AIM: To evaluate the biomechanical stability of the corneal scar treating with riboflavin and ultraviolet A(UVA). METHODS: Totally 86 New Zeal rabbits were divided into control group(group A, n=8) and trauma groups [g...AIM: To evaluate the biomechanical stability of the corneal scar treating with riboflavin and ultraviolet A(UVA). METHODS: Totally 86 New Zeal rabbits were divided into control group(group A, n=8) and trauma groups [group B(n=27), group C(n=24) and group D(n=27)]. Then groups B, C and D were divided into three sub-groups according to the time points of sacrifice, i.e. groups Ba, Ca and Da(4 wk, n=8); Bb, Cb and Db(6 wk, n=8); Bc(n=11), Cc(n=8) and Dc(8 wk, n=11). The right corneas of these 78 rabbits in the trauma groups were penetrated. Group B were only sutured. Group C were treated with corneal cross-linking(CXL) immediately after suturing. Group D were treated with CXL seven days after suturing. The corneal scar strips of 4.0×10.0 mm2 were cut and the stress and Young's modulus at 10% strain were evaluated. Samples from the three rabbits of group Bc and three of group Dc were used to measure the expression of alpha smooth muscle action(α-SMA). RESULTS: The mechanical strength of the corneal scar increased with time, and was strongest at 8 wk after the injury. The ultimate stress of corneal scar(group D) were 2.17±0.52 MPa, 2.92±0.63 MPa, and 4.21±0.68 Mpa at 4 wk, 6 wk and 8 wk, respectively; Young's modulus were 10.94±1.57 MPa, 11.16±2.50 MPa, and 13.36±2.10 Mpa, which were higher than that of other groups except for normal control. The expression of α-SMA in group B and group D were 0.28±0.11 and 0.65±0.20, respectively, and the difference was statistically significant(P=0.048). CONCLUSION: CXL with riboflavin/UVA at seven days after suturing improved the biomechanical properties of corneal scars most effectively in the present study.展开更多
Identifying semantic types for attributes in relations,known as attribute semantic type(AST)identification,plays an important role in many data analysis tasks,such as data cleaning,schema matching,and keyword search i...Identifying semantic types for attributes in relations,known as attribute semantic type(AST)identification,plays an important role in many data analysis tasks,such as data cleaning,schema matching,and keyword search in databases.However,due to a lack of unified naming standards across prevalent information systems(a.k.a.information islands),AST identification still remains as an open problem.To tackle this problem,we propose a context-aware method to figure out the ASTs for relations in this paper.We transform the AST identification into a multi-class classification problem and propose a schema context aware(SCA)model to learn the representation from a collection of relations associated with attribute values and schema context.Based on the learned representation,we predict the AST for a given attribute from an underlying relation,wherein the predicted AST is mapped to one of the labeled ASTs.To improve the performance for AST identification,especially for the case that the predicted semantic types of attributes are not included in the labeled ASTs,we then introduce knowledge base embeddings(a.k.a.KBVec)to enhance the above representation and construct a schema context aware model with knowledge base enhanced(SCA-KB)to get a stable and robust model.Extensive experiments based on real datasets demonstrate that our context-aware method outperforms the state-of-the-art approaches by a large margin,up to 6.14%and 25.17%in terms of macro average F1 score,and up to 0.28%and 9.56%in terms of weighted F1 score over high-quality and low-quality datasets respectively.展开更多
MongoDB is one of the first commercial distributed databases that support causal consistency.Its implementation of causal consistency combines several research ideas for achieving scalability,fault tolerance,and secur...MongoDB is one of the first commercial distributed databases that support causal consistency.Its implementation of causal consistency combines several research ideas for achieving scalability,fault tolerance,and security.Given its inherent complexity,a natural question arises:"Has MongoDB correctly implemented causal consistency as it claimed?"To address this concern,the Jepsen team has conducted black-box testing of MongoDB.However,this Jepsen testing has several drawbacks in terms of specification,test case generation,implementation of causal consistency checking algorithms,and testing scenarios,which undermine the credibility of its reports.In this work,we propose a more thorough design of Jepsen testing of causal consistency of MongoDB.Specifically,we fully implement the causal consistency checking algorithms proposed by Bouajjani et al.and test MongoDB against three well-known variants of causal consistency,namely CC,CCv,and CM,under various scenarios including node failures,data movement,and network partitions.In addition,we develop formal specifications of causal consistency and their checking algorithms in TLA^(+),and verify them using the TLC model checker.We also explain how TLA^(+) specification can be related to Jepsen testing.展开更多
基金Supported by the National Natural Science Foundation of China (No.81660169)the Science and Technology Foundation of Zunyi [No.(2014)94]
文摘AIM: To evaluate the biomechanical stability of the corneal scar treating with riboflavin and ultraviolet A(UVA). METHODS: Totally 86 New Zeal rabbits were divided into control group(group A, n=8) and trauma groups [group B(n=27), group C(n=24) and group D(n=27)]. Then groups B, C and D were divided into three sub-groups according to the time points of sacrifice, i.e. groups Ba, Ca and Da(4 wk, n=8); Bb, Cb and Db(6 wk, n=8); Bc(n=11), Cc(n=8) and Dc(8 wk, n=11). The right corneas of these 78 rabbits in the trauma groups were penetrated. Group B were only sutured. Group C were treated with corneal cross-linking(CXL) immediately after suturing. Group D were treated with CXL seven days after suturing. The corneal scar strips of 4.0×10.0 mm2 were cut and the stress and Young's modulus at 10% strain were evaluated. Samples from the three rabbits of group Bc and three of group Dc were used to measure the expression of alpha smooth muscle action(α-SMA). RESULTS: The mechanical strength of the corneal scar increased with time, and was strongest at 8 wk after the injury. The ultimate stress of corneal scar(group D) were 2.17±0.52 MPa, 2.92±0.63 MPa, and 4.21±0.68 Mpa at 4 wk, 6 wk and 8 wk, respectively; Young's modulus were 10.94±1.57 MPa, 11.16±2.50 MPa, and 13.36±2.10 Mpa, which were higher than that of other groups except for normal control. The expression of α-SMA in group B and group D were 0.28±0.11 and 0.65±0.20, respectively, and the difference was statistically significant(P=0.048). CONCLUSION: CXL with riboflavin/UVA at seven days after suturing improved the biomechanical properties of corneal scars most effectively in the present study.
基金supported by the National Key Research and Development Program of China under Grant No.2020YFB2104100the National Natural Science Foundation of China under Grant Nos.61972403 and U1711261the Fundamental Research Funds for the Central Universities of China,the Research Funds of Renmin University of China,and Tencent Rhino-Bird Joint Research Program.
文摘Identifying semantic types for attributes in relations,known as attribute semantic type(AST)identification,plays an important role in many data analysis tasks,such as data cleaning,schema matching,and keyword search in databases.However,due to a lack of unified naming standards across prevalent information systems(a.k.a.information islands),AST identification still remains as an open problem.To tackle this problem,we propose a context-aware method to figure out the ASTs for relations in this paper.We transform the AST identification into a multi-class classification problem and propose a schema context aware(SCA)model to learn the representation from a collection of relations associated with attribute values and schema context.Based on the learned representation,we predict the AST for a given attribute from an underlying relation,wherein the predicted AST is mapped to one of the labeled ASTs.To improve the performance for AST identification,especially for the case that the predicted semantic types of attributes are not included in the labeled ASTs,we then introduce knowledge base embeddings(a.k.a.KBVec)to enhance the above representation and construct a schema context aware model with knowledge base enhanced(SCA-KB)to get a stable and robust model.Extensive experiments based on real datasets demonstrate that our context-aware method outperforms the state-of-the-art approaches by a large margin,up to 6.14%and 25.17%in terms of macro average F1 score,and up to 0.28%and 9.56%in terms of weighted F1 score over high-quality and low-quality datasets respectively.
基金supported by the CCF-Tencent Open Fund under Grant No.RAGR20200124the National Natural Science Foundation of China under Grant Nos.61702253 and 61772258.
文摘MongoDB is one of the first commercial distributed databases that support causal consistency.Its implementation of causal consistency combines several research ideas for achieving scalability,fault tolerance,and security.Given its inherent complexity,a natural question arises:"Has MongoDB correctly implemented causal consistency as it claimed?"To address this concern,the Jepsen team has conducted black-box testing of MongoDB.However,this Jepsen testing has several drawbacks in terms of specification,test case generation,implementation of causal consistency checking algorithms,and testing scenarios,which undermine the credibility of its reports.In this work,we propose a more thorough design of Jepsen testing of causal consistency of MongoDB.Specifically,we fully implement the causal consistency checking algorithms proposed by Bouajjani et al.and test MongoDB against three well-known variants of causal consistency,namely CC,CCv,and CM,under various scenarios including node failures,data movement,and network partitions.In addition,we develop formal specifications of causal consistency and their checking algorithms in TLA^(+),and verify them using the TLC model checker.We also explain how TLA^(+) specification can be related to Jepsen testing.
