Semiautomatic dental recognition using a graph-based segmentation algorithm and teeth shapes features

Dental recognition is very important for forensic human identification, mainly regarding the mass disasters, which have frequently happened due to tsunamis, airplanes crashes, etc. Algorithms for automatic, precise, and robust teeth segmentation from radiograph images are crucial for dental recognition. In this work we propose the use of a graph-based algorithm to extract the teeth contours from panoramic dental radiographs that are used as dental features. In order to assess our proposal, we have carried out experiments using a database of 1126 tooth images, obtained from 40 panoramic dental radiograph images from 20 individuals. The results of the graph-based algorithm was qualitatively assessed by a human expert who reported excellent scores. For dental recognition we propose the use of the teeth shapes as biometric features, by the means of BAS (Bean Angle Statistics) and Shape Context descriptors. The BAS descriptors showed, on the same database, a better performance (EER 14%) than the Shape Context (EER 20%). © 2012 IEEE.

Fatores de risco de desordens osteomusculares presentes no posto de trabalho de estudantes de odontologia

Objective: To identify the perception of undergraduate dental students with respect of the risk factors present at the study/work place that could contribute to the development of musculoskeletal disorders. Method: A total of 348 undergraduate dental students from Araraquara Dental School - UNESP were evaluated. The Portuguese version of the Work-related activities that may contribute to job-related pain and/or injury questionnaire was used. Data were analyzed by descriptive statistics. Results: The mean perception of the undergraduates was 5.68 (SD=1.92). The factors with highest mean values were continue working with some kind of pain or lesion (7.49, SD=2.55), bending or twisting the back in an uncomfortable manner (7.39, SD=2.59), working in uncomfortable/inadequate positions (7.27. SD=2.60) and working in the same position for long periods (7.02. SD=2.53). Overall, female students presented higher mean scores of perception than male students. The body parts with highest prevalence of pain were the neck (67.0%) and lower back (65.8%). Conclusion: The students showed perception in relation to the risk factors for musculoskeletal disorders present at the study/work place.

Urea-induced unfolding of Glossoscolex paulistus hemoglobin, in oxy- and cyanomet-forms: A dissociation model

The urea effect on the giant extracellular hemoglobin of Glossoscolex paulistus (HbGp) stability was studied by analytical ultracentrifugation (AUC) and small angle X-ray scattering (SAXS). AUC data show that the sedimentation coefficient distributions curves c (S), at 1.0mol/L of urea, display a single peak at 57 S, associated to the undissociated protein. The increase in urea concentration, up to 4.0mol/L, induces the appearance of smaller species, due to oligomeric dissociation. The sedimentation coefficients and molecular masses are 9.2S and 204kDa for the dodecamer (abcd)3, 5.5S and 69kDa for the tetramer (abcd), 4.1S and 52kDa for the trimer (abc) and 2.0 S and 17kDa for the monomer d, respectively. SAXS data show initially a decrease in the I(0) values due to the oligomeric dissociation, and then, above 4.0mol/L of denaturant, for oxy-HbGp, and above 6.0mol/L for cyanomet-HbGp, an increase in the maximum dimension and gyration radius is observed, due to the unfolding process. According to AUC and SAXS data the HbGp unfolding is described by two phases: the first one, at low urea concentration, below 4.0mol/L, characterizes the oligomeric dissociation, while the second one, at higher urea concentration, is associated to the unfolding of dissociated species. Our results are complementary to a recent report based on spectroscopic observations. © 2012 Elsevier B.V.

Nutrição e produtividade do crambe em razão da adubação de semeadura na safrinha

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)