Exploring the experience of Year 10 South Korean students’ English language learning in immersive virtual reality.

A prescribed English language textbook often directs classroom teaching practices in secondary school classes in EFL contexts, such as in South Korea. The textbook is often accompanied by multimedia resources which are delivered to students as input at a regulated pace with limited opportunities for communicative interaction or spoken output. Such opportunities are further limited in the community outside of the English classroom. Immersive virtual reality (i-VR) has the potential to situate learners in a real-world context for authentic application of textbook language learning. English teachers in the formal classroom focus on linguistic competence development within time constraints by teaching new vocabulary and grammatical items in decontextualised forms. By comparison, i-VR environments focus on learning to construct meaning in communicative events in contextualised, real-world settings based on students’ existing linguistic knowledge and ability. In a small-scale pilot study, two teachers of Year 10 English classes in Seoul implemented four i-VR language learning modules in their classes: one as a self-directed learning experience that extended beyond formal classroom learning, and the other as a teacher-facilitated learning experience within the formal classroom. Both teachers were interviewed after the two-week implementation to seek their views on their perceptions of the value of such i-VR learning for their students. Beyond the motivational and entertainment value, the teachers viewed the i-VR experience as capable of incorporating pedagogical structures using the embedded multimodal resources that is not possible in other immersive forms of language learning. Moreover, the teachers believed that incorporation of authentic conversations and interactional opportunities could further enhance the learning potential

Understanding the Impact of Image Quality and Distance of Objects to Object Detection Performance

Deep learning has made great strides for object detection in images. The detection accuracy and computational cost of object detection depend on the spatial resolution of an image, which may be constrained by both the camera and storage considerations. Compression is often achieved by reducing either spatial or amplitude resolution or, at times, both, both of which have well-known effects on performance. Detection accuracy also depends on the distance of the object of interest from the camera. Our work examines the impact of spatial and amplitude resolution, as well as object distance, on object detection accuracy and computational cost. We develop a resolution-adaptive variant of YOLOv5 (RA-YOLO), which varies the number of scales in the feature pyramid and detection head based on the spatial resolution of the input image. To train and evaluate this new method, we created a dataset of images with diverse spatial and amplitude resolutions by combining images from the TJU and Eurocity datasets and generating different resolutions by applying spatial resizing and compression. We first show that RA-YOLO achieves a good trade-off between detection accuracy and inference time over a large range of spatial resolutions. We then evaluate the impact of spatial and amplitude resolutions on object detection accuracy using the proposed RA-YOLO model. We demonstrate that the optimal spatial resolution that leads to the highest detection accuracy depends on the 'tolerated' image size. We further assess the impact of the distance of an object to the camera on the detection accuracy and show that higher spatial resolution enables a greater detection range. These results provide important guidelines for choosing the image spatial resolution and compression settings predicated on available bandwidth, storage, desired inference time, and/or desired detection range, in practical applications

Niacin Inhibits Apoptosis and Rescues Premature Ovarian Failure

Background/Aims: Over 99% of mouse and human ovarian follicles will undergo specialized cell death including atresia and apoptosis. Reduction of apoptosis may help reduce infertility and maintain the reproductive ability in women. Methods: 3-day B6D2F1 mice were used to culture small follicle and ovary tissue with niacin and 18-day mice were intraperitoneal injected with niacin to determine its effect on follicle development. Then establish 8-weeks POF animal model with cytoxan (CTX) or radiation. Treatment group was given 0.1 mL of 100 mM niacin by an intraperitoneal injection twice before ovulation. The ovaries were collected and the follicles were counted and categorized, and ovarian histologic sections were stained for TUNEL. Ovarian function was then evaluated by monitoring ovulation. Microarray analyses, Western blot, immunofluorescence and real-time quantitative PCR were used to assess the mechanism of ovarian injury and repair. Results: We found that niacin promotes follicle growth in the immature oocyte and it increased the levels of a germ-line cell marker DDX4, and a cell proliferation marker PCNA in the ovary. Addition of niacin to the cell culture reduced oocyte apoptosis in vitro. Administration of niacin to treat premature ovarian failure (POF) in mouse models showed inhibition of follicular apoptosis under harmful conditions, such as radiation and chemotherapy damage, by markedly reducing cumulus cell apoptosis. Additionally, the number of developing follicles increased after administration of niacin. Conclusion: Niacin may have an important function in treating POF by reducing apoptosis in clinical applications

How we learn social norms: a three-stage model for social norm learning

As social animals, humans are unique to make the world function well by developing, maintaining, and enforcing social norms. As a prerequisite among these norm-related processes, learning social norms can act as a basis that helps us quickly coordinate with others, which is beneficial to social inclusion when people enter into a new environment or experience certain sociocultural changes. Given the positive effects of learning social norms on social order and sociocultural adaptability in daily life, there is an urgent need to understand the underlying mechanisms of social norm learning. In this article, we review a set of works regarding social norms and highlight the specificity of social norm learning. We then propose an integrated model of social norm learning containing three stages, i.e., pre-learning, reinforcement learning, and internalization, map a potential brain network in processing social norm learning, and further discuss the potential influencing factors that modulate social norm learning. Finally, we outline a couple of future directions along this line, including theoretical (i.e., societal and individual differences in social norm learning), methodological (i.e., longitudinal research, experimental methods, neuroimaging studies), and practical issues

Clinical application of superselective transarterial embolization of renal tumors in zero ischaemia robotic-assisted laparoscopic partial nephrectomy

ObjectiveTo assess the feasibility and safety of zero ischaemia robotic-assisted laparoscopic partial nephrectomy (RALPN) after preoperative superselective transarterial embolization (STE) of T1 renal cancer.MethodsWe retrospectively analyzed the data of 32 patients who underwent zero ischaemia RALPN after STE and 140 patients who received standard robot-assisted laparoscopic partial nephrectomy (S-RALPN). In addition, we selected 35 patients treated with off-clamp RALPN (O-RALPN) from September 2017 to March 2022 for comparison. STE was performed by the same interventional practitioner, and zero ischaemia laparoscopic partial nephrectomy (LPN) was carried out by experienced surgeon 1-12 hours after STE. The intraoperative data and postoperative complications were recorded. The postoperative renal function, routine urine test, urinary Computed Tomography (CT), and preoperative and postoperative glomerular filtration rate (GFR) data were analyzed.ResultsAll operations were completed successfully. There were no cases of conversion to opening and no deaths. The renal arterial trunk was not blocked. No blood transfusions were needed. The mean operation time was 91.5 ± 34.28 minutes. The mean blood loss was 58.59 ± 54.11 ml. No recurrence or metastasis occurred.ConclusionFor patients with renal tumors, STE of renal tumors in zero ischaemia RALPN can preserve more renal function, and it provides a safe and feasible surgical method

High Expression Levels of Total IGF-1R and Sensitivity of NSCLC Cells In Vitro to an Anti-IGF-1R Antibody (R1507)

© 2009 Gong et al

Coarse2Fine: Local Consistency Aware Re-prediction for Weakly Supervised Object Localization

Weakly supervised object localization aims to localize objects of interest by using only image-level labels. Existing methods generally segment activation map by threshold to obtain mask and generate bounding box. However, the activation map is locally inconsistent, i.e., similar neighboring pixels of the same object are not equally activated, which leads to the blurred boundary issue: the localization result is sensitive to the threshold, and the mask obtained directly from the activation map loses the fine contours of the object, making it difficult to obtain a tight bounding box. In this paper, we introduce the Local Consistency Aware Re-prediction (LCAR) framework, which aims to recover the complete fine object mask from locally inconsistent activation map and hence obtain a tight bounding box. To this end, we propose the self-guided re-prediction module (SGRM), which employs a novel superpixel aggregation network to replace the post-processing of threshold segmentation. In order to derive more reliable pseudo label from the activation map to supervise the SGRM, we further design an affinity refinement module (ARM) that utilizes the original image feature to better align the activation map with the image appearance, and design a self-distillation CAM (SD-CAM) to alleviate the locator dependence on saliency. Experiments demonstrate that our LCAR outperforms the state-of-the-art on both the CUB-200-2011 and ILSVRC datasets, achieving 95.89% and 70.72% of GT-Know localization accuracy, respectively