94 research outputs found

    Handling Attrition in Longitudinal Studies: The Case for Refreshment Samples

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    Panel studies typically suffer from attrition, which reduces sample size and can result in biased inferences. It is impossible to know whether or not the attrition causes bias from the observed panel data alone. Refreshment samples - new, randomly sampled respondents given the questionnaire at the same time as a subsequent wave of the panel - offer information that can be used to diagnose and adjust for bias due to attrition. We review and bolster the case for the use of refreshment samples in panel studies. We include examples of both a fully Bayesian approach for analyzing the concatenated panel and refreshment data, and a multiple imputation approach for analyzing only the original panel. For the latter, we document a positive bias in the usual multiple imputation variance estimator. We present models appropriate for three waves and two refreshment samples, including nonterminal attrition. We illustrate the three-wave analysis using the 2007-2008 Associated Press-Yahoo! News Election Poll.Comment: Published in at http://dx.doi.org/10.1214/13-STS414 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

    Learning Robotic Ultrasound Scanning Skills via Human Demonstrations and Guided Explorations

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    Medical ultrasound has become a routine examination approach nowadays and is widely adopted for different medical applications, so it is desired to have a robotic ultrasound system to perform the ultrasound scanning autonomously. However, the ultrasound scanning skill is considerably complex, which highly depends on the experience of the ultrasound physician. In this paper, we propose a learning-based approach to learn the robotic ultrasound scanning skills from human demonstrations. First, the robotic ultrasound scanning skill is encapsulated into a high-dimensional multi-modal model, which takes the ultrasound images, the pose/position of the probe and the contact force into account. Second, we leverage the power of imitation learning to train the multi-modal model with the training data collected from the demonstrations of experienced ultrasound physicians. Finally, a post-optimization procedure with guided explorations is proposed to further improve the performance of the learned model. Robotic experiments are conducted to validate the advantages of our proposed framework and the learned models

    Can Third-Party Sellers Benefit from a Platform’s Entry to the Market?

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    Because of the informational advantage of online marketplaces (i.e., platforms), it is a common belief that a platform’s market entry will be detrimental to third-party sellers who sell similar products on the platform. To examine the validity of this belief, we conduct an exploratory analysis using the sales data for a single product category provided by JD.com for the month of March 2018. Our analysis reveals an unexpected result. Upon the platform’s entry, third-party sellers who sell similar products can afford to charge a higher price, obtain a higher demand, and earn a higher profit. To provide a plausible explanation for this unexpected exploratory result, we develop a duopoly model that incorporates the changing competitive dynamic before and after the platform’s entry. Specifically, before entry, the platform earns a commission (based on the seller’s revenue), whereas the seller sets its retail price as a monopoly. After entry, the platform earns a profit generated by its direct sales in addition to the commission from the seller. In addition, the seller and the platform operate in a duopoly and engage in a sequential game. By examining the equilibrium outcomes associated with this sequential game, we identify conditions under which the platform’s entry can create a win-win situation for both parties. Specifically, these conditions hold when the platform’s market potential is moderate and when the platform’s entry creates a sufficiently high spillover effect on the seller, providing a plausible explanation for our empirical finding that the seller can benefit from a platform’s entry

    The Protective Effect of Magnesium Lithospermate B on Hepatic Ischemia/Reperfusion via Inhibiting the Jak2/Stat3 Signaling Pathway

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    Acute inflammation is an important component of the pathogenesis of hepatic ischemia/reperfusion injury (HIRI). Magnesium lithospermate B (MLB) has strong neuroprotective and cardioprotective effects. The purpose of this study was to determine whether MLB had underlying protective effects against hepatic I/R injury and to reveal the potential mechanisms related to the hepatoprotective effects. In this study, we first examined the protective effect of MLB on HIRI in mice that underwent 1 h ischemia followed by 6 h reperfusion. MLB pretreatment alleviated the abnormal liver function and hepatocyte damage induced by I/R injury. We found that serum inflammatory cytokines, including IL-6, IL-1β, and TNF-α, were significantly decreased by MLB during hepatic ischemia/reperfusion (I/R) injury, suggesting that MLB may alleviate hepatic I/R injury via inhibiting inflammatory signaling pathways. Second, we investigated the protein level of p-Jak2/Jak2 and p-Stat3/Stat3 using Western blotting and found that MLB could significantly inhibit the activation of the Jak2/Stat3 signaling pathway, which was further verified by AG490 in a mouse model. Finally, the effect of MLB on the Jak2/Stat3 pathway was further assessed in an in vitro model of RAW 264.7 cells; 1 µg/ml LPS induced the secretion of inflammatory mediators, including IL-6, TNF-α, and activation of the Jak2/Stat3 signaling pathway. MLB significantly inhibited the abnormal secretion of inflammatory factors and the activation of the Jak2/Stat3 signaling pathway in RAW264.7 cells. In conclusion, MLB was found for the first time to reduce inflammation induced by hepatic I/R via suppressing the Jak2/Stat3 pathway
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