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Lai Pan,* Chenyi Zhang,* Haizheng Zhang, Ting Ke, Mengyao Bian, Yuxuan Yang, Lili Chen, Jingyi Tan Department of Periodontology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, 310009, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jingyi Tan; Lili Chen, Email tanjingyi@zju.edu.cn; chenlili_1030@zju.edu.cnBackground: In chronic periodontitis, exosomes transport various informative substances between osteoclasts and osteoblasts in alveolar bone. Herein, we aimed to investigate the effect of exosomal micro-ribonucleic acid (miRNA/miR)-5134-5p derived from osteoclasts on osteoblastic proliferation and differentiation and the development of periodontitis in vivo and in vitro.Methods: The effects of OC-Exos on the proliferation and differentiation of osteoblasts were identified by Real-time quantitative reverse polymerase chain reaction (qRT-PCR), Western blot(WB), alkaline phosphatase(ALP) staining, etc. Exosomal miRNA expression was analyzed by sequencing. The sites of miRNA action were predicted through TargetScan and tested by double luciferase assay. After transfecting miR-5134-5p mimic/inhibitor into osteoblasts, we measured the proliferation and differentiation of osteoblasts by ALP staining and WB, etc. Furthermore, OC-Exos were injected into the gingival sulcus at the ligation site. Inflammation was observed by Hematoxylin-eosin (H&E) staining, the expression of inflammatory factors were detected by qRT-PCR, the resorption of alveolar bone was observed by Micro CT.Results: Osteoblastic proliferation and differentiation were negatively regulated by OC-Exos in vitro. miRNA sequencing analysis revealed that miR-5134-5p expression was significantly elevated in OC-Exos, which also increased in osteoblasts following OC-Exo intervention. The dual-luciferase assay revealed that miR-5134-5p and Janus kinase 2 (JAK2) had binding sites. miR-5134-5p-mimics could upre

Chenyi Zhang,1,* Lai Pan,1,* Haizheng Zhang,1 Ting Ke,1 Yuxuan Yang,1 Lan Zhang,2 Lili Chen,1 Jingyi Tan1 1Department of Periodontology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, People’s Republic of China; 2Stomatology Department, Zhejiang Hospital, Hangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Lili Chen; Jingyi Tan, Email chenlili_1030@zju.edu.cn; tanjingyi@zju.edu.cnObjective: Emerging studies have explained the crucial role of non-coding RNA (lncRNA) in various pathological progressions. The study was designed to examine the role of lncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) and miRNA-124 in the differentiation of osteoclasts, to provide new clues or evidences for the pathogenesis of periodontitis.Methods: We constructed an osteoblast-osteoclast Transwell co-culture system and osteoblast-derived exosomes (OB-exo) intervention model. We assessed the osteoclastogenesis as well as the level of lncRNA-MALAT1 and miRNA-124. The mechanism for lncRNA MALAT1 targeting miR-124 modulating the differentiation of osteoclasts was investigated by cell transfection, quantitative real-time reverse transcription PCR (RT-qPCR), Western blot, and Dual-Luciferase reporter assays.Results: Osteoblast-derived exosomes were isolated and identified. Co-culture and OB-exo intervention can promote osteoclastogenesis, also significantly up-regulate the expression of MALAT1, while the level of miR-124 is the opposite. Transfection of cells with small interfering RNA (si-MALAT1) and miR-124 mimic decreased the formation of TRAP+ osteoclasts and inhibited the expression of NFATc1. However, the effect was reversed when transfected with miR-124 inhibitor and si-MALAT1. The Dual-Luciferase reporter assay confirmed the binding sites between MALAT1 and miR-124, and miR-124 and NFATc1.Conclusion: LncRNA MALAT1 functioned as an endogenous sponge by competing

Simulation-based Dynamic Traffic Assignment models have important applications in real-time traffic management and control. The efficacy of these systems rests on the ability to generate accurate estimates and predictions of traffic states, which necessitates online calibration. A widely used solution approach for online calibration is the Extended Kalman Filter (EKF), which – although appealing in its flexibility to incorporate any class of parameters and measurements – poses several challenges with regard to calibration accuracy and scalability, especially in congested situations for large-scale networks. This paper addresses these issues in turn so as to improve the accuracy and efficiency of EKF-based online calibration approaches for large and congested networks. First, the concept of state augmentation is revisited to handle violations of the Markovian assumption typically implicit in online applications of the EKF. Second, a method based on graph-coloring is proposed to operationalize the partitioned finite-difference approach that enhances scalability of the gradient computations. Several synthetic experiments and a real world case study demonstrate that application of the proposed approaches yields improvements in terms of both prediction accuracy and computational performance. The work has applications in real-world deployments of simulation-based dynamic traffic assignment systems.

Simulation-based Dynamic Traffic Assignment models have important applications in real-time traffic management and control. The efficacy of these systems rests on the ability to generate accurate estimates and predictions of traffic states, which necessitates online calibration. A widely used solution approach for online calibration is the Extended Kalman Filter (EKF), which—although appealing in its flexibility to incorporate any class of parameters and measurements—poses several challenges with regard to calibration accuracy and scalability, especially in congested situations for large-scale networks. This paper addresses these issues in turn so as to improve the accuracy and efficiency of EKF-based online calibration approaches for large and congested networks. First, the concept of state augmentation is revisited to handle violations of the Markovian assumption typically implicit in online applications of the EKF. Second, a method based on graph-coloring is proposed to operationalize the partitioned finite-difference approach that enhances scalability of the gradient computations. Several synthetic experiments and a real world case study demonstrate that application of the proposed approaches yields improvements in terms of both prediction accuracy and computational performance. The work has applications in real-world deployments of simulation-based dynamic traffic assignment systems.

Simulation-based Dynamic Traffic Assignment models have important applications in real-time traffic management and control. The efficacy of these systems rests on the ability to generate accurate estimates and predictions of traffic states, which necessitates online calibration. A widely used solution approach for online calibration is the Extended Kalman Filter (EKF), which -- although appealing in its flexibility to incorporate any class of parameters and measurements -- poses several challenges with regard to calibration accuracy and scalability, especially in congested situations for large-scale networks. This paper addresses these issues in turn so as to improve the accuracy and efficiency of EKF-based online calibration approaches for large and congested networks. First, the concept of state augmentation is revisited to handle violations of the Markovian assumption typically implicit in online applications of the EKF. Second, a method based on graph-coloring is proposed to operationalize the partitioned finite-difference approach that enhances scalability of the gradient computations. Several synthetic experiments and a real world case study demonstrate that application of the proposed approaches yields improvements in terms of both prediction accuracy and computational performance. The work has applications in real-world deployments of simulation-based dynamic traffic assignment systems., Comment: 26 pages, 15 figures

Thesis: Ph. D. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018., This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections., Cataloged from student-submitted PDF version of thesis., Includes bibliographical references (pages 149-152)., The severity of traffic congestion is increasing each year in the US, resulting in higher travel times, and increased energy consumption and emissions. They have led to an increasing emphasis on the development of tools for trac management, which intends to alleviate congestion by more eciently utilizing the existing infrastructure. Eective trac management necessitates the generation of accurate short-term predictions of trac states and in this context, simulation-based Dynamic Trac Assignment (DTA) systems have gained prominence over the years. However, a key challenge that remains to be addressed with real-time DTA systems is their scalability and accuracy for applications to large-scale urban networks. A key component of real-time DTA systems that impacts scalability and accuracy is online calibration which attempts to adjust simulation parameters in real-time to match as closely as possible simulated measurements with real-time surveillance data. This thesis contributes to the existing literature on online calibration of DTA systems in three respects: (1) modeling explicitly the stochasticity in simulators and thereby improving accuracy; (2) augmenting the State Space Model (SSM) to capture the delayed measurements on large-scale and congested networks; (3) presenting a gradient estimation procedure called partitioned simultaneous perturbation (PSP) that utilizes an assumed sparse gradient structure to facilitate real-time performance. The results demonstrate that, first, the proposed approach to address stochasticity improves the accuracy of supply calibration on a synthetic network. Second, the augmented SSM improves both estimation and prediction accuracy on a congested synthetic network and the large-scale Singapore expressway network. Finally, compared with the traditional finite difference method, the PSP reduces the number of computations by 90% and achieves the same calibration accuracy on the Singapore expressway network. The proposed methodologies have, by Haizheng Zhang., Ph. D. in Transportation

Thesis: Ph. D. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018., This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections., Cataloged from student-submitted PDF version of thesis., Includes bibliographical references (pages 149-152)., The severity of traffic congestion is increasing each year in the US, resulting in higher travel times, and increased energy consumption and emissions. They have led to an increasing emphasis on the development of tools for trac management, which intends to alleviate congestion by more eciently utilizing the existing infrastructure. Eective trac management necessitates the generation of accurate short-term predictions of trac states and in this context, simulation-based Dynamic Trac Assignment (DTA) systems have gained prominence over the years. However, a key challenge that remains to be addressed with real-time DTA systems is their scalability and accuracy for applications to large-scale urban networks. A key component of real-time DTA systems that impacts scalability and accuracy is online calibration which attempts to adjust simulation parameters in real-time to match as closely as possible simulated measurements with real-time surveillance data. This thesis contributes to the existing literature on online calibration of DTA systems in three respects: (1) modeling explicitly the stochasticity in simulators and thereby improving accuracy; (2) augmenting the State Space Model (SSM) to capture the delayed measurements on large-scale and congested networks; (3) presenting a gradient estimation procedure called partitioned simultaneous perturbation (PSP) that utilizes an assumed sparse gradient structure to facilitate real-time performance. The results demonstrate that, first, the proposed approach to address stochasticity improves the accuracy of supply calibration on a synthetic network. Second, the augmented SSM improves both estimation and prediction accuracy on a congested synthetic network and the large-scale Singapore expressway network. Finally, compared with the traditional finite difference method, the PSP reduces the number of computations by 90% and achieves the same calibration accuracy on the Singapore expressway network. The proposed methodologies have, by Haizheng Zhang., Ph. D. in Transportation

The calibration of dynamic traffic assignment (DTA) models involves the estimation of model parameters to best replicate real-world measurements. Good calibration is essential to estimate and predict accurately traffic states, which are crucial for traffic management applications to alleviate congestion. A widely used approach to calibrate simulation-based DTA models is the extended Kalman filter (EKF). The EKF assumes that the DTA model parameters are unconstrained, although they are in fact constrained; for instance, origin–destination (O-D) flows are nonnegative. This assumption is typically not problematic for small- and medium-scale networks in which the EKF has been successfully applied. However, in large-scale networks (which typically contain numbers of O-D pairs with small magnitudes of flow), the estimates may severely violate constraints. In consequence, simply truncating the infeasible estimates may result in the divergence of EKF, leading to extremely poor state estimations and predictions. To address this issue, a constrained EKF (CEKF) approach is presented; it imposes constraints on the posterior distribution of the state estimators to obtain the maximum a posteriori (MAP) estimates that are feasible. The MAP estimates are obtained with a heuristic followed by the coordinate descent method. The procedure determines the optimum and are computationally faster by 31.5% over coordinate descent and by 94.9% over the interior point method. Experiments on the Singapore expressway network indicated that the CEKF significantly improved model accuracy and outperformed the traditional EKF (up to 78.17%) and generalized least squares (up to 17.13%) approaches in state estimation and prediction.

Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2016., Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016., This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections., Cataloged from student-submitted PDF version of thesis., Includes bibliographical references (pages 83-86)., The calibration (estimation of inputs and parameters) for dynamic traffic assignment (DTA) systems is a crucial process for traffic prediction accuracy, and thus critical to global traffic management applications to reduce traffic congestion. In support of the real-time traffic management, the DTA calibration algorithm should also be online, in terms of: 1) estimating inputs and parameters in a time interval only based on data up to that time; 2) performing calibration faster than real-time data generation. Generalized least squares (GLS) methods and Kalman filter-based methods are proved useful in online calibration. However, in literature, the road networks selected to test online calibration algorithms are usually simple and have small number of parameters. Thus their effectiveness when applied to high dimensions and large networks is not well proved. In this thesis, we implemented the extended Kalman filter (EKF) and tested it on the Singapore expressway network with synthetic data that replicate real world demand level. The EKF is diverging and the DTA system is even worse than when no calibration is applied. The problem lies in the truncation process in DTA systems. When estimated demand values are negative, they are truncated to 0 and the overall demand is overestimated. To overcome this problem, this thesis presents a modified EKF method, called constrained EKF. Constrained EKF solves the problem of over-estimating the overall demand by imposing constraints on the posterior distribution of the state estimators and obtain the maximum a posteriori (MAP) estimates within the feasible region. An algorithm of iteratively adding equality constraints followed by the coordinate descent method is applied to obtain the MAP estimates. In our case study, this constrained EKF implementation added less than 10 seconds of computation time and improved EKF significantly. Results show that it also outperforms GLS, probably because its inherent covariance update procedure has, by Haizheng Zhang., S.M. in Transportation, S.M.

Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2016., Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016., This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections., Cataloged from student-submitted PDF version of thesis., Includes bibliographical references (pages 83-86)., The calibration (estimation of inputs and parameters) for dynamic traffic assignment (DTA) systems is a crucial process for traffic prediction accuracy, and thus critical to global traffic management applications to reduce traffic congestion. In support of the real-time traffic management, the DTA calibration algorithm should also be online, in terms of: 1) estimating inputs and parameters in a time interval only based on data up to that time; 2) performing calibration faster than real-time data generation. Generalized least squares (GLS) methods and Kalman filter-based methods are proved useful in online calibration. However, in literature, the road networks selected to test online calibration algorithms are usually simple and have small number of parameters. Thus their effectiveness when applied to high dimensions and large networks is not well proved. In this thesis, we implemented the extended Kalman filter (EKF) and tested it on the Singapore expressway network with synthetic data that replicate real world demand level. The EKF is diverging and the DTA system is even worse than when no calibration is applied. The problem lies in the truncation process in DTA systems. When estimated demand values are negative, they are truncated to 0 and the overall demand is overestimated. To overcome this problem, this thesis presents a modified EKF method, called constrained EKF. Constrained EKF solves the problem of over-estimating the overall demand by imposing constraints on the posterior distribution of the state estimators and obtain the maximum a posteriori (MAP) estimates within the feasible region. An algorithm of iteratively adding equality constraints followed by the coordinate descent method is applied to obtain the MAP estimates. In our case study, this constrained EKF implementation added less than 10 seconds of computation time and improved EKF significantly. Results show that it also outperforms GLS, probably because its inherent covariance update procedure has, by Haizheng Zhang., S.M. in Transportation, S.M.

Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2016., Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016., This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections., Cataloged from student-submitted PDF version of thesis., Includes bibliographical references (pages 83-86)., The calibration (estimation of inputs and parameters) for dynamic traffic assignment (DTA) systems is a crucial process for traffic prediction accuracy, and thus critical to global traffic management applications to reduce traffic congestion. In support of the real-time traffic management, the DTA calibration algorithm should also be online, in terms of: 1) estimating inputs and parameters in a time interval only based on data up to that time; 2) performing calibration faster than real-time data generation. Generalized least squares (GLS) methods and Kalman filter-based methods are proved useful in online calibration. However, in literature, the road networks selected to test online calibration algorithms are usually simple and have small number of parameters. Thus their effectiveness when applied to high dimensions and large networks is not well proved. In this thesis, we implemented the extended Kalman filter (EKF) and tested it on the Singapore expressway network with synthetic data that replicate real world demand level. The EKF is diverging and the DTA system is even worse than when no calibration is applied. The problem lies in the truncation process in DTA systems. When estimated demand values are negative, they are truncated to 0 and the overall demand is overestimated. To overcome this problem, this thesis presents a modified EKF method, called constrained EKF. Constrained EKF solves the problem of over-estimating the overall demand by imposing constraints on the posterior distribution of the state estimators and obtain the maximum a posteriori (MAP) estimates within the feasible region. An algorithm of iteratively adding equality constraints followed by the coordinate descent method is applied to obtain the MAP estimates. In our case study, this constrained EKF implementation added less than 10 seconds of computation time and improved EKF significantly. Results show that it also outperforms GLS, probably because its inherent covariance update procedure has, by Haizheng Zhang., S.M. in Transportation, S.M.

Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2016., Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016., This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections., Cataloged from student-submitted PDF version of thesis., Includes bibliographical references (pages 83-86)., The calibration (estimation of inputs and parameters) for dynamic traffic assignment (DTA) systems is a crucial process for traffic prediction accuracy, and thus critical to global traffic management applications to reduce traffic congestion. In support of the real-time traffic management, the DTA calibration algorithm should also be online, in terms of: 1) estimating inputs and parameters in a time interval only based on data up to that time; 2) performing calibration faster than real-time data generation. Generalized least squares (GLS) methods and Kalman filter-based methods are proved useful in online calibration. However, in literature, the road networks selected to test online calibration algorithms are usually simple and have small number of parameters. Thus their effectiveness when applied to high dimensions and large networks is not well proved. In this thesis, we implemented the extended Kalman filter (EKF) and tested it on the Singapore expressway network with synthetic data that replicate real world demand level. The EKF is diverging and the DTA system is even worse than when no calibration is applied. The problem lies in the truncation process in DTA systems. When estimated demand values are negative, they are truncated to 0 and the overall demand is overestimated. To overcome this problem, this thesis presents a modified EKF method, called constrained EKF. Constrained EKF solves the problem of over-estimating the overall demand by imposing constraints on the posterior distribution of the state estimators and obtain the maximum a posteriori (MAP) estimates within the feasible region. An algorithm of iteratively adding equality constraints followed by the coordinate descent method is applied to obtain the MAP estimates. In our case study, this constrained EKF implementation added less than 10 seconds of computation time and improved EKF significantly. Results show that it also outperforms GLS, probably because its inherent covariance update procedure has, by Haizheng Zhang., S.M. in Transportation, S.M.

Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2016., Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016., This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections., Cataloged from student-submitted PDF version of thesis., Includes bibliographical references (pages 83-86)., The calibration (estimation of inputs and parameters) for dynamic traffic assignment (DTA) systems is a crucial process for traffic prediction accuracy, and thus critical to global traffic management applications to reduce traffic congestion. In support of the real-time traffic management, the DTA calibration algorithm should also be online, in terms of: 1) estimating inputs and parameters in a time interval only based on data up to that time; 2) performing calibration faster than real-time data generation. Generalized least squares (GLS) methods and Kalman filter-based methods are proved useful in online calibration. However, in literature, the road networks selected to test online calibration algorithms are usually simple and have small number of parameters. Thus their effectiveness when applied to high dimensions and large networks is not well proved. In this thesis, we implemented the extended Kalman filter (EKF) and tested it on the Singapore expressway network with synthetic data that replicate real world demand level. The EKF is diverging and the DTA system is even worse than when no calibration is applied. The problem lies in the truncation process in DTA systems. When estimated demand values are negative, they are truncated to 0 and the overall demand is overestimated. To overcome this problem, this thesis presents a modified EKF method, called constrained EKF. Constrained EKF solves the problem of over-estimating the overall demand by imposing constraints on the posterior distribution of the state estimators and obtain the maximum a posteriori (MAP) estimates within the feasible region. An algorithm of iteratively adding equality constraints followed by the coordinate descent method is applied to obtain the MAP estimates. In our case study, this constrained EKF implementation added less than 10 seconds of computation time and improved EKF significantly. Results show that it also outperforms GLS, probably because its inherent covariance update procedure has, by Haizheng Zhang., S.M. in Transportation, S.M.

Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2016., Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016., This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections., Cataloged from student-submitted PDF version of thesis., Includes bibliographical references (pages 83-86)., The calibration (estimation of inputs and parameters) for dynamic traffic assignment (DTA) systems is a crucial process for traffic prediction accuracy, and thus critical to global traffic management applications to reduce traffic congestion. In support of the real-time traffic management, the DTA calibration algorithm should also be online, in terms of: 1) estimating inputs and parameters in a time interval only based on data up to that time; 2) performing calibration faster than real-time data generation. Generalized least squares (GLS) methods and Kalman filter-based methods are proved useful in online calibration. However, in literature, the road networks selected to test online calibration algorithms are usually simple and have small number of parameters. Thus their effectiveness when applied to high dimensions and large networks is not well proved. In this thesis, we implemented the extended Kalman filter (EKF) and tested it on the Singapore expressway network with synthetic data that replicate real world demand level. The EKF is diverging and the DTA system is even worse than when no calibration is applied. The problem lies in the truncation process in DTA systems. When estimated demand values are negative, they are truncated to 0 and the overall demand is overestimated. To overcome this problem, this thesis presents a modified EKF method, called constrained EKF. Constrained EKF solves the problem of over-estimating the overall demand by imposing constraints on the posterior distribution of the state estimators and obtain the maximum a posteriori (MAP) estimates within the feasible region. An algorithm of iteratively adding equality constraints followed by the coordinate descent method is applied to obtain the MAP estimates. In our case study, this constrained EKF implementation added less than 10 seconds of computation time and improved EKF significantly. Results show that it also outperforms GLS, probably because its inherent covariance update procedure has, by Haizheng Zhang., S.M. in Transportation, S.M.