Towards Certain Fixes with Editing Rules and Master Data

A variety of integrity constraints have been studied for data cleaning. While these constraints can detect the presence of errors, they fall short of guiding us to correct the errors. Indeed, data repairing based on these constraints may not find certain fixes that are absolutely correct, and worse, may introduce new errors when repairing the data. We propose a method for finding certain fixes, based on master data, a notion of certain regions , and a class of editing rules . A certain region is a set of attributes that are assured correct by the users. Given a certain region and master data, editing rules tell us what attributes to fix and how to update them. We show how the method can be used in data monitoring and enrichment. We develop techniques for reasoning about editing rules, to decide whether they lead to a unique fix and whether they are able to fix all the attributes in a tuple, relative to master data and a certain region. We also provide an algorithm to identify minimal certain regions, such that a certain fix is warranted by editing rules and master data as long as one of the regions is correct. We experimentally verify the effectiveness and scalability of the algorithm. </jats:p

Rail-induced Traffic in China

The rapid development of China’s railway has exerted an enormous influence on the intercity passenger transport structure in recent years. However, it has not satisfied the passengers’ travel demand due to induced traffic. This paper is committed to solving such issue, with the aim of satisfying the current travel demand, and of anticipating the demand of the predicted traffic growth over the next 20 to 30 years. The paper has considered the increase in rail passenger kilometres caused by the growth of rail kilometres as rail-induced traffic. Based on the concept and former research of induced traffic, the panel data of 26 provinces and 3 municipalities of China between the year 2000 and 2014 were collected, and the elasticity models (including elasticity-based model, distributed lag model, high-speed rail (HSR) elasticity model and rail efficiency model) have been constructed. The results show the importance of model formation incorporation of rail-induced traffic. It is better to get the correct value in divided zones with different train frequencies or incorporation rail efficiency in cities or provinces. The lag time and rail types also need to be considered. In summary, the results analysis not only confirms the existence of rail-induced traffic, but also provides substantial recommendations to train operation planning.</p

Study on the tissue clearing process using different agents by Mueller matrix microscope

In this paper, we monitor the in vitro tissue clearing process of mouse dorsal skin immersed into two types of agents using Mueller matrix microscope. By Mueller matrix polar decomposition, we can see that the major difference between polarization changes due to two kinds of agents is the opposite trend of phase retardance with clearing. For the insight of the connection between different agents with the microstructural and optical changes of cleared tissues, we establish various models to mimic the dynamic process of microphysical features of tissues with clearing time. The mechanisms considered include refractive index matching, collagen shrinkage, more orderly fibers and birefringence variation. We compare the experimental results with simulations based on a single mechanism model and a combined model, respectively, which confirms that an individual possible mechanism cannot explain the polarization phenomena due to clearing. Also by simulations of various clearing models involving two possible mechanisms, we can speculate that formamide and saturated sucrose as agents have respective impacts on tissue features and then cause different polarization changes with clearing. Specifically, collagen shrinkage plus birefringence reduction can better explain the tissue cleared by formamide, and refractive index match plus increased birefringence model is likely to be a proper description of tissue cleared by sucrose. Both simulations and experiments also validate the potential of Mueller matrix microscope as a good tool to understand the interaction between clearing agents and tissues

PUBLIC OPINION ANALYSIS BASED ON PROBABILISTIC TOPIC MODELING AND DEEP LEARNING

With the rapid development of Internet, especially the social media technologies, the public have gradually published their perception of social events online through social media. In Web2.0 era, with the concept of extensive participation of public in social-event-related information sharing, the effective content analysis and better results presentation for these media published online thus possesses significant importance for public opinion analysis and monitoring. In view of this, this paper proposes a novel method for public opinion analysis on social media website. First, the probabilistic topic model of Latent Dirichlet Allocation (LDA) is adopted to extract the public ideas about the distinct topics of certain event, and then the deep learning model named word2vec is used to calculate the emotional intensity for each text. Next, the underlying themes in the whole as well as the events of emotional intensity are investigated, and the variation trend of public’s emotion intensities is tracked based on time series analysis. Finally, the rationality and effectiveness of the method are verified with the analysis of a real case

Anticancer Effects of Chenopodium ambrosiodes L. Essential Oil on Human Breast Cancer MCF-7 Cells In vitro

Purpose: To investigate the most effective compound of C. ambrosioides essential oil for the induction of cell death in human breast cancer cells (MCF-7), and the mechanism of induction.Methods: MCF-7 cells were treated with essential oil and its two main components, 1-isopropyl-4- methylbenzene and α-terpinene, respectively, for 24 and 48 h in vitro. To determine their cytotoxicity on MCF-7 cells, in vitro cytotoxicity, 3-(4, 5-dimethylthaizol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assay and live/dead cell fluorescent staining were used. MCF-7 cellular superoxide dismutase (SOD), catalase (CAT) vitality and malondialdehyde (MDA) content were also evaluated.Results: MTT results showed that essential oil and its two main compositions significantly inhibited the growth of MCF-7 cells in 24 h (p &lt; 0.05), which was consistent with the Live/dead cell fluorescent staining results. After 24 h incubation the average inhibition rate is 58.98 % for essential oil, 37.8 % for 1-isopropyl-4-methylbenzene and 32.09 % for α-terpinene. With increase in the concentration of essential oil and the two main components, the relative activity of SOD significantly decreased (p &lt; 0.05), while the relative activity of CAT was gradually increased (p &lt; 0.05), compared with control. MDA relative content significantly increased (p &lt; 0.05) until the concentration was 1.25, 0.21 and 0.17 μg/ml for essential oil , 1-isopropyl-4-methylbenzene and α-terpinene , and thereafter significantly decreased (p &lt; 0.05) , compared to control.Conclusion: The data suggest that the essential oil of C. ambrosioides and its two main components inhibit MCF-7 cell proliferation cell death by inducing oxidative damage. However, the two main components are less effective in their anticancer activity than the essential oilKeywords: Chenopodium ambrosioides L. Essential oil, 1-isopropyl-4-methylbenzene, α-Terpinene, Breast cancer MCF-7 cells, Antitumor activit