With recent advances in sensory and mobile computing technology, enormous amounts of data about moving objects are being collected. One important application with such data is automated identification of suspicious movements. Due to the sheer volume of data associated with moving objects, it is challenging to develop a method that can efficiently and effectively detect anomalies. The problem is exacerbated by the fact that anomalies may occur at arbitrary levels of abstraction and be associated with multiple granularity of spatiotemporal features. In this study, we propose a new framework named ROAM (Rule-and Motif-based Anomaly Detection in Moving Objects). In ROAM, object trajectories are expressed using discrete pattern fragments called motifs. Associated features are extracted to form a hierarchical feature space, which facilitates a multi-resolution view of the data. We also develop a general-purpose, rulebased classifier which explores the structured feature space and learns effective rules at multiple levels of granularity. We implemented ROAM and tested its components under a variety of conditions. Our experiments show that the system is efficient and effective at detecting abnormal moving objects.

PurposeGlycine and serine are well-known, classic metabolites of glycolysis. Here, we profiled the expression of enzymes associated with serine/glycine metabolism in different molecular subtypes of breast cancer and discuss their potential clinical implications.MethodsWe used western blotting and immunohistochemistry to examine five serine-/glycine-metabolism–associated proteins (PHGDH, PSAT, PSPH, SHMT, and GLDC) in six breast cancer cell lines and 709 breast cancer cases using tissue microarray (TMA).ResultsPHGDH and PSPH, associated with serine metabolism, were highly expressed in the TNBC cells. GLDC, associated with glycine metabolism, was highly expressed in HER-2-positive MDA-MB-453 and TNBC-related MDA-MB-435S. TMA showed that the TNBC-type breast cancer tissues highly expressed PHGDH, PSPH, and SHMT1, but not the luminal-A-type tissues (p<0.001). PSPH and SHMT1 expression in the tumor stroma of HER-2-type cancers was the highest, but the luminal-A tissues showed the lowest expression (p<0.001). GLDC was most frequently expressed in cancer cells and stroma of the HER-2-positive cancers and least frequently in TNBC (p<0.001). By Cox multivariate analysis, tumor PSPH positivity (hazard ratio [HR]: 2.068, 95% confidence interval [CI]: 1.049–4.079, p = 0.036), stromal PSPH positivity (HR: 2.152, 95% CI: 1.107–4.184, p = 0.024), and stromal SHMT1 negativity (HR: 2.142, 95% CI: 1.219–3.764, p = 0.008) were associated with short overall survival.ConclusionsExpression of serine-metabolism–associated proteins was increased in TNBC and decreased in the luminal-A cancers. Expression of glycine-metabolism–associated proteins was high in the tumor and stroma of HER-2-positive cancers.

In this paper we introduce a new type of pattern -a flipping correlation pattern. The flipping patterns are obtained from contrasting the correlations between items at different levels of abstraction. They represent surprising correlations, both positive and negative, which are specific for a given abstraction level, and which "flip" from positive to negative and vice versa when items are generalized to a higher level of abstraction. We design an efficient algorithm for finding flipping correlations, the Flipper algorithm, which outperforms naïve pattern mining methods by several orders of magnitude. We apply Flipper to real-life datasets and show that the discovered patterns are non-redundant, surprising and actionable. Flipper finds strong contrasting correlations in itemsets with low-to-medium support, while existing techniques cannot handle the pattern discovery in this frequency range.

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