Learning-aided Stochastic Network Optimization with Imperfect State Prediction

We investigate the problem of stochastic network optimization in the presence of imperfect state prediction and non-stationarity. Based on a novel distribution-accuracy curve prediction model, we develop the predictive learning-aided control (PLC) algorithm, which jointly utilizes historic and predicted network state information for decision making. PLC is an online algorithm that requires zero a-prior system statistical information, and consists of three key components, namely sequential distribution estimation and change detection, dual learning, and online queue-based control. Specifically, we show that PLC simultaneously achieves good long-term performance, short-term queue size reduction, accurate change detection, and fast algorithm convergence. In particular, for stationary networks, PLC achieves a near-optimal $[O(\epsilon)$, $O(\log(1/\epsilon)^2)]$ utility-delay tradeoff. For non-stationary networks, \plc{} obtains an $[O(\epsilon), O(\log^2(1/\epsilon)$ $+ \min(\epsilon^{c/2-1}, e_w/\epsilon))]$ utility-backlog tradeoff for distributions that last $\Theta(\frac{\max(\epsilon^{-c}, e_w^{-2})}{\epsilon^{1+a}})$ time, where $e_w$ is the prediction accuracy and $a=\Theta(1)>0$ is a constant (the Backpressue algorithm \cite{neelynowbook} requires an $O(\epsilon^{-2})$ length for the same utility performance with a larger backlog). Moreover, PLC detects distribution change $O(w)$ slots faster with high probability ($w$ is the prediction size) and achieves an $O(\min(\epsilon^{-1+c/2}, e_w/\epsilon)+\log^2(1/\epsilon))$ convergence time. Our results demonstrate that state prediction (even imperfect) can help (i) achieve faster detection and convergence, and (ii) obtain better utility-delay tradeoffs

Toward the clinical application of time-domain fluorescence lifetime imaging

High-speed (video-rate) fluorescence lifetime imaging (FLIM) through a flexible endoscope is reported based on gated optical image intensifier technology. The optimization and potential application of FLIM to tissue autofluorescence for clinical applications are discussed. (c) 2005 Society of Photo-Optical Instrumentation Engineers

The sources of parenchymal regeneration following chronic hepatocellular liver injury in mice

After liver injury, parenchymal regeneration occurs through hepatocyte replication. However, during regenerative stress, oval cells (OCs) and small hepatocyte like progenitor cells (SHPCs) contribute to the process. We systematically studied the intra-hepatic and extra-hepatic sources of liver cell replacement in the hepatitis B surface antigen (HBsAg-tg) mouse model of chronic liver injury. Female HBsAg-tg mice received a bone marrow (BM) transplant from male HBsAg-negative mice, and half of these animals received retrorsine to block indigenous hepatocyte proliferation. Livers were examined 3 and 6 months post-BM transplantation for evidence of BM-derived hepatocytes, OCs, and SHPCs. In animals that did not receive retrorsine, parenchymal regeneration occurred through hepatocyte replication, and the BM very rarely contributed to hepatocyte regeneration. In mice receiving retrorsine, 4.8% of hepatocytes were Y chromosome positive at 3 months, but this was frequently attributable to cell fusion between indigenous hepatocytes and donor BM, and their frequency decreased to 1.6% by 6 months, as florid OC reactions and nodules of SHPCs developed. By analyzing serial sections and reconstructing a 3-dimensional map, continuous streams of OCs could be seen that surrounded and entered deep into the nodules of SHPCs, connecting directly with SHPCs, suggesting a conversion of OCs into SHPCs. In conclusion, during regenerative stress, the contribution to parenchymal regeneration from the BM is minor and frequently attributable to cell fusion. OCs and SHPCs are of intrinsic hepatic origin, and OCs can form SHPC nodules

Agreement and Correlation Between Different Topical Corticosteroid Potency Classification Systems

Importance: Topical corticosteroids (TCSs) are available in multiple potencies that alter their effectiveness and safety. Pharmacoepidemiologic studies on TCSs are hampered by the absence of a universal potency classification system, limiting comparisons across studies, robust exposure classification, and clinical interpretation. Objective: To classify TCSs into 3 commonly used potency classification systems and evaluate the agreement and correlation between the 3 systems. Design, Setting, and Participants: In this classification study, a comprehensive list of TCS formulations was compiled using sources identified in the literature, the Ontario Drug Benefit Formulary, a recent Cochrane review on the use of TCSs in people with eczema, and the Anatomical Therapeutic Classification (ATC) of the World Health Organization from August 11, 2021, to January 6, 2022. Topical corticosteroid potency classifications were assigned and compared using the 7-category US classification system, a 4-category classification from a recent Cochrane review largely based on the UK formulary, and the 4-category ATC classification. To facilitate comparisons across systems, the 7-category US system was consolidated into 4 categories. Main Outcomes and Measures: Cohen weighted ? (?w) and Spearman rank correlation coefficients (r) were computed to examine agreement and correlation between the classification systems. Results: A total of 232 unique TCS formulations (ATC, n = 231; US classification, n = 232; Cochrane review, n = 89) were included. Overall, there was low-to-moderate agreement but strong correlation between the classification systems. The US classification had weak agreement with the ATC system (?w, 0.53; 95% CI, 0.45-0.60) and moderate agreement with the Cochrane review classification (?w, 0.60; 95% CI, 0.48-0.73); there was weak agreement between the ATC and Cochrane review classifications (?w, 0.58; 95% CI, 0.46-0.71). The US classification strongly correlated with the ATC system (r, 0.77; 95% CI, 0.71-0.82) and Cochrane review classification (r, 0.74; 95% CI, 0.62-0.82). There was also a strong correlation between the Cochrane review and ATC classifications (r, 0.71; 95% CI, 0.58-0.80). Conclusions and Relevance: This classification study used multiple resources to classify 232 TCS formulations into 3 potency classifications. Because these systems are often incongruent, they may yield different results in pharmacoepidemiologic studies; investigators need to be transparent in their classification approach and consider alternative potency definitions in sensitivity analyses.