Multiplayer Serious Games Supporting Programming Learning

Computational thinking (CT) is crucial in education for providing a multifaceted approach to problem-solving. However, challenges exist such as supporting teachers' knowledge of CT and students' desire to learn it, particularly for non-technical students. To combat these challenges, Computer Supported Collaborative Learning (CSCL) has been introduced in classrooms and implemented using a variety of technologies, including serious games, which have been adopted across several domains aiming to appeal to various demographics and skill levels. This research focuses on a Collaborative Multiplayer Serious Game (MSG) for CT skill training. The architecture is aimed at young students and is designed to aid in the learning of programming and the development of CT skills. The purpose of this research is to conduct an empirical study to assess the multiplayer game gameplay mechanics for collaborative CT learning. The proposed game leverages a card game structure and contains complex multi-team multi-player processes, allowing students to communicate and absorb sequential and conditional logics as well as graph routing in a 2D environment. A preliminary experiment was conducted with four fourth-graders and eight sixth-graders from a French school in Morocco who have varying levels of understanding of CT. Participants were split into three groups each with two teams and were required to complete a 16-question multiple-choice quiz before and after playing the same game to assess their initial structural programming logics and the effectiveness of the MSG. Questionnaires were collected along with an interview to gather feedback on their gaming experiences and the game’s role in teaching and learning. The results demonstrate that the proposed MSG had a favourable effect on the participants’ test scores as the scores of 4 of the teams increased and 1 remained the same. All students performed well on the sequential and conditional logics, which was significantly better than the achievement of the Bebras test of the graph routing. Furthermore, according to the participants, the game provides an appealing environment that allows players to immerse themselves in the game and the competitive aspect of the game adds to its appeal and helps develop teamwork, coordination, and communication skills

DisAsymNet: Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms using Self-adversarial Learning

Asymmetry is a crucial characteristic of bilateral mammograms (Bi-MG) when abnormalities are developing. It is widely utilized by radiologists for diagnosis. The question of 'what the symmetrical Bi-MG would look like when the asymmetrical abnormalities have been removed ?' has not yet received strong attention in the development of algorithms on mammograms. Addressing this question could provide valuable insights into mammographic anatomy and aid in diagnostic interpretation. Hence, we propose a novel framework, DisAsymNet, which utilizes asymmetrical abnormality transformer guided self-adversarial learning for disentangling abnormalities and symmetric Bi-MG. At the same time, our proposed method is partially guided by randomly synthesized abnormalities. We conduct experiments on three public and one in-house dataset, and demonstrate that our method outperforms existing methods in abnormality classification, segmentation, and localization tasks. Additionally, reconstructed normal mammograms can provide insights toward better interpretable visual cues for clinical diagnosis. The code will be accessible to the public

2.75D: Boosting learning by representing 3D Medical imaging to 2D features for small data

In medical-data driven learning, 3D convolutional neural networks (CNNs) have started to show superior performance to 2D CNNs in numerous deep learning tasks, proving the added value of 3D spatial information in feature representation. However, the difficulty in collecting more training samples to converge, more computational resources and longer execution time make this approach less applied. Also, applying transfer learning on 3D CNN is challenging due to a lack of publicly available pre-trained 3D models. To tackle these issues, we proposed a novel 2D strategical representation of volumetric data, namely 2.75D. In this work, the spatial information of 3D images is captured in a single 2D view by a spiral-spinning technique. As a result, 2D CNN networks can also be used to learn volumetric information. Besides, we can fully leverage pre-trained 2D CNNs for downstream vision problems. We also explore a multi-view 2.75D strategy, 2.75D 3 channels (2.75D × 3), to boost the advantage of 2.75D. We evaluated the proposed methods on three public datasets with different modalities or organs (Lung CT, Breast MRI, and Prostate MRI), against their 2D, 2.5D, and 3D counterparts in classification tasks. Results show that the proposed methods significantly outperform other counterparts when all methods were trained from scratch on the lung dataset. Such performance gain is more pronounced with transfer learning or in the case of limited training data. Our methods also achieved comparable performance on other datasets. In addition, our methods achieved a substantial reduction in time consumption of training and inference compared with the 2.5D or 3D method

Fang-Ji-Huang-Qi-Tang Attenuates Degeneration of Early-Stage KOA Mice Related to Promoting Joint Lymphatic Drainage Function

Osteoarthritis (OA) is a chronic degenerative joint disease characterized by the breakdown of articular cartilage, subchondral bone remodeling, and inflammation of the synovium. In this study, we investigated whether Fang-Ji-Huang-Qi-Tang (FJHQT) decoction improved the joint structure of OA or delayed the process of knee joint degeneration in OA mice by promoting lymphatic drain function. The mice were randomly divided into four groups, the sham group, the PBS group, the FJHQT-treated group, and the Mobic-treated group. The mice in each group were tested for lymphatic draining function at 4, 6, 8, and 10 weeks postsurgery (WPS), then sacrificed (N = 10/group). Using a near-infrared indocyanine green (NIR-ICG) lymphatic imaging system, we found that the lymphatic drain function was significantly reduced in the PBS group compared with the sham group. After treatment with the FJHQT decoction, the lymphatic draining function improved at 4 wps and 6 wps. The results of the analysis indicated a strong correlation between lymphatic draining function (ICG clearance) and the degree of joint structural damage (OARSI score). By Alcian blue/orange G (ABOG) staining of the paraffin sections, the FJHQT-treated group exhibited less cartilage destruction and lower OARSI scores. Moreover, the result of immunohistochemical staining (IHC) shows that FJHQT decoction increased the content of type II collagen in knee joints of OA mice at 4 wps and 6 wps. By the double immunofluorescence staining of podoplanin and smooth muscle actin in the paraffin sections, the capillaries and mature lymphatics in the FJHQT group increased at 4 wps. In conclusion, the FJHQT decoction can increase lymphatic vessel number, promote joint lymphatic draining function, and postpone knee osteoarthritis pathologic progression in the early stage of a collagen-induced mouse model. Therefore, the application of sufficient lymphatic drainage in the knee joint may be a new treatment method for knee joint osteoarthritis (KOA)