Assessment of spatial coupling coordination in the Three Gorges reservoir area- Taking Badong County as an example

The special geographical location leads to the mutual restriction of urban spatial natural resources, material space and economy and society in the Three Gorges reservoir area, which threatens the spatial security of the towns. Based on the characteristics of multidimensional coupling between systems, this study uses Badong County as an example to evaluate the trend of coupling coordination between natural resources-material space-economic society and society by using the coupling coordination model. The results show that although the coupling degree between systems in Badong County is as high as 0.9546 and the degree of interaction is very strong, the degree of coupling coordination is only a moderate imbalance development type. Therefore, it is an urgent problem to improve the level of coupling and coordination between systems and ensure the safety of urban space

Optical waveguides in LiTaO3 crystals fabricated by swift C5+ ion irradiation

We report on the optical waveguides, in both planar and ridge configurations, fabricated in LiTaO3 crystal by using carbon (C5+) ions irradiation at energy of 15 MeV. The planar waveguide was produced by direct irradiation of swift C5+ ions, whilst the ridge waveguides were manufactured by using femtosecond laser ablation of the planar layer. The reconstructed refractive index profile of the planar waveguide has showed a barrier-shaped distribution, and the near-field waveguide mode intensity distribution was in good agreement with the calculated modal profile. After thermal annealing at 260 °C in air, the propagation losses of both the planar and ridge waveguides were reduced to 10 dB/cm.This work is supported by the National Natural Science Foundation of China (No. U1332121) and the 973 Project (No. 2010CB832906) of China. S.Z. acknowledges the funding by the Helmholtz-Gemeinschaft Deutscher Forschungszentren (HGF-VHNG-713). J.R.V. thanks supports from Junta de Castilla y León under project SA086A12-2 and the Centro de Láseres Pulsados (CLPU)

Study on the relationship between dietary patterns and metabolic syndrome in Guangdong Province

Objective To analyze the relationship between dietary patterns and metabolic syndrome (MS) in Guangdong Province. Methods A survey was undertook based on the data from chronic disease and nutrition monitoring. Dietary patterns of adults in Guangdong Province were exploring by principal component analysis (PCA). PCA was used to identify dietary patterns among adult in Guangdong Province and unconditional Logistic regression model was used to analysis the effects of different dietary patterns on MS. Results Three evident dietary patterns were derived by PCA including "modern fast food dietary pattern", "high plant-based dietary pattern" and "coastal special dietary pattern". Rice and its products, fruits, milk, instant foods, noodles and their products, eggs were the main foods of "modern fast food dietary pattern"; light-colored vegetables, refined vegetable oil, salt, other livestock meat, starch/sugar, beans were the main foods of "high plant-based dietary pattern"; dark vegetables, light vegetables, fish and shrimps, refined animal oil, refined vegetable oil, and pork were the main foods of "coastal special dietary pattern". After adjusting for confounding factors, the modern fast food dietary pattern was a risk factor for hyperglycemia [odds ratio (OR)=2.161, confidence interval (95%CI)=1.173-3.981], and high plant-based dietary pattern was a protective factor for MS (OR=0.494, 95%CI=0.253-0.963). Conclusion High plant dietary patterns could help reduce the risk of MS or reduce the abnormal components of MS. The dietary structure and eating habits should be adjusted according to local conditions to prevent and control the occurrence of MS

Transcriptional regulation of BRD7 expression by Sp1 and c-Myc

<p>Abstract</p> <p>Background</p> <p>Bromodomain is an evolutionally conserved domain that is found in proteins strongly implicated in signal-dependent transcriptional regulation. Genetic alterations of bromodomain genes contributed to the development of many human cancers and other disorders. BRD7 is a recently identified bromodomain gene. It plays a critical role in cellular growth, cell cycle progression, and signal-dependent gene expression. Previous studies showed that BRD7 gene exhibited much higher-level of mRNA expression in normal nasopharyngeal epithelia than in nasopharyngeal carcinoma (NPC) biopsies and cell lines. However, little is known about its transcriptional regulation. In this study, we explored the transcriptional regulation of BRD7 gene.</p> <p>Method</p> <p>Potential binding sites of transcription factors within the promoter region of BRD7 gene were predicted with MatInspector Professional <url>http://genomatix.de/cgi-bin/matinspector_prof/mat_fam.pl</url>. Mutation construct methods and luciferase assays were performed to define the minimal promoter of BRD7 gene. RT-PCR and western blot assays were used to detect the endogenous expression of transcription factor Sp1, c-Myc and E2F6 in all cell lines used in this study. Electrophoretic mobility shift assays (EMSA) and Chromatin immunoprecipitation (ChIP) were used to detect the direct transcription factors that are responsible for the promoter activity of BRD7 gene. DNA vector-based siRNA technology and cell transfection methods were employed to establish clone pools that stably expresses SiRNA against c-Myc expression in nasopharyngeal carcinoma 5-8F cells. Real-time PCR was used to detect mRNA expression of BRD7 gene in 5-8F/Si-c-Myc cells.</p> <p>Results</p> <p>We defined the minimal promoter of BRD7 gene in a 55-bp region (from -266 to -212bp), and identified that its promoter activity is inversely related to c-Myc expression. Sp1 binds to the Sp1/Myc-Max overlapping site of BRD7 minimal promoter, and slightly positively regulate its promoter activity. c-Myc binds to this Sp1/Myc-Max overlapping site as well, and negatively regulates the promoter activity and endogenous mRNA expression of BRD7 gene. Knock-down of c-Myc increases the promoter activity and mRNA level of BRD7 gene. The luciferase activity of the mutated promoter constructs showed that Sp1/Myc-Max overlapping site is a positive regulation element of BRD7 promoter.</p> <p>Conclusion</p> <p>These studies provide for the first time the evidence that c-Myc is indeed a negative regulator of BRD7 gene. These findings will help to further understand and uncover the bio-functions of BRD7 gene involved in the pathogenesis of NPC.</p

Assessment of spatial coupling coordination in the Three Gorges reservoir area- Taking Badong County as an example

The special geographical location leads to the mutual restriction of urban spatial natural resources, material space and economy and society in the Three Gorges reservoir area, which threatens the spatial security of the towns. Based on the characteristics of multidimensional coupling between systems, this study uses Badong County as an example to evaluate the trend of coupling coordination between natural resources-material space-economic society and society by using the coupling coordination model. The results show that although the coupling degree between systems in Badong County is as high as 0.9546 and the degree of interaction is very strong, the degree of coupling coordination is only a moderate imbalance development type. Therefore, it is an urgent problem to improve the level of coupling and coordination between systems and ensure the safety of urban space

ML-MMAS: self-learning ant colony optimization for multi-criteria journey planning

Ant Colony Optimization (ACO) algorithms have been widely employed for solving optimization problems. Their ability to find optimal solutions depends heavily on the parameterization of the pheromone trails. However, the pheromone parameterization mechanisms in existing ACO algorithms have two major shortcomings: 1) pheromone trails are instance-specific; hence they need to be generated for each new problem instance, 2) solution is constructed based on static pheromone trails, which ignores the impact of the evolving decisions on the final solution. In this paper, we study the personalized journey route planning problem on multimodal public transport networks (MMPTN) that considers multiple travel criteria. The problem is addressed with a weighted sum method, which provides a journey route that best matches passenger's preference vector consisting of multiple travel criteria. We propose a Machine Learning (ML) based Max–Min Ant System (called ML-MMAS) to solve optimization problems by incorporating ML techniques into Ant Colony Optimization algorithms. ML-MMAS learns a pheromone function to directly produce prominent pheromone trails in order to construct solutions for any new instance, without the need to initialize and update the pheromone trails from scratch. We propose a self-learning framework to train the ML-MMAS using incremental solutions generated by MMAS, hence avoiding the need for pre-computed optimal solutions. Specifically, we develop a deep learning-based pheromone prediction model. We design several groups of features to train the model to characterize the evolving states of the search space during solution construction. Finally, we propose a solution component embedding (SCE) model to learn representations of solution components (transit services), which takes into account the transferability among transit services and passenger transfer preferences. The SCE model enables the extraction of high-quality features for the solution components. It can also be directly applied to solve other optimization problems with solutions that can be modeled as sequences of solution components. We evaluate the proposed ML-MMAS by comparing with exact algorithms and the underlying MMAS, using the MMPTN and passenger demands of Singapore. Results show that ML-MMAS is significantly faster than both the exact algorithm and the original MMAS, while achieving near-optimal solutions.National Research Foundation (NRF)This research project is supported in part by the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme with the Technical University of Munich at TUMCREATE

Learning congestion propagation behaviors for traffic prediction

Traffic prediction is a challenging task as the traffic flow is influenced by many seasonal, stochastic, and structural factors. In addition, the spatial and temporal distribution of traffic flow can induce direct and indirect congestion propagation patterns. While existing works have attempted to model spatial-temporal graphs to capture the spatial correlations and temporal dependencies, they fail to consider congestion propagation behavior among road segments. In this paper, we propose a novel traffic prediction model that takes into account the congestion propagation tendencies to improve prediction accuracy. A novel diffusion graph convolution network model is developed to capture the spatial traffic correlations while considering the congestion propagation behavior. Our model also jointly learns the importance of seasonal traffic speed correlations, road contextual information (structural information), and stochastic factors (external factors) through an attention layer. Experimental results on real-world data-set demonstrate the superiority of our method over state-of-the-art traffic prediction techniques, and confirm the significance of congestion propagation behavior in traffic prediction.National Research Foundation (NRF)Accepted versionThis research project is supported in part by the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme with the Technical University of Munich at TUMCREATE

Travel-time prediction of bus journey with multiple bus trips

Accurate travel-time prediction of public transport is essential for reliable journey planning in urban transportation systems. However, existing studies on bus travel-/arrival-time prediction often focus only on improving the prediction accuracy of a single bus trip. This is inadequate in modern public transportation systems, where a bus journey usually consists of multiple bus trips. In this paper, we investigate the problem of travel-time prediction for bus journeys that takes into account a passenger's riding time on multiple bus trips, and also his/her waiting time at transfer points (interchange stations or bus stops). A novel framework is proposed to separately predict the riding and waiting time of a given journey from multiple datasets (i.e., historical bus trajectories, bus route, and road network), and combining the results to form the final travel-time prediction. We empirically determine the impact factors of bus riding times and develop a long short-term memory model that can accurately predict the riding time of each segment of the bus lines/routes. We also demonstrate that the waiting time at transfer points significantly impacts the total journey travel time, and estimating the waiting time is non-trivial as we cannot assume a fixed distribution waiting time. In order to accurately predict the waiting time, we introduce a novel interval-based historical average method that can efficiently address the correlation and sensitivity issues in waiting time prediction. Experiments on real-world data show that the proposed method notably outperforms six baseline approaches for all the scenarios considered.National Research Foundation (NRF)This work was supported by the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) Programme with the Technical University of Munich at TUMCREATE