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Drosophila Myc (dMyc), as a broad-spectrum transcription factor, can regulate the expression of a large number of genes to control diverse cellular processes, such as cell cycle progression, cell growth, proliferation and apoptosis. However, it remains largely unknown about whether dMyc can be involved in Drosophila innate immune response. Here, we have identified dMyc to be a negative regulator of Drosophila Imd pathway via the loss- and gain-of-function screening. We demonstrate that dMyc inhibits Drosophila Imd immune response via directly activating miR-277 transcription, which further inhibit the expression of imd and Tab2-Ra/b. Importantly, dMyc can improve the survival of flies upon infection, suggesting inhibiting Drosophila Imd pathway by dMyc is vital to restore immune homeostasis that is essential for survival. Taken together, our study not only reports a new dMyc-miR-277-imd/Tab2 axis involved in the negative regulation of Drosophila Imd pathway, and provides a new insight into the complex regulatory mechanism of Drosophila innate immune homeostasis maintenance., Author summary Innate immunity is the first line of defense against pathogenic microorganisms. Both overactivation and depression of immune response are detrimental to the organism. It is indispensable to regulate the duration and intensity of immune response. In this work, we find that Drosophila Myc (dMyc) as a transcription factor activates the transcription of miR-277 to negatively regulate the Imd pathway via inhibiting the expression of imd/Tab2 gene in the middle and later stage of Drosophila innate immune. dMyc is required to restore Drosophila immune homeostasis for the survival of flies upon infection. Since dMyc is well conserved in animals, our findings will be important in understanding the complex regulatory mechanisms of innate immune responses in animals.

Wavelet is able to adapt to the requirements of time-frequency signal analysis automatically and can focus on any details of the signal and then decompose the function into the representation of a series of simple basis functions. It is of theoretical and practical significance. Therefore, this paper does subdivision on track irregularity time series based on the idea of wavelet decomposition-reconstruction and tries to find the best fitting forecast model of detail signal and approximate signal obtained through track irregularity time series wavelet decomposition, respectively. On this ideology, piecewise gray-ARMA recursive based on wavelet decomposition and reconstruction (PG-ARMARWDR) and piecewise ANN-ARMA recursive based on wavelet decomposition and reconstruction (PANN-ARMARWDR) models are proposed. Comparison and analysis of two models have shown that both these models can achieve higher accuracy.

Focusing on problems existing in track irregularity time series data quality, this paper first presents abnormal data identification, data offset correction algorithm, local outlier data identification, and noise cancellation algorithms. And then proposes track irregularity time series decomposition and reconstruction through the wavelet decomposition and reconstruction approach. Finally, the patterns and features of track irregularity standard deviation data sequence in unit sections are studied, and the changing trend of track irregularity time series is discovered and described.

The safety of railway permanent way is what the railway department highly concern about, railway permanent way department is one of the most important departments, it manages all railway infrastructure, railway infrastructure's good condition and normal operation is the foundation of railway transport safety. In this paper, management information system of railway permanent way safety risks and comprehensive evaluation is built through the integration of existing resources of the railway permanent way department, to achieve scientific and standardized management. It provides the effective solution for innovation of railway permanent way management and the mode of administration. This system is operational in February, 2008 in the Kunming railway bureau.

Railway distributed system integration needs to realize information exchange, resources sharing and coordination process across fields, departments and application systems. And railway data integration is essential to implement this integration. In order to resolve the problem of heterogeneity of data models among data sources of different railway operation systems, this paper has brought forth the concept of metadata dictionary that abstracts related information and information content of each data source. Based on the metadata dictionary, this paper presents a novel integration data model of spatial structure, a XML-oriented 3-dimension common data model. The proposed model accommodates both the flexibility of level relationship and syntax expression in data integration. In this model, a spatial data pattern is used to describe and express the characteristic relationship of data items among all types of data. Based on the data model with rooted directed graph and the organization of level as well as the flexibility of the expression, the model can represent the mapping between different data models, including relationship model and object-oriented model. A consistent concept and algebraic description of the data set is given to function as the metadata in data integration, so that the algebraic manipulation of data integration is standardized to support the data integration of distributed system.

As an important component of China’s transportation data sharing system, high-speed railway data sharing is a typical application of data-intensive computing. Currently, most high-speed railway data is shared in cloud computing environment. Thus, there is an urgent need for an effective cloud-computing-based data placement strategy in high-speed railway. In this paper, a new data placement strategy named hierarchical structure data placement strategy is proposed. The proposed method combines the semidefinite programming algorithm with the dynamic interval mapping algorithm. The semi-definite programming algorithm is suitable for the placement of files with various replications, ensuring that different replications of a file are placed on different storage devices, while the dynamic interval mapping algorithm ensures better self-adaptability of the data storage system. A hierarchical data placement strategy is proposed for large-scale networks. In this paper, a new theoretical analysis is provided, which is put in comparison with several other previous data placement approaches, showing the efficacy of the new analysis in several experiments.

Excellent condition of track geometry status is the foundation to ensure train travel security. The detection data of track inspection car contains many valuable features of the track status. The technique of gray forecast and Kalman filtering can be used to investigate the problem and predict the status change of the track geometry. In this paper, gray forecast is used in qualitative analysis of track geometry status changes, and we predict the development of track geometry status change using the Kalman filter prediction model and specific recursive algorithm, established prediction model of the track geometry to make an emulation experiment to analyze the data that track inspection car has detected, and predict changing trends of track geometry the state. Experiment results show that the application model of improved Kalman filter to predict the track geometry status changes gets a higher accuracy, and it can reflect the real change tendency of the track status.Copyright © 2011 by ASME

Railway distributed system integration needs to realize information exchange, resources sharing and coordination process across fields, departments and application systems. And railway data integration is essential to implement this integration. In order to resolve the problem of heterogeneity of data models among data sources of different railway operation systems, this paper presents a novel integration data model of spatial structure, a XML-oriented 3-dimension common data model. The proposed model accommodates both the flexibility of level relationship and syntax expression in data integration. In this model, a spatial data pattern is used to describe and express the characteristic relationship of data items among all types of data. Based on the data model with rooted directed graph and the organization of level as well as the flexibility of the expression, the model can represent the mapping between different data models, including relationship model and object-oriented model. A consistent concept and algebraic description of the data set is given to function as the metadata in data integration, so that the algebraic manipulation of data integration is standardized to support the data integration of distributed system.Copyright © 2011 by ASME

The combination of Kalman filtering technology and artificial neural networks technology has provided a new method for forecasting the changes of track geometry state. This article has established a forecasting model for the track geometry state of BP neural network based on Kalman filtering. The author analyzed some track detecting data of Chengdu-Kunming railway line of Kunming Railway Bureau, and made the state forecast based on this model. Simulation result showed that the application of BP neural network algorithm based on Kalman filtering in forecasting rail state has very high accuracy. The experiment showed that the application of this technology could forecast changes of the track geometry state in advance, and could make in-time adjustment to the track maintenance service work. Thus it is an effective measure for maintaining track geometry state in the best condition.

The condition of line equipment that track inspection car (or track recording car. which test geometry data of track, consist track gauge, cross level, longitudinal level, track alignment, twist, etc) examines has the attribute of high authenticity; as a result, it is an important way to obtain the true state of the track. However, this method also meets the problem of mileage excursion and noisy data. This paper employs the theoretical of gray relevancy of gray systems to analyze the waveform data that track inspection car examines, and makes corrections to mileage excursion, and then gets rid of the noisy data using the method of wavelet transform, so that the real track detecting data about track state can be achieved. This paper also makes the comparison between the effects of three threshold values in reducing noises, and pointed out their respective advantages and disadvantages.Copyright © 2010 by ASME

In the environment of cloud computing, real-time mass data about high-speed rail which is based on the intense monitoring of large scale perceived equipment provides strong support for the safety and maintenance of high-speed rail. In this paper, we focus on the Top- k algorithm of continuous distribution based on Multisource distributed data stream for high-speed rail monitoring. Specifically, we formalized Top- k monitoring model of high-speed rail and proposed DTMR that is the Top- k monitoring algorithm with random, continuous, or strictly monotone aggregation functions. The DTMR was proved to be valid by lots of experiments.