In silico evaluation of the Eucalyptus transcriptome

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

Quantitative cell-cycle protein expression in oral cancer assessed by computer-assisted system

The knowledge of cell-cycle control has shown that the capacity of malignant growth is acquired by the stepwise accumulation of defects in specific genes regulating cell growth. Histologic diagnosis might be improved by a quantitative evaluation of more specific diagnosis biomarkers, which could help to precisely identify pre-malignant and malignant oral lesions. The aim of the present study is to evaluate whether computer-based quantitative assessment of p53, PCNA and Ki-67 immunohistochemical expression, could be used clinically to foresee the risk of oral malignant transformation. This retrospective study was carried out in ninety-five oral biopsies, 27 were classified as fibrous inflammatory hyperplasia, 40 as leukoplakia and 28 as oral squamous cell carcinoma. Sixteen out of the 40 leukoplakia were diagnosed as non-dysplastic leukoplakia, the other 24 being dysplastic leukoplakia, of which 50.0% were classified as moderate to severe dysplasia. Comparison of the four groups of oral tissues showed significant rises in p53 and Ki-67 positivity index, which increased steadily in the order benign, pre-malignant, and malignant. In contrast, it was not possible to relate higher PCNA levels with pre-malignant and malignant oral lesions. We therefore conclude that PCNA immunohistochemistry expression is probably an inappropriate marker to identify oral carcinogenesis, whereas joint quantitative evaluation of p53 and Ki-67, appears to be useful as a tumor marker, providing a pre-diagnostic estimate of the potential for cell-cycle deregulation of the oral proliferate status.

The application of distributed virtual machines for enterprise computer management: A two-tier network file system for image provisioning and management

In order to simplify computer management, several system administrators are adopting advanced techniques to manage software configuration of enterprise computer networks, but the tight coupling between hardware and software makes every PC an individual managed entity, lowering the scalability and increasing the costs to manage hundreds or thousands of PCs. Virtualization is an established technology, however its use is been more focused on server consolidation and virtual desktop infrastructure, not for managing distributed computers over a network. This paper discusses the feasibility of the Distributed Virtual Machine Environment, a new approach for enterprise computer management that combines virtualization and distributed system architecture as the basis of the management architecture. © 2008 IEEE.

JSEG-based image segmentation in computer vision for agricultural mobile robot navigation

This project aims to apply image processing techniques in computer vision featuring an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained.

ANN statistical image recognition method for computer vision in agricultural mobile robot navigation

The main application area in this project, is to deploy image processing and segmentation techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. Thereby, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for image recognition. Hence, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave computational platforms, along with the application of customized Back-propagation Multilayer Perceptron (MLP) algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of segmented images in which reasonably accurate results were obtained. © 2010 IEEE.

Measuring and analyzing color and texture information in anatomical leaf cross sections: An approach using computer vision to aid plant species identification

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

A study on human error in the interaction with the computer systems

The term human factor is used by professionals of various fields meant for understanding the behavior of human beings at work. The human being, while developing a cooperative activity with a computer system, is subject to cause an undesirable situation in his/her task. This paper starts from the principle that human errors may be considered as a cause or factor contributing to a series of accidents and incidents in many diversified fields in which human beings interact with automated systems. We propose a simulator of performance in error with potentiality to assist the Human Computer Interaction (HCI) project manager in the construction of the critical systems. © 2011 Springer-Verlag.

Computer tools to aid the learning and design steps for photovoltaic systems

This paper presents novel simulation tools to assist the lecturers about learning processes on renewable energy sources, considering photovoltaic (PV) systems. The PV behavior, functionality and its interaction with power electronic converters are investigated in the simulation tools. The main PV output characteristics, I (current) versus V (voltage) and P (power) versus V (voltage), were implemented in the tools, in order to aid the users for the design steps. In order to verify the effectiveness of the developed tools the simulation results were compared with Matlab. Finally, a prototype was implemented with the purpose to compare the experimental results with the results from the proposed tools, validating its operational feasibility. © 2011 IEEE.

Illegal hunting cases detected with molecular forensics in Brazil

Background: Illegal hunting is one of the major threats to vertebrate populations in tropical regions. This unsustainable practice has serious consequences not only for the target populations, but also for the dynamics and structure of tropical ecosystems. Generally, in cases of suspected illegal hunting, the only evidence available is pieces of meat, skin or bone. In these cases, species identification can only be reliably determined using molecular technologies. Here, we reported an investigative study of three cases of suspected wildlife poaching in which molecular biology techniques were employed to identify the hunted species from remains of meat.Findings: By applying cytochrome b (cyt-b) and cytochrome oxidase subunit I (COI) molecular markers, the suspected illegal poaching was confirmed by the identification of three wild species, capybara (Hydrochoerus hydrochaeris), Chaco Chachalaca (Ortalis canicollis) and Pampas deer (Ozotoceros bezoarticus). In Brazil, hunting is a criminal offense, and based on this evidence, the defendants were found guilty and punished with fines; they may still be sentenced to prison for a period of 6 to 12 months.Conclusions: The genetic analysis used in this investigative study was suitable to diagnose the species killed and solve these criminal investigations. Molecular forensic techniques can therefore provide an important tool that enables local law enforcement agencies to apprehend illegal poachers. © 2012 Sanches et al.; licensee BioMed Central Ltd.

Scheduler simulation using iSPD, an iconic-based computer grid simulator

Increased accessibility to high-performance computing resources has created a demand for user support through performance evaluation tools like the iSPD (iconic Simulator for Parallel and Distributed systems), a simulator based on iconic modelling for distributed environments such as computer grids. It was developed to make it easier for general users to create their grid models, including allocation and scheduling algorithms. This paper describes how schedulers are managed by iSPD and how users can easily adopt the scheduling policy that improves the system being simulated. A thorough description of iSPD is given, detailing its scheduler manager. Some comparisons between iSPD and Simgrid simulations, including runs of the simulated environment in a real cluster, are also presented. © 2012 IEEE.

Boosting optimum-path forest clustering through harmony Search and its applications for intrusion detection in computer networks

In this paper we propose a nature-inspired approach that can boost the Optimum-Path Forest (OPF) clustering algorithm by optimizing its parameters in a discrete lattice. The experiments in two public datasets have shown that the proposed algorithm can achieve similar parameters' values compared to the exhaustive search. Although, the proposed technique is faster than the traditional one, being interesting for intrusion detection in large scale traffic networks. © 2012 IEEE.

Intrusion detection in computer networks using optimum-path forest clustering

Nowadays, organizations face the problem of keeping their information protected, available and trustworthy. In this context, machine learning techniques have also been extensively applied to this task. Since manual labeling is very expensive, several works attempt to handle intrusion detection with traditional clustering algorithms. In this paper, we introduce a new pattern recognition technique called Optimum-Path Forest (OPF) clustering to this task. Experiments on three public datasets have showed that OPF classifier may be a suitable tool to detect intrusions on computer networks, since it outperformed some state-of-the-art unsupervised techniques. © 2012 IEEE.

Computer techniques towards the automatic characterization of graphite particles in metallographic images of industrial materials

The automatic characterization of particles in metallographic images has been paramount, mainly because of the importance of quantifying such microstructures in order to assess the mechanical properties of materials common used in industry. This automated characterization may avoid problems related with fatigue and possible measurement errors. In this paper, computer techniques are used and assessed towards the accomplishment of this crucial industrial goal in an efficient and robust manner. Hence, the use of the most actively pursued machine learning classification techniques. In particularity, Support Vector Machine, Bayesian and Optimum-Path Forest based classifiers, and also the Otsu's method, which is commonly used in computer imaging to binarize automatically simply images and used here to demonstrated the need for more complex methods, are evaluated in the characterization of graphite particles in metallographic images. The statistical based analysis performed confirmed that these computer techniques are efficient solutions to accomplish the aimed characterization. Additionally, the Optimum-Path Forest based classifier demonstrated an overall superior performance, both in terms of accuracy and speed. © 2012 Elsevier Ltd. All rights reserved.

Socio-economic and geographic profiling of crime in Chile

Includes bibliography