Geographic information has spawned many novel Web applications where global positioning system (GPS) plays important roles in bridging the applications and end users. Learning knowledge from users' raw GPS data can provide rich context information for both geographic and mobile applications. However, so far, raw GPS data are still used directly without much understanding. In this paper, an approach based on supervised learning is proposed to automatically infer transportation mode from raw GPS data. The transportation mode, such as walking, driving, etc., implied in a user's GPS data can provide us valuable knowledge to understand the user. It also enables context-aware computing based on user's present transportation mode and design of an innovative user interface for Web users. Our approach consists of three parts: a change pointbased segmentation method, an inference model and a postprocessing algorithm based on conditional probability. The change point-based segmentation method was compared with two baselines including uniform duration based and uniform length based methods. Meanwhile, four different inference models including Decision Tree, Bayesian Net, Support Vector Machine (SVM) and Conditional Random Field (CRF) are studied in the experiments. We evaluated the approach using the GPS data collected by 45 users over six months period. As a result, beyond other two segmentation methods, the change point based method achieved a higher degree of accuracy in predicting transportation modes and detecting transitions between them. Decision Tree outperformed other inference models over the change point based segmentation method.

The increasing popularity of GPS device has boosted many applications where more and more GPS logs have been accumulating continuously. Managing and understanding the collected GPS data are two important issues for these applications. On one hand, by indexing the increasing GPS data, we can provide effective retrieval method for users to find the corresponding GPS data interests them. On the other hand, by understanding user's GPS data, we are more likely to enable novel services which would stimulate people's passion on contributing GPS data in turn. However, so far, GPS data are still used directly without much understanding. In our project, referred to as GeoLife, we focus on visualization, organization, fast retrieval, and effective understanding of GPS track logs for both personal and public use. It not only provides a powerful platform for people to effectively manage their GPS data but also help them well understand a person's past experience from GPS data.

No abstract

Increasing evidence suggests that deubiquitinase USP7 participates in tumor progression by various mechanisms and serves as a potential therapeutic target. However, its expression and role in esophageal cancer remains elusive; the anti-cancer effect by targeting USP7 still needs to be investigated. Here, we reported that USP7 was overexpressed in esophageal squamous cell carcinoma (ESCC) tissues compared with adjacent tissues, implying that USP7 was an attractive anticancer target of ESCC. Pharmaceutical or genetic inactivation of USP7 inhibited esophageal cancer cells growth in vitro and in vivo and induced apoptosis. Mechanistically, inhibition of USP7 accumulated poly-ubiquitinated proteins, activated endoplasmic reticulum stress, and increased expression of ATF4, which transcriptionally upregulated expression of NOXA and induced NOXA-mediated apoptosis. These results provide an evidence for clinical investigation of USP7 inhibitors for the treatment of ESCC.

No abstract

ZnS/CuS nanotubes exhibit visible-light photocatalytic activity for H2 evolution without cocatalysts, resulting from the heterojunctions between ZnS and CuS nanoparticles.

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The increasing popularity of GPS device has boosted many Web applications where people can upload, browse and exchange their GPS tracks. In these applications, spatial or temporal search function could provide an effective way for users to retrieve specific GPS tracks they are interested in. However, existing spatial-temporal index for trajectory data has not exploited the characteristic of user behavior in these online GPS track sharing applications. In most cases, when sharing a GPS track, people are more likely to upload GPS data of the near past than the distant past. Thus, the interval between the end time of a GPS track and the time it is uploaded, if viewed as a random variable, has a skewed distribution. In this paper, we first propose a probabilistic model to simulate user behavior of uploading GPS tracks onto an online sharing application. Then we propose a flexible spatio-temporal index scheme, referred to as Compressed Start-End Tree (CSE-tree), for large-scale GPS track retrieval. The CSE-tree combines the advantages of B+ Tree and dynamic array, and maintains different index structure for data with different update frequency. Experiments using synthetic data show that CSE-tree outperforms other schemes in requiring less index size and less update cost while keeping satisfactory retrieval performance.