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| 1 | Autoantibody to MOG suggests two distinct clinical subtypes of NMOSD显示文摘We characterized a unique group of patients with neuromyelitis optica spectrum disorder(NMOSD) who carried autoantibodies of aquaporin-4(AQP4) and myelin-oligodendrocyte glycoprotein(MOG). Among the 125 NMOSD patients, 10(8.0%) were AQP4- and MOG-ab double positive, and 14(11.2%) were MOG-ab single positive. The double-positive patients had a multiphase disease course with a high annual relapse rate(P=0.0431), and severe residual disability(P<0.0001). Of the double-positive patients, 70% had MS-like brain lesions, more severe edematous, multifocal regions on spinal magnetic resonance imaging(MRI), pronounced decreases of retinal nerve fiber layer thickness and atrophy of optic nerves. In contrast, patients with only MOG-ab had a higher ratio of monophasic disease course and mild residual disability. Spinal cord MRI illustrated multifocal cord lesions with mild edema, and brain MRIs showed more lesions around lateral ventricles. NMOSD patients carrying both autoantibodies to AQP4 and MOG existed and exhibited combined features of prototypic NMO and relapsing-remitting form of MS, whereas NMOSD with antibodies to MOG only exhibited an 'intermediate' phenotype between NMOSD and MS. Our study suggests that antibodies against MOG might be pathogenic in NMOSD patients and that determination of anti-MOG antibodies maybe instructive for management of NMOSD patients. | Yaping Yan Yujing Li Ying Fu Li Yang Lei Su Kaibin Shi Minshu Li Qiang Liu Aimee Borazanci Yaou Liu Yong He Jeffrey L Bennett Timothy L Vollmer Fu-Dong Shi | 2016 | Science China(Life Sciences)2016，59，12： | 20 |
| 2 | Wind erosion prediction over the Australian continent显示文摘 | SHAO Yaping LANCE M L | 1997 | Journal of Geophysical Research1997，102，30： | 1 |
| 3 | Recrystallization kinetics and microstructure evolution during annealing of a cold-rolled Fe-Mn-C alloy显示文摘 | Yaping L Dmitri A.Molodov | | 0，，59： | 1 |
| 4 | 显示文摘 | RIGGS J E GUO Zhixin CARROL D L SUN Yaping | 2000 | J Am Chem Soc2000，122，24： | 1 |
| 5 | Capturingand profiling adult hair follicle stem cells显示文摘 | Rebecca JM Yaping L Lee M | 2004 | Nature Biotech2004，22，： | 1 |
| 6 | Toxicokinetic and genomic analysis of chronic arsenic exposure in multidrug-resistance mdr1a/1b(–/–) double knockout mice显示文摘 | Yaxiong X Jie L Yaping L | 2004 | Molecular and Cellular Biochemistry2004，255，： | 1 |
| 7 | Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning显示文摘Elemental composition is a key parameter in solid waste treatment and disposal. This study has proposed a method based on infrared spectroscopy and machine learning algorithms that can rapidly predict the elemental composition (C, H, N, S) of solid waste. Both noise and moisture spectral interference that may occur in practical application are investigated. By comparing two feature selection methods and five machine learning algorithms, the most suitable models are selected. Moreover, the impacts of noise and moisture on the models are discussed, with paper, plastic, textiles, wood, and leather as examples of recyclable waste components. The results show that the combination of the feature selection and K-nearest neighbor (KNN) approaches exhibits the best prediction performance and generalization ability. Particularly, the coefficient of determination (R2) of the validation set, cross validation and test set are higher than 0.93, 0.89, and 0.97 for predicting the C, H, and N contents, respectively. Further, KNN is less sensitive to noise. Under moisture interference, the combination of feature selection and support vector regression or partial least-squares regression shows satisfactory results. Therefore, the elemental compositions of solid waste are quickly and accurately predicted under noise and moisture disturbances using infrared spectroscopy and machine learning algorithms. | Haoyang Xian Pinjing He Dongying Lan Yaping Qi Ruiheng Wang Fan Lü Hua Zhang Jisheng Long | 2023 | Frontiers of Environmental Science & Engineering2023，17，10： | 0 |
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