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G. Domínguez‑Rodríguez,J. A. González‑Sánchez,J. Rosado‑Carrasco,G. Canto 대한금속·재료학회 2020 METALS AND MATERIALS International Vol.26 No.9
The recognitions of phases and precise grain boundaries based on metallographic images are useful for conducting micromechanicalsimulations, such as finite element analysis and peridynamics. In this work, those processes are automatized byusing a Sobel operator for identifying edges, which is normalized by different proposed Gaussian filters (on intensity, rugosity,or both). After that, a threshold is used to discretize the edges. Different neighboring pixel configurations, sensitive to edgeintensity, are proposed for thinning and cleaning the discretized edges, and hence, grain boundaries with a one-pixel thicknessare obtained. Then, the phase is selected by averaging color of each delimited grain. Finally, the precision on the phaserecognition was found to increase from 75.61 to 83.6% for the unmodified and the normalized Sobel operator, respectively.
Muscle-specific color stability in fresh beef from grain-finished Bos indicus cattle
Ana Paula A. A. Salim,Surendranath P. Suman,Anna C.V.C.S. Canto,Bruno R.C. Costa-Lima,Fernanda M. Viana,Maria Lucia G. Monteiro,Teofilo J. P. Silva,Carlos A. Conte-Junior 아세아·태평양축산학회 2019 Animal Bioscience Vol.32 No.7
Objective: To investigate the color and oxidative stabilities of longissimus lumborum (LL) and psoas major (PM) muscles from grain-finished Bos indicus cattle in Brazil. Methods: The LL and PM muscles were obtained 24 h post-mortem from eight (n = 8) Nellore bull carcasses, fabricated into 1.5-cm steaks, aerobically packaged, and stored at 4°C for nine days. Steaks were analyzed for myoglobin concentration, pH, instrumental color, metmyoglobin reducing activity (MRA) and lipid oxidation. Results: The LL steaks exhibited greater (p<0.05) redness, color stability, and MRA than their PM counterparts on days 5 and 9. The LL and PM steaks demonstrated similar (p>0.05) lightness and yellowness on days 0, 5, and 9. On the other hand, PM steaks exhibited greater (p<0.05) myoglobin concentration, pH, and lipid oxidation than their LL counterparts. Conclusion: These results indicated that muscle source influenced the color and oxidative stabilities of beef from grain-finished Bos indicus animals. These results highlighted the necessity of muscle-specific strategies to improve the color stability of beef from grain-fed Bos indicus cattle.
Sun, Xiangqing,Elston, Robert C.,Barnholtz-Sloan, Jill S.,Falk, Gary W.,Grady, William M.,Faulx, Ashley,Mittal, Sumeet K.,Canto, Marcia,Shaheen, Nicholas J.,Wang, Jean S.,Iyer, Prasad G.,Abrams, Julia American Association for Cancer Research 2016 Cancer Epidemiology, Biomarkers & Prevention Vol.25 No.5
<P><B>Background:</B> Barrett's esophagus is often asymptomatic and only a small portion of Barrett's esophagus patients are currently diagnosed and under surveillance. Therefore, it is important to develop risk prediction models to identify high-risk individuals with Barrett's esophagus. Familial aggregation of Barrett's esophagus and esophageal adenocarcinoma, and the increased risk of esophageal adenocarcinoma for individuals with a family history, raise the necessity of including genetic factors in the prediction model. Methods to determine risk prediction models using both risk covariates and ascertained family data are not well developed.</P><P><B>Methods:</B> We developed a Barrett's Esophagus Translational Research Network (BETRNet) risk prediction model from 787 singly ascertained Barrett's esophagus pedigrees and 92 multiplex Barrett's esophagus pedigrees, fitting a multivariate logistic model that incorporates family history and clinical risk factors. The eight risk factors, age, sex, education level, parental status, smoking, heartburn frequency, regurgitation frequency, and use of acid suppressant, were included in the model. The prediction accuracy was evaluated on the training dataset and an independent validation dataset of 643 multiplex Barrett's esophagus pedigrees.</P><P><B>Results:</B> Our results indicate family information helps to predict Barrett's esophagus risk, and predicting in families improves both prediction calibration and discrimination accuracy.</P><P><B>Conclusions:</B> Our model can predict Barrett's esophagus risk for anyone with family members known to have, or not have, had Barrett's esophagus. It can predict risk for unrelated individuals without knowing any relatives' information.</P><P><B>Impact:</B> Our prediction model will shed light on effectively identifying high-risk individuals for Barrett's esophagus screening and surveillance, consequently allowing intervention at an early stage, and reducing mortality from esophageal adenocarcinoma. <I>Cancer Epidemiol Biomarkers Prev; 25(5); 727–35. ©2016 AACR</I>.</P>