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| 1 | Gold nanoflowers for 3D volumetric molecular imaging of tumors by photoacoustic tomography显示文摘By binding molecular probes that target tumor cells, gold nanoparticles (AuNPs) with superior characteristics have shown great potential in tumor molecular imaging studies. The non-invasive, high-resolution, and three-dimensional imaging of the targeted AuNPs within the tumor is desirable for both diagnosis and therapy. In this study, gold nanoflowers (AuNFs) are presented as a novel contrast agent for photoacoustic tomography (PAT). By binding to folic acid, the molecular probe, the tail-vein injected AuNFs concentrated within the tumor site in mice; this was clearly visualized by three-dimensional (3D) PAT imaging. In addition, toxicity assay proved that AuNFs were harmless to living cells and animals. Our results demonstrate that AuNFs have great potential in tumor molecular imaging. | Yuanyuan Jiang Zijian Deng Dan Yang Xin Deng Qi Li Yinlin Sha Changhui Li Dongsheng Xu | 2015 | Nano Research2015，8，7： | 8 |
| 2 | Introducing memo- ry and association mechanism into a biologically inspired visual model 显示文摘 | Qiao Hong Li Yinlin Tang Tang | 2013 | IEEE Transactions on Cybernetics2013，44，9： | 1 |
| 3 | Erratum to: Gold nanoflowers for 3D volumetric molecular imaging of tumors by photoacoustic tomography显示文摘 | Yuanyuan Jiang Zijian Deng Dan Yang Xin Deng Oi Li Yinlin Sha Changhui Li Dongsheng Xu | 2017 | Nano Research2017，10，3： | 0 |
| 4 | Deep Learning-based Moving Object Segmentation:Recent Progress and Research Prospects显示文摘Moving object segmentation(MOS),aiming at segmenting moving objects from video frames,is an important and challenging task in computer vision and with various applications.With the development of deep learning(DL),MOS has also entered the era of deep models toward spatiotemporal feature learning.This paper aims to provide the latest review of recent DL-based MOS methods proposed during the past three years.Specifically,we present a more up-to-date categorization based on model characteristics,then compare and discuss each category from feature learning(FL),and model training and evaluation perspectives.For FL,the methods reviewed are divided into three types:spatial FL,temporal FL,and spatiotemporal FL,then analyzed from input and model architectures aspects,three input types,and four typical preprocessing subnetworks are summarized.In terms of training,we discuss ideas for enhancing model transferability.In terms of evaluation,based on a previous categorization of scene dependent evaluation and scene independent evaluation,and combined with whether used videos are recorded with static or moving cameras,we further provide four subdivided evaluation setups and analyze that of reviewed methods.We also show performance comparisons of some reviewed MOS methods and analyze the advantages and disadvantages of reviewed MOS methods in terms of technology.Finally,based on the above comparisons and discussions,we present research prospects and future directions. | Rui Jiang Ruixiang Zhu Hu Su Yinlin Li Yuan Xie Wei Zou | 2023 | Machine Intelligence Research2023，20，3： | 0 |
| 5 | A Multicenter Metal–Organic Framework for Quantitative Detection of Multicomponent Organic Mixtures显示文摘Instant recognition and quantification of multicomponent organicmixtures,especially the oneswith similar characteristics,remains a key challenge in the chemical industry.Although luminescence sensing is considered a rapid and facile method for the detection of various chemical substances,the simultaneous and quantitative detection of three or more components has not yet been achieved.Herein,we report a rationally designed white-light-emitting metal–organic framework(MOF)EuTb@NKU-102,and demonstrate its application in the simultaneous discrimination of multicomponent organic mixtures. | Zongsu Han Kunyu Wang Yinlin Chen Jiangnan Li Simon JTeat Sihai Yang Wei Shi Peng Cheng | 2022 | CCS Chemistry2022，4，10： | 0 |
| 6 | Cascade adsorptive separation of light hydrocarbons by commercial zeolites显示文摘Adsorptive separation of light hydrocarbons by porous solids provides an energy-efficient alternative to state-of-the-art cryogenic distillation.However,an optimal balance between the cost,performance and stability of the sorbent material is yet to be achieved for industrial applications.Here,we report the efficient separation of C2 and C3 hydrocarbons by a faujasite zeolite(Na-X,Si/Al=1.23).A tandem configuration of two fixed-beds packed with Na-X affords complete dynamic separation of the ternary mixture of C_(2)H_(2)/C_(2)H_(4)/C_(2)H_(6)(1/49.5/49.5;v/v/v)under ambient conditions.Pressure-swing desorption on the latter fixed-bed gives ethylene(>99.50%,1.80 mmol g^(-1))and ethane(>99.99%,1.41 mmol g^(-1)).In situ synchrotron X-ray powder diffraction revealed the binding sites for C_(2)H_(2)and C_(2)H_(4)in Na-X.This study highlights the potential application of commercial zeolites for challenging industrial separations. | Shanshan Liu Yinlin Chen Bin Yue Yuanxin Nie Yuchao Chai Guangjun Wu Jiangnan Li Xue Han Sarah JDay Stephen PThompson Naijia Guan Sihai Yang Landong Li | 2022 | Journal of Energy Chemistry2022，31，9： | 0 |
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