Routine pattern discovery and anomaly detection in individual travel behavior

Discovering patterns and detecting anomalies in individual travel behavior is a crucial problem in both research and practice. In this paper, we address this problem by building a probabilistic framework to model individual spatiotemporal travel behavior data (e.g., trip records and trajectory data). We develop a two-dimensional latent Dirichlet allocation (LDA) model to characterize the generative mechanism of spatiotemporal trip records of each traveler. This model introduces two separate factor matrices for the spatial dimension and the temporal dimension, respectively, and use a two-dimensional core structure at the individual level to effectively model the joint interactions and complex dependencies. This model can efficiently summarize travel behavior patterns on both spatial and temporal dimensions from very sparse trip sequences in an unsupervised way. In this way, complex travel behavior can be modeled as a mixture of representative and interpretable spatiotemporal patterns. By applying the trained model on future/unseen spatiotemporal records of a traveler, we can detect her behavior anomalies by scoring those observations using perplexity. We demonstrate the effectiveness of the proposed modeling framework on a real-world license plate recognition (LPR) data set. The results confirm the advantage of statistical learning methods in modeling sparse individual travel behavior data. This type of pattern discovery and anomaly detection applications can provide useful insights for traffic monitoring, law enforcement, and individual travel behavior profiling

CD44(+)/CD24(- )breast cancer cells exhibit enhanced invasive properties: an early step necessary for metastasis

INTRODUCTION: A subpopulation (CD44(+)/CD24(-)) of breast cancer cells has been reported to have stem/progenitor cell properties. The aim of this study was to investigate whether this subpopulation of cancer cells has the unique ability to invade, home, and proliferate at sites of metastasis. METHODS: CD44 and CD24 expression was determined by flow cytometry. Northern blotting was used to determine the expression of proinvasive and 'bone and lung metastasis signature' genes. A matrigel invasion assay and intracardiac inoculation into nude mice were used to evaluate invasion, and homing and proliferation at sites of metastasis, respectively. RESULTS: Five among 13 breast cancer cell lines examined (MDA-MB-231, MDA-MB-436, Hs578T, SUM1315, and HBL-100) contained a higher percentage (>30%) of CD44(+)/CD24(- )cells. Cell lines with high CD44(+)/CD24(- )cell numbers express basal/mesenchymal or myoepithelial but not luminal markers. Expression levels of proinvasive genes (IL-1α, IL-6, IL-8, and urokinase plasminogen activator [UPA]) were higher in cell lines with a significant CD44(+)/CD24(- )population than in other cell lines. Among the CD44(+)/CD24(-)-positive cell lines, MDA-MB-231 has the unique property of expressing a broad range of genes that favor bone and lung metastasis. Consistent with previous studies in nude mice, cell lines with CD44(+)/CD24(- )subpopulation were more invasive than other cell lines. However, only a subset of CD44(+)/CD24(-)-positive cell lines was able to home and proliferate in lungs. CONCLUSION: Breast cancer cells with CD44(+)/CD24(- )subpopulation express higher levels of proinvasive genes and have highly invasive properties. However, this phenotype is not sufficient to predict capacity for pulmonary metastasis