Creative translanguaging in formative assessment : Chinese teachers’ perceptions and practices in the primary EFL classroom

Peer reviewe

Case report: Mononeuropathy multiplex of extranodal natural killer/T-cell lymphoma misdiagnosed as systemic vasculitis

BackgroundExtranodal NK/T-cell lymphoma (ENKTL) is an aggressive non-Hodgkin lymphoma that typically develops in the upper aerodigestive tract.Case presentationWe encountered an ENKTL patient who presented with purpura-like rashes and foot drops as initial symptoms and later developed other peripheral nerve involvement. The nerve conduction study of both the motor nerve and the sensory nerve showed axonal damage resembling mononeuropathy multiplex. Although the initial response to steroids was encouraging, the patient's symptoms reappeared and aggravated. A biopsy of the abdominal subcutaneous fat tissue with additional immunohistochemistry revealed neoplastic NK/T lymphocytes.ConclusionWe reported the first case presented as mononeuropathy multiplex as the initial clinical manifestation in ENKTL patients. Lymphoma should be considered in the diagnosis of atypical mononeuropathy in multiplex patients

Potential drug-drug interaction of olverembatinib (HQP1351) using physiologically based pharmacokinetic models

Olverembatinib (HQP1351) is a third-generation BCR-ABL tyrosine kinase inhibitor for the treatment of chronic myeloid leukemia (CML) (including T315I-mutant disease), exhibits drug-drug interaction (DDI) potential through cytochrome P450 (CYP) enzymes CYP3A4, CYP2C9, CYP2C19, CYP1A2, and CYP2B6. A physiologically-based pharmacokinetic (PBPK) model was constructed based on physicochemical and in vitro parameters, as well as clinical data to predict 1) potential DDIs between olverembatinib and CYP3A4 and CYP2C9 inhibitors or inducers 2), effects of olverembatinib on the exposure of CYP1A2, CYP2B6, CYP2C9, CYP2C19, and CYP3A4 substrates, and 3) pharmacokinetics in patients with liver function injury. The PBPK model successfully described observed plasma concentrations of olverembatinib from healthy subjects and patients with CML after a single administration, and predicted olverembatinib exposure increases when co-administered with itraconazole (strong CYP3A4 inhibitor) and decreases with rifampicin (strong CYP3A4 inducer), which were validated by observed data. The predicted results suggest that 1) strong, moderate, and mild CYP3A4 inhibitors (which have some overlap with CYP2C9 inhibitors) may increase olverembatinib exposure by approximately 2.39-, 1.80- to 2.39-, and 1.08-fold, respectively; strong, and moderate CYP3A4 inducers may decrease olverembatinib exposure by approximately 0.29-, and 0.35- to 0.56-fold, respectively 2); olverembatinib, as a “perpetrator,” would have no or limited impact on CYP1A2, CYP2B6, CYP2C9, CYP2C19, and CYP3A4 enzyme activity 3); systemic exposure of olverembatinib in liver function injury with Child-Pugh A, B, C may increase by 1.22-, 1.79-, and 2.13-fold, respectively. These simulations inform DDI risk for olverembatinib as either a “victim” or “perpetrator”

Washback of national matriculation English test on students' learning in the Chinese secondary school context

Tests play a powerful role in the Chinese educational system, and exert significant washback on students‘ learning. This study investigates the washback effect of the high-stakes National Matriculation English Test (NMET) within a Chinese high school from the students‘ perspective. It considers, in particular, the washback effect on the process and product of learning from the standpoint of reading strategies and skills. By considering data collected from two questionnaires, a reading test and a series of semi-structured interviews, the study shows that although the development of learning strategies and reading skills is overshadowed by the high-stakes nature of the test, students show an ability to use metacognitive, compensation and affective language 104 learning strategies, and attainment in reading skills, by coping well with the modified authentic texts used as the basis for the test paper administered by the researchers. The current study also offers some thoughts on issues behind the strong washback that emerges in the study, and makes suggestions in regards to bringing more formative types of assessment into the classroom

eHAPAC: A Privacy-Supported Access Control Model for IP-Enabled Wireless Sensor Networks

The implementation of IP technology in wireless sensor networks has promoted the development of many smart scenarios. To enhance secure access in IP-enabled wireless sensor networks, access control to sensor nodes is a necessary process. However, access control currently faces two challenges, feasibility and preservation of user access privacy. In this paper, we propose eHAPAC, a novel privacy-preserving access control model for IP-enabled wireless sensor networks. The contributions of our paper include three parts. First, this paper integrates the Hidra access control protocol and APAC privacy-preserving model, addressing the issue of privacy-preserving access control in resource-constrained devices. Second, this paper proposes an enhanced Hidra protocol to implement the unlinkability of protocol message exchanges. Third, to solve the problem of third party credibility, this paper improves the group signature-based APAC model and utilizes blockchain technology to manage the storage and publication of public group signature keys. Security analysis and performance evaluation prove that our protocol is secure and effective

RGBD Video Based Human Hand Trajectory Tracking and Gesture Recognition System

The task of human hand trajectory tracking and gesture trajectory recognition based on synchronized color and depth video is considered. Toward this end, in the facet of hand tracking, a joint observation model with the hand cues of skin saliency, motion and depth is integrated into particle filter in order to move particles to local peak in the likelihood. The proposed hand tracking method, namely, salient skin, motion, and depth based particle filter (SSMD-PF), is capable of improving the tracking accuracy considerably, in the context of the signer performing the gesture toward the camera device and in front of moving, cluttered backgrounds. In the facet of gesture recognition, a shape-order context descriptor on the basis of shape context is introduced, which can describe the gesture in spatiotemporal domain. The efficient shape-order context descriptor can reveal the shape relationship and embed gesture sequence order information into descriptor. Moreover, the shape-order context leads to a robust score for gesture invariant. Our approach is complemented with experimental results on the settings of the challenging hand-signed digits datasets and American sign language dataset, which corroborate the performance of the novel techniques

Prediction of Cavity Length Using an Interpretable Ensemble Learning Approach

The cavity length, which is a vital index in aeration and corrosion reduction engineering, is affected by many factors and is challenging to calculate. In this study, 10-fold cross-validation was performed to select the optimal input configuration. Additionally, the hyperparameters of three ensemble learning models—random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting tree (XGBOOST)—were fine-tuned by the Bayesian optimization (BO) algorithm to improve the prediction accuracy and compare the five empirical methods. The XGBOOST method was observed to present the highest prediction accuracy. Further interpretability analysis carried out using the Sobol method demonstrated its ability to reasonably capture the varying relative significance of different input features under different flow conditions. The Sobol sensitivity analysis also observed two patterns of extracting information from the input features in ML models: (1) the main effect of individual features in ensemble learning and (2) the interactive effect between each feature in SVR. From the results, the models obtaining individual information both predict the cavity length more accurately than that using interactive information. Subsequently, the XGBOOST captures more correct information from features, which leads to the varied Sobol index in accordance with outside phenomena; meanwhile, the predicted results fit the experimental points best

ASPDock: protein-protein docking algorithm using atomic solvation parameters model

Abstract Background Atomic Solvation Parameters (ASP) model has been proven to be a very successful method of calculating the binding free energy of protein complexes. This suggests that incorporating it into docking algorithms should improve the accuracy of prediction. In this paper we propose an FFT-based algorithm to calculate ASP scores of protein complexes and develop an ASP-based protein-protein docking method (ASPDock). Results The ASPDock is first tested on the 21 complexes whose binding free energies have been determined experimentally. The results show that the calculated ASP scores have stronger correlation (r ≈ 0.69) with the binding free energies than the pure shape complementarity scores (r ≈ 0.48). The ASPDock is further tested on a large dataset, the benchmark 3.0, which contain 124 complexes and also shows better performance than pure shape complementarity method in docking prediction. Comparisons with other state-of-the-art docking algorithms showed that ASP score indeed gives higher success rate than the pure shape complementarity score of FTDock but lower success rate than Zdock3.0. We also developed a softly restricting method to add the information of predicted binding sites into our docking algorithm. The ASP-based docking method performed well in CAPRI rounds 18 and 19. Conclusions ASP may be more accurate and physical than the pure shape complementarity in describing the feature of protein docking.</p