Delta-Aminolevulinic Acid-Mediated Photodiagnoses in Surgical Oncology: A Historical Review of Clinical Trials.

Fluorescence imaging is an emerging clinical technique for real-time intraoperative visualization of tumors and their boundaries. Though multiple fluorescent contrast agents are available in the basic sciences, few fluorescence agents are available for clinical use. Of the clinical fluorophores, delta aminolevulinic acid (5ALA) is unique for generating visible wavelength tumor-specific fluorescence. In 2017, 5ALA was FDA-approved for glioma surgery in the United States. Additionally, clinical studies suggest this agent may have utility in surgical subspecialties outside of neurosurgery. Data from dermatology, OB/GYN, urology, cardiothoracic surgery, and gastrointestinal surgery show 5ALA is helpful for intraoperative visualization of malignant tissues in multiple organ systems. This review summarizes data from English-language 5ALA clinical trials across surgical subspecialties. Imaging systems, routes of administration, dosing, efficacy, and related side effects are reviewed. We found that modified surgical microscopes and endoscopes are the preferred imaging devices. Systemic dosing across surgical specialties range between 5 and 30 mg/kg bodyweight. Multiple studies discussed potential for skin irritation with sun exposure, however this side effect is infrequently reported. Overall, 5ALA has shown high sensitivity for labeling malignant tissues and providing a means to visualize malignant tissue not apparent with standard operative light sources

The benefit of sequentiality in social networks *

Abstract This paper examines the benefit of sequentiality in the social networks. We adopt the elegant theoretical framework proposed by We then examine the structure of optimal mechanism and allow for arbitrary sequence of players' moves. We show that starting from any fixed sequence, the aggregate contribution always goes up while making simultaneous-moving players move sequentially. This suggests a robust rule of thumbs -any local modification towards the sequential-move game is beneficial. Pushing this idea to the extreme, the optimal sequence turns out to be a chain structure, i.e., players should move one by one. Our results continue to hold when either players exhibit strategic substitutes instead or the network designer's goal is to maximize the players' aggregate payoff rather than the aggregate contribution

Dynamics of Information Diffusion and Social Sensing

Statistical inference using social sensors is an area that has witnessed remarkable progress and is relevant in applications including localizing events for targeted advertising, marketing, localization of natural disasters and predicting sentiment of investors in financial markets. This chapter presents a tutorial description of four important aspects of sensing-based information diffusion in social networks from a communications/signal processing perspective. First, diffusion models for information exchange in large scale social networks together with social sensing via social media networks such as Twitter is considered. Second, Bayesian social learning models and risk averse social learning is considered with applications in finance and online reputation systems. Third, the principle of revealed preferences arising in micro-economics theory is used to parse datasets to determine if social sensors are utility maximizers and then determine their utility functions. Finally, the interaction of social sensors with YouTube channel owners is studied using time series analysis methods. All four topics are explained in the context of actual experimental datasets from health networks, social media and psychological experiments. Also, algorithms are given that exploit the above models to infer underlying events based on social sensing. The overview, insights, models and algorithms presented in this chapter stem from recent developments in network science, economics and signal processing. At a deeper level, this chapter considers mean field dynamics of networks, risk averse Bayesian social learning filtering and quickest change detection, data incest in decision making over a directed acyclic graph of social sensors, inverse optimization problems for utility function estimation (revealed preferences) and statistical modeling of interacting social sensors in YouTube social networks.Comment: arXiv admin note: text overlap with arXiv:1405.112

Dynamic Markets with Many Agents: Applications in Social Learning and Competition

This thesis considers two applications in dynamics economic models with many agents. The dynamics of the economic systems under consideration are intractable since they depend on the (stochastic) outcomes of the agents' actions. However, as the number of agents grows large, approximations to the aggregate behavior of agents come to light. I use this observation to characterize market dynamics and subsequently to study these applications. Chapter 2 studies the problem of devising a pricing strategy to maximize the revenues extracted from a stream of consumers with heterogenous preferences. Consumers, however, do not know the quality of the product or service and engage in a social learning process to learn it. Using a mean-field approximation the transient of this social learning process is uncovered and the pricing problem is analyzed. Chapter 3 adds to the previous chapter in analyzing features of this social learning process with finitely many agents. In addition, the chapter generalizes the information structure to include cases where consumers take into account the order in which reviews were submitted. Chapter 4 considers a model of dynamic oligopoly competition in the spirit of models that are widespread in industrial organization. The computation of equilibrium strategies of such models suffers from the curse of dimensionality when the number of agents (firms) is large. For a market structure with few dominant firms and many fringe firms, I study an alternative equilibrium concept in which fringe firms are represented succinctly with a low dimensional set of statistics. The chapter explores how this new equilibrium concept expands the class of dynamic oligopoly models that can be studied computationally in empirical work

Bayesian social learning with consumer reviews

We study a market of heterogeneous customers who rationally learn the mean quality of an offered product by observing the reviews of customers who purchased the product earlier in time. The seller, who is equally uniformed about the quality, prices dynamically to maximize her revenue. We find that social learning is successful|agents eventually learning the mean quality of the product. This result holds for an information structure when the sequence of past re- views and prices is observed, and, under some assumptions, even when only aggregate reviews are observed. The latter result hinges on the observation that earlier reviews are more inuential than later one. In addition, we find that under general conditions the seller benefits from social learning ex ante|before knowing the quality of her product. Finally, we draw conclusions on the sellers pricing problem when accounting for social learning. Under some assumptions, we find that lowering the price speeds social learning, in contrast with earlier results on social learning from privately observed signals

Monopoly Pricing in the Presence of Social Learning

To be submitted on November 2011 A monopolist offers a product to a market of consumers with heterogeneous quality preferences. Although initially uninformed about the product quality, they learn by observing past purchase decisions and reviews of other consumers. Our goal is to analyze the social learning mechanism and its effect on the seller’s pricing decision. This analysis borrows from the literature on social learning and on pricing and revenue management. Consumers follow a naive decision rule and, under some conditions, eventually learn the product’s quality. Using mean-field approximation, the dynamics of this learning process are characterized for markets with high demand intensity. The relationship between the price and the speed of learning depends on the heterogeneity of quality preferences. Two pricing strategies are studied: a static price and a single price change. Properties of the optimal prices are derived. Numerical experiments suggest that pricing strategies that account for social learning may increase revenues considerably relative to strategies that do not

Bayesian Social Learning from Consumer Reviews

Motivated by the proliferation of user-generated product-review information and its widespread use, this note studies a market where consumers are heterogeneous in terms of their willingness to pay for a new product. Each consumer observes the binary reviews (like or dislike) of consumers who purchased the product in the past and uses Bayesian updating to infer the product quality. We show that the learning process is successful as long as the price is not prohibitive, and therefore at least some consumers, with sufficiently high idiosyncratic willingness to pay, will purchase the product irrespective of their posterior quality estimate. We examine some structural properties of the dynamics of the posterior beliefs. Finally, we study the seller\u2019s pricing problem, and we show that, if the set of possible prices is finite, then a stationary optimal pricing policy exists. If it costs the seller a constant amount for each additional unit sold, then under the optimal policy learning fails with positive probability