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| 1 | In-situ synthe- sis of SiC particles by the structural evolution of TiC in A1- Si mett显示文摘 | Nie Jinfeng Li Dakui Wang Enzhao | 2014 | J Alloys Compd2014，613，： | 1 |
| 2 | Job scheduling algorithm based on Berger model in cloud environment 显示文摘 | Xu Baomin Zhao Chunyan Hua Enzhao | 2011 | Advances in Engi- neering Software2011，42，： | 1 |
| 3 | Job scheduling algo- rithm based on Berger model in cloud environment显示文摘 | Xu Baomin Zhao Chunyan Hu Enzhao et a! | 2011 | Advances in Engineering Software2011，42，7： | 1 |
| 4 | Job sched- uling algorithm based on Berger model in cloud envi- ronment显示文摘 | Xu Baomin Zhao Chunyan Hua Enzhao | 2011 | Advances in Engineering Software2011，42，： | 1 |
| 5 | Job sched- uling algorithm based on Berger model in cloud environ- ment显示文摘 | Xu Baomin Zhao Chunyan H u Enzhao | 2011 | Advances in Engineering Software2011，42，7： | 1 |
| 6 | Role of Probucol in Preventing Contrast-Induced Acute Kidney Injury After Coronary Interventional Procedure显示文摘 | Guangping Li Li Yin Tong Liu Xintian Zheng Gang Xu Yanmin Xu Ruyu Yuan Jingjin Che Hongmei Liu Lijuan Zhou Xin Chen Mei He Yiding Li Lei Wu Enzhao Liu | 2009 | The American Journal of Cardiology2009，，4： | 1 |
| 7 | Job scheduling algorithm based on Berger model in cloud environment 显示文摘 | Xu Baomin Zhao Chunyan Hua Enzhao Hu Bin | 2011 | Advances in Engineering Software2011，42，： | 1 |
| 8 | Statin use and development of atrial fibrillation: A systematic review and meta-analysis of randomized clinical trials and observational studies显示文摘 | Tong Liu Lijian Li Panagiotis Korantzopoulos Enzhao Liu Guangping Li | 2007 | International Journal of Cardiology2007，，2： | 1 |
| 9 | Soil bacterial communities interact with silicon fraction transformation and promote rice yield after long-term straw return显示文摘Returning crop straw into the soil is an important practice to balance biogenic and bioavailable silicon(Si)pool in paddy,which is crucial for the healthy growth of rice.However,owing to little knowledge about soil microbial communities responsible for straw degradation,how straw return affects Si bioavailability,its uptake,and rice yield remains elusive.Herein,we investigate the change of soil Si fractions and microbial community in a 39-year-old paddy field amended by a long-term straw return.Results show that rice straw return significantly increased soil bioavailable Si and rice yield from 29.9%to 61.6%and from 14.5%to 23.6%,respectively,when compared to NPK fertilization alone.Straw return significantly altered soil microbial community abundance.Acidobacteria was positively and significantly related to amorphous Si,while Rokubacteria at phylum level,Deltaproteobacteria,and Holophagae at class level was negatively and significantly related to organic matter adsorbed and Fe/Mn-oxide-combined Si in soils.Redundancy analysis of their correlations further demonstrated that Si status significantly explained 12%of soil bacterial community variation.These findings suggest that soil bacteria community and diversity interact with Si mobility by altering its transformation,thus resulting in the balance of various nutrient sources to drive biological Si cycle in agroecosystem. | Alin Song Zimin Li Yulin Liao Yongchao Liang Enzhao Wang Sai Wang Xu Li Jingjing Bi Zhiyuan Si Yanhong Lu Jun Nie Fenliang Fan | 2021 | Soil Ecology Letters2021，3，4： | 0 |
| 10 | Personalized surgical recommendations and quantitative therapeutic insights for patients with metastatic breast cancer: Insights from deep learning显示文摘Background:The role of surgery in metastatic breast cancer(MBC)is currently controversial.Several novel statistical and deep learning(DL)methods promise to infer the suitability of surgery at the individual level.Objective:The objective of this study was to identify the most applicable DL model for determining patients with MBC who could benefit from surgery and the type of surgery required.Methods:We introduced the deep survival regression with mixture effects(DSME),a semi-parametric DL model integrating three causal inference methods.Six models were trained to make individualized treatment recommendations.Patients who received treatments in line with the DL models'recommendations were compared with those who underwent treatments divergent from the recommendations.Inverse probability weighting(IPW)was used to minimize bias.The effects of various features on surgery selection were visualized and quantified using multivariate linear regression and causal inference.Results:In total,5269 female patients with MBC were included.DSME was an independent protective factor,outperforming other models in recommending surgery(IPW-adjusted hazard ratio[HR]=0.39,95%confidence interval[CI]:0.19–0.78)and type of surgery(IPW-adjusted HR=0.66,95%CI:0.48–0.93).DSME was superior to other models and traditional guidelines,suggesting a higher proportion of patients benefiting from surgery,especially breast-conserving surgery.The debiased effect of patient characteristics,including age,tumor size,metastatic sites,lymph node status,and breast cancer subtypes,on surgery decision was also quantified.Conclusions:Our findings suggested that DSME could effectively identify patients with MBC likely to benefit from surgery and the specific type of surgery needed.This method can facilitate the development of efficient,reliable treatment recommendation systems and provide quantifiable evidence for decision-making. | Enzhao Zhu Linmei Zhang Jiayi Wang Chunyu Hu Qi Jing Weizhong Shi Ziqin Xu Pu Ai Zhihao Dai Dan Shan Zisheng Ai | 2024 | Cancer Innovation2024，3，3： | 0 |
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