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

Lingual Leishmaniasis Presenting to Maxillofacial Surgery in UK with Successful Treatment with Miltefosine

Leishmaniasis is a disease that is caused by protozoa of the genus Leishmania, which is prevalent in tropical and subtropical areas. Clinical forms of leishmaniasis are particularly diverse representing a complex of diseases. We present a case of lingual Leishmaniasis in an immunocompetent man. The lesions were caused by Leishmania donovani/infantum species. The patient responded excellently to miltefosine treatment, with no reactivation during followup. To the authors’ knowledge, it is the first such case of successful miltefosine treatment in this unusual variant of leishmaniasis occurring on the tongue

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.

Control Yourself: ISPE‐Sponsored Guidance in the Application of Self‐Controlled Study Designs in Pharmacoepidemiology

Purpose Consensus is needed on conceptual foundations, terminology and relationships among the various self‐controlled “trigger” study designs that control for time‐invariant confounding factors and target the association between transient exposures (potential triggers) and abrupt outcomes. The International Society for Pharmacoepidemiology (ISPE) funded a working group of ISPE members to develop guidance material for the application and reporting of self‐controlled study designs, similar to Standards of Reporting Observational Epidemiology (STROBE). This first paper focuses on navigation between the types of self‐controlled designs to permit a foundational understanding with guiding principles. Methods We leveraged a systematic review of applications of these designs, that we term Self‐controlled Crossover Observational PharmacoEpidemiologic (SCOPE) studies. Starting from first principles and using case examples, we reviewed outcome‐anchored (case‐crossover [CCO], case‐time control [CTC], case‐case‐time control [CCTC]) and exposure‐anchored (self‐controlled case‐series [SCCS]) study designs. Results Key methodological features related to exposure, outcome and time‐related concerns were clarified, and a common language and worksheet to facilitate the design of SCOPE studies is introduced. Conclusions Consensus on conceptual foundations, terminology and relationships among SCOPE designs will facilitate understanding and critical appraisal of published studies, as well as help in the design, analysis and review of new SCOPE studies. This manuscript is endorsed by ISPE.</br