Proliferating cell nuclear antigen (PCNA) in non-Hodgkin's lymphomas: Correlation with working formulation and Kiel classification in formalin-fixed paraffin-embedded material

PCNA is a 36-KD proliferating cell nuclear antigen associated with the cell cycle. The immunocytochemical detection of PCNA represents a useful tool for the study of tumor proliferation activity. This study documents the detection of PCNA, using antibody PC 10 in formalin-fixed, paraffin-embedded tissue, and correlates the proliferative activity of the non-Hodgkin's lymphomas (NHL) with histological grading assessed by the International Working Formulation (WF) and Kiel classification. In 92 cases of NHLs we found a strong correlation between the PCNA index and lymphoma grading. Statistically significant differences were also found between the proliferative index (PI) in low and high grade lymphomas according to the Kiel classification (t = 9.519; p < 0.001) and between low, intermediate and high grade lymphomas according to the WF classification (F = 79.01; p < 0.001). In the Kiel classification the mean of low grade lymphomas was 39.5% and of high grade 75.7%. In the WF the average of low grade lymphomas was 29.7%, intermediate 53.1% and high 75.1%. Although the differences among the groups had been significant, we found variations inside each histological subgroup in both classifications. The intermediate lymphomas were the most heterogeneous group, with PI inside the same histologic subtypes coincident with low and high grade lymphomas. Since PCNA may be used as a marker of cell proliferation in clinical studies to estimate the biological aggressiveness of lymphomas, its determination in intermediate grade NHL could be very useful to evaluate individual cases in this group and determine prognosis and probably the appropriate therapy.

Oriented texture classification based on self-organizing neural network and Hough transform

This paper presents a technique for oriented texture classification which is based on the Hough transform and Kohonen's neural network model. In this technique, oriented texture features are extracted from the Hough space by means of two distinct strategies. While the first operates on a non-uniformly sampled Hough space, the second concentrates on the peaks produced in the Hough space. The described technique gives good results for the classification of oriented textures, a common phenomenon in nature underlying an important class of images. Experimental results are presented to demonstrate the performance of the new technique in comparison, with an implemented technique based on Gabor filters.

Brazilian consensus on gastroesophageal reflux disease: Proposals for assessment, classification, and management

The Brazilian Consensus on Gastroesophageal Reflux Disease considers gastroesophageal reflux disease to be a chronic disorder related to the retrograde flow of gastroduodenal contents into the esophagus and/or adjacent organs, resulting in a variable spectrum of symptoms, with or without tissue damage. Considering the limitations of classifications currently in use, a new classification is proposed that combines three criteria - clinical, endoscopic, and pH-metric - providing a comprehensive and more complete characterization of the disease. The diagnosis begins with the presence of heartburn, acid regurgitation, and alarm manifestations (dysphagia, odynophagia, weight loss, GI bleeding, nausea and/or vomiting, and family history of cancer). Also, atypical esophageal, pulmonary, otorhinolaryngological, and oral symptoms may occur. Endoscopy is the first approach, particularly in patients over 40 yr of age and in those with alarm symptoms. Other exams are considered in particular cases, such as contrast radiological examination, scyntigraphy, manometry, and prolonged pH measurement. The clinical treatment encompasses behavioral modifications in lifestyle and pharmacological measures. Proton pump inhibitors in manufacturers' recommended doses are indicated, with doubling of the dose in more severe cases of esophagitis. The minimum time of administration is 6 wk. Patients who do not respond to medical treatment, including those with atypical manifestations, should be considered for surgical treatment. Of the complications of gastroesophageal reflux disease, Barrett's esophagus presents a potential development of adenocarcinoma; biopsies should be performed, independent of Barrett's esophagus extent or location. In this regard the designation short Barrett's is not important in terms of management and prognosis. © 2002 by Am. Coll. of Gastroenterology.

Shape-based features for cat ganglion retinal cells classification

This article presents a quantitative and objective approach to cat ganglion cell characterization and classification. The combination of several biologically relevant features such as diameter, eccentricity, fractal dimension, influence histogram, influence area, convex hull area, and convex hull diameter are derived from geometrical transforms and then processed by three different clustering methods (Ward's hierarchical scheme, K-means and genetic algorithm), whose results are then combined by a voting strategy. These experiments indicate the superiority of some features and also suggest some possible biological implications.

Classification of petroleum well drilling operations using Support Vector Machine (SVM)

During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a Support Vector Machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert. © 2006 IEEE.

Microcalcification enhancement and classification on mammograms using the wavelet transform

This paper presents a method to enhance microcalcifications and classify their borders by applying the wavelet transform. Decomposing an image and removing its low frequency sub-band the microcalcifications are enhanced. Analyzing the effects of perturbations on high frequency subband it's possible to classify its borders as smooth, rugged or undefined. Results show a false positive reduction of 69.27% using a region growing algorithm. © 2008 IEEE.

Classification of lumber of Eucalyptus using non-destructive tests

Classification and standardization of the sawn wood is a usual activity, developed by countries that come as great consumers of this material. Brazil does not practice the classification of sawn wood. This work had the main objective of evaluating the sensibility of most common non-destructive tests in the classification of dimension lumber from fast grown Eucalyptus plantation. Wood was obtained from genetic material cultivated at Minas Gerais State, Brazil. 296 beams of structural dimensions (6 cm × 12 cm × 280 cm) from 10 different clones of Eucalyptus were sampled. Beams were non-destructively (stress wave, ultrasound and transverse vibration) and destructively (static bending and compression parallel to grain) tested. Non-destructive results showed sensibility in the classification of structural dimension lumber, being possible to establish wave velocity intervals that attend to the main strength classes reported by Wooden Structures Brazilian Code.

Molecular phylogeny of advanced snakes (Serpentes, Caenophidia) with an emphasis on South American xenodontines: A revised classification and descriptions of new taxa

We present a molecular phylogenetic analysis of caenophidian (advanced) snakes using sequences from two mitochondrial genes (12S and 16S rRNA) and one nuclear (c-mos) gene (1681 total base pairs), and with 131 terminal taxa sampled from throughout all major caenophidian lineages but focussing on Neotropical xenodontines. Direct optimization parsimony analysis resulted in a well-resolved phylogenetic tree, which corroborates some clades identified in previous analyses and suggests new hypotheses for the composition and relationships of others. The major salient points of our analysis are: (1) placement of Acrochordus, Xenodermatids, and Pareatids as successive outgroups to all remaining caenophidians (including viperids, elapids, atractaspidids, and all other colubrid groups); (2) within the latter group, viperids and homalopsids are sucessive sister clades to all remaining snakes; (3) the following monophyletic clades within crown group caenophidians: Afro-Asian psammophiids (including Mimophis from Madagascar), Elapidae (including hydrophiines but excluding Homoroselaps), Pseudoxyrhophiinae, Colubrinae, Natricinae, Dipsadinae, and Xenodontinae. Homoroselaps is associated with atractaspidids. Our analysis suggests some taxonomic changes within xenodontines, including new taxonomy for Alsophis elegans, Liophis amarali, and further taxonomic changes within Xenodontini and the West Indian radiation of xenodontines. Based on our molecular analysis, we present a revised classification for caenophidians and provide morphological diagnoses for many of the included clades; we also highlight groups where much more work is needed. We name as new two higher taxonomic clades within Caenophidia, one new subfamily within Dipsadidae, and, within Xenodontinae five new tribes, six new genera and two resurrected genera. We synonymize Xenoxybelis and Pseudablabes with Philodryas; Erythrolamprus with Liophis; and Lystrophis and Waglerophis with Xenodon.

Malware distributed collection and pre-classification system using honeypot technology

Malware has become a major threat in the last years due to the ease of spread through the Internet. Malware detection has become difficult with the use of compression, polymorphic methods and techniques to detect and disable security software. Those and other obfuscation techniques pose a problem for detection and classification schemes that analyze malware behavior. In this paper we propose a distributed architecture to improve malware collection using different honeypot technologies to increase the variety of malware collected. We also present a daemon tool developed to grab malware distributed through spam and a pre-classification technique that uses antivirus technology to separate malware in generic classes. © 2009 SPIE.

Automatic classification of enzyme family in protein annotation

Most of the tasks in genome annotation can be at least partially automated. Since this annotation is time-consuming, facilitating some parts of the process - thus freeing the specialist to carry out more valuable tasks - has been the motivation of many tools and annotation environments. In particular, annotation of protein function can benefit from knowledge about enzymatic processes. The use of sequence homology alone is not a good approach to derive this knowledge when there are only a few homologues of the sequence to be annotated. The alternative is to use motifs. This paper uses a symbolic machine learning approach to derive rules for the classification of enzymes according to the Enzyme Commission (EC). Our results show that, for the top class, the average global classification error is 3.13%. Our technique also produces a set of rules relating structural to functional information, which is important to understand the protein tridimensional structure and determine its biological function. © 2009 Springer Berlin Heidelberg.

Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.

Risk prediction for weed infestation using classification rules

This paper proposes a fuzzy classification system for the risk of infestation by weeds in agricultural zones considering the variability of weeds. The inputs of the system are features of the infestation extracted from estimated maps by kriging for the weed seed production and weed coverage, and from the competitiveness, inferred from narrow and broad-leaved weeds. Furthermore, a Bayesian network classifier is used to extract rules from data which are compared to the fuzzy rule set obtained on the base of specialist knowledge. Results for the risk inference in a maize crop field are presented and evaluated by the estimated yield loss. © 2009 IEEE.

Optimizing optimum-path forest classification for huge datasets

Traditional pattern recognition techniques can not handle the classification of large datasets with both efficiency and effectiveness. In this context, the Optimum-Path Forest (OPF) classifier was recently introduced, trying to achieve high recognition rates and low computational cost. Although OPF was much faster than Support Vector Machines for training, it was slightly slower for classification. In this paper, we present the Efficient OPF (EOPF), which is an enhanced and faster version of the traditional OPF, and validate it for the automatic recognition of white matter and gray matter in magnetic resonance images of the human brain. © 2010 IEEE.

Portable Hardness tester for timber classification

Hardness is a property largely used in material specifications, mechanical and metallurgical research and quality control of several materials. Specifically for timber, Janka hardness is a simple, quick and easy test, with good correlations with the compression parallel to grain strength, a strong reference in structural classification for this material. More recently, international studies have reported the use of Brinell hardness for timber assessment which resumes the advantages previously mentioned for Janka hardness and make it easier to be performed in the field, especially because of the lower magnitude of the involved loads. A first generation of an equipment for field evaluation of hardness in wood - Portable Hardness tester for wood - based on Brinell hardness has already been developed by the Research Group on Forest Products from FCA/UNESP, Brazil, with very good correlations between the evaluated hardness and several other mechanical properties of the material when performing tests with different species of native and reforested wood (traditionally used as ties - sleepers - in railways). This paper presents results obtained in the experimental program with the first generation of this equipment and preliminary tests with its second generation, which uses accelerometers to substitute the indentation measurements in wood. For the first generation of the equipment functional and calibration tests were carried out using 16 native and reforestation timber lots, among there E. citriodora, E. tereticornis, E. saligna, E. urophylla, E. grandis, Goupia glabra and Bagassa guianenses, with different origins and ages. The results obtained confirm its potential in the classification of specimens, with inclusion errors varying from 4.5% to 16.6%.

Land use image classification through optimum-path forest clustering

Land use classification has been paramount in the last years, since we can identify illegal land use and also to monitor deforesting areas. Although one can find several research works in the literature that address this problem, we propose here the land use recognition by means of Optimum-Path Forest Clustering (OPF), which has never been applied to this context up to date. Experiments among Optimum-Path Forest, Mean Shift and K-Means demonstrated the robustness of OPF for automatic land use classification of images obtained by CBERS-2B and Ikonos-2 satellites. © 2011 IEEE.