41 research outputs found

    A comparative in vitro evaluation of two different magnetic devices detecting the stability of osseo-integrated implants

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    Geckili O, Bilhan H, Cilingir A, Mumcu E, Bural C. A comparative in vitro evaluation of two different magnetic devices detecting the stability of osseo-integrated implants. J Periodont Res 2012; 47: 508513. (c) 2012 John Wiley & Sons A/S Background and Objective: It is unknown whether the resonance frequency analysis (RFA) measurements made by two different magnetic resonance frequency analysers are comparable. This in vitro study was designed to compare the RFA measurements made by the two magnetic resonance frequency analysers and to evaluate the intra- and interobserver reliability of the magnetic devices. Material and Methods: Thirty-two implants were placed in four cow ribs. The RFA value of each implant was measured by five different examiners. The measurements were repeated five times, in both the buccal and mesial directions, for each implant at 2 h intervals, and the averages of registered implant stability quotient (ISQ) units were recorded as the buccal ISQ value and the mesial ISQ value for every implant. Results: No statistically significant differences (p > 0.05) were observed between the RFA measurements made by the two magnetic devices. The intra-observer reliability of both devices was excellent, whereas the interobserver reliability of the devices was poor. Conclusion: The results of the RFA measurements of both tested devices overlap. Although both devices show excellent intra-observer reliability, there are variations between the measurements of different examiners

    GAYE: A face recognition system

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    In this paper, a new face recognition system, GAYE, is presented. GAYE is a fully automatic system that detects and recognizes faces in cluttered scenes. The input of the system is any digitized image/image sequence that includes face/faces. The basic building blocks of the system are face detection, feature extraction and feature comparison. Face detection is based on skin color segmentation. For feature extraction, a novel approach is proposed that depends on the Gabor wavelet transform of the face image. By comparing facial feature vectors system finally makes a decision if the incoming person is recognized or not. Real time system tests show that GAYE achieves a recognition ratio over %90

    ApicoAP: The First Computational Model for Identifying Apicoplast-Targeted Proteins in Multiple Species of Apicomplexa

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    <div><h3>Background</h3><p>Most of the parasites of the phylum Apicomplexa contain a relict prokaryotic-derived plastid called the apicoplast. This organelle is important not only for the survival of the parasite, but its unique properties make it an ideal drug target. The majority of apicoplast-associated proteins are nuclear encoded and targeted post-translationally to the organellar lumen via a bipartite signaling mechanism that requires an N-terminal signal and transit peptide (TP). Attempts to define a consensus motif that universally identifies apicoplast TPs have failed.</p> <h3>Methodology/Principal Findings</h3><p>In this study, we propose a generalized rule-based classification model to identify apicoplast-targeted proteins (ApicoTPs) that use a bipartite signaling mechanism. Given a training set specific to an organism, this model, called ApicoAP, incorporates a procedure based on a genetic algorithm to tailor a discriminating rule that exploits the known characteristics of ApicoTPs. Performance of ApicoAP is evaluated for four labeled datasets of <em>Plasmodium falciparum</em>, <em>Plasmodium yoelii</em>, <em>Babesia bovis</em>, and <em>Toxoplasma gondii</em> proteins. ApicoAP improves the classification accuracy of the published dataset for <em>P. falciparum</em> to 94%, originally 90% using PlasmoAP.</p> <h3>Conclusions/Significance</h3><p>We present a parametric model for ApicoTPs and a procedure to optimize the model parameters for a given training set. A major asset of this model is that it is customizable to different parasite genomes. The ApicoAP prediction software is available at <a href="http://code.google.com/p/apicoap/">http://code.google.com/p/apicoap/</a> and <a href="http://bcb.eecs.wsu.edu">http://bcb.eecs.wsu.edu</a>.</p> </div

    Breakdown of the labeled datasets into positive (ApicoTP) and negative (non-ApicoTP) classes.

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    <p><i>P. falciparum</i>* refers to the published dataset used in the development of PlasmoAP. We used only the SP-containing portion of this set.</p
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