Background: The Functional Assessment of Cancer Therapy-Lung (FACT-L) questionnaire, which consists of a core questionnaire (the General Measure of FACT [FACT-G]) and a 9-item Additional Concerns comprised of a 7-item Lung Cancer Subscale (LCS), was developed in an English-speaking culture. The validation of the Japanese FACT-G was reported previously, and this report describes the cross-cultural validation of the LCS. Methods: The Japanese version of the LCS was developed through an iterative forward-backward translation sequence used throughout the FACT Multilingual Translation Project. In evaluating psychometric performance, its construct validity was investigated with Cronbach's alpha coefficient and factor analysis. Clinical validities of a known-groups comparison and longitudinal validity were also investigated. Results: The FACT-L was administered twice to 180 patients with lung cancer within 2 weeks. The Japanese LCS had borderline values for Cronbachs alpha coefficients (0.62-0.67). Factor analysis indicated that the LCS had the three dimensions of respiratory symptoms, appetite plus body weight, and clear thinking. For clinical validity, a known-groups comparison showed that the LCS could differentiate patients according to truth disclosure, as Japanese doctors sometimes do not fully inform terminally ill patients. However, responsiveness was not proved when performance status was used as an anchor, probably owing to the short interval between the administration of the two measures. Conclusion: The Japanese version of the LCS asked questions about multiple symptoms of patients with lung cancer, as did the original English LCS. The longitudinal clinical validity of the Japanese version should be investigated in future clinical trials.

More engaging and accessible solutions that offer outstanding user experience are needed. The virtual desktop infrastructure (VDI) is the first user access part of the cloud. Therefore, the performance of the VDI has a significant influence on the network. The graphics processing unit (GPU) can accelerate performance by assigning each virtual machine a dedicated GPU for performance-boosting to improve the user experience. In this paper, we propose a high-performance VDI using an integration of a GPU on OpenStack with a PCI pass-through. It might have certain benefits over using virtualized hardware, such as lower latency, better performance, and more functionality. The evaluation and comparison of the computer processing unit (CPU) and GPU are presented among the different benchmark tools.