Aha! Measuring pre-service teachers' learning of content-based instruction

Page 127-153This research study investigated the effect of Content-Based Instruction (CBI) and its influence on pre-service teachers' (PSTs) perceptions of their existing knowledge and capabilities for teaching English language learners (ELs). Our goal was to examine the PSTs’ development in content-based instruction during a 16-week CBI course and their insights and changes resulting from the experience. The researchers hoped to determine what aspects of CBI methodology were new, noteworthy and important for PSTs in order to identify what they were not receiving in their general education teacher preparation courses. Over the course of three semesters, 49 participants took pre- and post-course surveys comprised of open-ended questions and five Likert scale close-ended questions to measure their insights and changes. Findings revealed substantial changes to pedagogical philosophy, instructional practices, curricular and materials development, awareness of ELs’ needs, and stance toward advocacy

NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenario

We introduce a novel visual question answering (VQA) task in the context of autonomous driving, aiming to answer natural language questions based on street-view clues. Compared to traditional VQA tasks, VQA in autonomous driving scenario presents more challenges. Firstly, the raw visual data are multi-modal, including images and point clouds captured by camera and LiDAR, respectively. Secondly, the data are multi-frame due to the continuous, real-time acquisition. Thirdly, the outdoor scenes exhibit both moving foreground and static background. Existing VQA benchmarks fail to adequately address these complexities. To bridge this gap, we propose NuScenes-QA, the first benchmark for VQA in the autonomous driving scenario, encompassing 34K visual scenes and 460K question-answer pairs. Specifically, we leverage existing 3D detection annotations to generate scene graphs and design question templates manually. Subsequently, the question-answer pairs are generated programmatically based on these templates. Comprehensive statistics prove that our NuScenes-QA is a balanced large-scale benchmark with diverse question formats. Built upon it, we develop a series of baselines that employ advanced 3D detection and VQA techniques. Our extensive experiments highlight the challenges posed by this new task. Codes and dataset are available at https://github.com/qiantianwen/NuScenes-QA

From Canteen Food to Daily Meals: Generalizing Food Recognition to More Practical Scenarios

The precise recognition of food categories plays a pivotal role for intelligent health management, attracting significant research attention in recent years. Prominent benchmarks, such as Food-101 and VIREO Food-172, provide abundant food image resources that catalyze the prosperity of research in this field. Nevertheless, these datasets are well-curated from canteen scenarios and thus deviate from food appearances in daily life. This discrepancy poses great challenges in effectively transferring classifiers trained on these canteen datasets to broader daily-life scenarios encountered by humans. Toward this end, we present two new benchmarks, namely DailyFood-172 and DailyFood-16, specifically designed to curate food images from everyday meals. These two datasets are used to evaluate the transferability of approaches from the well-curated food image domain to the everyday-life food image domain. In addition, we also propose a simple yet effective baseline method named Multi-Cluster Reference Learning (MCRL) to tackle the aforementioned domain gap. MCRL is motivated by the observation that food images in daily-life scenarios exhibit greater intra-class appearance variance compared with those in well-curated benchmarks. Notably, MCRL can be seamlessly coupled with existing approaches, yielding non-trivial performance enhancements. We hope our new benchmarks can inspire the community to explore the transferability of food recognition models trained on well-curated datasets toward practical real-life applications

A Finite Element Mesh Aggregating Approach to Multiple-Source Reconstruction in Bioluminescence Tomography

A finite element mesh aggregating approach is presented to reconstruct images of multiple internal bioluminescence sources. Rather than assuming independence between mesh nodes, the proposed reconstruction strategy exploits spatial structure of nodes and aggregation feature of density distribution on the finite element mesh to adaptively determine the number of sources and to improve the quality of reconstructed images. With the proposed strategy integrated in the regularization-based reconstruction process, reconstruction algorithms need no a priori knowledge of source number; even more importantly, they can automatically reconstruct multiple sources that differ greatly in density or power

MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection

Fusing LiDAR and camera information is essential for achieving accurate and reliable 3D object detection in autonomous driving systems. However, this is challenging due to the difficulty of combining multi-granularity geometric and semantic features from two drastically different modalities. Recent approaches aim at exploring the semantic densities of camera features through lifting points in 2D camera images (referred to as seeds) into 3D space for fusion, and they can be roughly divided into 1) early fusion of raw points that aims at augmenting the 3D point cloud at the early input stage, and 2) late fusion of BEV (bird-eye view) maps that merges LiDAR and camera BEV features before the detection head. While both have their merits in enhancing the representation power of the combined features, this single-level fusion strategy is a suboptimal solution to the aforementioned challenge. Their major drawbacks are the inability to interact the multi-granularity semantic features from two distinct modalities sufficiently. To this end, we propose a novel framework that focuses on the multi-scale progressive interaction of the multi-granularity LiDAR and camera features. Our proposed method, abbreviated as MDMSFusion, achieves state-of-the-art results in 3D object detection, with 69.1 mAP and 71.8 NDS on nuScenes validation set, and 70.8 mAP and 73.2 NDS on nuScenes test set, which rank 1st and 2nd respectively among single-model non-ensemble approaches by the time of submission

Influence of dietary zinc on growth, zinc bioaccumulation and expression of genes involved in antioxidant and innate immune in juvenile mud crabs (Scylla paramamosain)

The aim of present study was to investigate the effects of dietary Zn level on growth performance, Zn bioaccumulation, antioxidant capacity and innate immunity in juvenile mud crab (Scylla paramamosain). Six semi-purified diets were formulated to contain dietary Zn levels of 44.5, 56.9, 68.5, 97.3, 155.6 or 254.7 mg·kg-1, respectively. Dietary Zn level significantly influenced percent weight gain (PWG), with highest observed in crab fed the diet containing 97.3 mg·kg-1 Zn. Tissue Zn concentrations significantly increased as dietary Zn levels increased from 44.5 to 254.7 mg·kg-1. Retention of Zn in hepatopancreas increased with dietary Zn levels up to 68.5 mg·kg-1 and then significantly decreased. Moreover, inadequate dietary Zn (44.5 and 56.9 mg·kg-1) reduced anti-oxidation markers including total superoxide dismutase and copper/zinc superoxide dismutase activities and total anti-oxidant level. Crabs fed the diet with 44.5 mg·kg-1 Zn also showed significantly lower expression of genes involved in antioxidant status, such as Cu/Zn sod, glutathione peroxidase, catalase and thioredoxin than those fed diets containing 68.5 and 97.3 mg·kg-1 Zn. Highest activities of phenoloxidase and alkaline phosphatase were recorded in crab fed the diets containing 68.5 and 97.3 mg·kg-1 Zn. Expression levels of prophenoloxidase and toll-like receptor 2 were higher in crab fed the 97.3 mg·kg-1 Zn diet compared to crab fed the other diets. Based on PWG alone, the optimal dietary Zn level was estimated to be 82.9 mg·kg-1, with 68.5 to 97.3 mg·kg-1 recommended for maintaining optimal Zn bioaccumulation, oxidation resistance and innate immune response of juvenile mud crab

Spatial Molecular and Cellular Determinants of STAT3 Activation in Liver Fibrosis Progression in Non-alcoholic Fatty Liver Disease

BACKGROUND & AIMS: The prevalence of non-alcoholic fatty liver disease (NAFLD) and its severe form, non-alcoholic steatohepatitis (NASH), is increasing. Individuals with NASH often develop liver fibrosis and advanced liver fibrosis is the main determinant of mortality in individuals with NASH. We and others have reported that STAT3 contributes to liver fibrosis and hepatocellular carcinoma in mice. METHODS: Here, we explored whether STAT3 activation in hepatocyte and non-hepatocyte areas, measured by phospho-STAT3 (pSTAT3), is associated with liver fibrosis progression in 133 patients with NAFLD. We further characterized the molecular and cellular determinants of STAT3 activation by integrating spatial distribution and transcriptomic changes in fibrotic NAFLD livers.Results: pSTAT3 scores in non-hepatocyte areas progressively increased with fibrosis severity (r = 0.53, CONCLUSION: Increased understanding of the spatial dependence of STAT3 signaling in NASH and liver fibrosis progression could lead to novel targeted treatment approaches. IMPACT AND IMPLICATIONS: Advanced liver fibrosis is the main determinant of mortality in patients with NASH. This study showed using liver biopsies from 133 patients with NAFLD, that STAT3 activation in non-hepatocyte areas is strongly associated with fibrosis severity, inflammation, and progression to NASH. STAT3 activation was enriched in hepatic progenitor cells (HPCs) and sinusoidal endothelial cells (SECs), as determined by innovative technologies interrogating the spatial distribution of pSTAT3. Finally, STAT3 inhibition in mice resulted in reduced liver fibrosis and depletion of HPCs, suggesting that STAT3 activation in HPCs contributes to their expansion and fibrogenesis in NAFLD

Cellular and Molecular Mechanisms of Liver Fibrosis in Patients with NAFLD

The expression of immune- and cancer-related genes was measured in liver biopsies from 107 NAFLD patients. The strongest difference in overall gene expression was between liver fibrosis stages F3 and F4, with 162 cirrhosis-associated genes identified. Strong correlations with fibrosis progression from F1 to F4 were observed for 91 genes, including CCL21, CCL2, CXCL6, and CCL19. In addition, the expression of 21 genes was associated with fast progression to F3/F4 in an independent group of eight NAFLD patients. These included the four chemokines, SPP1, HAMP, CXCL2, and IL-8. A six-gene signature including SOX9, THY-1, and CD3D had the highest performance detecting the progressors among F1/F2 NAFLD patients. We also characterized immune cell changes using multiplex immunofluorescence platforms. Fibrotic areas were strongly enriched in CD3+ T cells compared to CD68+ macrophages. While the number of CD68+ macrophages increased with fibrosis severity, the increase in CD3+ T-cell density was more substantial and progressive from F1 to F4. The strongest correlation with fibrosis progression was observed for CD3+CD45R0+ memory T cells, while the most significant increase in density between F1/F2 and F3/F4 was for CD3+CD45RO+FOXP3+CD8- and CD3+CD45RO-FOXP3+CD8- regulatory T cells. A specific increase in the density of CD68+CD11b+ Kupffer cells with liver fibrosis progression was also observed