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Tumor microenvironment (TME) is closely associated with the progression and prognosis of head and neck squamous cell carcinoma (HNSCC). To investigate potential biomarkers for predicting therapeutic outcomes in HNSCC, we analyzed the immune and stromal status of HNSCC based on the genes associated with TME using the ESTIMATE algorithm. Immune and stromal genes were identified with differential gene expression and weighted gene co-expression network analysis (WGCNA). From these genes, 118 were initially selected through Cox univariate regression and then further input into least absolute shrinkage and selection operator (LASSO) regression analysis. As a result, 11 genes were screened out for the TME-related risk (TMErisk) score model which presented promising overall survival predictive potential. The TMErisk score was negatively associated with immune and stromal scores but positively associated with tumor purity. Individuals with high TMErisk scores exhibited decreased expression of most immune checkpoints and all human leukocyte antigen family genes, and reduced abundance of infiltrating immune cells. Divergent genes were mutated in HNSCC. In both high and low TMErisk score groups, the tumor protein P53 exhibited the highest mutation frequency. A higher TMErisk score was found to be associated with reduced overall survival probability and worse outcomes of immunotherapy. Therefore, the TMErisk score could serve as a valuable model for the outcome prediction of HNSCC in clinic.

Ensuring the stability of cable tunnels is crucial for power safety and urban reliability in light of the increasing demand for urban electricity. However, frequent waterlogging incidents within tunnels pose a significant threat to city safety by disrupting the power supply. Current methods have proven insufficient in timely and effective water detection within cable tunnels. While visual methods show promise, the complexity of cable tunnel environments degrades image quality under low-light conditions, intensifying the challenge of detection. To address these issues, this study proposes a novel hierarchical approach for waterlogging detection. The approach decomposes the problem into three subproblems: low-light image enhancement, image segmentation, and detection. Firstly, low-light image enhancement techniques are employed to improve image quality and enrich details for subsequent analysis. Next, image segmentation accurately delineates waterlogged road areas while mitigating interference from complex backgrounds. Finally, Faster R-CNN with ResNet as its backbone and integrated attention mechanisms enhances the model’s capacity to identify essential features amidst complex backgrounds, significantly improving accuracy in detecting waterlogged areas. Experimental results demonstrate our method’s superiority in accuracy and robustness for tunnel waterlogging detection tasks compared to traditional approaches. This proposed approach provides effective technical support for cable tunnel safety monitoring, contributing to ensuring urban power safety and reliability in the face of waterlogging challenges.

Abstract Objective To delineate the metabolomic differences in plasma samples between patients with coronary artery disease (CAD) and those with concomitant CAD and type 2 diabetes mellitus (T2DM), and to pinpoint distinctive metabolites indicative of T2DM risk. Method Plasma samples from CAD and CAD-T2DM patients across three centers underwent comprehensive metabolomic and lipidomic analyses. Multivariate logistic regression was employed to discern the relationship between the identified metabolites and T2DM risk. Characteristic metabolites' metabolic impacts were further probed through hepatocyte cellular experiments. Subsequent transcriptomic analyses elucidated the potential target sites explaining the metabolic actions of these metabolites. Results Metabolomic analysis revealed 192 and 95 significantly altered profiles in the discovery (FDR

With the rapid advancement of Artificial Intelligence (AI) in early childhood education (ECE), young children face the challenge of learning to use AI ethically and appropriately. Developing AI education programs requires an age- and culturally-appropriate AI literacy framework. This study addresses this fundamental gap by creating a Chinese framework for early childhood AI literacy through an expert interview study with a grounded theory approach. Seven Chinese experts, including ECE and AI professors, kindergarten principals, and Directors of ECE Information Departments, were purposely sampled and interviewed, representing scholars, policymakers, and practitioners. The synthesis of the transcribed evidence generated five dimensions of young children’s AI literacy, namely Safety, Identity, Attitude, Cognition, and Capability, collectively forming a holistic framework titled the ‘SIACC’ framework. The Chinese definition of early childhood AI literacy was also reported. This study introduces the Chinese framework of AI literacy and provides a scientific basis for policymakers to establish AI literacy standards for young children. Additionally, it offers a conceptual structure for developing systematic indicators and scales within AI literacy in ECE.

IntroductionMetropolitan governance’s efficacy is regularly gauged by its capability for public health preparedness, a critical component, particularly in the post-pandemic climate, as global cities reassess their mitigation abilities. This process has broader implications, curbing mortality rates and amplifying sustainability. Current methodologies for preparedness assessment lean primarily on either Subjective Evaluation-Based Assessment (SBA), predicated on experts’ input on various capacity indicators, or they opt for Data-Based quantitative Assessments (DBA), chiefly utilizing public statistic data.MethodsThe manuscript discusses an urgent need for integrating both SBA and DBA to adequately measure Metropolitan Public Health Pandemics Preparedness (MPHPP), thus proposing a novel entropy-TOPSIS-IF model for comprehensive evaluation of MPHPP. Within this proposed model, experts’ subjective communication is transformed into quantitative data via the aggregation of fuzzy decisions, while objective data is collected from public statistics sites. Shannon’s entropy and TOPSIS methods are enacted on these data sets to ascertain the optimal performer after normalization and data isotropy.Results and discussionThe core contribution of the entropy-TOPSIS-IF model lies in its assessment flexibility, making it universally applicable across various contexts, regardless of the availability of expert decisions or quantitative data. To illustrate the efficacy of the entropy-TOPSIS-IF model, a numerical application is presented, examining three Chinese metropolises through chosen criteria according to the evaluations of three experts. A sensitivity analysis is provided to further affirm the stability and robustness of the suggested MPHPP evaluation model.

Ischemic stroke (IS) is a leading cause of death and disability worldwide. Screening for marker genes in IS is crucial for its early diagnosis and improvement in clinical outcomes. In the study, the gene expression profiles in the GSE22255 and GSE37587 datasets were extracted from the public database Gene Expression Omnibus. Weighted gene co‑expression network analysis (WGCNA) was used to investigate the gene sets that were related to ubiquitination. A total of 33 ubiquitination-related differentially expressed genes (DEGs) were identified using "limma (version 3.50.0)". Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) analysis enriched multiple pathways that were closely related to IS. The correlations between the HALLMARK signaling pathways and DGEs were analyzed. Receiver operating characteristic analysis was used to validate the diagnostic value of the key genes. Among them, 16 genes were identified as hub genes. Single-sample GSEA was performed to evaluate the infiltration status of immune cells in IS. To understand the potential molecular mechanisms of the hub genes in IS, we constructed RBP-mRNA and mRNA-miRNA-lncRNA interaction networks. Additionally, we used the GeneMANIA database to create a PPI network for the signature genes to investigate their functions. As a result, there was a significant difference in the overall infiltration of immune cells between the IS and control groups. Among the 28 types of immune cells, the degree of infiltration of seven types was significantly different between the two groups (p

The key to electroencephalography (EEG)-based brain-computer interface (BCI) lies in neural decoding, and its accuracy can be improved by using hybrid BCI paradigms, that is, fusing multiple paradigms. However, hybrid BCIs usually require separate processing processes for EEG signals in each paradigm, which greatly reduces the efficiency of EEG feature extraction and the generalizability of the model. Here, we propose a two-stream convolutional neural network (TSCNN) based hybrid brain-computer interface. It combines steady-state visual evoked potential (SSVEP) and motor imagery (MI) paradigms. TSCNN automatically learns to extract EEG features in the two paradigms in the training process and improves the decoding accuracy by 25.4% compared with the MI mode, and 2.6% compared with SSVEP mode in the test data. Moreover, the versatility of TSCNN is verified as it provides considerable performance in both single-mode (70.2% for MI, 93.0% for SSVEP) and hybrid-mode scenarios (95.6% for MI-SSVEP hybrid). Our work will facilitate the real-world applications of EEG-based BCI systems.

In edge computing environments, limited storage and computational resources pose significant challenges to complex super-resolution network models. To address these challenges, we propose an agile super-resolution network via intelligent path selection (ASRN) that utilizes a policy network for dynamic path selection, thereby optimizing the inference process of super-resolution network models. Its primary objective is to substantially reduce the computational burden while maximally maintaining the super-resolution quality. To achieve this goal, a unique reward function is proposed to guide the policy network towards identifying optimal policies. The proposed ASRN not only streamlines the inference process but also significantly boosts inference speed on edge devices without compromising the quality of super-resolution images. Extensive experiments across multiple datasets confirm ASRN’s remarkable ability to accelerate inference speeds while maintaining minimal performance degradation. Additionally, we explore the broad applicability and practical value of ASRN in various edge computing scenarios, indicating its widespread potential in this rapidly evolving domain.

Summary: By a survey of metagenome-wide association studies (MWAS), we found a robust depletion of Bacteroides cellulosilyticus, Faecalibacterium prausnitzii, and Roseburia intestinalis in individuals with atherosclerotic cardiovascular disease (ACVD). From an established collection of bacteria isolated from healthy Chinese individuals, we selected B. cellulosilyticus, R. intestinalis, and Faecalibacterium longum, a bacterium related to F. prausnitzii, and tested the effects of these bacteria in an Apoe/− atherosclerosis mouse model. We show that administration of these three bacterial species to Apoe−/− mice robustly improves cardiac function, reduces plasma lipid levels, and attenuates the formation of atherosclerotic plaques. Comprehensive analysis of gut microbiota, plasma metabolome, and liver transcriptome revealed that the beneficial effects are associated with a modulation of the gut microbiota linked to a 7α-dehydroxylation–lithocholic acid (LCA)–farnesoid X receptor (FXR) pathway. Our study provides insights into transcriptional and metabolic impact whereby specific bacteria may hold promises for prevention/treatment of ACVD.

The present study identified a novel mechanism underlying the protective effect of Sirtuin 3 (SIRT3) against pathological cardiac hypertrophy, beyond its well-accepted role as a deacetylase in mitochondria. SIRT3 modulates the peroxisomes-mitochondria interplay by preserving the expression of peroxisomal biogenesis factor 5 (PEX5), thereby improving mitochondrial function. Downregulation of PEX5 was observed in the hearts of Sirt3−/− mice and angiotensin II-induced cardiac hypertrophic mice, as well as in cardiomyocytes with SIRT3 silencing. PEX5 knockdown abolished the protective effect of SIRT3 against cardiomyocyte hypertrophy, whereas PEX5 overexpression alleviated the hypertrophic response induced by SIRT3 inhibition. PEX5 was involved in the regulation of SIRT3 in mitochondrial homeostasis, including mitochondrial membrane potential, mitochondrial dynamic balance, mitochondrial morphology and ultrastructure, as well as ATP production. In addition, SIRT3 alleviated peroxisomal abnormalities in hypertrophic cardiomyocytes via PEX5, as implied by improvement of peroxisomal biogenesis and ultrastructure, as well as increase of peroxisomal catalase and repression of oxidative stress. Finally, the role of PEX5 as a key regulator of the peroxisomes-mitochondria interplay was confirmed, since peroxisomal defects caused by PEX5 deficiency led to mitochondrial impairment. Taken together, these observations indicate that SIRT3 could maintain mitochondrial homeostasis by preserving the peroxisomes-mitochondria interplay via PEX5. Our findings provide a new understanding of the role of SIRT3 in mitochondrial regulation via interorganelle communication in cardiomyocytes.

Analysis of social media during the COVID-19 quarantine period in Mainland China provides access to a large amount of user-generated content for sentiment analysis during this unexpected and stressful time period. This study focuses on emotions that were communicated in the context of interactions between parents and young children to explore their emotional attitudes and emotional contagion. Results suggest that positive emotional attitudes were more prevalent in parent-child interactions, which contrasts with previous research. In comparison to their children, parents expressed more negative moods. Nonetheless, Chinese preschoolers and their parents influenced each other's emotions with bi-directional effects, providing evidence of emotional contagion. Parents’ emotional transmission sometimes resulted in passive suppression by the young children. Emotions were manifested more through physical or behavioral interactions as opposed to verbal statements of feelings, especially during parent to child transmissions. The transmission of emotions from children to parents consisted mainly of two types: children's emotional catharsis and children's active emotional agency. The discussion explores explanations for the observed emotional contagion of positive emotions between parents and children, considers the role of power and agency during emotional contagion, and discusses the effects of Chinese socio-cultural factors on the sentiment analysis.

The teacher–child relationship is a key element in measuring the quality of childcare institutions and is essential to the current and future physical and mental developmental outcomes of children. The purpose of this study is to investigate the role of private preschool teachers’ readiness to teach in influencing the teacher–child relationship, and to explore the mechanisms by which teachers’ motivation and self-efficacy mediate their readiness to teach and the teacher–child relationship. Online questionnaires were administered to 289 early-childhood teachers in Shanghai, China. The findings of the study indicated a significant and positive correlation between early-childhood teachers’ readiness to teach and the quality of the teacher–child relationship. In addition, early-childhood teachers’ internal motivation to teach and self-efficacy mediated their readiness to teach and the teacher–child relationship, respectively. However, teachers’ external motivation did not mediate the effect of teaching readiness on the quality of the teacher–child relationship. Moreover, teachers’ motivation to teach (encompassing both internal and external factors) and their self-efficacy played chained roles in mediating the relationships between teaching readiness and the teacher–child relationship. This study highlights the significant roles of teaching readiness and instructional motivation, along with self-efficacy, in cultivating positive teacher–child relationships within early-childhood education settings.

Dentin matrix protein 1 (Dmp1) is a highly phosphorylated, extracellular matrix protein that is extensively expressed in bone and teeth but also found in soft tissues, including brain and muscle. However, the functions of Dmp1 in the mice cochlea are unknown. Our study showed that Dmp1 was expressed in auditory hair cells (HCs), with the role of Dmp1 in those cells identified using Dmp1 cKD mice. Immunostaining and scanning electron microscopy of the cochlea at P1 revealed that Dmp1 deficiency in mice resulted in an abnormal stereociliary bundle morphology and the mispositioning of the kinocilium. The following experiments further demonstrated that the cell-intrinsic polarity of HCs was affected without apparent effect on the tissue planer polarity, based on the observation that the asymmetric distribution of Vangl2 was unchanged whereas the Gαi3 expression domain was enlarged and Par6b expression was slightly altered. Then, the possible molecular mechanisms of Dmp1 involvement in inner ear development were explored via RNA-seq analysis. The study suggested that the Fgf23–Klotho endocrine axis may play a novel role in the inner ear and Dmp1 may regulate the kinocilium–stereocilia interaction via Fgf23–Klotho signaling. Together, our results proved the critical role of Dmp1 in the precise regulation of hair bundle morphogenesis in the early development of HCs.

"Research Findings": This study aims to explore the optimal roles, pressing challenges, and future developments of ChatGPT in early childhood education (ECE) by interviewing six expert professors from China and the United States. A content analysis approach that combined inductive and deductive coding was employed to analyze the transcribed interviews. The findings of this study revealed two primary roles of ChatGPT in ECE: (1) conversational agent for young children and (2) on-call facilitator for educators and caregivers. The challenges and risks associated with ChatGPT were categorized using the "3A2S" framework, which includes Accessibility, Affordability, Accountability, Sustainability, and Social Justice. The participants suggest that as knowledge, information retrieval, and integration skills become less critical, human agency and inquiry ability become more important. As a result, they predict that ChatGPT's future trajectory should be as a resource for Intelligence Augmentation (IA) rather than as a replacement for the actions of educators and caregivers. Furthermore, AI opens new domains in ECE, such as AI literacy and AI social interaction, paving the way for novel opportunities and challenges in the field. "Practice or Policy": This study contributes to the ongoing discourse on integrating AI technologies in ECE and offers valuable suggestions for policymakers, educators, and caregivers.

"Research Findings:" This study examined the financial literacy of 5 and 6-year-old children in Shanghai, including their understanding of financial concepts and their preference for and ability to use digital payment methods. Using a mixed-methods research design, a sample of children ranging in age from 62 to 73 months completed multiple tasks to assess their financial literacy and participated in a semi-structured interview. The results suggested that most young Chinese children had a basic understanding of coins and their value, but there was variation in their perceptions of the need to provide change after making a payment. The majority of young children preferred to pay with digital devices and had a positive attitude toward mobile payments, although some children expressed concerns about their ability to use this method accurately. "Practice or Policy:" The study findings suggest that perceptions of financial literacy may vary across families from diverse socioeconomic backgrounds. These results lay the groundwork for future digital financial literacy rating scales or behavior coding studies based on the initial findings of young children from middle-income families. The study also emphasizes the importance of providing formal and informal financial education to young children, particularly in regards to digital literacy, in order to prepare them for the future.

"Research Findings:" Digital leadership as an urgent and emerging topic has attracted public attention, but its nature, definition, and constructs have not been thoroughly explored for the Chinese culture. This study uses a grounded theory approach to examine how Chinese principles are utilized at "Model Level" preschools to define, understand, and practice digital leadership in preschools. Eight principals from "Model Level" preschools in Shanghai were purposefully sampled and interviewed individually. The interview transcripts' inductive coding and interpretive analysis revealed a Chinese definition of digital leadership and its three constructs: Attitude, Cognition, and Capability, resulting in a unique "ACC model." This model indicates that this Chinese definition of digital leadership has gone beyond the traditional process-capability dichotomy and is a combination of three major constructs. "Practice or Policy:" This model provides a scientific foundation for the policymakers to develop "Digital Leadership Standards for Chinese Preschool Principals;" a conceptual structure of digital leadership for developing systematic indicators and scales. Digital leadership training can begin with an emphasis on attitude and cognition.