Hepatitis B virus X antigen plays an important role in the development of human hepatocellular carcinoma (HCC). The key regulators controlling the temporal downstream gene expression for HCC progression remains unknown. In this study, we took advantage of systems biology approach and analyzed the microarray data of the HBx transgenic mouse as a screening process to identify the differentially expressed genes and applied the software Pathway Studio to identify potential pathways and regulators involved in HCC. Using subnetwork enrichment analysis, we identified five common regulator genes: EDN1, BMP7, BMP4, SPIB and SRC. Upregulation of the common regulators was validated in the other independent HBx transgenic mouse lines. Furthermore, we verified the correlation of their RNA expression levels by using the human HCC samples, and their protein levels by using the human liver disease tissue arrays. EDN1, bone morphogenetic protein (BMP) 4 and BMP7 were upregulated in cirrhosis, BMP4, BMP7 and SRC were further upregulated in hepatocellular or cholangiocellular carcinoma samples. The trend of increasing expression of the common regulators correlates well with the progression of human liver cancer. Overexpression of the common regulators increases the cell viability, promotes migration and invasiveness and enhances the colony formation ability in Hep3B cells. Our approach allows us to identify the critical genes in hepatocarcinogenesis in an HBx-induced mouse model. The validation of the gene expressions in the liver cancer of human patients and their cellular function assays suggests that the identified common regulators may serve as useful molecular targets for the early-stage diagnosis or therapy for HCC.

In scientometrics for trend analysis, parameter choices for observing trends are often made ad hoc in past studies. For examples, different year spans might be used to create the time sequence and different indices were chosen for trend observation. However, the effectiveness of these choices was hardly known, quantitatively and comparatively. This work provides clues to better interpret the results when a certain choice was made. Specifically, by sorting research topics in decreasing order of interest predicted by a trend index and then by evaluating this ordering based on information retrieval measures, we compare a number of trend indices (percentage of increase vs. regression slope), trend formulations (simple trend vs. eigen-trend), and options (various year spans and durations for prediction) in different domains (safety agriculture and information retrieval) with different collection scales (72500 papers vs. 853 papers) to know which one leads to better trend observation. Our results show that the slope of linear regression on the time series performs constantly better than the others. More interestingly, this index is robust under different conditions and is hardly affected even when the collection was split into arbitrary (e.g., only two) periods. Implications of these results are discussed. Our work does not only provide a method to evaluate trend prediction performance for scientometrics, but also provides insights and reflections for past and future trend observation studies.

Traditionally, intrusion detection systems detect intrusions at the operating system (OS) level. In this paper we explore the possibility of detecting intrusion at the application level by using rich application semantics. We use short sequences of language library calls as signatures. We consider library call signatures to be more application-oriented than system call signatures because they are a more direct reflection of application code. Most applications are written in a higher-level language with an associated support library, such as C or C++. We hypothesize that library call signatures can be used to detect attacks that cause perturbation in the application code. We are hopeful that this technique will be amenable to detecting attacks that are carried out by internal intruders, who are viewed as legitimate users by an operating system.