Effect of cucurbitacin on malignant biological behavior of breast cancer cells, and its possible underlying mechanism

Purpose: To study the influence of cucurbitacin on malignant biological behavior of mammary carcinoma cells, and the likely mechanism involved.Methods: Human mammary carcinoma cell line MDA-MB-436 was selected for cell culture and treated with different concentrations of cucurbitacin. The effect of cucurbitacin on cell activity, cell colonyformation capacity, cell invasion, migration potential, matrix metalloproteinase-9 (MMP-9) activity, and levels of vascular endothelial growth factor A (VEGFA), epithelial calcium adhesion (E-cadherin), and neurogenic calcium adhesion (N-cadherin) were measured. Moreover, levels of wave protein (vimentin), phosphorylated epidermal growth factor receptor (p-EGFR), phosphorylated signaling transduction, and transcription activation factor 3 (p-STAT3) and phosphorylated protein kinase B (p-Akt) were determined.Results: With increase in cucurbitacin dose, there was significant decrease in cell viability, cell colony ratio, cell invasion and migration capacity, and expression levels of MMP-9, VEGFA, e-cadherin, ncadherin, vimentin, P-EGFR, P-STAT3 and p-Akt (p < 0.05).Conclusion: Cucurbitacin inhibits the proliferation, invasion, and migration of breast cancer cells by down-regulating the expressions of EGFR/STAT3/Akt signaling-related proteins, and inhibiting epithelial-mesenchymal transition transformation

SE-shapelets: Semi-supervised Clustering of Time Series Using Representative Shapelets

Shapelets that discriminate time series using local features (subsequences) are promising for time series clustering. Existing time series clustering methods may fail to capture representative shapelets because they discover shapelets from a large pool of uninformative subsequences, and thus result in low clustering accuracy. This paper proposes a Semi-supervised Clustering of Time Series Using Representative Shapelets (SE-Shapelets) method, which utilizes a small number of labeled and propagated pseudo-labeled time series to help discover representative shapelets, thereby improving the clustering accuracy. In SE-Shapelets, we propose two techniques to discover representative shapelets for the effective clustering of time series. 1) A \textit{salient subsequence chain} ($SSC$) that can extract salient subsequences (as candidate shapelets) of a labeled/pseudo-labeled time series, which helps remove massive uninformative subsequences from the pool. 2) A \textit{linear discriminant selection} ($LDS$) algorithm to identify shapelets that can capture representative local features of time series in different classes, for convenient clustering. Experiments on UCR time series datasets demonstrate that SE-shapelets discovers representative shapelets and achieves higher clustering accuracy than counterpart semi-supervised time series clustering methods

DoSTra: Discovering common behaviors of objects using the duration of staying on each location of trajectories

Since semantic trajectories can discover more semantic meanings of a user\u27s interests without geographic restrictions, research on semantic trajectories has attracted a lot of attentions in recent years. Most existing work discover the similar behavior of moving objects through analysis of their semantic trajectory pattern, that is, sequences of locations. However, this kind of trajectories without considering the duration of staying on a location limits wild applications. For example, Tom and Anne have a common pattern of Home→Restaurant → Company → Restaurant, but they are not similar, since Tom works at Restaurant, sends snack to someone at Company and return to Restaurant while Anne has breakfast at Restaurant, works at Company and has lunch at Restaurant. If we consider duration of staying on each location we can easily to differentiate their behaviors. In this paper, we propose a novel approach for discovering common behaviors by considering the duration of staying on each location of trajectories (DoSTra). Our approach can be used to detect the group that has similar lifestyle, habit or behavior patterns and predict the future locations of moving objects. We evaluate the experiment based on synthetic dataset, which demonstrates the high effectiveness and efficiency of the proposed method

Building cloud-based healthcare data mining services

The linkage between healthcare service and cloud computing techniques has drawn much attention lately. Up to the present, most works focus on IT system migration and the management of distributed healthcare data rather than taking advantage of information hidden in the data. In this paper, we propose to explore healthcare data via cloud-based healthcare data mining services. Specifically, we propose a cloud-based healthcare data mining framework for healthcare data mining service development. Under such framework, we further develop a cloud-based healthcare data mining service to predict patients future length of stay in hospital

A clique-based WBAN scheduling for mobile wireless body area networks

Wireless-body-area-networks (WBAN) that generally comprises different types of sensors are useful to gather multiple parameters together, such as body temperature, blood pressure, pulse, heartbeat and blood sugar. However, a dense and mobile WBAN often suffers from interference, which causes serious problems, such as degrading throughput and wasting energy. So, the sensors in WBAN are not active together at the same time and they can be partitioned to different groups and each group works in turn to avoid interference. In this paper, we provide a Clique-Based WBAN Scheduling (CBWS) algorithm to cluster sensors of a single or multiple WBAN into different groups to avoid interference. Particularly, we propose a coloring based scheduling method to schedule all groups to work in a sequence of time slots. The experimental results demonstrate the performance of the proposed CBWS algorithm in terms of system throughput. © 2014 Published by Elsevier B.V

Analysis of Optimal Injection Ratio of Vapor Injection Heat Pump for Electric Railway Vehicles

Air-source heat pump with vapour injection is a prospective efficient heating method for electric railway vehicles in cold regions. A heating performance analysis modelling for air source heat pump with vapour injection is set up for performance optimization in this paper. The maximum errors of the program are within 15%. Heating performance, as well as the optimal injection ratio is analysed. The optimal injection ratio varies mainly from 0.12 to 0.3 under the typical working condition of railway vehicles in winter. It goes up with increasing inlet air temperature of condenser and goes down with increasing ambient temperature. The ambient temperature has very little effect on the optimal injection temperature of the internal heat exchanger. The results indicate that the expander valve opening of the injection branch can be controlled by its outlet temperature to get the optimal heating performance

Neural-PBIR Reconstruction of Shape, Material, and Illumination

Reconstructing the shape and spatially varying surface appearances of a physical-world object as well as its surrounding illumination based on 2D images (e.g., photographs) of the object has been a long-standing problem in computer vision and graphics. In this paper, we introduce a robust object reconstruction pipeline combining neural based object reconstruction and physics-based inverse rendering (PBIR). Specifically, our pipeline firstly leverages a neural stage to produce high-quality but potentially imperfect predictions of object shape, reflectance, and illumination. Then, in the later stage, initialized by the neural predictions, we perform PBIR to refine the initial results and obtain the final high-quality reconstruction. Experimental results demonstrate our pipeline significantly outperforms existing reconstruction methods quality-wise and performance-wise

Recommending tripleset interlinking through a social network approach

Tripleset interlinking is one of the main principles of Linked Data. However, the discovery of existing triplesets relevant to be linked with a new tripleset is a non-trivial task in the publishing process. Without prior knowledge about the entire Web of Data, a data publisher must perform an exploratory search, which demands substantial effort and may become impracticable, with the growth and dissemination of Linked Data. Aiming at alleviating this problem, this paper proposes a recommendation approach for this scenario, using a Social Network perspective. The experimental results show that the proposed approach obtains high levels of recall and reduces in up to 90% the number of triplesets to be further inspected for establishing appropriate links. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-41230-1_13.CNPq/160326/2012-5CNPq/301497/2006-0CNPq/475717/2011-2CNPq/57128/2009-9FAPERJ/E-26/170028/2008FAPERJ/E-26/103.070/2011CAPES/PROCAD/NF 1128/201