Search

Organoids are three-dimensional cell cultures that mimic organ functions and structures. The organoid model has been developed as a versatile in vitro platform for stem cell biology and diseases modeling. Tumor organoids are shown to share ~ 90% of genetic mutations with biopsies from same patients. However, it’s not clear whether tumor organoids recapitulate the cellular heterogeneity observed in patient tumors. Here, we used single-cell RNA-Seq to investigate the transcriptomics of tumor organoids derived from human colorectal tumors, and applied machine learning methods to unbiasedly cluster subtypes in tumor organoids. Computational analysis reveals cancer heterogeneity sustained in tumor organoids, and the subtypes in organoids displayed high diversity. Furthermore, we treated the tumor organoids with a first-line cancer drug, Oxaliplatin, and investigated drug response in single-cell scale. Diversity of tumor cell populations in organoids were significantly perturbed by drug treatment. Single-cell analysis detected the depletion of chemosensitive subgroups and emergence of new drug tolerant subgroups after drug treatment. Our study suggests that the organoid model is capable of recapitulating clinical heterogeneity and its evolution in response to chemotherapy.

Internet phishing attack is increasing and causing enormous economic loss in recent years. The network security literacy learning for educating students to defense phishing attack becomes more important. However, with the growing of attacking tricks, the anti-phishing knowledge is continuously increasing. The traditional learning platform with anti-phishing technical documents is not sufficient for students to prevent the phishing attacks. To transform the continuously increasing anti-phishing knowledge to learning materials, the Bloomi¦s taxonomy of cognitive domain was used to classify anti-phishing knowledge into different levels of learning skills. Since traditional anti-phishing documents are difficult to read for students, the Anti-phishing Education Game was developed to provide an interactive narrative learning environment for learning by doing. Thus, the game missions can be adaptively provided to students based on their learning achievements and engage studentsi¦ attention. The experimental result shows that students have significant progress in identifying phishing page and high learning motivation by using our approach

Low motivation is the first barrier when educating end-users about anti-phishing. Game-based learning utilizing interactive activities and interesting multimedia is a suitable solution for motivating end-users. To satisfy pedagogical needs, it is important to generate numerous game contents full of variety. However, it is costly and time consuming to generate new phishing page. Due to the complexity of combining different phishing attack technique with various deceiving scenario, the phishing pages modeling issue arises when automatically generate content of anti-phishing education game. In this paper, we propose the phishing attack frame hierarchy to describe stereotype features of phishing attack techniques. The inheritance and instantiation properties of the frame model allow us extend the original phishing pages to increase game contents. To evaluate the effectiveness of proposed frame hierarchy, we have developed an anti-phishing education game. The evaluation results show that most of lecturers and experts satisfy with our proposed system.

Dynamic contrast enhanced (DCE) MRI is a potential technique to monitor the temporal evolution of signal change for the specific tissue microcirculation or tumor angiogenesis. In this study, we implemented high-temperature superconducting (HTS) RF coil system on DCE application of in-vivo mice kidney metabolism. Compared to a conventional copper coil, the improvement of the signal-to-noise ratio (SNR) of approximately 2.7 folds and average signal change (DeltaS) of 4 folds was shown in the preliminary results. The capability of HTS coil on DCE MRI was demonstrated and further investigation of tumor cancer model is expected.