Performance of interferon-γ (IFN-γ) release assays still needs to be improved. The data on the performance of QuantiFERON-TB Gold Plus (QFT-Plus), a new-generation of QFT assay are limited. This study evaluated the diagnostic performance of QFT-Plus, and compared to that of QuantiFERON-TB Gold In-Tube (QFT-GIT). Blood samples were collected from 162 bacteriologically confirmed tuberculosis (TB) patients and 212 Mycobacterium tuberculosis-uninfected volunteers; these samples were then tested with QFT-GIT and QFT-Plus. The IFN-γ concentration of QFT-Plus was lower than that of QFT-GIT in TB patients (p < 0.001). Receiver operating characteristic curves were compared between QFT-GIT and QFT-Plus. Both assays showed area under the curve values over 0.99 without significant difference. Using the conventional cut-off (0.35 IU/mL) for QFT-GIT, QFT-Plus had a lower sensitivity of 91.1% compared to 96.2% (p = 0.008) at its optimum cut-off (0.168 IU/mL) with the same specificity. Moreover, IFN-γ values were significantly reduced with age in QFT-GIT (p = 0.035) but not in QFT-Plus. The diagnostic performance of QFT-Plus was as accurate as that of QFT-GIT despite a lack of TB7.7 antigen and despite the decrease in quantitative values. However, the cut-off value for QFT-Plus should be considered independently from that of QFT-GIT to obtain the best sensitivity without compromising specificity.

No abstract

No abstract

A knitted animal is made of a closed surface consisting of several knitted patches knitted out of yarn and stuffed with cotton ( Fig. 1). We introduce a system to create a knitting pattern from a given 3D surface model (mainly designed for rotund animal models). A knitting pattern is an instructional diagram describing how to knit yarn to obtain a desired shape. Since the creation of knitting patterns requires special skill, this is difficult for nonprofessionals. Our system automates the process and allows anyone to obtain his or her original knitting patterns from a 3D model. The system first covers the surface of the model with parallel winding strips of constant width. The system then samples the strip at constant intervals to convert it into a knitting pattern. The result is presented in a standard visual format so that the user can easily refer it during actual knitting. We show several examples of knitted animals created using the system.

In this paper, we compare feature-based and Neural Network-based approaches on the supervised stance classification task for tweets in SemEval-2016 Task 6 Subtask A (Mohammad et al., 2016). In the feature-based approach, we use external resources such as lexicons and crawled texts. The Neural Network based approach employs Convolutional Neural Network (CNN). Our results show that the feature-based model outperformed the CNN model on the test data although the CNN model was better than the feature-based model in the cross validation on the training data.