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| 1 | Disassembly of the fruit cell wall by the ripening-associated polygalacturonase and expansin influences tomato cracking显示文摘Fruit cracking is an important problem in horticultural crop production.Polygalacturonase(SlPG)and expansin(SlEXP1)proteins cooperatively disassemble the polysaccharide network of tomato fruit cell walls during ripening and thereby,enable softening.A Golden 2-like(GLK2)transcription factor,SlGLK2 regulates unripe fruit chloroplast development and results in elevated soluble solids and carotenoids in ripe fruit.To determine whether SlPG,SlEXP1,or SlGLK2 influence the rate of tomato fruit cracking,the incidence of fruit epidermal cracking was compared between wild-type,Ailsa Craig(WT)and fruit with suppressed SlPG and SlEXP1 expression(pg/exp)or expressing a truncated nonfunctional Slglk2(glk2).Treating plants with exogenous ABA increases xylemic flow into fruit.Our results showed that ABA treatment of tomato plants greatly increased cracking of fruit from WT and glk2 mutant,but not from pg/exp genotypes.The pg/exp fruit were firmer,had higher total soluble solids,denser cell walls and thicker cuticles than fruit of the other genotypes.Fruit from the ABA treated pg/exp fruit had cell walls with less water-soluble and more ionically and covalently-bound pectins than fruit from the other lines,demonstrating that ripening-related disassembly of the fruit cell wall,but not elimination of SlGLK2,influences cracking.Cracking incidence was significantly correlated with cell wall and wax thickness,and the content of cell wall protopectin and cellulose,but not with Ca^(2+)content. | Fangling Jiang Alfonso Lopez Shinjae Jeon Sergio Tonetto de Freitas Qinghui Yu Zhen Wu John M.Labavitch Shengke Tian Ann L.T.Powell Elizabeth Mitcham | 2019 | Horticulture Research2019，6，1： | 13 |
| 2 | Effects of bamboo charcoal on the growth performance, blood characteristics and noxious gas emission in fattening pigs显示文摘 | GyoMoon Chu JongHyun Kim HoiYun Kim JiHee Ha MinSeob Jung Yuno Song JaeHyun Cho ShinJa Lee RashidIsmael Hag Ibrahim SungSill Lee YoungMin Song | 2013 | Journal of Applied Animal Research2013，，1： | 2 |
| 3 | Effects of cordyceps militaris mycelia on in vitro rumen microbial fermentation显示文摘 | Joonmo Y Shinja L Sangmin L | 2009 | AsianAustralasian Journal of Animal Sciences2009，22，2： | 1 |
| 4 | Influence of N2 gas pressure on the chemical bonds of amorphous carbon nitride films 显示文摘 | ROH Kimin YOU Shinjae CHOI Sikyoung | 2009 | Plasma Processes Polym2009，9，7： | 1 |
| 5 | 动态血糖监测的临床应用[J]显示文摘 | KimHS ShinJA ChangJS等 | 2012 | 糖尿病/代谢研究和评论2012，28，： | 1 |
| 6 | Acute resveratrol treatment modulates multiple signaling pathways in the ischemic brain显示文摘 | ShinJA Lee KE Kim HS | 2012 | Neurochem Res2012，37，： | 1 |
| 7 | Three-dimensional coherent X-ray diffraction imaging via deep convolutional neural networks显示文摘As a critical component of coherent X-ray diffraction imaging(CDI),phase retrieval has been extensively applied in X-ray structural science to recover the 3D morphological information inside measured particles.Despite meeting all the oversampling requirements of Sayre and Shannon,current phase retrieval approaches still have trouble achieving a unique inversion of experimental data in the presence of noise.Here,we propose to overcome this limitation by incorporating a 3D Machine Learning(ML)model combining(optional)supervised learning with transfer learning.The trained ML model can rapidly provide an immediate result with high accuracy which could benefit real-time experiments,and the predicted result can be further refined with transfer learning.More significantly,the proposed ML model can be used without any prior training to learn the missing phases of an image based on minimization of an appropriate‘loss function’alone.We demonstrate significantly improved performance with experimental Bragg CDI data over traditional iterative phase retrieval algorithms. | Longlong Wu Shinjae Yoo Ana F.Suzana Tadesse A.Assefa Jiecheng Diao Ross J.Harder Wonsuk Cha Ian K.Robinson | 2021 | npj Computational Materials2021，，1： | 1 |
| 8 | Resolution-enhanced X-ray fluorescence microscopy via deep residual networks显示文摘Multimodal hard X-ray scanning probe microscopy has been extensively used to study functional materials providing multiple contrast mechanisms.For instance,combining ptychography with X-ray fluorescence(XRF)microscopy reveals structural and chemical properties simultaneously.While ptychography can achieve diffraction-limited spatial resolution,the resolution of XRF is limited by the X-ray probe size.Here,we develop a machine learning(ML)model to overcome this problem by decoupling the impact of the X-ray probe from the XRF signal.The enhanced spatial resolution was observed for both simulated and experimental XRF data,showing superior performance over the state-of-the-art scanning XRF method with different nano-sized X-ray probes.Enhanced spatial resolutions were also observed for the accompanying XRF tomography reconstructions.Using this probe profile deconvolution with the proposed ML solution to enhance the spatial resolution of XRF microscopy will be broadly applicable across both functional materials and biological imaging with XRF and other related application areas. | Longlong Wu Seongmin Bak Youngho Shin Yong S.Chu Shinjae Yoo Ian K.Robinson Xiaojing Huang | 2023 | npj Computational Materials2023，，1： | 0 |
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