Teacher professional identities and their impacts on translanguaging pedagogies in a STEM EMI classroom context in China: a nexus analysis

While translanguaging has gained increasing recognition as a multiliteracy pedagogy in English-medium instruction (EMI) education, research exploring its implementation in STEM classroom contexts remains limited. Furthermore, the interplay of EMI teachers’ professional identities and their instructional strategies has received little attention. This qualitative study explores how STEM academics in an EMI programme in China implemented translanguaging pedagogy, developed their professional identities, and examined the impact of identity on their classroom instructional language use. Drawing upon nexus analysis, the study maps the intersecting discourses influencing two EMI lecturers’ divergent language ideologies and translanguaging strategies. The findings highlight the role of teacher identity and agency in navigating institutional and classroom discourses, facilitating planned and effective translanguaging pedagogy. The study reveals identity struggles within the examined institution, where academic staff faced a challenge in balancing their roles as effective EMI teachers and successful researchers due to a discourse of research meritocracy and were constrained in exploring translanguaging pedagogy due to a discourse of internationalism. These challenges undermined their motivation to invest in teaching identity and pedagogical skills. This study underscores the need for a balanced view of research and teaching, more robust teacher evaluation systems, and institutional support to foster effective translanguaging pedagogy in EMI by incorporating teacher identity construction into EMI teacher preparedness

Using machine translation to support ESL pre-service teachers’ collaborative feedback for writing

The application of machine translation (MT) in ESL (English as a Second Language) writing has thrived over the past decade. However, how MT impacts pre-service teachers’ (PSTs) provision of collaborative feedback for writing remains an under-explored domain. Such knowledge will facilitate pre-service teacher education to be more scientific and practical. In this study, we investigated the effects of MT in assisting PSTs’ provision of feedback, drawing on six PSTs’ in-class discussions and the results of their revision. Additionally, we conducted interviews to learn their perception of MT. The findings revealed that MT assisted them in providing feedback on students’ L2 writing, especially in improving content, communicative achievement, and language issues. PSTs in the study held positive attitudes towards using MT in their collaborative revision for writing. The findings also suggest that applying MT to ESL pre-service teacher education is beneficial, especially in assisting their writing revision. It is also advisable to utilise MT with other tools. The implications of the findings are discussed

Improving the Brain-Computer Interface Learning Process with Gamification in Motor Imagery: A Review

Brain-computer-interface-based motor imagery (MI-BCI), a control method for transferring the imagination of motor behavior to computer-based commands, could positively impact neural functions. With the safety guaranteed by non-invasive BCI devices, this method has the potential to enhance rehabilitation and physical outcomes. Therefore, this MI-BCI control strategy has been highly researched. However, applying a non-invasive MI-BCI to real life is still not ideal. One of the main reasons is the monotonous training procedure. Although researchers have reviewed optimized signal processing methods, no suggestion is found in training feedback design. The authors believe that enhancing the engagement interface via gamification presents a potential method that could increase the MI-BCI outcome. After analyzing 2524 articles (from 2001 to 2020), 28 pieces of research are finally used to evaluate the feasibility of using gamified MI-BCI system for training. This paper claims that gamification is feasible for MI-BCI training with an average accuracy of 74.35% among 111 individuals and positive reports from 26 out of 28 studies. Furthermore, this literature review suggests more emphasis should be on immersive and humanoid design for a gaming system, which could support relieving distraction, stimulate correct MI and improve learning outcomes. Interruptive training issues such as disturbing graphical interface design and potential solutions have also been presented for further research

Ultrasonographic diagnosis of ovarian tumors through the deep convolutional neural network

Objectives: The objective of this study was to develop and validate an ovarian tumor ultrasonographic diagnostic model based on deep convolutional neural networks (DCNN) and compare its diagnostic performance with that of human experts. Material and methods: We collected 486 ultrasound images of 192 women with malignant ovarian tumors and 617 ultrasound images of 213 women with benign ovarian tumors, all confirmed by pathological examination. The image dataset was split into a training set and a validation set according to a 7:3 ratio. We selected 5 DCNNs to develop our model: MobileNet, Xception, Inception, ResNet and DenseNet. We compared the performance of the five models through the area under the curve (AUC), sensitivity, specificity, and accuracy. We then randomly selected 200 images from the validation set as the test set. We asked three expert radiologists to diagnose the images to compare the performance of radiologists and the DCNN model. Results: In the validation set, AUC of DenseNet was 0.997 while AUC was 0.988 of ResNet, 0.987 of Inception, 0.968 of Xception and 0.836 of MobileNet. In the test set, the accuracy was 0.975 with the DenseNet model versus 0.825 (p < 0.0001) with the radiologists, and sensitivity was 0.975 versus 0.700 (p < 0.0001), and specificity was 0.975 versus 0.908 (p < 0.001). Conclusions: DensNet performed better than other DCNNs and expert radiologists in identifying malignant ovarian tumors from benign ovarian tumors based on ultrasound images, a finding that needs to be further explored in clinical trials

C5aR1 shapes a non-inflammatory tumor microenvironment and mediates immune evasion in gastric cancer

C5a receptor 1 (C5aR1) is associated with various inflammatory processes, the pathogenesis of immune diseases, and tumor growth. However, its role in the tumor microenvironment of gastric cancer (GC) remains unclear. In this study, the expression of C5aR1 in GC and normal gastric mucosa tissues was compared using data retrieved from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, and the results were validated by in vitro qRT-PCR and immunohistochemical analyses. The relationship between C5aR1 expression and the overall survival of patients with GC was analyzed using the Kaplan–Meier method. Subsequently, enrichment analysis was performed, and the signaling pathways were screened. C5aR1 expression was also correlated with genes related to the immune checkpoint and immune cell infiltration. The results revealed that C5aR1 expression was enhanced in GC tissues compared to normal gastric tissues, and that patients with high expression of C5aR1 had a worse 10-year overall survival compared to those showing low expression of C5aR1. Functional analysis revealed that C5aR1 is a gene related to theimmune system and may play a crucial role in inflammatory and tumor immune responses. Additionally, C5aR1 showed a positive correlation with most immune checkpoint-related genes and a negative correlation with natural killer cells, dendritic cells, and CD8+ T cells. Immune evasion risk was observed to be significantly greater in patients with higher expression of C5aR1 than in those with lower expression. The results of this study reveal that C5aR1 shapes a non-inflammatory tumor microenvironment in GC and mediates immune evasion

Cell transcriptomic atlas of the non-human primate Macaca fascicularis.

Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis. This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell-cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M. fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs.We thank W. Liu and L. Xu from the Huazhen Laboratory Animal Breeding Centre for helping in the collection of monkey tissues, D. Zhu and H. Li from the Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory) for technical help, G. Guo and H. Sun from Zhejiang University for providing HCL and MCA gene expression data matrices, G. Dong and C. Liu from BGI Research, and X. Zhang, P. Li and C. Qi from the Guangzhou Institutes of Biomedicine and Health for experimental advice or providing reagents. This work was supported by the Shenzhen Basic Research Project for Excellent Young Scholars (RCYX20200714114644191), Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831), Shenzhen Bay Laboratory (SZBL2019062801012) and Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011). In addition, L.L. was supported by the National Natural Science Foundation of China (31900466), Y. Hou was supported by the Natural Science Foundation of Guangdong Province (2018A030313379) and M.A.E. was supported by a Changbai Mountain Scholar award (419020201252), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16030502), a Chinese Academy of Sciences–Japan Society for the Promotion of Science joint research project (GJHZ2093), the National Natural Science Foundation of China (92068106, U20A2015) and the Guangdong Basic and Applied Basic Research Foundation (2021B1515120075). M.L. was supported by the National Key Research and Development Program of China (2021YFC2600200).S

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Vaccine Data

The raw materials and proportion of Vaccine X

Introduction of the Clinical trials

The basic information of the trials