Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiote...Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.展开更多
Current experimental and computational methods have limitations in accurately and efficiently classifying ion channels within vast protein spaces.Here we have developed a deep learning algorithm,GPT2 Ion Channel Class...Current experimental and computational methods have limitations in accurately and efficiently classifying ion channels within vast protein spaces.Here we have developed a deep learning algorithm,GPT2 Ion Channel Classifier(GPT2-ICC),which effectively distinguishing ion channels from a test set containing approximately 239 times more non-ion-channel proteins.GPT2-ICC integrates representation learning with a large language model(LLM)-based classifier,enabling highly accurate identification of potential ion channels.Several potential ion channels were predicated from the unannotated human proteome,further demonstrating GPT2-ICC’s generalization ability.This study marks a significant advancement in artificial-intelligence-driven ion channel research,highlighting the adaptability and effectiveness of combining representation learning with LLMs to address the challenges of imbalanced protein sequence data.Moreover,it provides a valuable computational tool for uncovering previously uncharacterized ion channels.展开更多
The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF str...The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.展开更多
Background:Spinocerebellar ataxia type 2(SCA2)is a neurodegenerative disease marked by significant clinical and genetic heterogeneity,primarily caused by expanded CAG mutations in the ATXN2 gene.The unstable expansion...Background:Spinocerebellar ataxia type 2(SCA2)is a neurodegenerative disease marked by significant clinical and genetic heterogeneity,primarily caused by expanded CAG mutations in the ATXN2 gene.The unstable expansion of CAG repeats disrupts the genetic stability of animal models,which is detrimental to disease research.Methods:In this study,we established a mouse model in which CAG repeats do not undergo microsatellite instability(MSI)across generations.A humanized ATXN2 cDNA with four CAA interruptions within 73 CAG expansions was inserted into the Rosa26 locus of C57BL/6J mice.A 23 CAG control mouse model was also generated to verify ATXN2 integration and expression.Results:In our model,the number of CAG repeats remained stable during transmission,with no CAG repeat expansion observed in 64 parent-to-offspring transmissions.Compared with SCA2-Q23 mice,SCA2-Q73 mice exhibited progressive motor impairment,reduced Purkinje cell count and volume(indicative of cell atrophy),and muscle atrophy.These observations in the mice suggest that the behavioral and neuropathological phenotypes may reflect the features of SCA2 patients.RNA-seq analysis of the gastrocnemius muscle in SCA2-Q73 mice showed significant changes in muscle differentiation and development gene expression at 56 weeks,with no significant differences at 16 weeks compared to SCA2-Q23 mice.The expression level of the Myf6 gene significantly changed in the muscles of aged mice.Conclusion:In summary,the establishment of this model not only provides a stable animal model for studying CAG transmission in SCA2 but also indicates that the lack of long-term neural stimulation leads to muscle atrophy.展开更多
Background:New variants of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)continue to drive global epidemics and pose significant health risks.The pathogenicity of these variants evolves under immune press...Background:New variants of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)continue to drive global epidemics and pose significant health risks.The pathogenicity of these variants evolves under immune pressure and host factors.Understanding these changes is crucial for epidemic control and variant research.Methods:Human angiotensin-converting enzyme 2(hACE2)transgenic mice were in-tranasally challenged with the original strain WH-09 and the variants Delta,Beta,and Omicron BA.1,while BALB/c mice were challenged with Omicron subvariants BA.5,BF.7,and XBB.1.To compare the pathogenicity differences among variants,we con-ducted a comprehensive analysis that included clinical symptom observation,meas-urement of viral loads in the trachea and lungs,evaluation of pulmonary pathology,analysis of immune cell infiltration,and quantification of cytokine levels.Results:In hACE2 mice,the Beta variant caused significant weight loss,severe lung inflammation,increased inflammatory and chemotactic factor secretion,greater mac-rophage and neutrophil infiltration in the lungs,and higher viral loads with prolonged shedding duration.In contrast,BA.1 showed a significant reduction in pathogenicity.The BA.5,BF.7,and XBB.1 variants were less pathogenic than the WH-09,Beta,and Delta variants when infected in BALB/c mice.This was evidenced by reduced weight loss,diminished pulmonary pathology,decreased secretion of inflammatory factors and chemokines,reduced macrophage and neutrophil infiltration,as well as lower viral loads in both the trachea and lungs.Conclusion:In hACE2 mice,the Omicron variant demonstrated the lowest pathogenic-ity,while the Beta variant exhibited the highest.Pathogenicity of the Delta variant was comparable to the original WH-09 strain.Among BALB/c mice,Omicron subvari-ants BA.5,BF.7,and XBB.1 showed no statistically significant differences in virulence.展开更多
Cyberbullying on social media poses significant psychological risks,yet most detection systems over-simplify the task by focusing on binary classification,ignoring nuanced categories like passive-aggressive remarks or...Cyberbullying on social media poses significant psychological risks,yet most detection systems over-simplify the task by focusing on binary classification,ignoring nuanced categories like passive-aggressive remarks or indirect slurs.To address this gap,we propose a hybrid framework combining Term Frequency-Inverse Document Frequency(TF-IDF),word-to-vector(Word2Vec),and Bidirectional Encoder Representations from Transformers(BERT)based models for multi-class cyberbullying detection.Our approach integrates TF-IDF for lexical specificity and Word2Vec for semantic relationships,fused with BERT’s contextual embeddings to capture syntactic and semantic complexities.We evaluate the framework on a publicly available dataset of 47,000 annotated social media posts across five cyberbullying categories:age,ethnicity,gender,religion,and indirect aggression.Among BERT variants tested,BERT Base Un-Cased achieved the highest performance with 93%accuracy(standard deviation across±1%5-fold cross-validation)and an average AUC of 0.96,outperforming standalone TF-IDF(78%)and Word2Vec(82%)models.Notably,it achieved near-perfect AUC scores(0.99)for age and ethnicity-based bullying.A comparative analysis with state-of-the-art benchmarks,including Generative Pre-trained Transformer 2(GPT-2)and Text-to-Text Transfer Transformer(T5)models highlights BERT’s superiority in handling ambiguous language.This work advances cyberbullying detection by demonstrating how hybrid feature extraction and transformer models improve multi-class classification,offering a scalable solution for moderating nuanced harmful content.展开更多
The identification and optimization of mutations in nanobodies are crucial for enhancing their thera-peutic potential in disease prevention and control.However,this process is often complex and time-consuming,which li...The identification and optimization of mutations in nanobodies are crucial for enhancing their thera-peutic potential in disease prevention and control.However,this process is often complex and time-consuming,which limit its widespread application in practice.In this study,we developed a work-flow,named Evolutionary-Nanobody(EvoNB),to predict key mutation sites of nanobodies by combining protein language models(PLMs)and molecular dynamic(MD)simulations.By fine-tuning the ESM2 model on a large-scale nanobody dataset,the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced.The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies.Additionally,we selected four widely representative nanobodyeantigen complexes to verify the predicted effects of mutations.MD simulations analyzed the energy changes caused by these mu-tations to predict their impact on binding affinity to the targets.The results showed that multiple mu-tations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target,further validating the potential of this workflow for designing and optimizing nanobody mutations.Additionally,sequence-based predictions are generally less dependent on structural absence,allowing them to be more easily integrated with tools for structural predictions,such as AlphaFold 3.Through mutation prediction and systematic analysis of key sites,we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes.The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field.展开更多
Characterized by special morphologic,geographic,hydrologic,and societal behaviors,the water resources management of the Mediterranean catchment often shows a higher level of complexity including security issues of wat...Characterized by special morphologic,geographic,hydrologic,and societal behaviors,the water resources management of the Mediterranean catchment often shows a higher level of complexity including security issues of water supply,inundation risks,and environment management under the perspective of climate change.To have a comprehensive understanding of the Mediterranean water-cycle system,a deterministic distributed hydrologic modeling approach has been developed and presented in this study based on an application in the Var catchment(2800 km^(2))located at the French Mediterranean region.A 1D and 2D coupled model of MIKE SHE and MIKE 11 has been set up under a series of hypotheses to represent the whole hydrologic and hydrodynamic processes including rainfall-runoff,snow-melting,channel flow,overland flow,and the water exchange between land surface and unsaturated/saturated zones.The developed model was first calibrated with 4 years daily records from 2008 to 2011,then to be validated and further run within hourly time interval to produce detailed representation of the catchment water-cycle from 2012 to 2014.The deterministic distributed modeling approach presented in this study is able to represent its complicated water-cycle and used for supporting the decision‐making process of the water resources management of the catchment.展开更多
Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help...Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help refining enterprises determine the optimal operating parameters to maximize product quality while ensuring product yield,or to increase product yield while reducing energy consumption.This paper presents a multi-objective optimization scheme for hydrocracking based on an improved SPEA2-PE algorithm,which combines path evolution operator and adaptive step strategy to accelerate the convergence speed and improve the computational accuracy of the algorithm.The reactor model used in this article is simulated based on a twenty-five lumped kinetic model.Through model and test function verification,the proposed optimization scheme exhibits significant advantages in the multiobjective optimization process of hydrocracking.展开更多
Radio frequency capacitively coupled plasmas(RF CCPs)operated in Ar/O_(2)gas mixtures which are widely adopted in microelectronics,display,and photovoltaic industry,are investigated based on an equivalent circuit mode...Radio frequency capacitively coupled plasmas(RF CCPs)operated in Ar/O_(2)gas mixtures which are widely adopted in microelectronics,display,and photovoltaic industry,are investigated based on an equivalent circuit model coupled with a global model.This study focuses on the effects of singlet metastable molecule O_(2)(b^(1)∑_(8)^(+)),highly excited Herzberg states O_(2)(A^(3)∑_(u)^(+),A^(3)△_(u),c^(1)∑_(u)^(-)),and the negative ion O_(2)^(-),which are usually neglected in simulation studies.Specifically,their impact on particle densities,electronegativity,electron temperature,voltage drop across the sheath,and absorbed power in the discharge is analyzed.The results indicate that O_(2)(b^(1)∑_(8)^(+))and O_(2)^(-)exhibit relatively high densities in argon-oxygen discharges.While O_(2)(A^(3)∑_(u)^(+),A^(3)△_(u),c^(1)∑_(u)^(-))play a critical role in O_(2)b1S+g production,especially at higher pressure.The inclusion of these particles reduces the electronegativity,electron temperature,and key species densities,especially the O^(-)and O^(*)densities.Moreover,the sheath voltage drop,as well as the inductance and resistance of the plasma bulk are enhanced,while the sheath dissipation power and total absorbed power decrease slightly.With the increasing pressure,the influence of these particles on the discharge properties becomes more significant.The study also explores the generation and loss of main neutral species and charged particles within the pressure range of 20 mTorr-100 mTorr(1 Torr=1.33322×10^(2)Pa),offering insights into essential and non-essential reactions for future low-pressure O_(2)and Ar/O_(2)CCP discharge modeling.展开更多
The acetylpolyamine oxidase(APAO),spermine oxidase(SMO),and spermidine/spermine N1-acetyltransferase(SSAT)are pivotal enzymes in polyamine metabolism,exerting direct influence on polyamine homeostasis regulation.Dysfu...The acetylpolyamine oxidase(APAO),spermine oxidase(SMO),and spermidine/spermine N1-acetyltransferase(SSAT)are pivotal enzymes in polyamine metabolism,exerting direct influence on polyamine homeostasis regulation.Dysfunctions in these enzymes are intricately linked to inflammatory diseases and cancers.Establishing their three-dimensional structures is essential for exploring enzymatic catalytic mechanisms and designing inhibitors at the atomic level.This article primarily assesses the precision of AlphaFold2 and molecular dynamics simulations in determining the three-dimensional structures of these enzymes,utilizing protein conformation rationality assessment,residue correlation matrix,and other techniques.This provides robust models for subsequent polyamine catabolic metabolism calculations and offers valuable insights for modeling proteins that have yet to acquire crystal structures.展开更多
Background:SARS-CoV-2,first identified in late 2019,has given rise to numerous variants of concern(VOCs),posing a significant threat to human health.The emer-gence of Omicron BA.1.1 towards the end of 2021 led to a pa...Background:SARS-CoV-2,first identified in late 2019,has given rise to numerous variants of concern(VOCs),posing a significant threat to human health.The emer-gence of Omicron BA.1.1 towards the end of 2021 led to a pandemic in early 2022.At present,the lethal mouse model for the study of SARS-CoV-2 needs supplementation,and the alterations in neutrophils and monocytes caused by different strains remain to be elucidated.Methods:Human ACE2 transgenic mice were inoculated with the SARS-CoV-2 proto-type and Omicron BA.1,respectively.The pathogenicity of the two strains was evalu-ated by observing clinical symptoms,viral load and pathology.Complete blood count,immunohistochemistry and flow cytometry were performed to detect the alterations of neutrophils and monocytes caused by the two strains.Results:Our findings revealed that Omicron BA.1 exhibited significantly lower vir-ulence compared to the SARS-CoV-2 prototype in the mouse model.Additionally,we observed a significant increase in the proportion of neutrophils late in infection with the SARS-CoV-2 prototype and Omicron BA.1.We found that the proportion of monocytes increased at first and then decreased.The trends in the changes in the proportions of neutrophils and monocytes induced by the two strains were similar.Conclusion:Our study provides valuable insights into the utility of mouse models for simulating the severe disease of SARS-CoV-2 prototype infection and the milder manifestation associated with Omicron BA.1.SARS-CoV-2 prototype and Omicron BA.1 resulted in similar trends in the changes in neutrophils and monocytes.展开更多
BACKGROUND Type 2 diabetes mellitus(T2DM)is a prevalent metabolic disorder increasingly linked with hypertension,posing significant health risks.The need for a predictive model tailored for T2DM patients is evident,as...BACKGROUND Type 2 diabetes mellitus(T2DM)is a prevalent metabolic disorder increasingly linked with hypertension,posing significant health risks.The need for a predictive model tailored for T2DM patients is evident,as current tools may not fully capture the unique risks in this population.This study hypothesizes that a nomogram incorporating specific risk factors will improve hypertension risk prediction in T2DM patients.AIM To develop and validate a nomogram prediction model for hypertension in T2DM patients.METHODS A retrospective observational study was conducted using data from 26850 T2DM patients from the Anhui Provincial Primary Medical and Health Information Management System(2022 to 2024).The study included patients aged 18 and above with available data on key variables.Exclusion criteria were type 1 diabetes,gestational diabetes,insufficient data,secondary hypertension,and abnormal liver and kidney function.The Least Absolute Shrinkage and Selection Operator regression and multivariate logistic regression were used to construct the nomogram,which was validated on separate datasets.RESULTS The developed nomogram for T2DM patients incorporated age,low-density lipoprotein,body mass index,diabetes duration,and urine protein levels as key predictive factors.In the training dataset,the model demonstrated a high discriminative power with an area under the receiver operating characteristic curve(AUC)of 0.823,indicating strong predictive accuracy.The validation dataset confirmed these findings with an AUC of 0.812.The calibration curve analysis showed excellent agreement between predicted and observed outcomes,with absolute errors of 0.017 for the training set and 0.031 for the validation set.The Hosmer-Lemeshow test yielded non-significant results for both sets(χ^(2)=7.066,P=0.562 for training;χ^(2)=6.122,P=0.709 for validation),suggesting good model fit.CONCLUSION The nomogram effectively predicts hypertension risk in T2DM patients,offering a valuable tool for personalized risk assessment and guiding targeted interventions.This model provides a significant advancement in the management of T2DM and hypertension comorbidity.展开更多
BACKGROUND The risk factors and prediction models for diabetic foot(DF)remain incompletely understood,with several potential factors still requiring in-depth investigations.AIM To identify risk factors for new-onset D...BACKGROUND The risk factors and prediction models for diabetic foot(DF)remain incompletely understood,with several potential factors still requiring in-depth investigations.AIM To identify risk factors for new-onset DF and develop a robust prediction model for hospitalized patients with type 2 diabetes.METHODS We included 6301 hospitalized patients with type 2 diabetes from January 2016 to December 2021.A univariate Cox model and least absolute shrinkage and selection operator analyses were applied to select the appropriate predictors.Nonlinear associations between continuous variables and the risk of DF were explored using restricted cubic spline functions.The Cox model was further employed to evaluate the impact of risk factors on DF.The area under the curve(AUC)was measured to evaluate the accuracy of the prediction model.RESULTS Seventy-five diabetic inpatients experienced DF.The incidence density of DF was 4.5/1000 person-years.A long duration of diabetes,lower extremity arterial disease,lower serum albumin,fasting plasma glucose(FPG),and diabetic nephropathy were independently associated with DF.Among these risk factors,the serum albumin concentration was inversely associated with DF,with a hazard ratio(HR)and 95%confidence interval(CI)of 0.91(0.88-0.95)(P<0.001).Additionally,a U-shaped nonlinear relationship was observed between the FPG level and DF.After adjusting for other variables,the HRs and 95%CI for FPG<4.4 mmol/L and≥7.0 mmol/L were 3.99(1.55-10.25)(P=0.004)and 3.12(1.66-5.87)(P<0.001),respectively,which was greater than the mid-range level(4.4-6.9 mmol/L).The AUC for predicting DF over 3 years was 0.797.CONCLUSION FPG demonstrated a U-shaped relationship with DF.Serum albumin levels were negatively associated with DF.The prediction nomogram model of DF showed good discrimination ability using diabetes duration,lower extremity arterial disease,serum albumin,FPG,and diabetic nephropathy(Clinicaltrial.gov NCT05519163).展开更多
Blended learning is an important practice of teaching reform in universities,which effectively integrates online and offline teaching resources.Through the participation of teachers in the learning process and helping...Blended learning is an important practice of teaching reform in universities,which effectively integrates online and offline teaching resources.Through the participation of teachers in the learning process and helping students construct knowledge,the teaching philosophy of“learning as the center”is realized,which plays an important role in improving the quality of teaching courses and cultivating professional talents.This article analyzes the problems in course teaching,proposes a hybrid teaching design strategy based on the ADDIO2OE model,analyzes the specific requirements of each stage,and conducts research and discussion to form a complete teaching model,aiming to deepen teaching reform and improve teaching quality.展开更多
Based on the hindcasts from five subseasonal-to-seasonal(S2S)models participating in the S2S Prediction Project,this study evaluates the performance of the multimodel ensemble(MME)approach in predicting the subseasona...Based on the hindcasts from five subseasonal-to-seasonal(S2S)models participating in the S2S Prediction Project,this study evaluates the performance of the multimodel ensemble(MME)approach in predicting the subseasonal precipitation anomalies during summer in China and reveals the contributions of possible driving factors.The results suggest that while single-model ensembles(SMEs)exhibit constrained predictive skills within a limited forecast lead time of three pentads,the MME illustrates an enhanced predictive skill at a lead time of up to four pentads,and even six pentads,in southern China.Based on both deterministic and probabilistic verification metrics,the MME consistently outperforms SMEs,with a more evident advantage observed in probabilistic forecasting.The superior performance of the MME is primarily attributable to the increase in ensemble size,and the enhanced model diversity is also a contributing factor.The reliability of probabilistic skill is largely improved due to the increase in ensemble members,while the resolution term does not exhibit consistent improvement.Furthermore,the Madden–Julian Oscillation(MJO)is revealed as the primary driving factor for the successful prediction of summer precipitation in China using the MME.The improvement by the MME is not solely attributable to the enhancement in the inherent predictive capacity of the MJO itself,but derives from its capability in capturing the more realistic relationship between the MJO and subseasonal precipitation anomalies in China.This study establishes a scientific foundation for acknowledging the advantageous predictive capability of the MME approach in subseasonal predictions of summer precipitation in China,and sheds light on further improving S2S predictions.展开更多
Objective: To analyze the clinical effects of the patient participation health model in the health management of type 2 diabetes mellitus. Methods: A total of 124 patients with type 2 diabetes admitted to the hospital...Objective: To analyze the clinical effects of the patient participation health model in the health management of type 2 diabetes mellitus. Methods: A total of 124 patients with type 2 diabetes admitted to the hospital from June 2023 to June 2024 were randomly assigned to either the control group (64 patients) or the intervention group (60 patients). Patients in the control group received routine health management, while those in the intervention group were managed using a patient-participation health model with progressive, stage-based interventions. Outcomes were assessed based on blood glucose control, disease awareness, and self-management behaviors. Adverse reactions during health management were closely monitored in both groups. Results: Patients in the intervention group showed significantly better outcomes in blood glucose control, disease awareness, and self-management behaviors compared to the control group. Conclusion: The patient participation health model demonstrated significant clinical value, effectively enhancing self-management abilities, improving glycemic control, and increasing disease awareness. This model is recommended for widespread adoption in the health management of type 2 diabetes to achieve better therapeutic outcomes and improve patient quality of life.展开更多
With the development of the Semantic Web,the number of ontologies grows exponentially and the semantic relationships between ontologies become more and more complex,understanding the true semantics of specific terms o...With the development of the Semantic Web,the number of ontologies grows exponentially and the semantic relationships between ontologies become more and more complex,understanding the true semantics of specific terms or concepts in an ontology is crucial for the matching task.At present,the main challenges facing ontology matching tasks based on representation learning methods are how to improve the embedding quality of ontology knowledge and how to integrate multiple features of ontology efficiently.Therefore,we propose an Ontology Matching Method Based on the Gated Graph Attention Model(OM-GGAT).Firstly,the semantic knowledge related to concepts in the ontology is encoded into vectors using the OWL2Vec^(*)method,and the relevant path information from the root node to the concept is embedded to understand better the true meaning of the concept itself and the relationship between concepts.Secondly,the ontology is transformed into the corresponding graph structure according to the semantic relation.Then,when extracting the features of the ontology graph nodes,different attention weights are assigned to each adjacent node of the central concept with the help of the attention mechanism idea.Finally,gated networks are designed to further fuse semantic and structural embedding representations efficiently.To verify the effectiveness of the proposed method,comparative experiments on matching tasks were carried out on public datasets.The results show that the OM-GGAT model can effectively improve the efficiency of ontology matching.展开更多
基金supported by the National Key Research and Development Program of China[grant number 2022YFE0106800]an Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number 311024001]+3 种基金a project supported by the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number SML2023SP209]a Research Council of Norway funded project(MAPARC)[grant number 328943]a Nansen Center´s basic institutional funding[grant number 342624]the high-performance computing support from the School of Atmospheric Science at Sun Yat-sen University。
文摘Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.
基金funded by grants from the National Key Research and Development Program of China(Grant Nos.:2022YFE0205600 and 2022YFC3400504)the National Natural Science Foundation of China(Grant Nos.:82373792 and 82273857)the Fundamental Research Funds for the Central Universities,China,and the East China Normal University Medicine and Health Joint Fund,China(Grant No.:2022JKXYD07001).
文摘Current experimental and computational methods have limitations in accurately and efficiently classifying ion channels within vast protein spaces.Here we have developed a deep learning algorithm,GPT2 Ion Channel Classifier(GPT2-ICC),which effectively distinguishing ion channels from a test set containing approximately 239 times more non-ion-channel proteins.GPT2-ICC integrates representation learning with a large language model(LLM)-based classifier,enabling highly accurate identification of potential ion channels.Several potential ion channels were predicated from the unannotated human proteome,further demonstrating GPT2-ICC’s generalization ability.This study marks a significant advancement in artificial-intelligence-driven ion channel research,highlighting the adaptability and effectiveness of combining representation learning with LLMs to address the challenges of imbalanced protein sequence data.Moreover,it provides a valuable computational tool for uncovering previously uncharacterized ion channels.
基金financial support from the National Key Research and Development Program of China(2021YFB 3501501)the National Natural Science Foundation of China(No.22225803,22038001,22108007 and 22278011)+1 种基金Beijing Natural Science Foundation(No.Z230023)Beijing Science and Technology Commission(No.Z211100004321001).
文摘The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.
基金CAMS Innovation Fund for Medical Sciences,Grant/Award Number:CIFMS,2021-I2M-1-024The Joint Fund for the Department of Science and Technology of Yunnan Province-Kunming Medical University,Grant/Award Number:202201AY070001-007+1 种基金Open Research Fund Project of Yunnan Provincial Key Laboratory of Pharmacology of Natural Medicines,Grant/Award Number:YKLPNP-G2403The Science and Technology Leading Talent Program of Yunnan Province,Grant/Award Number:202405AB350002。
文摘Background:Spinocerebellar ataxia type 2(SCA2)is a neurodegenerative disease marked by significant clinical and genetic heterogeneity,primarily caused by expanded CAG mutations in the ATXN2 gene.The unstable expansion of CAG repeats disrupts the genetic stability of animal models,which is detrimental to disease research.Methods:In this study,we established a mouse model in which CAG repeats do not undergo microsatellite instability(MSI)across generations.A humanized ATXN2 cDNA with four CAA interruptions within 73 CAG expansions was inserted into the Rosa26 locus of C57BL/6J mice.A 23 CAG control mouse model was also generated to verify ATXN2 integration and expression.Results:In our model,the number of CAG repeats remained stable during transmission,with no CAG repeat expansion observed in 64 parent-to-offspring transmissions.Compared with SCA2-Q23 mice,SCA2-Q73 mice exhibited progressive motor impairment,reduced Purkinje cell count and volume(indicative of cell atrophy),and muscle atrophy.These observations in the mice suggest that the behavioral and neuropathological phenotypes may reflect the features of SCA2 patients.RNA-seq analysis of the gastrocnemius muscle in SCA2-Q73 mice showed significant changes in muscle differentiation and development gene expression at 56 weeks,with no significant differences at 16 weeks compared to SCA2-Q23 mice.The expression level of the Myf6 gene significantly changed in the muscles of aged mice.Conclusion:In summary,the establishment of this model not only provides a stable animal model for studying CAG transmission in SCA2 but also indicates that the lack of long-term neural stimulation leads to muscle atrophy.
基金National Science and Technology Infrastructure of China,Grant/Award Number:National Pathogen Resource Center-NPRC-32National Key Research and Development Program of China,Grant/Award Number:2023YFF0724800CAMS Innovation Fund for Medical Sciences,Grant/Award Number:2021-I2M-1-035。
文摘Background:New variants of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)continue to drive global epidemics and pose significant health risks.The pathogenicity of these variants evolves under immune pressure and host factors.Understanding these changes is crucial for epidemic control and variant research.Methods:Human angiotensin-converting enzyme 2(hACE2)transgenic mice were in-tranasally challenged with the original strain WH-09 and the variants Delta,Beta,and Omicron BA.1,while BALB/c mice were challenged with Omicron subvariants BA.5,BF.7,and XBB.1.To compare the pathogenicity differences among variants,we con-ducted a comprehensive analysis that included clinical symptom observation,meas-urement of viral loads in the trachea and lungs,evaluation of pulmonary pathology,analysis of immune cell infiltration,and quantification of cytokine levels.Results:In hACE2 mice,the Beta variant caused significant weight loss,severe lung inflammation,increased inflammatory and chemotactic factor secretion,greater mac-rophage and neutrophil infiltration in the lungs,and higher viral loads with prolonged shedding duration.In contrast,BA.1 showed a significant reduction in pathogenicity.The BA.5,BF.7,and XBB.1 variants were less pathogenic than the WH-09,Beta,and Delta variants when infected in BALB/c mice.This was evidenced by reduced weight loss,diminished pulmonary pathology,decreased secretion of inflammatory factors and chemokines,reduced macrophage and neutrophil infiltration,as well as lower viral loads in both the trachea and lungs.Conclusion:In hACE2 mice,the Omicron variant demonstrated the lowest pathogenic-ity,while the Beta variant exhibited the highest.Pathogenicity of the Delta variant was comparable to the original WH-09 strain.Among BALB/c mice,Omicron subvari-ants BA.5,BF.7,and XBB.1 showed no statistically significant differences in virulence.
基金funded by Scientific Research Deanship at University of Hail-Saudi Arabia through Project Number RG-23092.
文摘Cyberbullying on social media poses significant psychological risks,yet most detection systems over-simplify the task by focusing on binary classification,ignoring nuanced categories like passive-aggressive remarks or indirect slurs.To address this gap,we propose a hybrid framework combining Term Frequency-Inverse Document Frequency(TF-IDF),word-to-vector(Word2Vec),and Bidirectional Encoder Representations from Transformers(BERT)based models for multi-class cyberbullying detection.Our approach integrates TF-IDF for lexical specificity and Word2Vec for semantic relationships,fused with BERT’s contextual embeddings to capture syntactic and semantic complexities.We evaluate the framework on a publicly available dataset of 47,000 annotated social media posts across five cyberbullying categories:age,ethnicity,gender,religion,and indirect aggression.Among BERT variants tested,BERT Base Un-Cased achieved the highest performance with 93%accuracy(standard deviation across±1%5-fold cross-validation)and an average AUC of 0.96,outperforming standalone TF-IDF(78%)and Word2Vec(82%)models.Notably,it achieved near-perfect AUC scores(0.99)for age and ethnicity-based bullying.A comparative analysis with state-of-the-art benchmarks,including Generative Pre-trained Transformer 2(GPT-2)and Text-to-Text Transfer Transformer(T5)models highlights BERT’s superiority in handling ambiguous language.This work advances cyberbullying detection by demonstrating how hybrid feature extraction and transformer models improve multi-class classification,offering a scalable solution for moderating nuanced harmful content.
基金supported by the National Natural Science Foundation of China(Grant Nos.:92477103,22273023,12474285 and 22373116)the National Key R&D Program of China(Grant No.:2019YFA0905200)+5 种基金Shanghai Municipal Natural Science Foundation(Grant No.:23ZR1418200)Natural Science Foundation of Chongqing,China(Grant No.:CSTB2023NSCQ-MSX0616)Shanghai Frontiers Science Center of Molecule Intelligent SynthesesShanghai Future Discipline Program(Quantum Science and Tech-nology)Shanghai Municipal Education Commission’s“Artificial Intelligence-Driven Research Paradigm Reform and Discipline Advancement Program”the Fundamental Research Funds for the Central Universities.
文摘The identification and optimization of mutations in nanobodies are crucial for enhancing their thera-peutic potential in disease prevention and control.However,this process is often complex and time-consuming,which limit its widespread application in practice.In this study,we developed a work-flow,named Evolutionary-Nanobody(EvoNB),to predict key mutation sites of nanobodies by combining protein language models(PLMs)and molecular dynamic(MD)simulations.By fine-tuning the ESM2 model on a large-scale nanobody dataset,the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced.The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies.Additionally,we selected four widely representative nanobodyeantigen complexes to verify the predicted effects of mutations.MD simulations analyzed the energy changes caused by these mu-tations to predict their impact on binding affinity to the targets.The results showed that multiple mu-tations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target,further validating the potential of this workflow for designing and optimizing nanobody mutations.Additionally,sequence-based predictions are generally less dependent on structural absence,allowing them to be more easily integrated with tools for structural predictions,such as AlphaFold 3.Through mutation prediction and systematic analysis of key sites,we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes.The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field.
基金supported by the National Key R&D Program of China(No.2023YFC3006702)the Natural Science Foundation of Beijing Municipality(IS23117).
文摘Characterized by special morphologic,geographic,hydrologic,and societal behaviors,the water resources management of the Mediterranean catchment often shows a higher level of complexity including security issues of water supply,inundation risks,and environment management under the perspective of climate change.To have a comprehensive understanding of the Mediterranean water-cycle system,a deterministic distributed hydrologic modeling approach has been developed and presented in this study based on an application in the Var catchment(2800 km^(2))located at the French Mediterranean region.A 1D and 2D coupled model of MIKE SHE and MIKE 11 has been set up under a series of hypotheses to represent the whole hydrologic and hydrodynamic processes including rainfall-runoff,snow-melting,channel flow,overland flow,and the water exchange between land surface and unsaturated/saturated zones.The developed model was first calibrated with 4 years daily records from 2008 to 2011,then to be validated and further run within hourly time interval to produce detailed representation of the catchment water-cycle from 2012 to 2014.The deterministic distributed modeling approach presented in this study is able to represent its complicated water-cycle and used for supporting the decision‐making process of the water resources management of the catchment.
基金supported by National Key Research and Development Program of China (2023YFB3307800)National Natural Science Foundation of China (Key Program: 62136003, 62373155)+1 种基金Major Science and Technology Project of Xinjiang (No. 2022A01006-4)the Fundamental Research Funds for the Central Universities。
文摘Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help refining enterprises determine the optimal operating parameters to maximize product quality while ensuring product yield,or to increase product yield while reducing energy consumption.This paper presents a multi-objective optimization scheme for hydrocracking based on an improved SPEA2-PE algorithm,which combines path evolution operator and adaptive step strategy to accelerate the convergence speed and improve the computational accuracy of the algorithm.The reactor model used in this article is simulated based on a twenty-five lumped kinetic model.Through model and test function verification,the proposed optimization scheme exhibits significant advantages in the multiobjective optimization process of hydrocracking.
基金supported by the National Natural Science Foundation of China(Grant Nos.12020101005,12475202,12347131,and 12405289).
文摘Radio frequency capacitively coupled plasmas(RF CCPs)operated in Ar/O_(2)gas mixtures which are widely adopted in microelectronics,display,and photovoltaic industry,are investigated based on an equivalent circuit model coupled with a global model.This study focuses on the effects of singlet metastable molecule O_(2)(b^(1)∑_(8)^(+)),highly excited Herzberg states O_(2)(A^(3)∑_(u)^(+),A^(3)△_(u),c^(1)∑_(u)^(-)),and the negative ion O_(2)^(-),which are usually neglected in simulation studies.Specifically,their impact on particle densities,electronegativity,electron temperature,voltage drop across the sheath,and absorbed power in the discharge is analyzed.The results indicate that O_(2)(b^(1)∑_(8)^(+))and O_(2)^(-)exhibit relatively high densities in argon-oxygen discharges.While O_(2)(A^(3)∑_(u)^(+),A^(3)△_(u),c^(1)∑_(u)^(-))play a critical role in O_(2)b1S+g production,especially at higher pressure.The inclusion of these particles reduces the electronegativity,electron temperature,and key species densities,especially the O^(-)and O^(*)densities.Moreover,the sheath voltage drop,as well as the inductance and resistance of the plasma bulk are enhanced,while the sheath dissipation power and total absorbed power decrease slightly.With the increasing pressure,the influence of these particles on the discharge properties becomes more significant.The study also explores the generation and loss of main neutral species and charged particles within the pressure range of 20 mTorr-100 mTorr(1 Torr=1.33322×10^(2)Pa),offering insights into essential and non-essential reactions for future low-pressure O_(2)and Ar/O_(2)CCP discharge modeling.
基金National Natural Science Foundation of China(22073023)Natural Science Foundation of Henan Province(242300421134)+1 种基金the Young Backbone Teacher in Colleges and Universities of Henan Province(2021GGJS020)Foundation of State Key Laboratory of Antiviral Drugs。
文摘The acetylpolyamine oxidase(APAO),spermine oxidase(SMO),and spermidine/spermine N1-acetyltransferase(SSAT)are pivotal enzymes in polyamine metabolism,exerting direct influence on polyamine homeostasis regulation.Dysfunctions in these enzymes are intricately linked to inflammatory diseases and cancers.Establishing their three-dimensional structures is essential for exploring enzymatic catalytic mechanisms and designing inhibitors at the atomic level.This article primarily assesses the precision of AlphaFold2 and molecular dynamics simulations in determining the three-dimensional structures of these enzymes,utilizing protein conformation rationality assessment,residue correlation matrix,and other techniques.This provides robust models for subsequent polyamine catabolic metabolism calculations and offers valuable insights for modeling proteins that have yet to acquire crystal structures.
基金supported by Beijing Natural Science Foundation(Grant No.Z210014)National Natural Science Foundation of China(Grant No.32070543)+1 种基金National Key Research and Development Project of China(Grant No.2022YFC2303404)CAMS Innovation Fund for Medical Sciences(CIFMS)(Grant No.2022-12M-CoV19-002)
文摘Background:SARS-CoV-2,first identified in late 2019,has given rise to numerous variants of concern(VOCs),posing a significant threat to human health.The emer-gence of Omicron BA.1.1 towards the end of 2021 led to a pandemic in early 2022.At present,the lethal mouse model for the study of SARS-CoV-2 needs supplementation,and the alterations in neutrophils and monocytes caused by different strains remain to be elucidated.Methods:Human ACE2 transgenic mice were inoculated with the SARS-CoV-2 proto-type and Omicron BA.1,respectively.The pathogenicity of the two strains was evalu-ated by observing clinical symptoms,viral load and pathology.Complete blood count,immunohistochemistry and flow cytometry were performed to detect the alterations of neutrophils and monocytes caused by the two strains.Results:Our findings revealed that Omicron BA.1 exhibited significantly lower vir-ulence compared to the SARS-CoV-2 prototype in the mouse model.Additionally,we observed a significant increase in the proportion of neutrophils late in infection with the SARS-CoV-2 prototype and Omicron BA.1.We found that the proportion of monocytes increased at first and then decreased.The trends in the changes in the proportions of neutrophils and monocytes induced by the two strains were similar.Conclusion:Our study provides valuable insights into the utility of mouse models for simulating the severe disease of SARS-CoV-2 prototype infection and the milder manifestation associated with Omicron BA.1.SARS-CoV-2 prototype and Omicron BA.1 resulted in similar trends in the changes in neutrophils and monocytes.
文摘BACKGROUND Type 2 diabetes mellitus(T2DM)is a prevalent metabolic disorder increasingly linked with hypertension,posing significant health risks.The need for a predictive model tailored for T2DM patients is evident,as current tools may not fully capture the unique risks in this population.This study hypothesizes that a nomogram incorporating specific risk factors will improve hypertension risk prediction in T2DM patients.AIM To develop and validate a nomogram prediction model for hypertension in T2DM patients.METHODS A retrospective observational study was conducted using data from 26850 T2DM patients from the Anhui Provincial Primary Medical and Health Information Management System(2022 to 2024).The study included patients aged 18 and above with available data on key variables.Exclusion criteria were type 1 diabetes,gestational diabetes,insufficient data,secondary hypertension,and abnormal liver and kidney function.The Least Absolute Shrinkage and Selection Operator regression and multivariate logistic regression were used to construct the nomogram,which was validated on separate datasets.RESULTS The developed nomogram for T2DM patients incorporated age,low-density lipoprotein,body mass index,diabetes duration,and urine protein levels as key predictive factors.In the training dataset,the model demonstrated a high discriminative power with an area under the receiver operating characteristic curve(AUC)of 0.823,indicating strong predictive accuracy.The validation dataset confirmed these findings with an AUC of 0.812.The calibration curve analysis showed excellent agreement between predicted and observed outcomes,with absolute errors of 0.017 for the training set and 0.031 for the validation set.The Hosmer-Lemeshow test yielded non-significant results for both sets(χ^(2)=7.066,P=0.562 for training;χ^(2)=6.122,P=0.709 for validation),suggesting good model fit.CONCLUSION The nomogram effectively predicts hypertension risk in T2DM patients,offering a valuable tool for personalized risk assessment and guiding targeted interventions.This model provides a significant advancement in the management of T2DM and hypertension comorbidity.
基金Supported by National Natural Science Foundation of China,No.81972947Academic Promotion Programme of Shandong First Medical University,No.2019LJ005.
文摘BACKGROUND The risk factors and prediction models for diabetic foot(DF)remain incompletely understood,with several potential factors still requiring in-depth investigations.AIM To identify risk factors for new-onset DF and develop a robust prediction model for hospitalized patients with type 2 diabetes.METHODS We included 6301 hospitalized patients with type 2 diabetes from January 2016 to December 2021.A univariate Cox model and least absolute shrinkage and selection operator analyses were applied to select the appropriate predictors.Nonlinear associations between continuous variables and the risk of DF were explored using restricted cubic spline functions.The Cox model was further employed to evaluate the impact of risk factors on DF.The area under the curve(AUC)was measured to evaluate the accuracy of the prediction model.RESULTS Seventy-five diabetic inpatients experienced DF.The incidence density of DF was 4.5/1000 person-years.A long duration of diabetes,lower extremity arterial disease,lower serum albumin,fasting plasma glucose(FPG),and diabetic nephropathy were independently associated with DF.Among these risk factors,the serum albumin concentration was inversely associated with DF,with a hazard ratio(HR)and 95%confidence interval(CI)of 0.91(0.88-0.95)(P<0.001).Additionally,a U-shaped nonlinear relationship was observed between the FPG level and DF.After adjusting for other variables,the HRs and 95%CI for FPG<4.4 mmol/L and≥7.0 mmol/L were 3.99(1.55-10.25)(P=0.004)and 3.12(1.66-5.87)(P<0.001),respectively,which was greater than the mid-range level(4.4-6.9 mmol/L).The AUC for predicting DF over 3 years was 0.797.CONCLUSION FPG demonstrated a U-shaped relationship with DF.Serum albumin levels were negatively associated with DF.The prediction nomogram model of DF showed good discrimination ability using diabetes duration,lower extremity arterial disease,serum albumin,FPG,and diabetic nephropathy(Clinicaltrial.gov NCT05519163).
文摘Blended learning is an important practice of teaching reform in universities,which effectively integrates online and offline teaching resources.Through the participation of teachers in the learning process and helping students construct knowledge,the teaching philosophy of“learning as the center”is realized,which plays an important role in improving the quality of teaching courses and cultivating professional talents.This article analyzes the problems in course teaching,proposes a hybrid teaching design strategy based on the ADDIO2OE model,analyzes the specific requirements of each stage,and conducts research and discussion to form a complete teaching model,aiming to deepen teaching reform and improve teaching quality.
基金sponsored by the National Natural Science Foundation of China(Grant Nos.42175052 and U2442206)the Joint Research Project for Meteorological Capacity Improvement(Grant No.23NLTSQ007,23NLTSZ003)+2 种基金the Innovative Development Special Project of the China Meteorological Administration(Grant No.CXFZ2023J002)the National Key R&D Program of China(Grant No.2023YFC3007700,2024YFC3013100)the China Meteorological Administration Youth Innovation Team(Grant No.CMA2024QN06)。
文摘Based on the hindcasts from five subseasonal-to-seasonal(S2S)models participating in the S2S Prediction Project,this study evaluates the performance of the multimodel ensemble(MME)approach in predicting the subseasonal precipitation anomalies during summer in China and reveals the contributions of possible driving factors.The results suggest that while single-model ensembles(SMEs)exhibit constrained predictive skills within a limited forecast lead time of three pentads,the MME illustrates an enhanced predictive skill at a lead time of up to four pentads,and even six pentads,in southern China.Based on both deterministic and probabilistic verification metrics,the MME consistently outperforms SMEs,with a more evident advantage observed in probabilistic forecasting.The superior performance of the MME is primarily attributable to the increase in ensemble size,and the enhanced model diversity is also a contributing factor.The reliability of probabilistic skill is largely improved due to the increase in ensemble members,while the resolution term does not exhibit consistent improvement.Furthermore,the Madden–Julian Oscillation(MJO)is revealed as the primary driving factor for the successful prediction of summer precipitation in China using the MME.The improvement by the MME is not solely attributable to the enhancement in the inherent predictive capacity of the MJO itself,but derives from its capability in capturing the more realistic relationship between the MJO and subseasonal precipitation anomalies in China.This study establishes a scientific foundation for acknowledging the advantageous predictive capability of the MME approach in subseasonal predictions of summer precipitation in China,and sheds light on further improving S2S predictions.
文摘Objective: To analyze the clinical effects of the patient participation health model in the health management of type 2 diabetes mellitus. Methods: A total of 124 patients with type 2 diabetes admitted to the hospital from June 2023 to June 2024 were randomly assigned to either the control group (64 patients) or the intervention group (60 patients). Patients in the control group received routine health management, while those in the intervention group were managed using a patient-participation health model with progressive, stage-based interventions. Outcomes were assessed based on blood glucose control, disease awareness, and self-management behaviors. Adverse reactions during health management were closely monitored in both groups. Results: Patients in the intervention group showed significantly better outcomes in blood glucose control, disease awareness, and self-management behaviors compared to the control group. Conclusion: The patient participation health model demonstrated significant clinical value, effectively enhancing self-management abilities, improving glycemic control, and increasing disease awareness. This model is recommended for widespread adoption in the health management of type 2 diabetes to achieve better therapeutic outcomes and improve patient quality of life.
基金supported by the National Natural Science Foundation of China(grant numbers 62267005 and 42365008)the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing.
文摘With the development of the Semantic Web,the number of ontologies grows exponentially and the semantic relationships between ontologies become more and more complex,understanding the true semantics of specific terms or concepts in an ontology is crucial for the matching task.At present,the main challenges facing ontology matching tasks based on representation learning methods are how to improve the embedding quality of ontology knowledge and how to integrate multiple features of ontology efficiently.Therefore,we propose an Ontology Matching Method Based on the Gated Graph Attention Model(OM-GGAT).Firstly,the semantic knowledge related to concepts in the ontology is encoded into vectors using the OWL2Vec^(*)method,and the relevant path information from the root node to the concept is embedded to understand better the true meaning of the concept itself and the relationship between concepts.Secondly,the ontology is transformed into the corresponding graph structure according to the semantic relation.Then,when extracting the features of the ontology graph nodes,different attention weights are assigned to each adjacent node of the central concept with the help of the attention mechanism idea.Finally,gated networks are designed to further fuse semantic and structural embedding representations efficiently.To verify the effectiveness of the proposed method,comparative experiments on matching tasks were carried out on public datasets.The results show that the OM-GGAT model can effectively improve the efficiency of ontology matching.
