Confocal laser scanning microscopy was used to observe the spatio-temporal expression of the pathway-specific gene redD during S. coelicolor cell cultivation. The corresponding mutant S. coelicolor lyqRY1522 carrying ...Confocal laser scanning microscopy was used to observe the spatio-temporal expression of the pathway-specific gene redD during S. coelicolor cell cultivation. The corresponding mutant S. coelicolor lyqRY1522 carrying redD::eyfp in the chro- mosome was constructed. The temporal expression results of the fusion protein during submerged cultivation demonstrated that expression of redD began in the transition phase, continuing through the exponential growth phase to the stationary phase, and reached maximum in the stationary phase. On the other hand, redD was expressed only in substrate mycelia during solid-state culture, while aerial mycelia remained essentially non-fluorescent throughout culture. Results demonstrated that the expression pattern of redD coincides with that of the biosynthesis of the antibiotics during culture, revealing a direct correlation between the spatio-temporal distribution of regulatory gene expression and second metabolism.展开更多
Coconut(Cocos nucifera L.),a major oil and fruit crop of the Arecaceae family,is extensively cultivated across the Asia—Pacific region.Despite its agricultural importance,genome assembly in coconut remains challengin...Coconut(Cocos nucifera L.),a major oil and fruit crop of the Arecaceae family,is extensively cultivated across the Asia—Pacific region.Despite its agricultural importance,genome assembly in coconut remains challenging due to its large genome size and high proportion of repetitive sequences.Allele-specific expression(ASE)plays a key role in regulating plant development and evolution,yet research on ASE in coconut is limited(Shao et al.,2019;Li et al.,2021;Zhang et al.,2021;Hu et al.,2022).Among phenotypic traits,fruit color is especially important as an indicator of maturity,guiding harvest timing and post-harvest processes(Kapoor et al.,2022).While prior studies have explored various coconut traits such as salt tolerance,fiber content,and plant height(Wang et al.,2021;Yang et al.,2021),investigations into ASE and fruit color remain scarce.展开更多
Abstract: To explore the mode of the spatio-temporal expression of six newly discovered ginsenoside biosynthesis candidate gene transcripts, both Northern blotting and semi-quantitative reverse transcription-polymeras...Abstract: To explore the mode of the spatio-temporal expression of six newly discovered ginsenoside biosynthesis candidate gene transcripts, both Northern blotting and semi-quantitative reverse transcription-polymerase chain reaction (RT-PCR) were used to elucidate the mRNA expression levels of the transcripts in various tissues and organs of Panax ginseng C. A. Meyer during different growth development stages. The six gene transcripts were all differentially expressed in cultured callus, root, stem, leaf, and seed. The mRNA expression levels were significantly higher in four-year-old roots than in one-year-old roots, and results of semi-quantitative RT-PCR assays were in accordance with those of Northern blotting analyses. The results strongly suggest that all six genes were differentially expressed at root-specific developmental stages. In particular, when a quiescent early stage culture suspension of P. ginseng cells was exposed to the ginsenoside biosynthesis-promoting elicitor Aspergillus niger polysaccharide, the GBR6 gene transcript response showed time-dependent increments and was parallel with ginsenoside productivity (P < 0.01). Overexpressionof the GBR6 gene is likely to play a critically important role in the biosynthesis of ginsenosides. The results of the present study provided a background for the further elucidation of the structure and physiological function of these six candidate genes.展开更多
Due to water conflicts and allocation in the Lancang-Mekong River Basin(LMRB),the spatio-temporal differentiation of total water resources and the natural-human influence need to be clarified.This work investigated LM...Due to water conflicts and allocation in the Lancang-Mekong River Basin(LMRB),the spatio-temporal differentiation of total water resources and the natural-human influence need to be clarified.This work investigated LMRB's terrestrial water storage anomaly(TWSA)and its spatio-temporal dynamics during 2002–2020.Considering the effects of natural factors and human activities,the respective contributions of climate variability and human activities to terrestrial water storage change(TWSC)were separated.Results showed that:(1)LMRB's TWSA decreased by 0.3158 cm/a.(2)TWSA showed a gradual increase in distribution from southwest of MRB to middle LMRB and from northeast of LRB to middle LMRB.TWSA positively changed in Myanmar while slightly changed in Laos and China.It negatively changed in Vietnam,Thailand and Cambodia.(3)TWSA components decreased in a descending order of soil moisture,groundwater and precipitation.(4)Natural factors had a substantial and spatial differentiated influence on TWSA over the LMRB.(5)Climate variability contributed 79%of TWSC in the LMRB while human activities contributed 21%with an increasing impact after 2008.The TWSC of upstream basin countries was found to be controlled by climate variability while Vietnam and Cambodia's TWSC has been controlled by human activities since 2012.展开更多
Artificial intelligence,such as deep learning technology,has advanced the study of facial expression recognition since facial expression carries rich emotional information and is significant for many naturalistic situ...Artificial intelligence,such as deep learning technology,has advanced the study of facial expression recognition since facial expression carries rich emotional information and is significant for many naturalistic situations.To pursue a high facial expression recognition accuracy,the network model of deep learning is generally designed to be very deep while the model’s real-time performance is typically constrained and limited.With MobileNetV3,a lightweight model with a good accuracy,a further study is conducted by adding a basic ResNet module to each of its existing modules and an SSH(Single Stage Headless Face Detector)context module to expand the model’s perceptual field.In this article,the enhanced model named Res-MobileNetV3,could alleviate the subpar of real-time performance and compress the size of large network models,which can process information at a rate of up to 33 frames per second.Although the improved model has been verified to be slightly inferior to the current state-of-the-art method in aspect of accuracy rate on the publically available face expression datasets,it can bring a good balance on accuracy,real-time performance,model size and model complexity in practical applications.展开更多
The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to u...The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.展开更多
Rice is a poor source of folate,an essential micronutrient for the body.Biofortification offers an effective way to enhance the folate content of rice and alleviate folate deficiencies in humans.In this study,we confi...Rice is a poor source of folate,an essential micronutrient for the body.Biofortification offers an effective way to enhance the folate content of rice and alleviate folate deficiencies in humans.In this study,we confirmed that OsADCS and OsGTPCHI,encoding the initial enzymes necessary for folate synthesis,positively regulate folate accumulation in knockout mutants of both japonica and indica rice backgrounds.The folate content in the low-folate japonica variety was slightly increased by the expression of the indica alleles driven by the endosperm-specific promoter.We further obtained co-expression lines by stacking OsADCS and OsGTPCHI genes;the folate accumulation in brown rice and polished rice reached 5.65μg/g and 2.95μg/g,respectively,representing 37.9-fold and 26.5-fold increases compared with the wild type.Transcriptomic analysis of rice grains from six transgenic lines showed that folate changes affected biological pathways involved in the synthesis and metabolism of rice seed storage substances,while the expression of other folate synthesis genes was weakly regulated.In addition,we identified Aus rice as a high-folate germplasm carrying superior haplotypes of OsADCS and OsGTPCHI through natural variation.This study provides an alternative and effective complementary strategy for rice biofortification,promoting the rational combination of metabolic engineering and conventional breeding to breed high-folate varieties.展开更多
Oral expression skills play an essential role in the development of EFL students’language abilities,and how to improve EFL students’oral expression skills is an essential and challenging task.This study adopts a qua...Oral expression skills play an essential role in the development of EFL students’language abilities,and how to improve EFL students’oral expression skills is an essential and challenging task.This study adopts a quasi-experimental research method to carry out the research and proposes an AI-based reflective dialogue model.Based on this,an analysis of the impact brought by this model on EFL students’oral expression performance and learning anxiety levels.The results show that students in the experimental group have significantly higher oral expression performance than those in the control group in the three dimensions of grammatical accuracy,expressive fluency,and word accuracy.In addition,the students in the experimental group produced facilitated anxiety after using the AI-based reflective dialogue model for oral expression learning,which prompted the students to learn more diligently.展开更多
Objective INF2 is a member of the formins family.Abnormal expression and regulation of INF2 have been associated with the progression of various tumors,but the expression and role of INF2 in hepatocellular carcinoma(H...Objective INF2 is a member of the formins family.Abnormal expression and regulation of INF2 have been associated with the progression of various tumors,but the expression and role of INF2 in hepatocellular carcinoma(HCC)remain unclear.HCC is a highly lethal malignant tumor.Given the limitations of traditional treatments,this study explored the expression level,clinical value and potential mechanism of INF2 in HCC in order to seek new therapeutic targets.Methods In this study,we used public databases to analyze the expression of INF2 in pan-cancer and HCC,as well as the impact of INF2 expression levels on HCC prognosis.Quantitative real time polymerase chain reaction(RT-qPCR),Western blot,and immunohistochemistry were used to detect the expression level of INF2 in liver cancer cells and human HCC tissues.The correlation between INF2 expression and clinical pathological features was analyzed using public databases and clinical data of human HCC samples.Subsequently,the effects of INF2 expression on the biological function and Drp1 phosphorylation of liver cancer cells were elucidated through in vitro and in vivo experiments.Finally,the predictive value and potential mechanism of INF2 in HCC were further analyzed through database and immunohistochemical experiments.Results INF2 is aberrantly high expression in HCC samples and the high expression of INF2 is correlated with overall survival,liver cirrhosis and pathological differentiation of HCC patients.The expression level of INF2 has certain diagnostic value in predicting the prognosis and pathological differentiation of HCC.In vivo and in vitro HCC models,upregulated expression of INF2 triggers the proliferation and migration of the HCC cell,while knockdown of INF2 could counteract this effect.INF2 in liver cancer cells may affect mitochondrial division by inducing Drp1 phosphorylation and mediate immune escape by up-regulating PD-L1 expression,thus promoting tumor progression.Conclusion INF2 is highly expressed in HCC and is associated with poor prognosis.High expression of INF2 may promote HCC progression by inducing Drp1 phosphorylation and up-regulation of PD-L1 expression,and targeting INF2 may be beneficial for HCC patients with high expression of INF2.展开更多
The xylitol dehydrogenase(XDH)is a crucial enzyme involved in the xylose utilization in pentose⁃catabolizing yeasts and fungi.In addition to producing xylulose,XDH can also be employed to develop a biosensor for monit...The xylitol dehydrogenase(XDH)is a crucial enzyme involved in the xylose utilization in pentose⁃catabolizing yeasts and fungi.In addition to producing xylulose,XDH can also be employed to develop a biosensor for monitoring xylitol concentration.In this study,the gene encoding the thermophilic fungus Talaromyces emersonii XDH(TeXDH)was heterologously expressed in Escherichia coli BL21(DE3)at 16℃in the soluble form.Recombinant TeXDH with high purity was purified by using a Ni⁃NTA affinity column.Size⁃exclusion chromatography and SDS⁃PAGE analysis demonstrated that the puri⁃fied recombinant TeXDH exists as a native trimer with a molecular mass of approximately 116 kD,and is composed of three identical subunits,each with a molecular weight of around 39 kD.The TeXDH strictly preferred NAD^(+)as a coenzyme to NADP^(+).The optimal temperature and pH of the TeXDH were 40℃and 10.0,respectively.After EDTA treatment,the enzyme activity of TeXDH decreased to 43.26%of the initial enzyme activity,while the divalent metal ions Mg^(2+)or Ca^(2+)could recover the enzyme activity of TeXDH,reaching 103.32%and 110.69%of the initial enzyme activity,respectively,making them the optimal divalent metal ion cofactors for TeXDH enzyme.However,the divalent metal ions of Mn^(2+),Ni^(2+),Cu^(2+),Zn^(2+),Co^(2+),and Cd^(2+)significantly inhibited the activity of TeXDH.ICP⁃MS and molecular doc⁃king studies revealed that 1 mol/L of TeXDH bound 2 mol/L Zn^(2+)ions and 1 mol/L Mg^(2+)ion.Further⁃more,TeXDH exhibited a high specificity for xylitol,laying the foundation for the development of future xylitol biosensors.展开更多
Objective To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis(TB)in the Guangdong Province between 2010 and 2019.Method Spatial and temporal variations in TB ...Objective To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis(TB)in the Guangdong Province between 2010 and 2019.Method Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering.Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive(ST-CAR)model.Results Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000in 2019.Spatial hotspots were found in northeastern Guangdong,particularly in Heyuan,Shanwei,and Shantou,while Shenzhen,Dongguan,and Foshan had the lowest rates in the Pearl River Delta.The STCAR model showed that the TB risk was lower with higher per capita Gross Domestic Product(GDP)[Relative Risk(RR),0.91;95%Confidence Interval(CI):0.86–0.98],more the ratio of licensed physicians and physician(RR,0.94;95%CI:0.90-0.98),and higher per capita public expenditure(RR,0.94;95%CI:0.90–0.97),with a marginal effect of population density(RR,0.86;95%CI:0.86–1.00).Conclusion The incidence of TB in Guangdong varies spatially and temporally.Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection.Strategies focusing on equitable health resource distribution and economic development are the key to TB control.展开更多
DNA microarray technology is an extremely effective technique for studying gene expression patterns in cells, and the main challenge currently faced by this technology is how to analyze the large amount of gene expres...DNA microarray technology is an extremely effective technique for studying gene expression patterns in cells, and the main challenge currently faced by this technology is how to analyze the large amount of gene expression data generated. To address this, this paper employs a mixed-effects model to analyze gene expression data. In terms of data selection, 1176 genes from the white mouse gene expression dataset under two experimental conditions were chosen, setting up two conditions: pneumococcal infection and no infection, and constructing a mixed-effects model. After preprocessing the gene chip information, the data were imported into the model, preliminary results were calculated, and permutation tests were performed to biologically validate the preliminary results using GSEA. The final dataset consists of 20 groups of gene expression data from pneumococcal infection, which categorizes functionally related genes based on the similarity of their expression profiles, facilitating the study of genes with unknown functions.展开更多
This study examines the effects of rapid land use changes in India,with a specific focus on Sonipat District in Haryana—a region undergoing significant urban expansion.Over the past two decades,rural landscapes in So...This study examines the effects of rapid land use changes in India,with a specific focus on Sonipat District in Haryana—a region undergoing significant urban expansion.Over the past two decades,rural landscapes in Sonipat have undergone notable transformation,as open spaces and agricultural lands are increasingly converted into residential colonies,commercial hubs,and industrial zones.While such changes reflect economic development and urban growth,they also raise critical concerns about sustainability,especially in terms of food security,groundwater depletion,and environmental degradation.The study examines land use changes between 2000 and 2024 using remote sensing techniques and spatial analysis.It further incorporates secondary data and insights from community-level interactions to assess the socio-economic and ecological impacts of this transformation.The findings indicate rising land fragmentation,loss of agricultural livelihoods,pressure on civic infrastructure,and increasing pollution—factors that threaten long-term regional sustainability.The study underscores the urgent need to reconcile urban development with environmental and social sustainability.By offering a detailed case study of Sonipat,this research contributes to the broader discourse on India’s urbanisation pathways.It aims to provide policymakers,planners,and researchers with evidence-based recommendations to manage land transitions more responsibly,promoting urban growth models that ensure ecological integrity,equitable development,and long-term resilience.展开更多
Neuronal activity,synaptic transmission,and molecular changes in the basolateral amygdala play critical roles in fear memory.Cylindromatosis(CYLD)is a deubiquitinase that negatively regulates the nuclear factor kappa-...Neuronal activity,synaptic transmission,and molecular changes in the basolateral amygdala play critical roles in fear memory.Cylindromatosis(CYLD)is a deubiquitinase that negatively regulates the nuclear factor kappa-B pathway.CYLD is well studied in non-neuronal cells,yet underinvestigated in the brain,where it is highly expressed.Emerging studies have shown involvement of CYLD in the remodeling of glutamatergic synapses,neuroinflammation,fear memory,and anxiety-and autism-like behaviors.However,the precise role of CYLD in glutamatergic neurons is largely unknown.Here,we first proposed involvement of CYLD in cued fear expression.We next constructed transgenic model mice with specific deletion of Cyld from glutamatergic neurons.Our results show that glutamatergic CYLD deficiency exaggerated the expression of cued fear in only male mice.Further,loss of CYLD in glutamatergic neurons resulted in enhanced neuronal activation,impaired excitatory synaptic transmission,and altered levels of glutamate receptors accompanied by over-activation of microglia in the basolateral amygdala of male mice.Altogether,our study suggests a critical role of glutamatergic CYLD in maintaining normal neuronal,synaptic,and microglial activation.This may contribute,at least in part,to cued fear expression.展开更多
As Deepfake technology continues to evolve,the distinction between real and fake content becomes increasingly blurred.Most existing Deepfake video detectionmethods rely on single-frame facial image features,which limi...As Deepfake technology continues to evolve,the distinction between real and fake content becomes increasingly blurred.Most existing Deepfake video detectionmethods rely on single-frame facial image features,which limits their ability to capture temporal differences between frames.Current methods also exhibit limited generalization capabilities,struggling to detect content generated by unknown forgery algorithms.Moreover,the diversity and complexity of forgery techniques introduced by Artificial Intelligence Generated Content(AIGC)present significant challenges for traditional detection frameworks,whichmust balance high detection accuracy with robust performance.To address these challenges,we propose a novel Deepfake detection framework that combines a two-stream convolutional network with a Vision Transformer(ViT)module to enhance spatio-temporal feature representation.The ViT model extracts spatial features from the forged video,while the 3D convolutional network captures temporal features.The 3D convolution enables cross-frame feature extraction,allowing the model to detect subtle facial changes between frames.The confidence scores from both the ViT and 3D convolution submodels are fused at the decision layer,enabling themodel to effectively handle unknown forgery techniques.Focusing on Deepfake videos and GAN-generated images,the proposed approach is evaluated on two widely used public face forgery datasets.Compared to existing state-of-theartmethods,it achieves higher detection accuracy and better generalization performance,offering a robust solution for deepfake detection in real-world scenarios.展开更多
Sandfly fever is a viral infectious disease transmitted by sand flies that is widely prevalent in tropical and subtropical regions.Previous studies on its infection mechanism,immune response and diagnosis and treatmen...Sandfly fever is a viral infectious disease transmitted by sand flies that is widely prevalent in tropical and subtropical regions.Previous studies on its infection mechanism,immune response and diagnosis and treatment methods were lack of systematic.This study applied spatio-temporal omics technology to comprehensively explain the dynamic changes of immunity in the incubation period,exacerbation period,peak period and recovery period of Sandfl y fever,and integrated with diff erent coping strategies.To provide new research ideas for its overall research.展开更多
Agriculture holds a pivotal position in the economic fabric of every nation,yet concerns about agricultural carbon emission intensity(ACI)have become a major hurdle to achieving global economic sustainability.Focusing...Agriculture holds a pivotal position in the economic fabric of every nation,yet concerns about agricultural carbon emission intensity(ACI)have become a major hurdle to achieving global economic sustainability.Focusing on 31 provincial-level regions in China,this study uses the Exploratory Spatio-temporal Data Analysis(ESTDA)and Panel Quantile Regression(PQR)model to analyze the spatio-temporal interaction characteristics and influencing factors of ACI in China from 2004 to 2023.The findings are as follows:(1)ACI showed an overall downward trend,and the spatial distribution pattern was characterized by“high in the western region and low along the southeastern coast”.Although the overall disparity tended to converge,some high-carbon-intensity regions exhibited extreme trends.ACI displayed clear spatial directionality,with the spatial center shifting steadily toward the northeast.(2)Regions in the northwest,northeast,and central-south parts exhibited strong local spatial structural dynamics,and the local spatial dependence of ACI in each region showed a nonlinear trend.Generally speaking,the spatial association pattern demonstrated a certain degree of inertia in spatial transfer,reflecting strong path dependence or spatial lock-in characteristics.(3)Optimization of industrial structure and improvement in agricultural mechanization will increase ACI,while economic development can effectively reduce it.The impact of urbanization on ACI exhibits a nonlinear pattern.The coordinated development of economic growth and urbanization significantly reduces ACI,with a stronger emission reduction observed in regions with low ACI.The optimization of industrial structure,when combined with urbanization and environmental regulation,contributes to significant emission reductions particularly in high-ACI areas.Similarly,the synergy between agricultural mechanization and urbanization effectively lowers emissions in low-ACI regions,though this effect diminishes in areas with higher ACI.展开更多
Sloping farmland,particularly in mountainous and hilly areas,constitutes a significant component of regional farmland resources.An investigation into the spatio-temporal pattern of sloping farmland and its influencing...Sloping farmland,particularly in mountainous and hilly areas,constitutes a significant component of regional farmland resources.An investigation into the spatio-temporal pattern of sloping farmland and its influencing factors in China is imperative for the efficient utilization of farmland and the optimization of land space.We used land use transfer matrix,geographically weighted regression model and geographical detector to conduct this study.Results showed that sloping farmland in China firstly decreased and then increased from 2000 to 2020.The proportion of sloping farmland decreased radially outward from Sichuan basin to the surrounding areas.Change rates of sloping farmland with different slopes varied and the slope with 6°-15°underwent the fastest changes.The influencing factors of farmland at various slope degrees were different.For sloping farmland below 15°,land use intensity and elevation had the greatest contribution.For sloping farmland between 15°and 25°,elevation,land use intensity,and population density were the main influencing factors.Sloping farmland above 25°was mostly affected by natural factors.This study can provide scientific basis for rational development and protection of sloping farmland.展开更多
Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decode...Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decoder (ACSF-ED) network to predict the action and locate the object efficiently. In the Adaptive Cross-Scale Fusion Spatio-Temporal Encoder (ACSF ST-Encoder), the Asymptotic Cross-scale Feature-fusion Module (ACCFM) is designed to address the issue of information degradation caused by the propagation of high-level semantic information, thereby extracting high-quality multi-scale features to provide superior features for subsequent spatio-temporal information modeling. Within the Shared-Head Decoder structure, a shared classification and regression detection head is constructed. A multi-constraint loss function composed of one-to-one, one-to-many, and contrastive denoising losses is designed to address the problem of insufficient constraint force in predicting results with traditional methods. This loss function enhances the accuracy of model classification predictions and improves the proximity of regression position predictions to ground truth objects. The proposed method model is evaluated on the popular dataset UCF101-24 and JHMDB-21. Experimental results demonstrate that the proposed method achieves an accuracy of 81.52% on the Frame-mAP metric, surpassing current existing methods.展开更多
Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to...Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to perform multi-perspective learning of temporal signals and Electrocardiogram images, nor can they fully extract the latent information within the data, falling short of the accuracy required by clinicians. Therefore, this paper proposes an innovative hybrid multimodal spatiotemporal neural network to address these challenges. The model employs a multimodal data augmentation framework integrating visual and signal-based features to enhance the classification performance of rare arrhythmias in imbalanced datasets. Additionally, the spatiotemporal fusion module incorporates a spatiotemporal graph convolutional network to jointly model temporal and spatial features, uncovering complex dependencies within the Electrocardiogram data and improving the model’s ability to represent complex patterns. In experiments conducted on the MIT-BIH arrhythmia dataset, the model achieved 99.95% accuracy, 99.80% recall, and a 99.78% F1 score. The model was further validated for generalization using the clinical INCART arrhythmia dataset, and the results demonstrated its effectiveness in terms of both generalization and robustness.展开更多
基金Project (No. 2004-527) supported by the Scientific Research Foun-dation for the Returned Overseas Chinese Scholars, State EducationMinistry, China
文摘Confocal laser scanning microscopy was used to observe the spatio-temporal expression of the pathway-specific gene redD during S. coelicolor cell cultivation. The corresponding mutant S. coelicolor lyqRY1522 carrying redD::eyfp in the chro- mosome was constructed. The temporal expression results of the fusion protein during submerged cultivation demonstrated that expression of redD began in the transition phase, continuing through the exponential growth phase to the stationary phase, and reached maximum in the stationary phase. On the other hand, redD was expressed only in substrate mycelia during solid-state culture, while aerial mycelia remained essentially non-fluorescent throughout culture. Results demonstrated that the expression pattern of redD coincides with that of the biosynthesis of the antibiotics during culture, revealing a direct correlation between the spatio-temporal distribution of regulatory gene expression and second metabolism.
基金supported by Central Public-interest Scientific Institution Basal Research Fund(CATAS-Nos.1630152023007,1630152023011,1630152023012,1630152023013)the National Natural Science Foundation of China(Grant No.32071805).
文摘Coconut(Cocos nucifera L.),a major oil and fruit crop of the Arecaceae family,is extensively cultivated across the Asia—Pacific region.Despite its agricultural importance,genome assembly in coconut remains challenging due to its large genome size and high proportion of repetitive sequences.Allele-specific expression(ASE)plays a key role in regulating plant development and evolution,yet research on ASE in coconut is limited(Shao et al.,2019;Li et al.,2021;Zhang et al.,2021;Hu et al.,2022).Among phenotypic traits,fruit color is especially important as an indicator of maturity,guiding harvest timing and post-harvest processes(Kapoor et al.,2022).While prior studies have explored various coconut traits such as salt tolerance,fiber content,and plant height(Wang et al.,2021;Yang et al.,2021),investigations into ASE and fruit color remain scarce.
文摘Abstract: To explore the mode of the spatio-temporal expression of six newly discovered ginsenoside biosynthesis candidate gene transcripts, both Northern blotting and semi-quantitative reverse transcription-polymerase chain reaction (RT-PCR) were used to elucidate the mRNA expression levels of the transcripts in various tissues and organs of Panax ginseng C. A. Meyer during different growth development stages. The six gene transcripts were all differentially expressed in cultured callus, root, stem, leaf, and seed. The mRNA expression levels were significantly higher in four-year-old roots than in one-year-old roots, and results of semi-quantitative RT-PCR assays were in accordance with those of Northern blotting analyses. The results strongly suggest that all six genes were differentially expressed at root-specific developmental stages. In particular, when a quiescent early stage culture suspension of P. ginseng cells was exposed to the ginsenoside biosynthesis-promoting elicitor Aspergillus niger polysaccharide, the GBR6 gene transcript response showed time-dependent increments and was parallel with ginsenoside productivity (P < 0.01). Overexpressionof the GBR6 gene is likely to play a critically important role in the biosynthesis of ginsenosides. The results of the present study provided a background for the further elucidation of the structure and physiological function of these six candidate genes.
基金National Natural Science Foundation of China,No.42161006Yunnan Fundamental Research Projects No.202201AT070094,No.202301BF070001-004+1 种基金Special Project for High-level Talents of Yunnan Province for Young Top Talents,No.C6213001159European Research Council(ERC)Starting-Grant STORIES,No.101040939。
文摘Due to water conflicts and allocation in the Lancang-Mekong River Basin(LMRB),the spatio-temporal differentiation of total water resources and the natural-human influence need to be clarified.This work investigated LMRB's terrestrial water storage anomaly(TWSA)and its spatio-temporal dynamics during 2002–2020.Considering the effects of natural factors and human activities,the respective contributions of climate variability and human activities to terrestrial water storage change(TWSC)were separated.Results showed that:(1)LMRB's TWSA decreased by 0.3158 cm/a.(2)TWSA showed a gradual increase in distribution from southwest of MRB to middle LMRB and from northeast of LRB to middle LMRB.TWSA positively changed in Myanmar while slightly changed in Laos and China.It negatively changed in Vietnam,Thailand and Cambodia.(3)TWSA components decreased in a descending order of soil moisture,groundwater and precipitation.(4)Natural factors had a substantial and spatial differentiated influence on TWSA over the LMRB.(5)Climate variability contributed 79%of TWSC in the LMRB while human activities contributed 21%with an increasing impact after 2008.The TWSC of upstream basin countries was found to be controlled by climate variability while Vietnam and Cambodia's TWSC has been controlled by human activities since 2012.
基金supported by China Academy of Railway Sciences Corporation Limited(No.2021YJ127).
文摘Artificial intelligence,such as deep learning technology,has advanced the study of facial expression recognition since facial expression carries rich emotional information and is significant for many naturalistic situations.To pursue a high facial expression recognition accuracy,the network model of deep learning is generally designed to be very deep while the model’s real-time performance is typically constrained and limited.With MobileNetV3,a lightweight model with a good accuracy,a further study is conducted by adding a basic ResNet module to each of its existing modules and an SSH(Single Stage Headless Face Detector)context module to expand the model’s perceptual field.In this article,the enhanced model named Res-MobileNetV3,could alleviate the subpar of real-time performance and compress the size of large network models,which can process information at a rate of up to 33 frames per second.Although the improved model has been verified to be slightly inferior to the current state-of-the-art method in aspect of accuracy rate on the publically available face expression datasets,it can bring a good balance on accuracy,real-time performance,model size and model complexity in practical applications.
文摘The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.
基金supported by the Central Public-Interest Scientific Institution Basal Research Fund,China(Grant No.CPSIBRF-CNRRI-202403)。
文摘Rice is a poor source of folate,an essential micronutrient for the body.Biofortification offers an effective way to enhance the folate content of rice and alleviate folate deficiencies in humans.In this study,we confirmed that OsADCS and OsGTPCHI,encoding the initial enzymes necessary for folate synthesis,positively regulate folate accumulation in knockout mutants of both japonica and indica rice backgrounds.The folate content in the low-folate japonica variety was slightly increased by the expression of the indica alleles driven by the endosperm-specific promoter.We further obtained co-expression lines by stacking OsADCS and OsGTPCHI genes;the folate accumulation in brown rice and polished rice reached 5.65μg/g and 2.95μg/g,respectively,representing 37.9-fold and 26.5-fold increases compared with the wild type.Transcriptomic analysis of rice grains from six transgenic lines showed that folate changes affected biological pathways involved in the synthesis and metabolism of rice seed storage substances,while the expression of other folate synthesis genes was weakly regulated.In addition,we identified Aus rice as a high-folate germplasm carrying superior haplotypes of OsADCS and OsGTPCHI through natural variation.This study provides an alternative and effective complementary strategy for rice biofortification,promoting the rational combination of metabolic engineering and conventional breeding to breed high-folate varieties.
基金2024 Provincial Teaching Reform Program for Graduate Students in the Second Batch of the 14th Five-Year Plan of Zhejiang Provincial Office of Education:Innovation and Practice of“Six Synergistic”Graduate Teaching Guided by Educator’s Spirit(No.JGCG2024406)Key Project of Zhejiang Provincial Education Science Planning:Research on an interdisciplinary teaching model to promote students’computational thinking from multiple analytical perspectives[No.2025SB103].
文摘Oral expression skills play an essential role in the development of EFL students’language abilities,and how to improve EFL students’oral expression skills is an essential and challenging task.This study adopts a quasi-experimental research method to carry out the research and proposes an AI-based reflective dialogue model.Based on this,an analysis of the impact brought by this model on EFL students’oral expression performance and learning anxiety levels.The results show that students in the experimental group have significantly higher oral expression performance than those in the control group in the three dimensions of grammatical accuracy,expressive fluency,and word accuracy.In addition,the students in the experimental group produced facilitated anxiety after using the AI-based reflective dialogue model for oral expression learning,which prompted the students to learn more diligently.
文摘Objective INF2 is a member of the formins family.Abnormal expression and regulation of INF2 have been associated with the progression of various tumors,but the expression and role of INF2 in hepatocellular carcinoma(HCC)remain unclear.HCC is a highly lethal malignant tumor.Given the limitations of traditional treatments,this study explored the expression level,clinical value and potential mechanism of INF2 in HCC in order to seek new therapeutic targets.Methods In this study,we used public databases to analyze the expression of INF2 in pan-cancer and HCC,as well as the impact of INF2 expression levels on HCC prognosis.Quantitative real time polymerase chain reaction(RT-qPCR),Western blot,and immunohistochemistry were used to detect the expression level of INF2 in liver cancer cells and human HCC tissues.The correlation between INF2 expression and clinical pathological features was analyzed using public databases and clinical data of human HCC samples.Subsequently,the effects of INF2 expression on the biological function and Drp1 phosphorylation of liver cancer cells were elucidated through in vitro and in vivo experiments.Finally,the predictive value and potential mechanism of INF2 in HCC were further analyzed through database and immunohistochemical experiments.Results INF2 is aberrantly high expression in HCC samples and the high expression of INF2 is correlated with overall survival,liver cirrhosis and pathological differentiation of HCC patients.The expression level of INF2 has certain diagnostic value in predicting the prognosis and pathological differentiation of HCC.In vivo and in vitro HCC models,upregulated expression of INF2 triggers the proliferation and migration of the HCC cell,while knockdown of INF2 could counteract this effect.INF2 in liver cancer cells may affect mitochondrial division by inducing Drp1 phosphorylation and mediate immune escape by up-regulating PD-L1 expression,thus promoting tumor progression.Conclusion INF2 is highly expressed in HCC and is associated with poor prognosis.High expression of INF2 may promote HCC progression by inducing Drp1 phosphorylation and up-regulation of PD-L1 expression,and targeting INF2 may be beneficial for HCC patients with high expression of INF2.
基金湖南省教育厅基金优秀青年项目(No.22B0482)湖南科技大学博士启动基金(No.E51992 and E51993)资助。
文摘The xylitol dehydrogenase(XDH)is a crucial enzyme involved in the xylose utilization in pentose⁃catabolizing yeasts and fungi.In addition to producing xylulose,XDH can also be employed to develop a biosensor for monitoring xylitol concentration.In this study,the gene encoding the thermophilic fungus Talaromyces emersonii XDH(TeXDH)was heterologously expressed in Escherichia coli BL21(DE3)at 16℃in the soluble form.Recombinant TeXDH with high purity was purified by using a Ni⁃NTA affinity column.Size⁃exclusion chromatography and SDS⁃PAGE analysis demonstrated that the puri⁃fied recombinant TeXDH exists as a native trimer with a molecular mass of approximately 116 kD,and is composed of three identical subunits,each with a molecular weight of around 39 kD.The TeXDH strictly preferred NAD^(+)as a coenzyme to NADP^(+).The optimal temperature and pH of the TeXDH were 40℃and 10.0,respectively.After EDTA treatment,the enzyme activity of TeXDH decreased to 43.26%of the initial enzyme activity,while the divalent metal ions Mg^(2+)or Ca^(2+)could recover the enzyme activity of TeXDH,reaching 103.32%and 110.69%of the initial enzyme activity,respectively,making them the optimal divalent metal ion cofactors for TeXDH enzyme.However,the divalent metal ions of Mn^(2+),Ni^(2+),Cu^(2+),Zn^(2+),Co^(2+),and Cd^(2+)significantly inhibited the activity of TeXDH.ICP⁃MS and molecular doc⁃king studies revealed that 1 mol/L of TeXDH bound 2 mol/L Zn^(2+)ions and 1 mol/L Mg^(2+)ion.Further⁃more,TeXDH exhibited a high specificity for xylitol,laying the foundation for the development of future xylitol biosensors.
基金supported by the Guangdong Provincial Clinical Research Center for Tuberculosis(No.2020B1111170014)。
文摘Objective To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis(TB)in the Guangdong Province between 2010 and 2019.Method Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering.Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive(ST-CAR)model.Results Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000in 2019.Spatial hotspots were found in northeastern Guangdong,particularly in Heyuan,Shanwei,and Shantou,while Shenzhen,Dongguan,and Foshan had the lowest rates in the Pearl River Delta.The STCAR model showed that the TB risk was lower with higher per capita Gross Domestic Product(GDP)[Relative Risk(RR),0.91;95%Confidence Interval(CI):0.86–0.98],more the ratio of licensed physicians and physician(RR,0.94;95%CI:0.90-0.98),and higher per capita public expenditure(RR,0.94;95%CI:0.90–0.97),with a marginal effect of population density(RR,0.86;95%CI:0.86–1.00).Conclusion The incidence of TB in Guangdong varies spatially and temporally.Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection.Strategies focusing on equitable health resource distribution and economic development are the key to TB control.
文摘DNA microarray technology is an extremely effective technique for studying gene expression patterns in cells, and the main challenge currently faced by this technology is how to analyze the large amount of gene expression data generated. To address this, this paper employs a mixed-effects model to analyze gene expression data. In terms of data selection, 1176 genes from the white mouse gene expression dataset under two experimental conditions were chosen, setting up two conditions: pneumococcal infection and no infection, and constructing a mixed-effects model. After preprocessing the gene chip information, the data were imported into the model, preliminary results were calculated, and permutation tests were performed to biologically validate the preliminary results using GSEA. The final dataset consists of 20 groups of gene expression data from pneumococcal infection, which categorizes functionally related genes based on the similarity of their expression profiles, facilitating the study of genes with unknown functions.
文摘This study examines the effects of rapid land use changes in India,with a specific focus on Sonipat District in Haryana—a region undergoing significant urban expansion.Over the past two decades,rural landscapes in Sonipat have undergone notable transformation,as open spaces and agricultural lands are increasingly converted into residential colonies,commercial hubs,and industrial zones.While such changes reflect economic development and urban growth,they also raise critical concerns about sustainability,especially in terms of food security,groundwater depletion,and environmental degradation.The study examines land use changes between 2000 and 2024 using remote sensing techniques and spatial analysis.It further incorporates secondary data and insights from community-level interactions to assess the socio-economic and ecological impacts of this transformation.The findings indicate rising land fragmentation,loss of agricultural livelihoods,pressure on civic infrastructure,and increasing pollution—factors that threaten long-term regional sustainability.The study underscores the urgent need to reconcile urban development with environmental and social sustainability.By offering a detailed case study of Sonipat,this research contributes to the broader discourse on India’s urbanisation pathways.It aims to provide policymakers,planners,and researchers with evidence-based recommendations to manage land transitions more responsibly,promoting urban growth models that ensure ecological integrity,equitable development,and long-term resilience.
基金supported by the National Natural Science Foundation of China,Nos.32371065(to CL)and 32170950(to LY)the Natural Science Foundation of the Guangdong Province,No.2023A1515010899(to CL)the Science and Technology Projects in Guangzhou,Nos.2023A4J0578 and 2024A03J0180(to CW)。
文摘Neuronal activity,synaptic transmission,and molecular changes in the basolateral amygdala play critical roles in fear memory.Cylindromatosis(CYLD)is a deubiquitinase that negatively regulates the nuclear factor kappa-B pathway.CYLD is well studied in non-neuronal cells,yet underinvestigated in the brain,where it is highly expressed.Emerging studies have shown involvement of CYLD in the remodeling of glutamatergic synapses,neuroinflammation,fear memory,and anxiety-and autism-like behaviors.However,the precise role of CYLD in glutamatergic neurons is largely unknown.Here,we first proposed involvement of CYLD in cued fear expression.We next constructed transgenic model mice with specific deletion of Cyld from glutamatergic neurons.Our results show that glutamatergic CYLD deficiency exaggerated the expression of cued fear in only male mice.Further,loss of CYLD in glutamatergic neurons resulted in enhanced neuronal activation,impaired excitatory synaptic transmission,and altered levels of glutamate receptors accompanied by over-activation of microglia in the basolateral amygdala of male mice.Altogether,our study suggests a critical role of glutamatergic CYLD in maintaining normal neuronal,synaptic,and microglial activation.This may contribute,at least in part,to cued fear expression.
基金supported by National Natural Science Foundation of China(Nos.62477026,62177029,61807020)Humanities and Social Sciences Research Program of the Ministry of Education of China(No.23YJAZH047)the Startup Foundation for Introducing Talent of Nanjing University of Posts and Communications under Grant NY222034.
文摘As Deepfake technology continues to evolve,the distinction between real and fake content becomes increasingly blurred.Most existing Deepfake video detectionmethods rely on single-frame facial image features,which limits their ability to capture temporal differences between frames.Current methods also exhibit limited generalization capabilities,struggling to detect content generated by unknown forgery algorithms.Moreover,the diversity and complexity of forgery techniques introduced by Artificial Intelligence Generated Content(AIGC)present significant challenges for traditional detection frameworks,whichmust balance high detection accuracy with robust performance.To address these challenges,we propose a novel Deepfake detection framework that combines a two-stream convolutional network with a Vision Transformer(ViT)module to enhance spatio-temporal feature representation.The ViT model extracts spatial features from the forged video,while the 3D convolutional network captures temporal features.The 3D convolution enables cross-frame feature extraction,allowing the model to detect subtle facial changes between frames.The confidence scores from both the ViT and 3D convolution submodels are fused at the decision layer,enabling themodel to effectively handle unknown forgery techniques.Focusing on Deepfake videos and GAN-generated images,the proposed approach is evaluated on two widely used public face forgery datasets.Compared to existing state-of-theartmethods,it achieves higher detection accuracy and better generalization performance,offering a robust solution for deepfake detection in real-world scenarios.
基金College Students Innovation and Entrepreneurship Training Program(X202511049398)College Students Innovation and Entrepreneurship Training Program(X202511049201)+1 种基金College Students Innovation and Entrepreneurship Training Program(X202511258005S)University-Level Research Funding Program of Hainan Science and Technology Vocational University(HKKY2024-87)。
文摘Sandfly fever is a viral infectious disease transmitted by sand flies that is widely prevalent in tropical and subtropical regions.Previous studies on its infection mechanism,immune response and diagnosis and treatment methods were lack of systematic.This study applied spatio-temporal omics technology to comprehensively explain the dynamic changes of immunity in the incubation period,exacerbation period,peak period and recovery period of Sandfl y fever,and integrated with diff erent coping strategies.To provide new research ideas for its overall research.
基金National Natural Science Foundation of China,No.42230106,No.42171250State Key Laboratory of Earth Surface Processes and Resource Ecology,No.2022-ZD-04。
文摘Agriculture holds a pivotal position in the economic fabric of every nation,yet concerns about agricultural carbon emission intensity(ACI)have become a major hurdle to achieving global economic sustainability.Focusing on 31 provincial-level regions in China,this study uses the Exploratory Spatio-temporal Data Analysis(ESTDA)and Panel Quantile Regression(PQR)model to analyze the spatio-temporal interaction characteristics and influencing factors of ACI in China from 2004 to 2023.The findings are as follows:(1)ACI showed an overall downward trend,and the spatial distribution pattern was characterized by“high in the western region and low along the southeastern coast”.Although the overall disparity tended to converge,some high-carbon-intensity regions exhibited extreme trends.ACI displayed clear spatial directionality,with the spatial center shifting steadily toward the northeast.(2)Regions in the northwest,northeast,and central-south parts exhibited strong local spatial structural dynamics,and the local spatial dependence of ACI in each region showed a nonlinear trend.Generally speaking,the spatial association pattern demonstrated a certain degree of inertia in spatial transfer,reflecting strong path dependence or spatial lock-in characteristics.(3)Optimization of industrial structure and improvement in agricultural mechanization will increase ACI,while economic development can effectively reduce it.The impact of urbanization on ACI exhibits a nonlinear pattern.The coordinated development of economic growth and urbanization significantly reduces ACI,with a stronger emission reduction observed in regions with low ACI.The optimization of industrial structure,when combined with urbanization and environmental regulation,contributes to significant emission reductions particularly in high-ACI areas.Similarly,the synergy between agricultural mechanization and urbanization effectively lowers emissions in low-ACI regions,though this effect diminishes in areas with higher ACI.
基金supported by the Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region,Ministry of Natural Resources(NRMSSHR2023Y02)Yunnan Key Laboratory of Plateau Geographic Processes and Environmental Changes,Faculty of Geography,Yunnan Normal University(PGPEC2304)China Scholarship Council。
文摘Sloping farmland,particularly in mountainous and hilly areas,constitutes a significant component of regional farmland resources.An investigation into the spatio-temporal pattern of sloping farmland and its influencing factors in China is imperative for the efficient utilization of farmland and the optimization of land space.We used land use transfer matrix,geographically weighted regression model and geographical detector to conduct this study.Results showed that sloping farmland in China firstly decreased and then increased from 2000 to 2020.The proportion of sloping farmland decreased radially outward from Sichuan basin to the surrounding areas.Change rates of sloping farmland with different slopes varied and the slope with 6°-15°underwent the fastest changes.The influencing factors of farmland at various slope degrees were different.For sloping farmland below 15°,land use intensity and elevation had the greatest contribution.For sloping farmland between 15°and 25°,elevation,land use intensity,and population density were the main influencing factors.Sloping farmland above 25°was mostly affected by natural factors.This study can provide scientific basis for rational development and protection of sloping farmland.
基金support for this work was supported by Key Lab of Intelligent and Green Flexographic Printing under Grant ZBKT202301.
文摘Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decoder (ACSF-ED) network to predict the action and locate the object efficiently. In the Adaptive Cross-Scale Fusion Spatio-Temporal Encoder (ACSF ST-Encoder), the Asymptotic Cross-scale Feature-fusion Module (ACCFM) is designed to address the issue of information degradation caused by the propagation of high-level semantic information, thereby extracting high-quality multi-scale features to provide superior features for subsequent spatio-temporal information modeling. Within the Shared-Head Decoder structure, a shared classification and regression detection head is constructed. A multi-constraint loss function composed of one-to-one, one-to-many, and contrastive denoising losses is designed to address the problem of insufficient constraint force in predicting results with traditional methods. This loss function enhances the accuracy of model classification predictions and improves the proximity of regression position predictions to ground truth objects. The proposed method model is evaluated on the popular dataset UCF101-24 and JHMDB-21. Experimental results demonstrate that the proposed method achieves an accuracy of 81.52% on the Frame-mAP metric, surpassing current existing methods.
基金supported by The Henan Province Science and Technology Research Project(242102211046)the Key Scientific Research Project of Higher Education Institutions in Henan Province(25A520039)+1 种基金theNatural Science Foundation project of Zhongyuan Institute of Technology(K2025YB011)the Zhongyuan University of Technology Graduate Education and Teaching Reform Research Project(JG202424).
文摘Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to perform multi-perspective learning of temporal signals and Electrocardiogram images, nor can they fully extract the latent information within the data, falling short of the accuracy required by clinicians. Therefore, this paper proposes an innovative hybrid multimodal spatiotemporal neural network to address these challenges. The model employs a multimodal data augmentation framework integrating visual and signal-based features to enhance the classification performance of rare arrhythmias in imbalanced datasets. Additionally, the spatiotemporal fusion module incorporates a spatiotemporal graph convolutional network to jointly model temporal and spatial features, uncovering complex dependencies within the Electrocardiogram data and improving the model’s ability to represent complex patterns. In experiments conducted on the MIT-BIH arrhythmia dataset, the model achieved 99.95% accuracy, 99.80% recall, and a 99.78% F1 score. The model was further validated for generalization using the clinical INCART arrhythmia dataset, and the results demonstrated its effectiveness in terms of both generalization and robustness.
