Zobrazeno 1 - 10
of 1 314
pro vyhledávání: '"Jianfeng LU"'
Autor:
Guanhao Wang, Lining Cao, Shuanqing Li, Meihui Zhang, Yingqi Li, Jinjin Duan, You Li, Zhangsen Hu, Jiaan Wu, Jianbo Ni, Danmei Lan, Tianming Li, Jianfeng Lu
Publikováno v:
npj Biofilms and Microbiomes, Vol 10, Iss 1, Pp 1-15 (2024)
Abstract The role of gut microbiota (GM) dysbiosis in the pathogenesis of depression has received widespread attention, but the mechanism remains elusive. Corticosterone (CORT)-treated mice showed depression-like behaviors, reduced hippocampal neurog
Externí odkaz:
https://doaj.org/article/a4acf2088e604157887588bfb3062a1c
Publikováno v:
Alexandria Engineering Journal, Vol 114, Iss , Pp 543-555 (2025)
With the rise of artificial intelligence approaches, fully automatic tooth segmentation models from Cone-beam Computed Tomography (CBCT) images become more popular for dental clinical diagnosis. Recently, many deep learning-based tooth segmentation t
Externí odkaz:
https://doaj.org/article/371c2e300caa4be89dadb60b94bce150
Autor:
Chenxing Du, Ge Zhu, Hanwen Hu, Zhangqun Duan, Shuizhong Luo, Lin Lin, Jianfeng Lu, Zhi Zheng
Publikováno v:
Food Chemistry: X, Vol 24, Iss , Pp 101866- (2024)
This study investigated the mechanisms underlying the influence of droplet size and emulsifier wettability on gel properties when oil-in-water (O/W) emulsions serve as fillers in myofibrillar protein (MP) gels. Pickering emulsions with varying drople
Externí odkaz:
https://doaj.org/article/f013831fa7814bab8c86ea4b897603ff
Autor:
Xiujun Liu, Yitong Ji, Zezhou Xia, Dongyang Zhang, Yingying Cheng, Xiangda Liu, Xiaojie Ren, Xiaotong Liu, Haoran Huang, Yanqing Zhu, Xueyuan Yang, Xiaobin Liao, Long Ren, Wenliang Tan, Zhi Jiang, Jianfeng Lu, Christopher McNeill, Wenchao Huang
Publikováno v:
Advanced Science, Vol 11, Iss 37, Pp n/a-n/a (2024)
Abstract Sol–gel processed zinc oxide (ZnO) is one of the most widely used electron transport layers (ETLs) in inverted organic solar cells (OSCs). The high annealing temperature (≈200 °C) required for sintering to ensure a high electron mobilit
Externí odkaz:
https://doaj.org/article/7c100fcafa474a37ae3048718acc1e7b
Autor:
Yanqing Zhu, Chenglong Li, JiaHui Chen, Yuxi Zhang, Jianfeng Lu, Min Hu, Wangnan Li, Fuzhi Huang, Yi‐Bing Cheng, Hyesung Park, Shengqiang Xiao
Publikováno v:
Interdisciplinary Materials, Vol 3, Iss 3, Pp 369-379 (2024)
Abstract Fullerene derivatives are highly attractive materials in solar cells, organic thermoelectrics, and other devices. However, the intrinsic low electron mobility and electrical conductivity restrict their potential device performance, such as p
Externí odkaz:
https://doaj.org/article/7774794e07c7430182720f4505b2787a
Autor:
Yingqi Li, Jinjin Duan, You Li, Meihui Zhang, Jiaan Wu, Guanhao Wang, Shuanqing Li, Zhangsen Hu, Yi Qu, Yunhe Li, Xiran Hu, Fei Guo, Lining Cao, Jianfeng Lu
Publikováno v:
Stem Cell Research & Therapy, Vol 15, Iss 1, Pp 1-16 (2024)
Abstract Background The detailed transcriptomic profiles during human serotonin neuron (SN) differentiation remain elusive. The establishment of a reporter system based on SN terminal selector holds promise to produce highly-purified cells with an ea
Externí odkaz:
https://doaj.org/article/06e6e4f9f65a44be8acc6c99549514a4
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 3729-3740 (2024)
Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a powerful tool for exploring interactions among brain regions. A growing body of research is actively investigating various computational approaches for estimating causal e
Externí odkaz:
https://doaj.org/article/fbd9cfbefb7649d2bf8deb4606f2a46b
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 3084-3094 (2024)
Brain networks/graphs have been widely recognized as powerful and efficient tools for identifying neurological disorders. In recent years, various graph neural network models have been developed to automatically extract features from brain networks.
Externí odkaz:
https://doaj.org/article/2e0f23fb5e5a4d6098f65dea80c9bf7d
Publikováno v:
IEEE Access, Vol 12, Pp 52255-52266 (2024)
Although federated learning (FL) represents a distributed machine learning paradigm that ensures privacy protection, the failure of stragglers to upload local models in a timely manner results in an overall degradation of the global model’s perform
Externí odkaz:
https://doaj.org/article/6b0931608de24904aff810bfe5c4ad70
Publikováno v:
IEEE Access, Vol 12, Pp 24735-24750 (2024)
With the growing public attention to data privacy protection, the problem of data silos has been exacerbated, which makes it more difficult for crowd intelligence technologies to get off the ground. Meanwhile, Federated Learning (FL) has received gre
Externí odkaz:
https://doaj.org/article/ee313c0249424c1f99b0d4890afabcf4