Pedestrian Walking Behavior Revealed through a Random Walk Model

This paper applies method of continuous-time random walks for pedestrian flow simulation. In the model, pedestrians can walk forward or backward and turn left or right if there is no block. Velocities of pedestrian flow moving forward or diffusing are dominated by coefficients. The waiting time preceding each jump is assumed to follow an exponential distribution. To solve the model, a second-order two-dimensional partial differential equation, a high-order compact scheme with the alternating direction implicit method, is employed. In the numerical experiments, the walking domain of the first one is two-dimensional with two entrances and one exit, and that of the second one is two-dimensional with one entrance and one exit. The flows in both scenarios are one way. Numerical results show that the model can be used for pedestrian flow simulation

Metabonomic Profiles Delineate the Effect of Traditional Chinese Medicine Sini Decoction on Myocardial Infarction in Rats

Background: In spite of great advances in target-oriented Western medicine for treating myocardial infarction (MI), it is still a leading cause of death in a worldwide epidemic. In contrast to Western medicine, Traditional Chinese medicine (TCM) uses a holistic and synergistic approach to restore the balance of Yin-Yang of body energy so the body’s normal function can be restored. Sini decoction (SND) is a well-known formula of TCM which has been used to treat MI for many years. However, its holistic activity evaluation and mechanistic understanding are still lacking due to its complex components. Methodology/Principal Findings: A urinary metabonomic method based on nuclear magnetic resonance and ultra highperformance liquid chromatography coupled to mass spectrometry was developed to characterize MI-related metabolic profiles and delineate the effect of SND on MI. With Elastic Net for classification and selection of biomarkers, nineteen potential biomarkers in rat urine were screened out, primarily related to myocardial energy metabolism, including the glycolysis, citrate cycle, amino acid metabolism, purine metabolism and pyrimidine metabolism. With the altered metabolism pathways as possible drug targets, we systematically analyze the therapeutic effect of SND, which demonstrated that SND administration could provide satisfactory effect on MI through partially regulating the perturbed myocardial energy metabolism. Conclusions/Significance: Our results showed that metabonomic approach offers a useful tool to identify MI-relate

Traffic Speed Data Imputation Method Based on Tensor Completion

Traffic speed data plays a key role in Intelligent Transportation Systems (ITS); however, missing traffic data would affect the performance of ITS as well as Advanced Traveler Information Systems (ATIS). In this paper, we handle this issue by a novel tensor-based imputation approach. Specifically, tensor pattern is adopted for modeling traffic speed data and then High accurate Low Rank Tensor Completion (HaLRTC), an efficient tensor completion method, is employed to estimate the missing traffic speed data. This proposed method is able to recover missing entries from given entries, which may be noisy, considering severe fluctuation of traffic speed data compared with traffic volume. The proposed method is evaluated on Performance Measurement System (PeMS) database, and the experimental results show the superiority of the proposed approach over state-of-the-art baseline approaches

Robust Missing Traffic Flow Imputation Considering Nonnegativity and Road Capacity

There are increasing concerns about missing traffic data in recent years. In this paper, a robust missing traffic flow data imputation approach based on matrix completion is proposed. In the proposed method, the similarity of traffic flow from day to day is exploited to impute missing data by the low-rank hypothesis of constructed traffic flow matrix. And the physical limitation of road capacity and nonnegativity is also considered through the optimization process, which avoids the possibility of producing negative and overcapacity values. Moreover, the proposed algorithm can impute missing data and recover outlier in a unify framework. The experiment results show that the proposed method is more accurate, stable, and reasonable

Robust Missing Traffic Flow Imputation Considering Nonnegativity and Road Capacity

There are increasing concerns about missing traffic data in recent years. In this paper, a robust missing traffic flow data imputation approach based on matrix completion is proposed. In the proposed method, the similarity of traffic flow from day to day is exploited to impute missing data by the low-rank hypothesis of constructed traffic flow matrix. And the physical limitation of road capacity and nonnegativity is also considered through the optimization process, which avoids the possibility of producing negative and overcapacity values. Moreover, the proposed algorithm can impute missing data and recover outlier in a unify framework. The experiment results show that the proposed method is more accurate, stable, and reasonable

Mixture Augmented Lagrange Multiplier Method for Tensor Recovery and Its Applications

The problem of data recovery in multiway arrays (i.e., tensors) arises in many fields such as computer vision, image processing, and traffic data analysis. In this paper, we propose a scalable and fast algorithm for recovering a low-n-rank tensor with an unknown fraction of its entries being arbitrarily corrupted. In the new algorithm, the tensor recovery problem is formulated as a mixture convex multilinear Robust Principal Component Analysis (RPCA) optimization problem by minimizing a sum of the nuclear norm and the ℓ1-norm. The problem is well structured in both the objective function and constraints. We apply augmented Lagrange multiplier method which can make use of the good structure for efficiently solving this problem. In the experiments, the algorithm is compared with the state-of-art algorithm both on synthetic data and real data including traffic data, image data, and video data

Potential Biomarkers in Mouse Myocardium of Doxorubicin-Induced Cardiomyopathy: A Metabonomic Method and Its Application

BACKGROUND: Doxorubicin (DOX) is one of the most potent antitumor agents available; however, its clinical use is limited because of the risk of severe cardiotoxicity. Though numerous studies have ascribed DOX cardiomyopathy to specific cellular pathways, the precise mechanism remains obscure. Sini decoction (SND) is a well-known formula of Traditional Chinese Medicine (TCM) and is considered as efficient agents against DOX-induced cardiomyopathy. However, its action mechanisms are not well known due to its complex components. METHODOLOGY/PRINCIPAL FINDINGS: A tissue-targeted metabonomic method using gas chromatography-mass spectrometry was developed to characterize the metabolic profile of DOX-induced cardiomyopathy in mice. With Elastic Net for classification and selection of biomarkers, twenty-four metabolites corresponding to DOX-induced cardiomyopathy were screened out, primarily involving glycolysis, lipid metabolism, citrate cycle, and some amino acids metabolism. With these altered metabolic pathways as possible drug targets, we systematically analyzed the protective effect of TCM SND, which showed that SND administration could provide satisfactory effect on DOX-induced cardiomyopathy through partially regulating the perturbed metabolic pathways. CONCLUSIONS/SIGNIFICANCE: The results of the present study not only gave rise to a systematic view of the development of DOX-induced cardiomyopathy but also provided the theoretical basis to prevent or modify expected damage

Особенности вазомоторной функции эндотелия у больных стабильной стенокардией с факторами риска (артериальной гипертензией, гиперхолестеринемией, курением)

ВАЗОМОТОРНАЯ СИСТЕМАЭНДОТЕЛИЙ /ФИЗИОЛЭПИТЕЛИЙСТЕНОКАРДИЯКОРОНАРНАЯ БОЛЕЗНЬГРУДНАЯ КЛЕТКА, БОЛИФАКТОРЫ РИСКАГИПЕРТЕНЗИЯКРОВЕНОСНЫХ СОСУДОВ БОЛЕЗНИГИПЕРХОЛЕСТЕРИНЕМИЯГИПЕРЛИПИДЕМИЯКУРЕНИЕ /ВРЕД ВОЗДТАБАКА УПОТРЕБЛЕНИЕ, РАССТРОЙСТВА ЗДОРОВЬ

Molecular Modeling Study of Chiral Separation and Recognition Mechanism of β-Adrenergic Antagonists by Capillary Electrophoresis

Chiral separations of five β-adrenergic antagonists (propranolol, esmolol, atenolol, metoprolol, and bisoprolol) were studied by capillary electrophoresis using six cyclodextrins (CDs) as the chiral selectors. Carboxymethylated-β-cyclodextrin (CM-β-CD) exhibited a higher enantioselectivity power compared to the other tested CDs. The influences of the concentration of CM-β-CD, buffer pH, buffer concentration, temperature, and applied voltage were investigated. The good chiral separation of five β-adrenergic antagonists was achieved using 50 mM Tris buffer at pH 4.0 containing 8 mM CM-β-CD with an applied voltage of 24 kV at 20 °C. In order to understand possible chiral recognition mechanisms of these racemates with CM-β-CD, host-guest binding procedures of CM-β-CD and these racemates were studied using the molecular docking software Autodock. The binding free energy was calculated using the Autodock semi-empirical binding free energy function. The results showed that the phenyl or naphthyl ring inserted in the hydrophobic cavity of CM-β-CD and the side chain was found to point out of the cyclodextrin rim. Hydrogen bonding between CM-β-CD and these racemates played an important role in the process of enantionseparation and a model of the hydrogen bonding interaction positions was constructed. The difference in hydrogen bonding formed with the –OH next to the chiral center of the analytes may help to increase chiral discrimination and gave rise to a bigger separation factor. In addition, the longer side chain in the hydrophobic phenyl ring of the enantiomer was not beneficial for enantioseparation and the chiral selectivity factor was found to correspond to the difference in binding free energy