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您的检索式：作者名="Jake Huang"
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| 1 | Exercise-induced irisin in bone and systemic irisin administration reveal new regulatory mechanisms of bone metabolism显示文摘Irisin is a polypeptide hormone derived from the proteolytic cleavage of fibronectin-type III domaincontaining 5(FNDC5) protein. Once released to circulation upon exercise or cold exposure, irisin stimulates browning of white adipose tissue(WAT) and uncoupling protein 1(UCP1) expression, leading to an increase in total body energy expenditure by augmented UCP1-mediated thermogenesis. It is currently unknown whether irisin is secreted by bone upon exercise or whether it regulates bone metabolism in vivo. In this study, we found that 2 weeks of voluntary wheel-running exercise induced high levels of FNDC5 messenger RNA as well as FNDC5/irisin protein expression in murine bone tissues. Increased immunoreactivity due to exercise-induced FNDC5/irisin expression was detected in different regions of exercised femoral bones,including growth plate, trabecular bone, cortical bone, articular cartilage, and bone–tendon interface. Exercise also increased expression of osteogenic markers in bone and that of UCP1 in WAT, and led to bodyweight loss. Irisin intraperitoneal(IP) administration resulted in increased trabecular and cortical bone thickness and osteoblasts numbers, and concurrently induced UCP1 expression in subcutaneous WAT. Lentiviral FNDC5 IP administration increased cortical bone thickness. In vitro studies in bone cells revealed irisin increases osteoblastogenesis and mineralization, and inhibits receptor activator of nuclear factor-k B ligand(RANKL)-induced osteoclastogenesis. Taken together, our findings show that voluntary exercise increases irisin production in bone, and that an increase in circulating irisin levels enhances osteogenesis in mice. | Jin Zhang Paloma Valverde Xiaofang Zhu Dana Murray Yuwei Wu Liming Yu Hua Jiang Michel M Dard Jin Huang Zhiwei Xu Qisheng Tu Jake Chen | 2017 | Bone Research2017，5，1： | 23 |
| 2 | Prediction of impedance responses of protonic ceramic cells using artificial neural network tuned with the distribution of relaxation times显示文摘A deep-learning-based framework is proposed to predict the impedance response and underlying electrochemical behavior of the reversible protonic ceramic cell(PCC) across a wide variety of different operating conditions.Electrochemical impedance spectra(EIS) of PCCs were first acquired under a variety of opera ting conditions to provide a dataset containing 36 sets of EIS spectra for the model.An artificial neural network(ANN) was then trained to model the relationship between the cell operating condition and EIS response.Finally,ANN model-predicted EIS spectra were analyzed by the distribution of relaxation times(DRT) and compared to DRT spectra obtained from the experimental EIS data,enabling an assessment of the accumulative errors from the predicted EIS data vs the predicted DRT.We show that in certain cases,although the R^(2)of the predicted EIS curve may be> 0.98,the R^(2)of the predicted DRT may be as low as~0.3.This can lead to an inaccurate ANN prediction of the underlying time-resolved electrochemical response,although the apparent accuracy as evaluated from the EIS prediction may seem acceptable.After adjustment of the parameters of the ANN framework,the average R^(2)of the DRTs derived from the predicted EIS can be improved to 0.9667.Thus,we demonstrate that a properly tuned ANN model can be used as an effective tool to predict not only the EIS,but also the DRT of complex electrochemical systems. | Xuhao Liu Zilin Yan Junwei Wu Jake Huang Yifeng Zheng Neal PSullivan Ryan O'Hayre Zheng Zhong Zehua Pan | 2023 | Journal of Energy Chemistry2023，，3： | 0 |
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