Case Study of Altruistic Behavior and Relational Network with Business Value on Local Travel Agency

The present study based on a local travel agency in Tainan, investigation how does the altruistic behavior affected the relational network and created the business value. The case company’s CEO had voluntary participated in charity societies more than 20 years. The present study first showed how the case company’s CEO to build an emotional relational network through altruistic behavior. Second, how does the emotional relational network form mixed relational network based on the key features demonstrated by altruistic behavior. Finally, also showed how the mixed relational network is transfer into an instrumental relational network. The results showed that the altruistic behavior can help local travel agency develop and increase their business value via relational network. The key factors to maximize the business value are the professional knowledge and altruistic behavior

Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation

While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated. However, one cannot easily address this task without observing ground truth annotation for the training data. To address this problem, we propose a novel deep learning model of Cross-Domain Representation Disentangler (CDRD). By observing fully annotated source-domain data and unlabeled target-domain data of interest, our model bridges the information across data domains and transfers the attribute information accordingly. Thus, cross-domain joint feature disentanglement and adaptation can be jointly performed. In the experiments, we provide qualitative results to verify our disentanglement capability. Moreover, we further confirm that our model can be applied for solving classification tasks of unsupervised domain adaptation, and performs favorably against state-of-the-art image disentanglement and translation methods.Comment: CVPR 2018 Spotligh

Adaptive computation of multiscale entropy and its application in EEG signals for monitoring depth of anesthesia during surgery

Entropy as an estimate of complexity of the electroencephalogram is an effective parameter for monitoring the depth of anesthesia (DOA) during surgery. Multiscale entropy (MSE) is useful to evaluate the complexity of signals over different time scales. However, the limitation of the length of processed signal is a problem due to observing the variation of sample entropy (SE) on different scales. In this study, the adaptive resampling procedure is employed to replace the process of coarse-graining in MSE. According to the analysis of various signals and practical EEG signals, it is feasible to calculate the SE from the adaptive resampled signals, and it has the highly similar results with the original MSE at small scales. The distribution of the MSE of EEG during the whole surgery based on adaptive resampling process is able to show the detailed variation of SE in small scales and complexity of EEG, which could help anesthesiologists evaluate the status of patients.The Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan which is sponsored by National Science Council (Grant Number: NSC 100-2911-I-008-001). Also, it was supported by Chung-Shan Institute of Science & Technology in Taiwan (Grant Numbers: CSIST-095-V101 and CSIST-095-V102). Furthermore, it was supported by the National Science Foundation of China (No.50935005)

Angelica Sinensis promotes myotube hypertrophy through the PI3K/Akt/mTOR pathway

BACKGROUND: Angelica Sinensis (AS), a folk medicine, has long been used in ergogenic aids for athletes, but there is little scientific evidence supporting its effects. We investigated whether AS induces hypertrophy in myotubes through the phosphatidylinositol 3-kinase (PI3K)/Akt (also termed PKB)/mammalian target of the rapamycin (mTOR) pathway. METHODS: An in vitro experiment investigating the induction of hypertrophy in myotubes was conducted. To investigate whether AS promoted the hypertrophy of myotubes, an established in vitro model of myotube hypertrophy with and without AS was used and examined using microscopic images. The role of the PI3K/Akt/mTOR signaling pathway in AS-induced myotube hypertrophy was evaluated. Two inhibitors, wortmannin (an inhibitor of PI3K) and rapamycin (an inhibitor of mTOR), were used. RESULT: The results revealed that the myotube diameters in the AS-treated group were significantly larger than those in the untreated control group (P < 0.05). Wortmannin and rapamycin inhibited AS-induced hypertrophy. Furthermore, AS increased Akt and mTOR phosphorylation through the PI3K pathway and induced myotube hypertrophy. CONCLUSION: The results confirmed that AS induces hypertrophy in myotubes through the PI3K/Akt/mTOR pathway