| 1 | Hyaluronic acid-based hydrogels with tobacco mosaic virus containing cell adhesive peptide induce bone repair in normal and osteoporotic rats显示文摘Tobacco mosaic virus(TMV)has been studied as a multi-functional agent for bone tissue engineering.An osteo-inductive effect of wild-type TMV has been reported,as it can significantly enhance the bone differentiation potential of bone marrow stromal cells both on a two-dimensional substrate and in a three-dimensional(3D)hydrogel system.A TMV mutant(TMV-RGD1)was created which featured the adhesion peptide arginyl-glycyl-aspartic acid(RGD),the most common peptide motif responsible for cell adhesion to the extracellular matrix,on the surface of the virus particle to enhance the bio-functionality of the scaffold material.We hypothesised that the incorporation of either wild-type TMV or TMV-RGD1 in the 3D hydrogel scaffold would induce bone healing in critical size defects of the cranial segmental bone.We have previously tested the virus-functionalised scaffolds,in vitro,with a hyaluronic acid-based system as an in-situ hydrogel platform for 3D cell encapsulation,culture,and differentiation.The results of these experiments suggested the potential of the virus-functionalised hydrogel to promote in vitro stem cell differentiation.The hydrogel-forming system we employed was shown to be safe and biocompatible in vivo.Here,we further explored the physiological responses regarding bone regeneration of a calvarial defect in both normal and osteoporotic ovariectomized rat models.Our results,based on histological analysis in both animal models,suggested that both wild-type TMV and TMV-RGD1 functionalised hydrogels could accelerate bone regeneration,without systemic toxicity,evaluated by blood counts.New bone formation was intensified by the incorporation of the RGD-mutant viral particles.This finding increased the potential for use of the rodshaped plant virus as a platform for the addition of powerful biofunctionality for tissue engineering applications.This study was approved by the Ethics Committee on Animal Use of the Zhenjiang Affiliated First People’s Hospital affiliated to Jiangsu University. | Jishan Yuan Panita Maturavongsadit Zhihui Zhou Bin Lv Yuan Lin Jia Yang Jittima Amie Luckanagul | 2020 | Biomaterials Translational2020，1，1： | 2 |
| 2 | The early Japanese books reorganization by combining image processing and deep learning显示文摘Many early Japanese books record a large amount of information,including historical politics,economics,culture,and so on,which are all valuable legacies.These books are waiting to be reorganized at the moment.However,a large amount of the books are described by Kuzushiji,a type of handwriting cursive script that is no longer in use today and only readable by a few experts.Therefore,researchers are trying to detect and recognise the characters from these books through modern techniques.Unfortunately,the characteristics of the Kuzushiji,such as Connect-Separate-characters and Manyvariation,hinder the modern technique assisted re-organisation.Connect-Separatecharacters refer to the case of some characters connecting each other or one character being separated into unconnected parts,which makes character detection hard.Manyvariation is one of the typical characteristics of Kuzushiji,defined as the case that the same character has several variations even if they are written by the same person in the same book at the same time,which increases the difficulty of character recognition.In this sense,this paper aims to construct an early Japanese book reorganisation system by combining image processing and deep learning techniques.The experimentation has been done by testing two early Japanese books.In terms of character detection,the final Recall,Precision and F-value reaches 79.8%,80.3%,and 80.0%,respectively.The deep learning based character recognition accuracy of Top3 reaches 69.52%,and the highest recognition rate reaches 82.57%,which verifies the effectiveness of our proposal. | Bing Lyu Hengyi Li Ami Tanaka Lin Meng | 2022 | CAAI Transactions on Intelligence Technology2022，7，4： | 1 |
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