Additive effects of rapamycin and aspirin on dendritic cell allostimulatory capacity.

Acting as antigen presenting cells, mature dendritic cells (DCs) initiate both innate and adaptive alloimmune responses. However, immature DCs are weak immunostimulators and mediate tolerogenic effects under certain conditions. Tolerogenic activities of immature DCs can be enhanced by pharmacological agents. Here, we compared pharmacological DC preconditioning with rapamycin and aspirin, applied alone or in combination, on LPS-induced DC maturation and T-cell allostimulatory capacity. Preconditioning with aspirin but not rapamycin tended to reduce the number of mouse bone marrow-derived immature DCs expressing CD40 and major histocompatibility complex class II molecules upon LPS stimulation. Conversely, DC preconditioning with rapamycin, but not aspirin, reduced T-cell alloproliferative responses. A combination of rapamycin and aspirin was more effective than either drug applied alone with respect to inhibition of T-cell alloproliferation. The two agents in combination reduced numbers of CD4(+)IFN-γ(+) Th1 and CD4(+)IL-17(+) Th17 effector cells while maintaining Foxp3(+) regulatory T cells. These results suggest aspirin may moderately enhance rapamycin-mediated inhibition of DC allostimulatory capacity.

Change points detection in crime-related time series: an on-line fuzzy approach based on a shape space representation

The extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.