Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Yifan Sheng"'
Autor:
Yuanhui Jia, Wenjing Lu, Han Xie, Yifan Sheng, Luyao Wang, Wenqi Lv, Lijun Ling, Jiaqi Dong, Xinrui Jia, Shengyu Wu, Wenqiang Liu, Hao Ying
Publikováno v:
Journal of Translational Medicine, Vol 22, Iss 1, Pp 1-18 (2024)
Abstract Background Preeclampsia, especially early-onset preeclampsia (EO-PE), is a pregnancy complication that has serious consequences for the health of both the mother and the fetus. Although abnormal placentation due to mitochondrial dysfunction
Externí odkaz:
https://doaj.org/article/d61dd68bfba448ee8e032b9a056fa334
Publikováno v:
Shuiwen dizhi gongcheng dizhi, Vol 50, Iss 3, Pp 125-137 (2023)
When using machine learning models for landslide susceptibility evaluation, the non-landslide sample points are usually selected randomly outside the landslide influence area, leading to a certain error. To improve the accuracy of landslide susceptib
Externí odkaz:
https://doaj.org/article/c7c6c8fc2df349d0a7366e1be0418849
Autor:
Herbert Gnetegha Fotsidie, Aurele Gnetegha Ayemele, Gaojuan Zhao, Xiang Li, Yifan Sheng, Lu Ma, Dengpan Bu
Publikováno v:
Circular Agricultural Systems, Vol 3, Iss 1, Pp 1-4 (2023)
This study aimed at evaluating the milk production after supplementing Calotropis gigantea (Giant milkweed, GM) silage as a new functional feed additive for ruminants. Cows refused to eat GM plants so, we processed it into silage before feeding. Afte
Externí odkaz:
https://doaj.org/article/a4681a3337ca466090c70219659ad535
Publikováno v:
Shipin Kexue, Vol 44, Iss 2, Pp 173-180 (2023)
An unknown laboratory strain was identified and named as Bacillus cereus HMPM18123 based on the 16S rRNA gene and conserved protein-coding gene (gyrA and gyrB) sequences combined with virulence genes (nheA, nheB, nheC, hblA, hblC, hblD, becT, cytK an
Externí odkaz:
https://doaj.org/article/49447587fb2b4b56899190d97926271d
Publikováno v:
Remote Sensing, Vol 15, Iss 21, p 5256 (2023)
Landslide susceptibility mapping (LSM) is significant for landslide risk assessment. However, there remains no consensus on which method is optimal for LSM. This study implements a dynamic approach to landslide hazard mapping by integrating spatio-te
Externí odkaz:
https://doaj.org/article/07328885029b44db9c0454d95f4e711a
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 11, Iss 1, Pp 510-534 (2020)
Machine learning models are regarded as efficient and popular models for natural disaster susceptibility prediction. However, few studies have focussed on the applications of the latest popular machine learning models in collapse susceptibility asses
Externí odkaz:
https://doaj.org/article/b3c55ba5054c4a6086927215a65d3052
Publikováno v:
Journal of Functional Foods, Vol 77, Iss , Pp 104149- (2021)
Rhizoma Polygonati (huangjing in Chinese, 黄精) is a medicine food homology herb used as a component of traditional Chinese medicine treating COVID-19 in the current pandemic emergency in China but the mechanisms remain elusive. Here using TCMSP an
Externí odkaz:
https://doaj.org/article/bf53b0761ed743ac94881edd85431e68
Publikováno v:
Data in Brief, Vol 33, Iss , Pp 106475- (2020)
Rhizoma Polygonati (Chinese name as 黄精, pinyin as huangjing), as medicine and food homology of Traditional Chinese Medicine, has been recently applied for the complex prescriptions of alternative medicine for treatment of COVID-19 but the mechani
Externí odkaz:
https://doaj.org/article/09e2b55d76a4484490f2554f929e0f74
Autor:
Taiyi Chen, Guangli Xu, Jiangfeng Li, Huizhong Li, Yifan Sheng, Ran Qi, Xueping Li, Yang Wang, Yuqing Ding, Junyu Zhang, Junning Liu
Publikováno v:
Journal of Geoscience and Environment Protection. 10:160-169
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4dfcdd411d290572669c142d45af8666
https://doi.org/10.4236/gep.2022.105012
https://doi.org/10.4236/gep.2022.105012
Publikováno v:
Bulletin of Engineering Geology and the Environment. 81
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f87501dc3ed0c72c3af9ea7b23c7720f
https://doi.org/10.1007/s10064-022-02890-x
https://doi.org/10.1007/s10064-022-02890-x