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| 1 | Rhizobial infection triggers systemic transport of endogenous RNAs between shoots and roots in soybean显示文摘Legumes have evolved a symbiotic relationship with rhizobial bacteria and their roots form unique nitrogen-fixing organs called nodules.Studies have shown that abiotic and biotic stresses alter the profile of gene expression and transcript mobility in plants.However,little is known about the systemic transport of RNA between roots and shoots in response to rhizobial infection on a genome-wide scale during the formation of legume-rhizobia symbiosis.In our study,we found that two soybean(Glycine max)cultivars,Peking and Williams,show a high frequency of single nucleotide polymorphisms;this allowed us to characterize the origin and mobility of transcripts in hetero-grafts of these two cultivars.We identified 4,552 genes that produce mobile RNAs in soybean,and found that rhizobial infection triggers mass transport of m RNAs between shoots and roots at the early stage of nodulation.The majority of these mRNAs are of relatively low abundance and their transport occurs in a selective manner in soybean plants.Notably,the mRNAs that moved from shoots to roots at the early stage of nodulation were enriched in many nodule-related responsive processes.Moreover,the transcripts of many known symbiosis-related genes that are induced by rhizobial infection can move between shoots and roots.Our findings provide a deeper understanding of endogenous RNA transport in legume-rhizobia symbiotic processes. | Chen Zhang Meifang Qi Xiaxia Zhang Qi Wang Yanjun Yu Yijing Zhang Zhaosheng Kong | 2020 | Science China(Life Sciences)2020，63，8： | 2 |
| 2 | Novel oral fast-disintegrating drug delivery devices with predefined inner structure fabricated by three-dimensional printing显示文摘 | YU Dengguang SHEN Xiaxia WHITE C B | 2009 | J Pharm Pharmacol2009，61，： | 1 |
| 3 | Use of Deep Learning for Continuous Prediction of Mortality for All Admissions in Intensive Care Units显示文摘The mortality rate in the intensive care unit(ICU)is a key metric of hospital clinical quality.To enhance hospital performance,many methods have been proposed for the stratification of patients’different risk categories,such as severity scoring systems and machine learning models.However,these methods make capturing time sequence information difficult,posing challenges to the continuous assessment of a patient’s severity during their hospital stay.Therefore,we built a predictive model that can make predictions throughout the patient’s stay and obtain the patient’s risk of death in real time.Our proposed model performed much better than other machine learning methods,including logistic regression,random forest,and XGBoost,in a full set of performance evaluation processes.Thus,the proposed model can support physicians’decisions by allowing them to pay more attention to high-risk patients and anticipate potential complications to reduce ICU mortality. | Guangjian Zeng Jinhu Zhuang Haofan Huang Mu Tian Yi Gao Yong Liu Xiaxia Yu | 2023 | Tsinghua Science and Technology2023，28，4： | 0 |
| 4 | VdMKK1-mediated cell wall integrity is essential for virulence in vascular wilt pathogen Verticillium dahliae显示文摘The fungal cell wall is the front-line in host-pathogen interactions,which is an essential dynamic structure maintaining cellular integrity and protecting the fungal cell from external aggressors,such as environmental stress,or during host infection(Geoghegan et al.,2017;Gow et al.,2017).Its synthesizing and remodeling/reinforcement are controlled by the cell wall integrity(CWl)pathway(Riquelme et al.,2018).The CWI pathway is a conserved signalling transduction cascade and is well characterized in the model yeast Saccharomyces cerevisiae.In phytopathogenic fungi,it exhibits more speciesspecific functions to suit their distinct invasion strategies.For many appressorium-forming plant pathogens such as the rice blast fungus Magnaporthe oryzae and Colletotrichum gloeosporioide,CWl pathway regulates the development and functioning of appressorium,which is required for pathogenicity(Jeon et al.,2008;Yin et al.,2016,2020;Fang et al.,2018). | Jiaqi Li Juan Tian Huan Cao Mengli Pu Xiaxia Zhang Yanjun Yu Zhi Wang Zhaosheng Kong | 2023 | Journal of Genetics and Genomics2023，50，8： | 0 |
| 5 | Open-source algorithm and software for computed tomography-based virtual pancreatoscopy and other applications显示文摘Pancreatoscopy plays a significant role in the diagnosis and treatment of pancreatic diseases.However,the risk of pancreatoscopy is remarkably greater than that of other endoscopic procedures,such as gastroscopy and bronchoscopy,owing to its severe invasiveness.In comparison,virtual pancreatoscopy(VP)has shown notable advantages.However,because of the low resolution of current computed tomography(CT)technology and the small diameter of the pancreatic duct,VP has limited clinical use.In this study,an optimal path algorithm and super-resolution technique are investigated for the development of an open-source software platform for VP based on 3D Slicer.The proposed segmentation of the pancreatic duct from the abdominal CT images reached an average Dice coefficient of 0.85 with a standard deviation of 0.04.Owing to the excellent segmentation performance,a fly-through visualization of both the inside and outside of the duct was successfully reconstructed,thereby demonstrating the feasibility of VP.In addition,a quantitative analysis of the wall thickness and topology of the duct provides more insight into pancreatic diseases than a fly-through visualization.The entire VP system developed in this study is available at http://gffzz188fe103f8f1460as9w5kxvpopo906nw6.ffgz.tsg.suse.edu.cn/gaoyi/VirtualEndoscopy.git. | Haofan Huang Xiaxia Yu Mu Tian Weizhen He Shawn Xiang Li Zhengrong Liang Yi Gao | 2022 | Visual Computing for Industry,Biomedicine,and Art2022，5，1： | 0 |
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