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This paper is a collection of three articles on the topic of trust in online professional recommendations. Online professional recommendations are a prominent feature of Online Professional Networks (OPNs), which are used heavily by recruiters to find candidates to fill open positions. These recommendations are meant to validate the credibility of the user’s profile by offering first-hand accounts from other users who can provide their experience working with the user. However, these recommendations tend to be overly positive, full of superlatives, and lacking in any information regarding areas where the user needs to improve. Because of the functionality inherent to OPNs, users can choose which recommendations to make visible to the network and then hide any that they do not like. This amount of control afforded to the user creates credibility issues as recruiters are disposed to question the accuracy of the content. This unique feature of online recommendations makes them inherently different from traditional letters of recommendations, which have been studied in the past. In these articles, we first examine how the use of superlatives and the inclusion of information regarding a user’s weakness can affect the perceived trustworthiness of the recommender, and how that, in turn, affects the effectiveness and usefulness of the recommendation. In the second article, we examine how factors such as gender, gender stereotypes, and physical attractiveness may provide boundary conditions on the effects observed in the first study. The last article focuses on reinforcing the proposition that online and traditional recommendations are inherently different, confirming the need for future research to further study online recommendations and their impact on decision making in employment scenarios. Collectively, these articles introduce online professional recommendations to the field of academia and provide a foundation for future research upon which to build.

Technological developments and greater rigor in the quantitative measurement of biological features in medical images have given rise to an increased interest in using quantitative imaging biomarkers to measure changes in these features. Critical to the performance of a quantitative imaging biomarker in preclinical or clinical settings are three primary metrology areas of interest: measurement linearity and bias, repeatability, and the ability to consistently reproduce equivalent results when conditions change, as would be expected in any clinical trial. Unfortunately, performance studies to date differ greatly in designs, analysis method, and metrics used to assess a quantitative imaging biomarker for clinical use. It is therefore difficult or not possible to integrate results from different studies or to use reported results to design studies. The Radiological Society of North America and the Quantitative Imaging Biomarker Alliance with technical, radiological, and statistical experts developed a set of technical performance analysis methods, metrics, and study designs that provide terminology, metrics, and methods consistent with widely accepted metrological standards. This document provides a consistent framework for the conduct and evaluation of quantitative imaging biomarker performance studies so that results from multiple studies can be compared, contrasted, or combined.

Celebrities sharing their on-set temptations are presented. These include, Andy Cohen, Emily VanCamp, and Julianne Hough.

Several scoops on which casts will feed fan frenzy, are presented. These include, Veronica Mars, Mean Girls, and Full House.

An interview with The Real Housewives of New York City newbie Kristen Taekman, about how was her six seasons joining the reality show. Among others, Taekman tells whether she has regrets about her tagline.

An interview with actor Theo James is presented. Among others, James talks about the highly anticipated film Divergent.