Student engagement and learning outcomes:an empirical study applying a four-dimensional framework

Introduction: This study applies Reeve’s four-dimensional student engagement framework to a medical education context to elucidate the relationship between behavioral, emotional, cognitive, and agentic engagement and learning outcomes. Meanwhile, we categorize learning outcomes in knowledge and skills, and added taxonomies to the cognitive education objectives for the knowledge part, including memorization, comprehension, and application. Methods: We used the China Medical Student Survey to investigate student engagement, and combined it with the Clinical Medicine Proficiency Test for Medical Schools results as a standardized measurement of learning outcomes. We performed multivariate regression analyses to delve into the effectiveness of different types of student engagement. Moreover, we evaluated the moderating roles of gender and the National College Entrance Examination (NCEE) within the relationships between student engagement and learning outcomes. Results: We observed that emotional engagement is most effective in promoting learning outcomes in basic medical knowledge and basic clinical skills. Emotional engagement and cognitive engagement could effectively contribute to learning outcomes in all three aspects of basic medical knowledge. In contrast, behavioral and agentic engagement showed negative effects on learning outcomes. Besides, we found that the results of the NCEE played a positive moderating role. Conclusion: This study provides robust evidence for the effectiveness of emotional engagement and cognitive engagement in promoting learning outcomes. Whereas behavioral and agentic engagement may not be good predictors of learning outcomes in macro-level general competence tests. We suggest a combined effort by students and institutions to promote student engagement and bridge the distance between general competency tests and daily learning activities.</p

Schrodinger Bridges Beat Diffusion Models on Text-to-Speech Synthesis

In text-to-speech (TTS) synthesis, diffusion models have achieved promising generation quality. However, because of the pre-defined data-to-noise diffusion process, their prior distribution is restricted to a noisy representation, which provides little information of the generation target. In this work, we present a novel TTS system, Bridge-TTS, making the first attempt to substitute the noisy Gaussian prior in established diffusion-based TTS methods with a clean and deterministic one, which provides strong structural information of the target. Specifically, we leverage the latent representation obtained from text input as our prior, and build a fully tractable Schrodinger bridge between it and the ground-truth mel-spectrogram, leading to a data-to-data process. Moreover, the tractability and flexibility of our formulation allow us to empirically study the design spaces such as noise schedules, as well as to develop stochastic and deterministic samplers. Experimental results on the LJ-Speech dataset illustrate the effectiveness of our method in terms of both synthesis quality and sampling efficiency, significantly outperforming our diffusion counterpart Grad-TTS in 50-step/1000-step synthesis and strong fast TTS models in few-step scenarios. Project page: https://bridge-tts.github.io

Treatment effect, postoperative complications, and their reasons in juvenile thoracic and lumbar spinal tuberculosis surgery

OBJECTIVE: Fifty-four juvenile cases under 18 years of age with thoracic and lumbar spinal tuberculosis underwent focus debridement, deformity correction, bone graft fusion, and internal fixation. The treatment effects, complications, and reasons were analyzed retrospectively. MATERIAL AND METHOD: There were 54 juvenile cases under 18 years of age with thoracolumbar spinal tuberculosis. The average age was 9.2 years old, and the sample comprised 38 males and 16 females. The disease types included 28 thoracic cases, 17 thoracolumbar cases, and 9 lumbar cases. Nerve function was evaluated with the Frankel classification. Thirty-six cases were performed with focus debridement and deformity correction and were supported with allograft or autograft in mesh and fixed with pedicle screws from a posterior approach. Eight cases underwent a combined anterior and posterior surgical approach. Nine cases underwent osteotomy and deformity correction, and one case received focus debridement. The treatment effects, complications, and bone fusions were tracked for an average of 52 months. RESULTS: According to the Frankel classification, paralysis was improved from 3 cases of B, 8 cases of C, 18 cases of D, and 25 cases of E preoperatively. This improvement was found in 3 cases of C, 6 cases of D, and 45 cases of E at a final follow-up postoperatively. No nerve dysfunction was aggravated. VAS was improved from 7.8 ± 1.7 preoperatively to 3.2 ± 2.1 at final follow-up postoperatively. ODI was improved from 77.5 ± 17.3 preoperatively to 28.4 ± 15.9 at final follow-up postoperatively. Kyphosis Cobb angle improved from 62.2° ± 3.7° preoperatively to 37° ± 2.4° at final follow-up postoperatively. Both of these are significant improvements, and all bone grafts were fused. Complications related to the operation occurred in 31.5 % (17/54) of cases. Six cases suffered postoperative aggravated kyphosis deformity, eight cases suffered proximal kyphosis deformity, one case suffered pedicle penetration, one case suffered failure of internal devices, and one case suffered recurrence of tuberculosis. CONCLUSION: As long as the treatment plan is fully prepared, the surgical option can achieve a satisfactory curative effect in treating juvenile spinal tuberculosis despite some complications

A novel prognostic 7-methylguanosine signature reflects immune microenvironment and alternative splicing in glioma based on multi-omics analysis

Glioma is the most common type of central nervous system tumor with increasing incidence. 7-methylguanosine (m7G) is one of the diverse RNA modifications that is known to regulate RNA metabolism and its dysregulation was associated with various cancers. However, the expression pattern of m7G regulators and their roles in regulating tumor immune microenvironments (TIMEs) as well as alternative splicing events (ASEs) in glioma has not been reported. In this study, we showed that m7G regulators displayed a close correlation with each other and most of them were differentially expressed between normal and glioma tissues. Two m7G signatures were then constructed to predict the overall survival of both GBM and LGG patients with moderate predictive performance. The risk score calculated from the regression coefficient and expression level of signature genes was proved to be an independent prognostic factor for patients with LGG, thus, a nomogram was established on the risk score and other independent clinical parameters to predict the survival probability of LGG patients. We also investigated the correlation of m7G signatures with TIMEs in terms of immune scores, expression levels of HLA and immune checkpoint genes, immune cell composition, and immune-related functions. While exploring the correlation between signature genes and the ASEs in glioma, we found that EIF4E1B was a key regulator and might play dual roles depending on glioma grade. By incorporating spatial transcriptomic data, we found a cluster of cells featured by high expression of PTN exhibited the highest m7G score and may communicate with adjacent cancer cells via SPP1 and PTN signaling pathways. In conclusion, our work brought novel insights into the roles of m7G modification in TIMEs and ASEs in glioma, suggesting that evaluation of m7G in glioma could predict prognosis. Moreover, our data suggested that blocking SPP1 and PTN pathways might be a strategy for combating glioma

Periostin: A Downstream Mediator of EphB4-Induced Osteogenic Differentiation of Human Bone Marrow-Derived Mesenchymal Stem Cells

Erythropoietin-producing hepatocyte B4 (EphB4) has been reported to be a key molecular switch in the regulation of bone homeostasis, but the underlying mechanism remains poorly understood. In this study, we investigated the role of EphB4 in regulating the expression of periostin (POSTN) within bone marrow-derived mesenchymal stem cells (MSCs) and assessed its effect and molecular mechanism of osteogenic induction in vitro. Treatment with ephrinB2-FC significantly increased the expression of POSTN in MSCs, and the inhibition of EphB4 could abrogate this effect. In addition, osteogenic markers were upregulated especially in MSCs overexpressing EphB4. To elucidate the underlying mechanism of cross talk between EphB4 and the Wnt pathway, we detected the change in protein expression of phosphorylated-glycogen synthase kinase 3β-serine 9 (p-GSK-3β-Ser9) and β-catenin, as well as the osteogenic markers Runx2 and COL1. The results showed that GSK-3β activation and osteogenic marker expression levels were downregulated by ephrinB2-FC treatment, but these effects were inhibited by blocking integrin αvβ3 in MSCs. Our findings demonstrate that EphB4 can promote osteogenic differentiation of MSCs via upregulation of POSTN expression. It not only helps to reveal the interaction mechanism between EphB4 and Wnt pathway but also brings a better understanding of EphB4/ephrinB2 signaling in bone homeostasis

The Screening of the Protective Antigens of Aeromonas hydrophila Using the Reverse Vaccinology Approach: Potential Candidates for Subunit Vaccine Development

The threat of bacterial septicemia caused by Aeromonas hydrophila infection to aquaculture growth can be prevented through vaccination, but differences among A. hydrophila strains may affect the effectiveness of non-conserved subunit vaccines or non-inactivated A. hydrophila vaccines, making the identification and development of conserved antigens crucial. In this study, a bioinformatics analysis of 4268 protein sequences encoded by the A. hydrophila J-1 strain whole genome was performed based on reverse vaccinology. The specific analysis included signal peptide prediction, transmembrane helical structure prediction, subcellular localization prediction, and antigenicity and adhesion evaluation, as well as interspecific and intraspecific homology comparison, thereby screening the 39 conserved proteins as candidate antigens for A. hydrophila vaccine. The 9 isolated A. hydrophila strains from diseased fish were categorized into 6 different molecular subtypes via enterobacterial repetitive intergenic consensus (ERIC)-PCR technology, and the coding regions of 39 identified candidate proteins were amplified via PCR and sequenced to verify their conservation in different subtypes of A. hydrophila and other Aeromonas species. In this way, conserved proteins were screened out according to the comparison results. Briefly, 16 proteins were highly conserved in different A. hydrophila subtypes, of which 2 proteins were highly conserved in Aeromonas species, which could be selected as candidate antigens for vaccines development, including type IV pilus secretin PilQ (AJE35401.1) and TolC family outer membrane protein (AJE35877.1). The present study screened the conserved antigens of A. hydrophila by using reverse vaccinology, which provided basic foundations for developing broad-spectrum protective vaccines of A. hydrophila

T2MAC: Targeted and Trusted Multi-Agent Communication through Selective Engagement and Evidence-Driven Integration

Communication stands as a potent mechanism to harmonize the behaviors of multiple agents. However, existing works primarily concentrate on broadcast communication, which not only lacks practicality, but also leads to information redundancy. This surplus, one-fits-all information could adversely impact the communication efficiency. Furthermore, existing works often resort to basic mechanisms to integrate observed and received information, impairing the learning process. To tackle these difficulties, we propose Targeted and Trusted Multi-Agent Communication (T2MAC), a straightforward yet effective method that enables agents to learn selective engagement and evidence-driven integration. With T2MAC, agents have the capability to craft individualized messages, pinpoint ideal communication windows, and engage with reliable partners, thereby refining communication efficiency. Following the reception of messages, the agents integrate information observed and received from different sources at an evidence level. This process enables agents to collectively use evidence garnered from multiple perspectives, fostering trusted and cooperative behaviors. We evaluate our method on a diverse set of cooperative multi-agent tasks, with varying difficulties, involving different scales and ranging from Hallway, MPE to SMAC. The experiments indicate that the proposed model not only surpasses the state-of-the-art methods in terms of cooperative performance and communication efficiency, but also exhibits impressive generalization.Comment: AAAI2