Recent publications
Natural polysaccharides have complex structural properties and a wide range of health-promoting effects. Accumulating evidence suggests that the effects are significantly mediated through fermentation by gut microbiota. In recent years, the relationship between the structures of natural polysaccharides and their properties in regulating gut microbiota has garnered significant research attention as researchers attempt to precisely understand the role of gut microbiota in the bioactivities of natural polysaccharides. Progress in this niche, however, remains limited. In this review, we first provide an overview of current research investigating this structure-property relationship. We then present a detailed correlation analysis between the structural characteristics of 159 purified natural polysaccharides and their effects on gut microbiota reported over the past two decades. The analysis revealed that diverse gut bacteria show specific correlations with the molecular weight, glycosidic linkages, and monosaccharide composition of natural polysaccharides. Multifaceted molecular mechanisms, including carbohydrate binding, enzymatic degradation, and cross-feeding, were proposed to be collectively involved in these correlations. Finally, we offer our perspective on future studies to further improve our understanding of the relationship between polysaccharide structure and gut microbiota regulation.
Food waste (FW) presents a significant opportunity for renewable energy production through anaerobic digestion (AD) when subjected to appropriate treatment. This study investigates the impact of thermal hydrolysis pretreatment (THP) on FW at varying temperature levels (90 °C, 120 °C, and 140 °C) prior to mesophilic anaerobic co-digestion with sewage sludge (SS). Results demonstrate enhanced FW hydrolysis at 120 °C, leading to a cumulative methane yield of 324.39 ± 4.5 mL/gVSadd, representing a 41.75% increase over untreated FW (228.83 ± 1.13 mL/gVSadd). Shifts in microbial communities, particularly Methanosarcina, Methanobactrium, and Methanobrevibacter, support efficient methanogenesis. Co-digestion of FW pretreated at 120 °C yields maximum energy production of 11.48 MJ/t, a 49.47% improvement compared to untreated processes. The economic analysis underscores the profitability of co-digestion with FW pretreated at 120 °C. These findings highlight the potential for enhanced methane production and energy conversion efficiency with hydrothermally pretreated FW and SS co-digestion.
Graph Neural Networks (GNNs) have significantly advanced recommendation systems by modeling user-item interactions through bipartite graphs. However, real-world user-item interaction data are often sparse and noisy. Traditional bipartite graph modeling fails to capture higher-order relationships between users and items, limiting the ability of GNNs to learn high-quality node embeddings. While existing graph contrastive learning methods address data sparsity by partitioning nodes into positive and negative pairs, they also neglect these higher-order relationships, thus limiting the effectiveness of contrastive learning in recommendation systems. Furthermore, due to the inherent limitations of graph convolution, noise can propagate and amplify with increasing layers in deep graph convolutional networks. To address these challenges, Neighbor Enhancement and Embedding Perturbation for Graph Contrastive Learning (NPGCL) is proposed, which introduces two key modules - Relational Neighbor Enhancement Module and Collaborative Neighbor Enhancement Module - to capture higher-order relationships between homogeneous nodes and calculate interaction importance for noise suppression. Moreover, NPGCL employs an Embedding Perturbation Strategy and applies inter-layer contrastive learning to mitigate the noise impact caused by multi-layer graph convolutions. Experimental results demonstrate that NPGCL significantly improves performance across four publicly available datasets, with a notable enhancement in robustness, especially in noisy environments. Specifically, NPGCL achieves performance improvements of 1.77%-3.34% and 3.87%-9.07% on the Gowalla and Amazon-books datasets, respectively. In noisy datasets, NPGCL improves Recall@20 by 4.98% and 10.92%, respectively.
This study investigates the mediating role of reflective thinking practice in the relationship between practicum and positive mirror effect in early childhood special education teacher training. The study participants comprise students enrolled in undergraduate early childhood teacher education programs specific to special education. This study entails a post-evaluation using the Experiential Learning Experiences Scale, Reflective Practice Questionnaire, and Mirror Effects Inventory to assess practicum experiences, reflective thinking practices, and mirror effects via an online survey after engaging in experiential learning, particularly a teaching practicum. The hypothetical serial multiple mediation model is analyzed using PROCESS macro. The results reveal that the relationship between practicum experiences and the positive mirror effect was at least partially explained by the desire for improvement, reflective capacity, and confidence. However, the desire for improvement is not sufficient to demonstrate a mediation effect. Moreover, the findings suggest that reflective capacity fostering student teachers’ confidence may generate a positive mirror effect. These findings highlight the beneficial effects of reflective capacity in the relationship between practicum experiences and positive mirror effects in early childhood special education teacher training. This implies that practicum experiences can contribute positively to reflective capacity and foster student teachers’ affective learning experiences, ultimately enhancing the overall professionalism of early childhood special education. This study provides essential insights indicating that further research should focus on extracting practical knowledge and reflective experiences in special education from pre-service and experienced in-service teachers, along with methods to cultivate positive affective experience and confidence, thereby generating a positive mirror effect in practicums across diverse disciplines.
Neurodegenerative diseases (NDs) pose significant challenges to both longevity and quality of life, affecting millions worldwide and ranking as a major cause of global morbidity and disability. There is an urgent need for less invasive, cost-effective diagnostic methods that can reliably detect ND-related biomarkers in readily available biofluids such as serum. In response to this need, we have developed an ultra-sensitive assay that utilizes magnetic nanoparticles and a novel, tailor-made turn-on fluorescent probe named F-SPG. This innovative assay enables the detection of neurofilament light chain (NfL) at femtomolar concentrations, with a remarkable detection limit of 24 fM which is at least 18 times more sensitive than the conventional ELISA kits. Our assay’s design eliminates the need for traditional secondary antibody in immunoassay, thereby streamlining the diagnostic process. Recoveries exceeding 95% underscore the assay’s precision and reliability. It has proven effective in distinguishing Alzheimer’s Disease patients from healthy individuals by quantifying serum NfL levels, showcasing its potential as a cost-effective, ultra-sensitive tool for the early screening of neurodegenerative diseases.
Graphical abstract
Tensor decomposition is a powerful tool for extracting physically meaningful latent factors from multi‐dimensional non‐negative data, and has been an increasing interest in a variety of fields such as image processing, machine learning, and computer vision. In this paper, we propose a sparse non‐negative Tucker decomposition and completion approach for the recovery of underlying non‐negative data under incompleted and generally noisy observations. Here the underlying non‐negative tensor data is decomposed into a core tensor and several factor matrices with all entries being non‐negative and the factor matrices being sparse. The loss function is derived by the maximum likelihood estimation of the noisy observations, and the norm is employed to enhance the sparsity of the factor matrices. We establish the error bound of the estimator of the proposed model under generic noise scenarios, which is then specified to the observations with additive Gaussian noise, additive Laplace noise, and Poisson observations, respectively. Our theoretical results are better than those by existing tensor‐based or matrix‐based methods. Moreover, the minimax lower bounds are shown to be matched with the derived upper bounds up to logarithmic factors. Numerical experiments on both synthetic data and real‐world data sets demonstrate the superiority of the proposed method for non‐negative tensor data completion.
Using the staggered adoption of anti-recharacterization laws (ARLs) as an exogenous shock to creditor rights, we study the effects of creditor rights on borrowers’ accounting conservatism. By forbidding securitized assets from being recharacterized as collateral for secured debt, ARLs protect securitization creditors at the expense of non-securitization creditors. Given the conflict of interest between these creditors, we argue that non-securitization creditors may demand more conservative accounting in response to their decreasing rights. Consistent with this argument, we find an increase in borrowers’ accounting conservatism after ARL adoption. To further support our demand-side argument, we present evidence that the effect of ARLs on accounting conservatism is stronger for borrowers with a higher likelihood of securitizing assets, those with unsecured creditors, and those with loan contracts that include more financial covenants. We also find a stronger effect for borrowers that are more likely to default, as captured by their credit risk, and for borrowers that are more difficult to monitor, as indicated by their poor information environment. Overall, our study establishes a link between creditor rights and borrowers’ accounting conservatism. We also add to the literature by offering novel evidence that creditors’ demand for accounting conservatism shapes borrowers’ financial reporting practices.
Marine fisheries resources are under increasing threat, necessitating the development of new effective monitoring and management strategies. Environmental DNA (eDNA) and RNA (eRNA) metabarcoding has emerged as a non-invasive and sensitive alternative method for monitoring fish biodiversity and fisheries resources and assessing the fisheries impact of anthropogenic activities. Here, we summarize crucial technical details about eDNA metabarcoding for marine fish monitoring and provide meta-analytical trends in primer selection and sample size, assessment standards, fish and fisheries databases, reference fish genomic databases, and other relevant metrics. The pressing need for better reference databases and standardization methods is discussed. We further highlight the potency of emerging eDNA metabarcoding studies for monitoring global fish diversity and revealed regional study hotspots in South China, Atlantic and Mediterranean Seas. The innovative advances in using eDNA/eRNA metabarcoding for fish diversity monitoring and assessment from the detection of rare or invasive species to branching applications in biomass estimation, population genetics, food web analysis, fish migration and feeding studies were reviewed. We also explore the potential of eRNA metabarcoding as an upcoming extension of eDNA metabarcoding in marine fish monitoring and assessment with improved functional relevance. We envision the integration of eDNA/eRNA metabarcoding-based fish monitoring methods with traditional monitoring approaches to significantly improve marine fish surveillance, ecological research, and conservation efforts.
The rising application of Generative Artificial Intelligence (GenAI) tools like ChatGPT, Bing Chat, and Bard in language teaching and learning heralds a transformative era. Yet, the experiences and perspectives of university students on integrating these tools into their translation studies remain underexplored. This qualitative study, conducted in a research-intensive, Sino-foreign cooperative university in southern China, explored university students’ perceived benefits and challenges of utilizing GenAI in translation practices, as well as their preferred support mechanisms for addressing encountered issues. Data was collected through open-ended questionnaires and semi-structured interviews and analyzed by using reflexive thematic analysis. Results underscored the advantages of GenAI in enhancing translation efficiency, quality, learning, and practice, fostering a positive outlook and social benefits. Nevertheless, issues such as adequacy, prompt engineering efficacy, practical application, technical limitations, accountability, transparency, and potential AI dependency were noted. Beyond existing self-help strategies, there was an expressed need for additional guidance from educators and institutions. This study enriches our comprehension of how university students perceive and engage with GenAI tools in translation, offering insights for educators and academic institutions to optimize future teaching strategies. It also outlines the study’s limitations and proposes directions for subsequent research.
“This exposition discusses continuous-time reinforcement learning (CT-RL) for the control of affine nonlinear systems. We review four seminal methods that are the centerpieces of the most recent results on CT-RL control. We survey the theoretical results of the four methods, highlighting their fundamental importance and successes by including discussions on problem formulation, key assumptions, algorithm procedures, and theoretical guarantees. Subsequently, we evaluate the performance of the control designs to provide analyses and insights on the feasibility of these design methods for applications from a control designer's point of view. Through systematic evaluations, we point out when theory diverges from practical controller synthesis. We, furthermore, introduce a new quantitative analytical framework to diagnose the observed discrepancies. Based on the analyses and the insights gained through quantitative evaluations, we point out potential future research directions to unleash the potential of CT-RL control algorithms in addressing the identified challenges.”
Background: The latest Global Burden of Disease study indicates that stroke remains the second major cause of death. Promising protective effects have been shown in most studies of ischemic stroke (IS) protection in preclinical animal models, but have failed to enter clinical trials. The objective of this study is to investigate the brain cytoprotective effects of the natural compound MBS-1 in IS, using a novel, rigorous, and feasible paradigm inspired by Stroke Preclinical Assessment Network (SPAN).
Methods: This study performs the SPAN methodology in two centers, and takes including a randomization, blinding, and placebo control approach, utilizing a novel multi-arm (multiple groups) and multi-stage (multiple time points) design along with the establishment of comorbidity models. Specifically, the study uses wild-type (WT) C57BL/6J mice (8~10 weeks, Half male and half female), aged mice, and diabetic mice to establish a middle cerebral artery occlusion (MCAO) model. We assess the recovery of motor and sensory functions using behavioral tests such as the corner test, cylinder test, modified neurological severity score (mNSS), and Longa score. To identify the protein expression and localization, we use Western blot analysis and immunofluorescence staining. The infarct volume is detected using magnetic resonance imaging (MRI) or triphenyltetrazolium chloride (TTC) staining. Transmission electron microscopy (TEM) and Golgi staining are applied to assess neuronal morphology and number, while Nissl and TUNEL staining are used to evaluate neuronal function and apoptosis levels.
Results: In the permanent MCAO model induced by chloride iron in WT young mice, MBS-1 significantly reduces the infarct volume, improves neurological function, and decreases neuronal apoptosis. In the MCAO/reperfusion (MCAO/R) model conducted by intraluminal filament in WT young mice, MBS-1 also effectively reduces the infarct volume, enhances neurological function, and decreases neuronal apoptosis. While in the MCAO/R model conducted by intraluminal filament in WT aged mice, MBS-1 visibly improves the survival rate of mice. Furthermore, in the MCAO/R model of diabetic mice, MBS-1 significantly reduces the infarct volume and improves neurological function.
Conclusion: Using SPAN paradigm, MBS-l exhibits brain cytoprotection in various IS models.
Key words: Stroke Preclinical Assessment Network, MBS-1, ischemic stroke, brain cytoprotection
Objective
To investigate the characteristics and interrelationships between knowledge, preventive practice, and information sources of infectious diseases among Chinese children.
Methods
This study used data collected from the baseline survey of a China national multi-centered cluster-randomized controlled trial in 2013. A total of 30,287 children completed a questionnaire package that included measures for knowledge, preventive practice and information sources related to infectious diseases.
Results
The mean scores of knowledge and prevention practices of infectious diseases were 2.35 and 12.16, respectively. Children received information about infectious diseases primarily through school, other individuals, and electronic media. Knowledge and practices among children differed significantly across gender, age, single-child, living with parents or not, residence(urban/rural), regions, parental age and parents’ education levels. Multivariable linear regression analysis showed that higher levels of knowledge(b = 0.102), and receiving information through schools(b = 0.054), electronic media(b = 0.016), and paper media(b = 0.054) were significantly associated with better preventive practice.
Conclusions
Children’s knowledge and various sources of access to information significantly predicted the prevention practice score. It might add value to future interventions and policy-making in promoting preventive measures for infectious diseases.
Triple-negative breast cancer (TNBC) is an aggressive and challenging type of cancer, characterized by the absence of specific receptors targeted by current therapies, which limits effective targeted treatment options. TNBC has a high risk of recurrence and distant metastasis, resulting in lower survival rates. Additionally, TNBC exhibits significant heterogeneity at histopathological, proteomic, transcriptomic, and genomic levels, further complicating the development of effective treatments. While some TNBC subtypes may initially respond to chemotherapy, resistance frequently develops, increasing the risk of aggressive recurrence. Therefore, precisely classifying and characterizing the distinct features of TNBC subtypes is crucial for identifying the most suitable molecular-based therapies for individual patients. In this review, we provide a comprehensive overview of these subtypes, highlighting their unique profiles as defined by various classification systems. We also address the limitations of conventional therapeutic approaches and explore innovative biological strategies, all aimed at advancing the development of targeted and effective therapeutic strategies for TNBC.
Large language models (LLMs) have the potential to enhance evidence synthesis efficiency and accuracy. This study assessed LLM-only and LLM-assisted methods in data extraction and risk of bias assessment for 107 trials on complementary medicine. Moonshot-v1-128k and Claude-3.5-sonnet achieved high accuracy (≥95%), with LLM-assisted methods performing better (≥97%). LLM-assisted methods significantly reduced processing time (14.7 and 5.9 min vs. 86.9 and 10.4 min for conventional methods). These findings highlight LLMs' potential when integrated with human expertise.
Aerosol pollution is anticipated to decrease in the future, yet the associated effects of reduced aerosol loading on precipitation remain insufficiently explored. Widespread reductions in anthropogenic emission during COVID-19 lockdowns offer a unique opportunity to understand precipitation responses to changes in anthropogenic aerosols. Based on observations and regional and global climate-chemistry coupled model simulations, we attribute unprecedented precipitation in India during the 2021 lockdown to decreased aerosol levels due to emission reductions. Reduced aerosol loading leads to a northward shift of the subtropical westerly jet, which induces a westward movement of the subtropical southern branch trough and negative sea-level pressure anomalies over the eastern Arabian Sea. This shift facilitates water vapor transport from surrounding oceans to land, increasing precipitation in India by approximately 24.2% in May according to the Weather Research and Forecasting model coupled with chemistry simulations and by 28.5% over the entire lockdown period according to the Community Earth System Model version 2.1.3 simulations. Future projections under the lower aerosol emission scenario indicate an additional enhancement in monsoon precipitation in India. Our findings highlight the complex interplay between aerosol emissions and hydrometeorological dynamics, with implications for understanding future precipitation changes and providing theoretical reference for water resource management.
Purpose
This study analyzes China’s strategic initiatives in metaverse and artificial intelligence (AI) development, examining their impact on academic research, industry innovation, and policy formulation. It aims to understand how government policies and investments have shaped research agendas and to identify challenges and opportunities in these fields.
Design/methodology/approach
The research employs a comprehensive analysis of government documents, funding schemes, and research output. It examines key policies, investment programs, and academic publications to track trends in metaverse and AI development in China. The study utilizes bibliometric analysis to assess publication trends, citation patterns, and international collaboration networks.
Findings
China’s proactive approach, characterized by strong government support and significant private sector investment, has led to a substantial increase in research output and quality in metaverse and AI fields. Chinese institutions have become major contributors to global publications, with growing citation rates and presence at international conferences. The research identifies emerging challenges in privacy, ethical AI development, and digital divide concerns.
Practical implications
The findings provide insights for policymakers, researchers, and industry stakeholders on the development trajectory of metaverse and AI technologies in China. They highlight the need for balanced approaches to innovation, regulation, and ethical considerations in these rapidly evolving fields.
Social implications
The study underscores the potential of metaverse and AI technologies to transform various sectors of society, from education and healthcare to entertainment and social interactions. It emphasizes the importance of addressing digital equity and ethical AI deployment to ensure broad societal benefits.
Originality/value
This research offers a comprehensive overview of China’s approach to metaverse and AI development, providing a unique perspective on the interplay between government initiatives, academic research, and industry innovation. It contributes to the broader discussion on the global development of these transformative technologies and their implications for future technological landscapes.
Formative assessment is essential in music education as it supports the music learning process, which relies mostly on feedback from self and others to improve performance. Despite growing interest in formative assessment across various subjects, there is a lack of empirical evidence on how it is applied specifically in music education. Given the crucial role of teachers in the effective implementation of formative assessment, this study aims to examine music teachers’ intentions and implementation of formative assessment, along with the factors influencing them, based on the Theory of Planned Behavior. A total of 671 music teachers from 29 cities/provinces of Mainland China were surveyed. The structural equation modeling results indicate that in the Chinese Mainland school music education context, a positive attitude toward formative assessment, a supportive and collaborative social environment, and strong confidence in using formative assessment enhance teachers’ intentions to adopt it. Additionally, greater confidence directly increases actual implementation. However, increased school support did not significantly impact teachers’ intentions or their implementation of formative assessment. The findings suggest that school administrators should focus on helping music teachers build confidence and fostering a collaborative, supportive culture for using formative assessment practices to improve music learning.
Objective
Compared to adulthood-onset obesity (AO), those with childhood-onset obesity (CO) are at greater risk of metabolic disease. However, the differences between these two obesity phenotypes are not clear. The aim of this study is to investigate how the age of obesity onset (CO vs. AO) affects the use of intramyocellular (IMCL) and extramyocellular (EMCL) lipids in response to exercise.
Methods
Males with CO (n = 5) and AO (n = 5) were recruited. At the first study visit, body composition was measured via dual-energy x-ray absorptiometry (DEXA). Energy expenditure and substrate oxidation were measured via indirect calorimetry. Participants were provided with standardized meals for 3 days prior to the exercise study visit. At the exercise study visit, IMCL and EMCL were measured via magnetic resonance spectroscopy (MRS) before and after 90-minutes of moderate intensity cycling with indirect calorimetry.
Results
Substrate oxidation at rest and during exercise was not different between groups. Post-exercise, a decrease in IMCL was observed in the AO group that was not demonstrated in the CO group. There were no changes in EMCL post-exercise in either group.
Conclusions
This was the first study to compare the effects of exercise on IMCL and EMCL use in males with CO and AO. The decreases in IMCL of the AO group is similar with those observed in the literature in lean individuals. We made the novel observation that with moderate intensity cycling, males with CO do not appear to use IMCL as effectively as those with AO, suggesting perturbations in IMCL metabolism.
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