outcome in these patients.Methods Tissue microarrays constructed using tissue samples from 209 endometrial adenocarcinomas, 49 endometrial polyps and 48 endometrial hyperplasias were analysed for RKIP expression by immunohistochemistry.Results The authors found that RKIP expression decreases significantly during malignant progression of endometrial cancer; it is highly expressed in non-neoplastic tissues (polyps 79.6%; hyperplasias 87.5%) and expressed at very low levels in endometrioid adenocarcinomas (29.7%). No correlations were observed between RKIP expression, clinicopathological data and survival.Conclusion This study demonstrated for the first time that RKIP expression is lost during the carcinogenic evolution of endometrial tumours and that the loss of RKIP expression is associated with a malignant phenotype. Functional studies are needed to address the biological role of RKIP downregulation in endometrial cancer.

Effect of floating macrophyte cover on the water quality in fishpond

A study was conducted during 23 days in order to evaluate the impact of floating aquatic macrophyte on the water quality of a fishpond. Water samples were collected in four points, three inside the pond and one in water inlet. Drastic reduction of dissolved oxygen was observed in the pond, down to 0.87 mg/L. No significant differences (P > 0.05) were observed for total CO 2, nitrite and ammonia with respect to inlet water (P1) and inside the pond (P2, P3 e P4). Chlorophyll a displayed an inverse relationship with phosphorus. Among nitrogen compounds, ammonia presented the highest concentrations except in water inlet where nitrate was higher, 513.33 μg/l, as well as the highest conductivity values. The pH was slightly acid. The results obtained showed that the macrophyte cover promoted an adverse effect in the medium. Under control, aquatic plants might impact positively due to its capacity to reduce total phosphorus and nitrate in the water column as observed in this study.

Metáforas e metamorfoses na poesia de cláudio manuel da costa

This paper explains the physical space allegorization as the main figurative device of the human actor dishumanization theme, in Cláudio Manuel da Costa poetry.

Classificação climática para os municípios de Botucatu e São Manuel, SP

Climatic classification defines the geographical limits of different climate types all over the world, and it is considered essential to study similar areas. This work updates the climatic classification of the municipal districts of Botucatu and of São Manuel, State of Sao Paulo, where the experimental farms of the Schools of Agronomical Sciences - UNESP, Campus of Botucatu, State of São Paulo, are located. Koppen's and Thornthwaite's methods were used for the air temperature and precipitation data, in a 36-year period (from 1971 to 2006). For both municipal districts of Botucatu and São Manuel, the climate was characterized as being Cfa, hot climate with rains in the summer and drought in the winter, and the average temperature in the hottest month is above 22 °C. According to Thornthwaite's classification, there was a small difference due to the humidity index, characterized as B2rB′3a′ (humid climate with small hydro deficiency - April, July and August, with annual potential evapotranspiration of 945.15 mm and concentration of the potential evapotranspiration in the summer of 33%) in the district of Botucatu, and as B1rB′3a′ (humid climate with small hidric deficiency - April, July and August, with annual potential evapotranspiration of 994.21 mm and concentration of the potential evapotranspiration in the summer of 33%)in the district of São Manuel.

Manuel d'utilisation de l'Observatoire de l'égalité de genre de l'Amérique Latine et des Caraïbes

Inclut la bibliographie

Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest

In this work we propose a novel automatic cast iron segmentation approach based on the Optimum-Path Forest classifier (OPF). Microscopic images from nodular, gray and malleable cast irons are segmented using OPF, and Support Vector Machines (SVM) with Radial Basis Function and SVM without kernel mapping. Results show accurate and fast segmented images, in which OPF outperformed SVMs. Our work is the first into applying OPF for automatic cast iron segmentation. © 2010 Springer-Verlag.

Escritos de Manuel Balboa, 1952-1976: bibliografía existente en la Biblioteca de la CEPAL

Se incluyó además en la investigación la Biblioteca de la Fundación Educacional "Roberto Bellarmino"

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.

Identification of foliar diseases in cotton crop

The pathogens manifestation in plantations are the largest cause of damage in several cultivars, which may cause increase of prices and loss of crop quality. This paper presents a method for automatic classification of cotton diseases through feature extraction of leaf symptoms from digital images. Wavelet transform energy has been used for feature extraction while Support Vector Machine has been used for classification. Five situations have been diagnosed, namely: Healthy crop, Ramularia disease, Bacterial Blight, Ascochyta Blight, and unspecified disease. © 2012 Taylor & Francis Group.

Fast identification of individuals based on iris characteristics for biometric systems

Nowadays, systems based on biométrie techniques have a wide acceptance in many different areas, due to their levels of safety and accuracy. A biometrie technique that is gaining prominence is the identification of individuals through iris recognition. However, to be proficiently used these systems must process their recognition task as fast as possible. The goal of this work has been the development of an iris recognition method to produce results rapidly, yet without losing the recognition accuracy. The experimental results show that the method is quite promising. © 2012 Taylor & Francis Group.

Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metal

Duplex and superduplex stainless steels are class of materials of a high importance for engineering purposes, since they have good mechanical properties combination and also are very resistant to corrosion. It is known as well that the chemical composition of such steels is very important to maintain some desired properties. In the past years, some works have reported that γ 2 precipitation improves the toughness of such steels, and its quantification may reveals some important information about steel quality. Thus, we propose in this work the automatic segmentation of γ 2 precipitation using two pattern recognition techniques: Optimum-Path Forest (OPF) and a Bayesian classifier. To the best of our knowledge, this if the first time that machine learning techniques are applied into this area. The experimental results showed that both techniques achieved similar and good recognition rates. © 2012 Taylor & Francis Group.

Characterization of texture in image of skin lesions by support vector machine

Due to the increased incidence of skin cancer, computational methods based on intelligent approaches have been developed to aid dermatologists in the diagnosis of skin lesions. This paper proposes a method to classify texture in images, since it is an important feature for the successfully identification of skin lesions. For this is defined a feature vector, with the fractal dimension of images through the box-counting method (BCM), which is used with a SVM to classify the texture of the lesions in to non-irregular or irregular. With the proposed solution, we could obtain an accuracy of 72.84%. © 2012 AISTI.