B Cell Activation in Insulin Resistance and Obesity

Our group has demonstrated that inflammatory diseases such as type 2 diabetes (DM), inflammatory bowel disease (IBD), and periodontal disease (PD) are associated with altered B cell function that may contribute to disease pathogenesis. B cells were found to be highly activated with characteristics of inflammatory cells. Obesity is a pre-disease state for cardiovascular disease and type 2 diabetes and is considered a state of chronic inflammation. Therefore, we sought to better characterize B cell function and phenotype in obese patients. We demonstrate that (Toll-like receptor) TLR4 and CD36 expression by B cells is elevated in obese subjects, suggesting increased sensing of lipopolysaccharide (LPS) and other TLR ligands. These ligands may be of microbial, from translocation from a leaky gut, or host origin. To better assess microbial ligand burden and host response in the bloodstream, we measured LPS binding protein (LBP), bacterial/permeability increasing protein (BPI), and high mobility group box 1 (HMGB1). Thus far, our data demonstrate an increase in LBP in DM and obesity indicating increased responses to TLR ligands in the blood. Interestingly, B cells responded to certain types of LPS by phosphorylating extracellular-signal-regulated kinases (ERK) 1/2. A better understanding of the immunological state of obesity and the microbial and endogenous TLR ligands that may be activating B cells will help identify novel therapeutics to reduce the risk of more dangerous conditions, such as cardiovascular disease.

Determining WWW User's Next Access and Its Application to Pre-fetching

World-Wide Web (WWW) services have grown to levels where significant delays are expected to happen. Techniques like pre-fetching are likely to help users to personalize their needs, reducing their waiting times. However, pre-fetching is only effective if the right documents are identified and if user's move is correctly predicted. Otherwise, pre-fetching will only waste bandwidth. Therefore, it is productive to determine whether a revisit will occur or not, before starting pre-fetching. In this paper we develop two user models that help determining user's next move. One model uses Random Walk approximation and the other is based on Digital Signal Processing techniques. We also give hints on how to use such models with a simple pre-fetching technique that we are developing.