This erratum corrects an error in the original interpretation of the images from the two pancreatic cancer cases reported in the manuscript. 18 F-FluorThanaTrace (18 F-FTT) images from patients with pancreatic cancer obtained subsequent to the publication of this manuscript prompted a re-review of the images published in this text. It was discovered that the histories for the two reported cases were switched. Also, on re-review of the images, the pancreatic tumor for the case shown in figure 5 of the manuscript was found to be located more superiorly and to have more limited inferior extension than shown in the original publication. The following erratum corrects the reported information and data interpretation for these two cases. An additional correction to the time of image acquisition for the one liver cancer case shown in figure 6 is also reported. This information does not affect the validity of the remainder of the data as originally reported in the manuscript, including the animal data supporting specificity of tracer binding and the estimated human radiation dosimetry.

The automatic detection of bias in news articles can have a high impact on society because undiscovered news bias may influence the political opinions, social views, and emotional feelings of readers. While various analyses and approaches to news bias detection have been proposed, large data sets with rich bias annotations on a fine-grained level are still missing. In this paper, we firstly aggregate the aspects of news bias in related works by proposing a new annotation schema for labeling news bias. This schema covers the overall bias, as well as the bias dimensions (1) hidden assumptions, (2) subjectivity, and (3) representation tendencies. Secondly, we propose a methodology based on crowdsourcing for obtaining a large data set for news bias analysis and identification. We then use our methodology to create a data set consisting of more than 2,000 sentences annotated with 43,000 bias and bias dimension labels. Thirdly, we perform an in-depth analysis of the collected data. We show that the annotation task is difficult with respect to bias and specific bias dimensions. While crowdworkers' labels of representation tendencies correlate with experts' bias labels for articles, subjectivity and hidden assumptions do not correlate with experts' bias labels and, thus, seem to be less relevant when creating data sets with crowdworkers. The experts' article labels better match the inferred crowdworkers' article labels than the crowdworkers' sentence labels. The crowdworkers' countries of origin seem to affect their judgements. In our study, non-Western crowdworkers tend to annotate more bias either directly or in the form of bias dimensions (e.g., subjectivity) than Western crowdworkers do.

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Computer Aid Education has been considered as a powerful tool for teaching and learning second languages. However, the frequency of its use is considerable low than expectation. It is because of boring and monotonous characters of educational applications. To complete all tasks in educational applications, it is asked great efforts and patient due to lacking fun and amusement. Then for the developers of e-learning applications for language learning, how to add amusement elements on applications is a big deal. In this paper, in order to seek a solution for this, we will show a way to add these elements by gamification of an application for practicing building phrase. The reason we choose phrase building application is that it is mostly difficult to attract leaners' interests in spite of its importance in language learning.