A programme for computer aided identification of Phlebotomine sandflies of the Americas (CIPA): presentation and check-list of American species

The CIPA programme is a collaborative project including two entomologists from France and seven South and Central America countries. Its objective is the development of an expert system for computer aided identification of phlebotomine sandflies from the Americas. It also includes the formation of data bases for bibliographic, taxonomic and biogeographic data. Participant consensus on taxonomic prerequisites, standardization in bibliographic data collections and selection of descriptive variables for the final programme has been established through continous communication among participants and annual meetings. The adopted check-list of American sandflies presented here includes 386 specific taxa, ordered into genera and 28 sub-genera or species groups.

Identification by computer-simulated RFLP of phytoplasmas associated with eggplant giant calyx representative of two subgroups, a lineage of 16SrIII-J and the new subgroup 16SrIII-U

Symptoms resembling giant calyx, a graft-transmissible disease, were observed on 1-5% of eggplant (aubergine; Solanum melongena L.) plants in production fields in Sao Paulo state, Brazil. Phytoplasmas were detected in 1 2 of 1 2 samples from symptomatic plants that were analysed by a nested PCR assay employing 16S rRNA gene primers R16mF2/R16mR1 followed by R16F2n/R16R2. RFLP analysis of the resulting rRNA gene products (1.2 kb) indicated that all plants contained similar phytoplasmas, each closely resembling strains previously classified as members of RFLP group 16SrIII (X-disease group). Virtual RFLP and phylogenetic analyses of sequences derived from PCR products identified phytoplasmas infecting eggplant crops grown in Piracicaba as a lineage of the subgroup 16SrIII-J, whereas phytoplasmas detected in plants grown in Braganca Paulista were tentatively classified as members of a novel subgroup 16SrIII-U. These findings confirm eggplant as a new host of group 16SrIII-J phytoplasmas and extend the known diversity of strains belonging to this group in Brazil.

Computer-aided drug design and ADMET predictions for identification and evaluation of novel potential farnesyltransferase inhibitors in cancer therapy

We have used various computational methodologies including molecular dynamics, density functional theory, virtual screening, ADMET predictions and molecular interaction field studies to design and analyze four novel potential inhibitors of farnesyltransferase (FTase). Evaluation of two proposals regarding their drug potential as well as lead compounds have indicated them as novel promising FTase inhibitors, with theoretically interesting pharmacotherapeutic profiles, when Compared to the very active and most cited FTase inhibitors that have activity data reported, which are launched drugs or compounds in clinical tests. One of our two proposals appears to be a more promising drug candidate and FTase inhibitor, but both derivative molecules indicate potentially very good pharmacotherapeutic profiles in comparison with Tipifarnib and Lonafarnib, two reference pharmaceuticals. Two other proposals have been selected with virtual screening approaches and investigated by LIS, which suggest novel and alternatives scaffolds to design future potential FTase inhibitors. Such compounds can be explored as promising molecules to initiate a research protocol in order to discover novel anticancer drug candidates targeting farnesyltransferase, in the fight against cancer. (C) 2009 Elsevier Inc. All rights reserved.

Elementary computer-mediated online distance education : identification of criteria for successful implementation and practice

This study had three purposes related to the effective implem,entation and practice of computer-mediated online distance education (C-MODE) at the elementary level: (a) To identify a preliminary framework of criteria 'or guidelines for effective implementation and practice, (b) to identify areas ofC-MODE for which criteria or guidelines of effectiveness have not yet been developed, and (c) to develop an implementation and practice criteria questionnaire based on a review of the distance education literature, and to use the questionnaire in an exploratory survey of elementary C-MODE practitioners. Using the survey instrument, the beliefs and attitudes of 16 elementary C'- MODE practitioners about what constitutes effective implementation and practice principles were investigated. Respondents, who included both administrators and instructors, provided information about themselves and the program in which they worked. They rated 101 individual criteria statenlents on a 5 point Likert scale with a \. point range that included the values: 1 (Strongly Disagree), 2 (Disagree), 3 (Neutral or Undecided), 4 (Agree), 5 (Strongly Agree). Respondents also provided qualitative data by commenting on the individual statements, or suggesting other statements they considered important. Eighty-two different statements or guidelines related to the successful implementation and practice of computer-mediated online education at the elementary level were endorsed. Response to a small number of statements differed significantly by gender and years of experience. A new area for investigation, namely, the role ofparents, which has received little attention in the online distance education literature, emerged from the findings. The study also identified a number of other areas within an elementary context where additional research is necessary. These included: (a) differences in the factors that determine learning in a distance education setting and traditional settings, (b) elementary students' ability to function in an online setting, (c) the role and workload of instructors, (d) the importance of effective, timely communication with students and parents, and (e) the use of a variety of media.

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 computer vision approach for weeds identification through Support Vector Machines

This paper outlines an automatic computervision system for the identification of avena sterilis which is a special weed seed growing in cereal crops. The final goal is to reduce the quantity of herbicide to be sprayed as an important and necessary step for precision agriculture. So, only areas where the presence of weeds is important should be sprayed. The main problems for the identification of this kind of weed are its similar spectral signature with respect the crops and also its irregular distribution in the field. It has been designed a new strategy involving two processes: image segmentation and decision making. The image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based attributes measuring the relations among the crops and weeds. The decision making is based on the SupportVectorMachines and determines if a cell must be sprayed. The main findings of this paper are reflected in the combination of the segmentation and the SupportVectorMachines decision processes. Another important contribution of this approach is the minimum requirements of the system in terms of memory and computation power if compared with other previous works. The performance of the method is illustrated by comparative analysis against some existing strategies.

Gender identification of five genera of stingless bees (Apidae, Meliponini) based on wing morphology

Currently, the identification of pollinators is a critical necessity of conservation programs. After it was found that features extracted from patterns of wing venation are sufficient to discriminate among insect species, various studies have focused on this structure. We examined wing venation patterns of males and workers of five stingless bee species in order to determine if there are differences between sexes and if these differences are greater within than between species. Geometric morphometric analyses were made of the forewings of males and workers of Nannotrigona testaceicornis, Melipona quadrifasciata, Frieseomelitta varia, and Scaptotrigona aff. depilis and Plebeia remota. The patterns of males and workers from the same species were more similar than the patterns of individuals of the same sex from different species, and the patterns of both males and workers, when analyzed alone, were sufficiently different to distinguish among these five species. This demonstrates that we can use this kind of analysis for the identification of stingless bee species and that the sex of the individual does not impede identification. Computer-assisted morphometric analysis of bee wing images can be a useful tool for biodiversity studies and conservation programs.

Dynamical models for computer viruses propagation

Nowadays, digital computer systems and networks are the main engineering tools, being used in planning, design, operation, and control of all sizes of building, transportation, machinery, business, and life maintaining devices. Consequently, computer viruses became one of the most important sources of uncertainty, contributing to decrease the reliability of vital activities. A lot of antivirus programs have been developed, but they are limited to detecting and removing infections, based on previous knowledge of the virus code. In spite of having good adaptation capability, these programs work just as vaccines against diseases and are not able to prevent new infections based on the network state. Here, a trial on modeling computer viruses propagation dynamics relates it to other notable events occurring in the network permitting to establish preventive policies in the network management. Data from three different viruses are collected in the Internet and two different identification techniques, autoregressive and Fourier analyses, are applied showing that it is possible to forecast the dynamics of a new virus propagation by using the data collected from other viruses that formerly infected the network. Copyright (c) 2008 J. R. C. Piqueira and F. B. Cesar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Genome Survey Sequence Analysis and Identification of Homologs of Major Surface Protease (gp63) Genes in Trypanosoma rangeli

In this study, 222 genome survey sequences were generated for Trypanosoma rangeli strain P07 isolated from an opossum (Didelphis albiventris) in Minas Gerais State, Brazil. T. rangeli sequences were compared by BLASTX (Basic Local Alignment Search Tool X) analysis with the assembled contigs of Leishmania braziliensis, Leishmania infantum, Leishmania major, Trypanosoma brucei, and Trypanosoma cruzi. Results revealed that 82% (182/222) of the sequences were associated with predicted proteins described, whereas 18% (40/222) of the sequences did not show significant identity with sequences deposited in databases, suggesting that they may represent T. rangeli-specific sequences. Among the 182 predicted sequences, 179 (80.6%) had the highest similarity with T. cruzi, 2 (0.9%) with T. brucei, and 1 (0.5%) with L. braziliensis. Computer analysis permitted the identification of members of various gene families described for trypanosomatids in the genome of T. rangeli, such as trans-sialidases, mucin-associated surface proteins, and major surface proteases (MSP or gp63). This is the first report identifying sequences of the MSP family in T. rangeli. Multiple sequence alignments showed that the predicted MSP of T. rangeli presented the typical characteristics of metalloproteases, such as the presence of the HEXXH motif, which corresponds to a region previously associated with the catalytic site of the enzyme, and various cysteine and proline residues, which are conserved among MSPs of different trypanosomatid species. Reverse transcriptase-polymerase chain reaction analysis revealed the presence of MSP transcripts in epimastigote forms of T. rangeli.

Gene Expression Complex Networks: Synthesis, Identification, and Analysis

Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erdos-Renyi (ER), the small-world Watts-Strogatz (WS), the scale-free Barabasi-Albert (BA), and geographical networks (GG). The experimental results indicate that the inference method was sensitive to average degree k variation, decreasing its network recovery rate with the increase of k. The signal size was important for the inference method to get better accuracy in the network identification rate, presenting very good results with small expression profiles. However, the adopted inference method was not sensible to recognize distinct structures of interaction among genes, presenting a similar behavior when applied to different network topologies. In summary, the proposed framework, though simple, was adequate for the validation of the inferred networks by identifying some properties of the evaluated method, which can be extended to other inference methods.

Harmonic identification using parallel neural networks in single-phase systems

In this paper, artificial neural networks are employed in a novel approach to identify harmonic components of single-phase nonlinear load currents, whose amplitude and phase angle are subject to unpredictable changes, even in steady-state. The first six harmonic current components are identified through the variation analysis of waveform characteristics. The effectiveness of this method is tested by applying it to the model of a single-phase active power filter, dedicated to the selective compensation of harmonic current drained by an AC controller. Simulation and experimental results are presented to validate the proposed approach. (C) 2010 Elsevier B. V. All rights reserved.

Data Mining Applied to Harmonic Current Sources Identification in Residential Consumers

This work proposes a method based on both preprocessing and data mining with the objective of identify harmonic current sources in residential consumers. In addition, this methodology can also be applied to identify linear and nonlinear loads. It should be emphasized that the entire database was obtained through laboratory essays, i.e., real data were acquired from residential loads. Thus, the residential system created in laboratory was fed by a configurable power source and in its output were placed the loads and the power quality analyzers (all measurements were stored in a microcomputer). So, the data were submitted to pre-processing, which was based on attribute selection techniques in order to minimize the complexity in identifying the loads. A newer database was generated maintaining only the attributes selected, thus, Artificial Neural Networks were trained to realized the identification of loads. In order to validate the methodology proposed, the loads were fed both under ideal conditions (without harmonics), but also by harmonic voltages within limits pre-established. These limits are in accordance with IEEE Std. 519-1992 and PRODIST (procedures to delivery energy employed by Brazilian`s utilities). The results obtained seek to validate the methodology proposed and furnish a method that can serve as alternative to conventional methods.

A hybrid Particle Swarm Optimization - Simplex algorithm (PSOS) for structural damage identification

This study proposes a new PSOS-model based damage identification procedure using frequency domain data. The formulation of the objective function for the minimization problem is based on the Frequency Response Functions (FRFs) of the system. A novel strategy for the control of the Particle Swarm Optimization (PSO) parameters based on the Nelder-Mead algorithm (Simplex method) is presented; consequently, the convergence of the PSOS becomes independent of the heuristic constants and its stability and confidence are enhanced. The formulated hybrid method performs better in different benchmark functions than the Simulated Annealing (SA) and the basic PSO (PSO(b)). Two damage identification problems, taking into consideration the effects of noisy and incomplete data, were studied: first, a 10-bar truss and second, a cracked free-free beam, both modeled with finite elements. In these cases, the damage location and extent were successfully determined. Finally, a non-linear oscillator (Duffing oscillator) was identified by PSOS providing good results. (C) 2009 Elsevier Ltd. All rights reserved

Learning Profile Identification Based on the Analysis of the User Context of Interaction

One of the e-learning environment goal is to attend the individual needs of students during the learning process. The adaptation of contents, activities and tools into different visualization or in a variety of content types is an important feature of this environment, bringing to the user the sensation that there are suitable workplaces to his profile in the same system. Nevertheless, it is important the investigation of student behaviour aspects, considering the context where the interaction happens, to achieve an efficient personalization process. The paper goal is to present an approach to identify the student learning profile analyzing the context of interaction. Besides this, the learning profile could be analyzed in different dimensions allows the system to deal with the different focus of the learning.

Dynamic models for computer viruses

Computer viruses are an important risk to computational systems endangering either corporations of all sizes or personal computers used for domestic applications. Here, classical epidemiological models for disease propagation are adapted to computer networks and, by using simple systems identification techniques a model called SAIC (Susceptible, Antidotal, Infectious, Contaminated) is developed. Real data about computer viruses are used to validate the model. (c) 2008 Elsevier Ltd. All rights reserved.