Identifying the Components of Tenderness Using Differential Scanning Calorimetry

Several components have been identified as general predictors of beef tenderness. Most of these components include the collagen in the connective tissue, myofibrillar proteins and fat. Berry et al. (1974} reported that muscles rated low in tenderness exhibited a lower percent of soluble collagen content than those with high collagen solubility, while other studies (Kruggel et al. 1970; Smith and Carpenter, 1970; Cross et al, 1973} found lower correlation between total collagen content and tenderness of muscles. In addition, Bouton and Harris (1972b) concluded myofibrils reflected the major differences in tenderness of muscles. Moreover, Cross et al. (1972) and McKeith et al. (1985) reported muscles with higher percent fat tended to be more tender. Differential scanning calorimetry (DSC) has been used to study the thermal denaturation of proteins in post rigor muscle (Ledward and Lawrie, 1975; Wright et al. 1977; Stabursvik and Martens, 1980; Findlay and Stanley, 1984). Wagner and Anon (1985) determined the thermal denaturation kinetics of myofibrillar proteins in bovine muscle. Also, Wu et al. (1985) investigated thermal transitions of fish mince and actomyosin from croaker by using DSC. Therefore, the objective of this study was to use differential scanning calorimetry to study thermal denaturation of muscle protein from selected beef muscles differing in tenderness.Food Scienc

Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal

Deep Convolutional Neural Networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many kernels to extract the knowledge behind it. However, with the depth of convolutional layers getting deeper and deeper in recent years, the enormous computational complexity makes it difficult to be deployed on embedded systems with limited hardware resources. In this paper, we propose two computation-performance optimization methods to reduce the redundant convolution kernels of a CNN with performance and architecture constraints, and apply it to a network for super resolution (SR). Using PSNR drop compared to the original network as the performance criterion, our method can get the optimal PSNR under a certain computation budget constraint. On the other hand, our method is also capable of minimizing the computation required under a given PSNR drop.Comment: This paper was accepted by 2018 The International Symposium on Circuits and Systems (ISCAS

Development of a short and universal learning self-efficacy scale for clinical skills

Background Learning self-efficacy, defined as learners’ confidence in their capability to learn specific subjects, is crucial for the enhancement of academic progress, because it is positively correlated with academic achievements and effective learning strategy use. In this study, we developed a universal scale called the Learning Self-Efficacy Scale (L-SES) for Clinical Skills for undergraduate medical students and validated it through item analysis and content validity index (CVI) calculation. Design The L-SES was developed based on the framework of Bloom’s taxonomy, and the questions were generated through expert consensus and CVI calculation. A pilot version of the L-SES was administered to 235 medical students attending a basic clinical skills course. The collected data were then examined through item analysis. Results The first draft of the L-SES comprised 15 questions. After expert consensus and CVI calculation, 3 questions were eliminated; hence, the pilot version comprised 12 questions. The CVI values of the 12 questions were between .88 and 1, indicating high content validity. Moreover, the item analysis indicated that the quality of L-SES reached the qualified threshold. The results showed that the L-SES scores were unaffected by gender (t = −0.049; 95% confidence interval [−.115, .109], p > .05). Conclusion The L-SES is a short, well-developed scale that can serve as a generic assessment tool for measuring medical students’ learning self-efficacy for clinical skills. Moreover, the L-SES is unaffected by gender differences. However, additional analyses in relevant educational settings are needed