Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)mutations are influenced by random and uncontrollable factors,and the risk of the next widespread epidemic remains.Dual-target drugs that synergistically act ...Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)mutations are influenced by random and uncontrollable factors,and the risk of the next widespread epidemic remains.Dual-target drugs that synergistically act on two targets exhibit strong therapeutic effects and advantages against mutations.In this study,a novel computational workflow was developed to design dual-target SARS-CoV-2 candidate inhibitors with the Envelope protein and Main protease selected as the two target proteins.The drug-like molecules of our self-constructed 3D scaffold database were used as high-throughput molecular docking probes for feature extraction of two target protein pockets.A multi-layer perceptron(MLP)was employed to embed the binding affinities into a latent space as conditional vectors to control conditional distribution.Utilizing a conditional generative neural network,cG-SchNet,with 3D Euclidean group(E3)symmetries,the conditional probability distributions of molecular 3D structures were acquired and a set of novel SARS-CoV-2 dual-target candidate inhibitors were generated.The 1D probability,2D joint probability,and 2D cumulative probability distribution results indicate that the generated sets are significantly enhanced compared to the training set in the high binding affinity area.Among the 201 generated molecules,42 molecules exhibited a sum binding affinity exceeding 17.0 kcal/mol while 9 of them having a sum binding affinity exceeding 19.0 kcal/mol,demonstrating structure diversity along with strong dual-target affinities,good absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties,and ease of synthesis.Dual-target drugs are rare and difficult to find,and our“high-throughput docking-multi-conditional generation”workflow offers a wide range of options for designing or optimizing potent dual-target SARS-CoV-2 inhibitors.展开更多
Aimed at the problem that the traditional ART-2 neural network can not recognize a gradually changing course, an eternal term memory (ETM) vector is introduced into ART-2 to simulate the function of human brain, i.e. ...Aimed at the problem that the traditional ART-2 neural network can not recognize a gradually changing course, an eternal term memory (ETM) vector is introduced into ART-2 to simulate the function of human brain, i.e. the deep remembrance for the initial impression.. The eternal term memory vector is determined only by the initial vector that establishes category neuron node and is used to keep the remembrance for this vector for ever. Two times of vigilance algorithm are put forward, and the posterior input vector must first pass the first vigilance of this eternal term memory vector, only succeeded has it the qualification to begin the second vigilance of long term memory vector. The long term memory vector can be revised only when both of the vigilances are passed. Results of recognition examples show that the improved ART-2 overcomes the defect of traditional ART-2 and can recognize a gradually changing course effectively.展开更多
BACKGROUND Current evidence suggests that commonly used antidiabetic drugs have varying effects on cancer risk.Some antidiabetics offer protective effects against cancer,whereas others may increase risk in specific po...BACKGROUND Current evidence suggests that commonly used antidiabetic drugs have varying effects on cancer risk.Some antidiabetics offer protective effects against cancer,whereas others may increase risk in specific populations.AIM To comprehensively compare the effects of different antidiabetic drugs on the risk of various cancers in patients with type 2 diabetes mellitus(T2DM)through a systematic review and network meta-analysis.METHODS Four databases(PubMed,EMBASE,Cochrane Library,and Web of Science)were searched from their inception until April 11,2025.Published randomized controlled trials that enrolled at least 100 participants and had an intervention duration of at least 1 year were included.The inclusion criteria were studies involving adult patients with T2DM and interventions that compared different classes of antidiabetic drugs with a placebo or another antidiabetic drug.Network meta-analysis was conducted using Stata 17.0 software.Confidence in network meta-analysis was used to assess the quality of evidence regarding the risk of cancer associated with different antidiabetic drugs.RESULTS A total of 13535 articles were identified.After applying the inclusion and exclusion criteria,87 high-quality studies involving 216106 patients and 26 different drugs across seven classes were included in this study.Indirect evidence from network meta-analysis revealed some heterogeneity;however,this did not affect the reliability of the results.The results indicated that antidiabetic drugs did not increase the overall risk of cancer compared with placebo.In contrast,some antidiabetic medications demonstrated a more pronounced advantage in reducing cancer risk,such as dipeptidyl peptidase-4 inhibitors for thyroid and rectal cancers;sodium-glucose co-transporter type 2 inhibitors for lung and bronchial cancers;sulfonylureas for gastric and colon cancers;biguanides for pancreatic cancer;insulin for bladder cancer;glucagon-like peptide-1 receptor agonists for prostate,uterine,hepatocellular,renal,and hematologic cancers;and thiazolidinediones for breast cancer.CONCLUSION Antidiabetic drugs reduce cancer risk in patients with T2DM.However,given the limitations in the number and quality of the included studies,our conclusions should be interpreted with caution.More large-scale,high-quality clinical trials are required to validate our findings towards the optimization of comprehensive cancer management strategies for patients with T2DM.展开更多
Objective Epidemiological studies have shown that vitamin D status affects glycemic control in individuals with type 2 diabetes mellitus(T2DM).However,findings from intervention studies remain inconsistent.Therefore,a...Objective Epidemiological studies have shown that vitamin D status affects glycemic control in individuals with type 2 diabetes mellitus(T2DM).However,findings from intervention studies remain inconsistent.Therefore,a network meta-analysis was conducted to evaluate the comparative efficacy of various vitamin D supplementation strategies on glucose indicators in adults with T2DM.Methods Eligible studies published before September 12,2024,were retrieved from PubMed,EMBASE,Cochrane Library,and Web of Science.A network meta-analysis of multiple dosage strategies—low(<1,000 IU/day,LDS),medium(1,000–2,000 IU/day,MDS),high(2,000–4,000 IU/day,HDS),and extremely high(≥4,000 IU/day,EHDS)—was performed.Results The network meta-analysis of 40 RCTs indicated that,compared with placebo,vitamin D_(3)supplementation increased 25-hydroxyvitamin D[25-(OH)-D]levels,with pooled mean difference(MD)showing a stepwise increase from LDS to EHDS.Ranking probabilities showed a corresponding rise in 25-(OH)-D levels from LDS(46.7%)to EHDS(91.2%).EHDS reduced fasting blood glucose(FBG)relative to no treatment.LDS significantly decreased hemoglobin A1c(HbA1c),and vitamin D_(2) significantly affected FBG levels.MDS led to a significant change in fasting insulin(FIN)compared to both placebo(MD:-4.76;95%CI-8.91 to-0.61)and no treatment(MD:-7.30;95%CI-14.44 to-0.17).Conclusion The findings suggest that vitamin D supplementation may be a viable approach for improving glycemic control in adults with T2DM,with lower doses potentially offering benefit.The analysis also showed a dose-dependent increase in 25-(OH)-D levels.展开更多
With the advancement of Vehicle-to-Everything(V2X)technology,efficient resource allocation in dynamic vehicular networks has become a critical challenge for achieving optimal performance.Existing methods suffer from h...With the advancement of Vehicle-to-Everything(V2X)technology,efficient resource allocation in dynamic vehicular networks has become a critical challenge for achieving optimal performance.Existing methods suffer from high computational complexity and decision latency under high-density traffic and heterogeneous network conditions.To address these challenges,this study presents an innovative framework that combines Graph Neural Networks(GNNs)with a Double Deep Q-Network(DDQN),utilizing dynamic graph structures and reinforcement learning.An adaptive neighbor sampling mechanism is introduced to dynamically select the most relevant neighbors based on interference levels and network topology,thereby improving decision accuracy and efficiency.Meanwhile,the framework models communication links as nodes and interference relationships as edges,effectively capturing the direct impact of interference on resource allocation while reducing computational complexity and preserving critical interaction information.Employing an aggregation mechanism based on the Graph Attention Network(GAT),it dynamically adjusts the neighbor sampling scope and performs attention-weighted aggregation based on node importance,ensuring more efficient and adaptive resource management.This design ensures reliable Vehicle-to-Vehicle(V2V)communication while maintaining high Vehicle-to-Infrastructure(V2I)throughput.The framework retains the global feature learning capabilities of GNNs and supports distributed network deployment,allowing vehicles to extract low-dimensional graph embeddings from local observations for real-time resource decisions.Experimental results demonstrate that the proposed method significantly reduces computational overhead,mitigates latency,and improves resource utilization efficiency in vehicular networks under complex traffic scenarios.This research not only provides a novel solution to resource allocation challenges in V2X networks but also advances the application of DDQN in intelligent transportation systems,offering substantial theoretical significance and practical value.展开更多
Speech Emotion Recognition(SER)has received widespread attention as a crucial way for understanding human emotional states.However,the impact of irrelevant information on speech signals and data sparsity limit the dev...Speech Emotion Recognition(SER)has received widespread attention as a crucial way for understanding human emotional states.However,the impact of irrelevant information on speech signals and data sparsity limit the development of SER system.To address these issues,this paper proposes a framework that incorporates the Attentive Mask Residual Network(AM-ResNet)and the self-supervised learning model Wav2vec 2.0 to obtain AM-ResNet features and Wav2vec 2.0 features respectively,together with a cross-attention module to interact and fuse these two features.The AM-ResNet branch mainly consists of maximum amplitude difference detection,mask residual block,and an attention mechanism.Among them,the maximum amplitude difference detection and the mask residual block act on the pre-processing and the network,respectively,to reduce the impact of silent frames,and the attention mechanism assigns different weights to unvoiced and voiced speech to reduce redundant emotional information caused by unvoiced speech.In the Wav2vec 2.0 branch,this model is introduced as a feature extractor to obtain general speech features(Wav2vec 2.0 features)through pre-training with a large amount of unlabeled speech data,which can assist the SER task and cope with data sparsity problems.In the cross-attention module,AM-ResNet features and Wav2vec 2.0 features are interacted with and fused to obtain the cross-fused features,which are used to predict the final emotion.Furthermore,multi-label learning is also used to add ambiguous emotion utterances to deal with data limitations.Finally,experimental results illustrate the usefulness and superiority of our proposed framework over existing state-of-the-art approaches.展开更多
The increasing demand for radioauthorized applications in the 6G era necessitates enhanced monitoring and management of radio resources,particularly for precise control over the electromagnetic environment.The radio m...The increasing demand for radioauthorized applications in the 6G era necessitates enhanced monitoring and management of radio resources,particularly for precise control over the electromagnetic environment.The radio map serves as a crucial tool for describing signal strength distribution within the current electromagnetic environment.However,most existing algorithms rely on sparse measurements of radio strength,disregarding the impact of building information.In this paper,we propose a spectrum cartography(SC)algorithm that eliminates the need for relying on sparse ground-based radio strength measurements by utilizing a satellite network to collect data on buildings and transmitters.Our algorithm leverages Pix2Pix Generative Adversarial Network(GAN)to construct accurate radio maps using transmitter information within real geographical environments.Finally,simulation results demonstrate that our algorithm exhibits superior accuracy compared to previously proposed methods.展开更多
OBJECTIVE:To explore the mechanism of Tangfukang formula(糖复康方,TFK)in treating type 2 diabetes mellitus(T2DM).METHODS:We employed network pharmacology combined with experimental validation to explore the potential ...OBJECTIVE:To explore the mechanism of Tangfukang formula(糖复康方,TFK)in treating type 2 diabetes mellitus(T2DM).METHODS:We employed network pharmacology combined with experimental validation to explore the potential mechanism of TFK against T2DM.Initially,we filtered bioactive compounds with the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP)and Symptom Mapping(Sym Map),and gathered targets of TFK and T2DM.Subsequently,we constructed a protein-protein interaction(PPI)network,enriched core targets through Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG),and adopted molecular docking to study the binding mode of compounds and the signaling pathway.Finally,we employed a KKAy mice model to investigate the effect and mechanism of TFK against T2DM.Biochemical assay,histology assay,and Western blot(WB)were used to assess the mechanism.RESULTS:There were 492 bioactive compounds of TFK screened,and 1226 overlapping targets of TFK against T2DM identified.A compound-T2DM-related target network with 997 nodes and 4439 edges was constructed.KEGG enrichment analysis identified some core pathways related to T2DM,including adenosine 5-monophosphate-activated protein kinase(AMPK)signaling pathway.Molecular docking study revealed that compounds of TFK,including citric acid,could bind to the active pocket of AMPK crystal structure with free binding energy of-4.8,-8 and-7.9,respectively.Animal experiments indicated that TFK decreased body weight,fasting blood glucose,fasting serum insulin,homeostasis model of insulin resistance,glycosylated serum protein,total cholesterol,triglyceride,and low-density lipoprotein cholesterol,and improve oral glucose tolerance test results.TFK reduced steatosis in liver tissue,and infiltration of inflammatory cells,and protected liver cells to a certain extent.WB analysis revealed that,TFK upregulated the phosphorylation of AMPK and branchedchainα-ketoacid dehydrogenase proteins.CONCLUSION:TFK has the potential to effectively manage T2DM,possibly by regulating the AMPK signaling pathway.The present study lays a new foundation for the therapeutic application of TFK in the treatment of T2DM.展开更多
High-entropy alloy(HEA)offer tunable composition and surface structures,enabling the creation of novel active sites that enhance catalytic performance in renewable energy application.However,the inherent surface compl...High-entropy alloy(HEA)offer tunable composition and surface structures,enabling the creation of novel active sites that enhance catalytic performance in renewable energy application.However,the inherent surface complexity and tendency for elemental segregation,which results in discrepancies between bulk and surface compositions,pose challenges for direct investigation via density functional theory.To address this,Monte Carlo simulations combined with molecular dynamics were employed to model surface segregation across a broad range of elements,including Cu,Ag,Au,Pt,Pd,and Al.The analysis revealed a trend in surface segregation propensity following the order Ag>Au>Al>Cu>Pd>Pt.To capture the correlation between surface site characteristics and the free energy of multi-dentate CO_(2)reduction intermediates,a graph neural network was designed,where adsorbates were transformed into pseudo-atoms at their centers of mass.This model achieved mean absolute errors of 0.08–0.15 eV for the free energies of C_(2)intermediates,enabling precise site activity quantification.Results indicated that increasing the concentration of Cu,Ag,and Al significantly boosts activity for CO and C_(2)formation,whereas Au,Pd,and Pt exhibit negative effects.By screening stable composition space,promising HEA bulk compositions for CO,HCOOH,and C_(2)products were predicted,offering superior catalytic activity compared to pure Cu catalysts.展开更多
Photocatalytic H_(2) evolution from wastewater exhibits fascinating prospects in environment and energy fields.Here,we propose a novel 3D cross-linked g-C_(3)N_(4) network(SCN)assembling with 1D nanowires.This network...Photocatalytic H_(2) evolution from wastewater exhibits fascinating prospects in environment and energy fields.Here,we propose a novel 3D cross-linked g-C_(3)N_(4) network(SCN)assembling with 1D nanowires.This network structure endows SCN with abundant carbon defects,creating a defect energy level and shallow charge trapping centres,which significantly prolongs the photocarrier lifetime,suppresses their recombination and facilitates the mass transfer process during the dye photodegradation.Consequently,in photocatalytic H_(2) evolution coupled with Rhodamine B(RhB)photodegradation under visible light,the H_(2) production rate of SCN is 283μmol h^(-1)g^(-1),accompanying by 97%RhB photodegradation efficiency,much higher than UCN's 31μmol h^(-1)g^(-1)and 64%.In particular,AQY of SCN for H_(2) evolution from RhB solution reaches 23.7%at 380 nm.Furthermore,the calculated transition states demonstrate that the N1 site connected to the defect in SCN has a minimum Gibbs free energy ΔG(H^(*)),indicating that H~+undergoes an H^(+)→H^(*)→H_(2) evolution process.展开更多
In dynamic 5G network environments,user mobility and heterogeneous network topologies pose dual challenges to the effort of improving performance of mobile edge caching.Existing studies often overlook the dynamic natu...In dynamic 5G network environments,user mobility and heterogeneous network topologies pose dual challenges to the effort of improving performance of mobile edge caching.Existing studies often overlook the dynamic nature of user locations and the potential of device-to-device(D2D)cooperative caching,limiting the reduction of transmission latency.To address this issue,this paper proposes a joint optimization scheme for edge caching that integrates user mobility prediction with deep reinforcement learning.First,a Transformer-based geolocation prediction model is designed,leveraging multi-head attention mechanisms to capture correlations in historical user trajectories for accurate future location prediction.Then,within a three-tier heterogeneous network,we formulate a latency minimization problem under a D2D cooperative caching architecture and develop a mobility-aware Deep Q-Network(DQN)caching strategy.This strategy takes predicted location information as state input and dynamically adjusts the content distribution across small base stations(SBSs)andmobile users(MUs)to reduce end-to-end delay inmulti-hop content retrieval.Simulation results show that the proposed DQN-based method outperforms other baseline strategies across variousmetrics,achieving a 17.2%reduction in transmission delay compared to DQNmethods withoutmobility integration,thus validating the effectiveness of the joint optimization of location prediction and caching decisions.展开更多
In Saharan climates,greenhouses face extreme diurnal temperature fluctuations that generate thermal stress,reduce crop productivity,and hinder sustainable agricultural practices.Passive thermal storage using Phase Cha...In Saharan climates,greenhouses face extreme diurnal temperature fluctuations that generate thermal stress,reduce crop productivity,and hinder sustainable agricultural practices.Passive thermal storage using Phase Change Materials(PCM)is a promising solution to stabilize microclimatic conditions.This study aims to evaluate experimentally and numerically the effectiveness of PCM integration for moderating greenhouse temperature fluctuations under Saharan climatic conditions.Two identical greenhouse prototypes were constructed in Ghardaia,Algeria:a reference greenhouse and a PCM-integrated greenhouse using calcium chloride hexahydrate(CaCl_(2)⋅6H_(2)O).Thermal performance was assessed during a five-day experimental period(7–11May 2025)under severe ambient conditions.To complement this,a Nonlinear Auto-Regressive with eXogenous inputs(NARX)neural network model was developed and trained using a larger dataset(7–25 May 2025)to predict greenhouse thermal dynamics.The PCM greenhouse reduced peak daytime air temperature by an average of 8.14℃and decreased the diurnal temperature amplitude by 53.6%compared to the reference greenhouse.The NARX model achieved high predictive accuracy(R^(2)=0.990,RMSE=0.425℃,MAE=0.223℃,MBE=0.008℃),capturing both sensible and latent heat transfer mechanisms,including PCM melting and solidification.The combined experimental and predictive modeling results confirm the potential of PCM integration as an effective passive thermal regulation strategy for greenhouses in arid regions.This approach enhances microclimatic stability,improves energy efficiency,and supports the sustainability of protected agriculture under extreme climatic conditions.展开更多
基金supported by Interdisciplinary Innova-tion Project of“Bioarchaeology Laboratory”of Jilin University,China,and“MedicineþX”Interdisciplinary Innovation Team of Norman Bethune Health Science Center of Jilin University,China(Grant No.:2022JBGS05).
文摘Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)mutations are influenced by random and uncontrollable factors,and the risk of the next widespread epidemic remains.Dual-target drugs that synergistically act on two targets exhibit strong therapeutic effects and advantages against mutations.In this study,a novel computational workflow was developed to design dual-target SARS-CoV-2 candidate inhibitors with the Envelope protein and Main protease selected as the two target proteins.The drug-like molecules of our self-constructed 3D scaffold database were used as high-throughput molecular docking probes for feature extraction of two target protein pockets.A multi-layer perceptron(MLP)was employed to embed the binding affinities into a latent space as conditional vectors to control conditional distribution.Utilizing a conditional generative neural network,cG-SchNet,with 3D Euclidean group(E3)symmetries,the conditional probability distributions of molecular 3D structures were acquired and a set of novel SARS-CoV-2 dual-target candidate inhibitors were generated.The 1D probability,2D joint probability,and 2D cumulative probability distribution results indicate that the generated sets are significantly enhanced compared to the training set in the high binding affinity area.Among the 201 generated molecules,42 molecules exhibited a sum binding affinity exceeding 17.0 kcal/mol while 9 of them having a sum binding affinity exceeding 19.0 kcal/mol,demonstrating structure diversity along with strong dual-target affinities,good absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties,and ease of synthesis.Dual-target drugs are rare and difficult to find,and our“high-throughput docking-multi-conditional generation”workflow offers a wide range of options for designing or optimizing potent dual-target SARS-CoV-2 inhibitors.
基金Sponsored by the National Natural Science Foundation of China (Grant No. 50305005)
文摘Aimed at the problem that the traditional ART-2 neural network can not recognize a gradually changing course, an eternal term memory (ETM) vector is introduced into ART-2 to simulate the function of human brain, i.e. the deep remembrance for the initial impression.. The eternal term memory vector is determined only by the initial vector that establishes category neuron node and is used to keep the remembrance for this vector for ever. Two times of vigilance algorithm are put forward, and the posterior input vector must first pass the first vigilance of this eternal term memory vector, only succeeded has it the qualification to begin the second vigilance of long term memory vector. The long term memory vector can be revised only when both of the vigilances are passed. Results of recognition examples show that the improved ART-2 overcomes the defect of traditional ART-2 and can recognize a gradually changing course effectively.
基金Supported by National Natural Science Foundation of China,No.82305205Young Elite Scientists Sponsorship Program by China Association of Chinese Medicine,No.2023-QNRC2-A05+1 种基金Safeguard Project of Guang’anmen Hospital,China Academy of Chinese Medical Sciences,No.GAMHH9324001Special Program for the Training of Outstanding Young Scientific and Technological Talents Under the Basic Scientific Research Operating Expenses of the China Academy of Chinese Medical Sciences,No.ZZ18-YQ-011.
文摘BACKGROUND Current evidence suggests that commonly used antidiabetic drugs have varying effects on cancer risk.Some antidiabetics offer protective effects against cancer,whereas others may increase risk in specific populations.AIM To comprehensively compare the effects of different antidiabetic drugs on the risk of various cancers in patients with type 2 diabetes mellitus(T2DM)through a systematic review and network meta-analysis.METHODS Four databases(PubMed,EMBASE,Cochrane Library,and Web of Science)were searched from their inception until April 11,2025.Published randomized controlled trials that enrolled at least 100 participants and had an intervention duration of at least 1 year were included.The inclusion criteria were studies involving adult patients with T2DM and interventions that compared different classes of antidiabetic drugs with a placebo or another antidiabetic drug.Network meta-analysis was conducted using Stata 17.0 software.Confidence in network meta-analysis was used to assess the quality of evidence regarding the risk of cancer associated with different antidiabetic drugs.RESULTS A total of 13535 articles were identified.After applying the inclusion and exclusion criteria,87 high-quality studies involving 216106 patients and 26 different drugs across seven classes were included in this study.Indirect evidence from network meta-analysis revealed some heterogeneity;however,this did not affect the reliability of the results.The results indicated that antidiabetic drugs did not increase the overall risk of cancer compared with placebo.In contrast,some antidiabetic medications demonstrated a more pronounced advantage in reducing cancer risk,such as dipeptidyl peptidase-4 inhibitors for thyroid and rectal cancers;sodium-glucose co-transporter type 2 inhibitors for lung and bronchial cancers;sulfonylureas for gastric and colon cancers;biguanides for pancreatic cancer;insulin for bladder cancer;glucagon-like peptide-1 receptor agonists for prostate,uterine,hepatocellular,renal,and hematologic cancers;and thiazolidinediones for breast cancer.CONCLUSION Antidiabetic drugs reduce cancer risk in patients with T2DM.However,given the limitations in the number and quality of the included studies,our conclusions should be interpreted with caution.More large-scale,high-quality clinical trials are required to validate our findings towards the optimization of comprehensive cancer management strategies for patients with T2DM.
文摘Objective Epidemiological studies have shown that vitamin D status affects glycemic control in individuals with type 2 diabetes mellitus(T2DM).However,findings from intervention studies remain inconsistent.Therefore,a network meta-analysis was conducted to evaluate the comparative efficacy of various vitamin D supplementation strategies on glucose indicators in adults with T2DM.Methods Eligible studies published before September 12,2024,were retrieved from PubMed,EMBASE,Cochrane Library,and Web of Science.A network meta-analysis of multiple dosage strategies—low(<1,000 IU/day,LDS),medium(1,000–2,000 IU/day,MDS),high(2,000–4,000 IU/day,HDS),and extremely high(≥4,000 IU/day,EHDS)—was performed.Results The network meta-analysis of 40 RCTs indicated that,compared with placebo,vitamin D_(3)supplementation increased 25-hydroxyvitamin D[25-(OH)-D]levels,with pooled mean difference(MD)showing a stepwise increase from LDS to EHDS.Ranking probabilities showed a corresponding rise in 25-(OH)-D levels from LDS(46.7%)to EHDS(91.2%).EHDS reduced fasting blood glucose(FBG)relative to no treatment.LDS significantly decreased hemoglobin A1c(HbA1c),and vitamin D_(2) significantly affected FBG levels.MDS led to a significant change in fasting insulin(FIN)compared to both placebo(MD:-4.76;95%CI-8.91 to-0.61)and no treatment(MD:-7.30;95%CI-14.44 to-0.17).Conclusion The findings suggest that vitamin D supplementation may be a viable approach for improving glycemic control in adults with T2DM,with lower doses potentially offering benefit.The analysis also showed a dose-dependent increase in 25-(OH)-D levels.
基金Project ZR2023MF111 supported by Shandong Provincial Natural Science Foundation。
文摘With the advancement of Vehicle-to-Everything(V2X)technology,efficient resource allocation in dynamic vehicular networks has become a critical challenge for achieving optimal performance.Existing methods suffer from high computational complexity and decision latency under high-density traffic and heterogeneous network conditions.To address these challenges,this study presents an innovative framework that combines Graph Neural Networks(GNNs)with a Double Deep Q-Network(DDQN),utilizing dynamic graph structures and reinforcement learning.An adaptive neighbor sampling mechanism is introduced to dynamically select the most relevant neighbors based on interference levels and network topology,thereby improving decision accuracy and efficiency.Meanwhile,the framework models communication links as nodes and interference relationships as edges,effectively capturing the direct impact of interference on resource allocation while reducing computational complexity and preserving critical interaction information.Employing an aggregation mechanism based on the Graph Attention Network(GAT),it dynamically adjusts the neighbor sampling scope and performs attention-weighted aggregation based on node importance,ensuring more efficient and adaptive resource management.This design ensures reliable Vehicle-to-Vehicle(V2V)communication while maintaining high Vehicle-to-Infrastructure(V2I)throughput.The framework retains the global feature learning capabilities of GNNs and supports distributed network deployment,allowing vehicles to extract low-dimensional graph embeddings from local observations for real-time resource decisions.Experimental results demonstrate that the proposed method significantly reduces computational overhead,mitigates latency,and improves resource utilization efficiency in vehicular networks under complex traffic scenarios.This research not only provides a novel solution to resource allocation challenges in V2X networks but also advances the application of DDQN in intelligent transportation systems,offering substantial theoretical significance and practical value.
基金supported by Chongqing University of Posts and Telecommunications Ph.D.Innovative Talents Project(Grant No.BYJS202106)Chongqing Postgraduate Research Innovation Project(Grant No.CYB21203).
文摘Speech Emotion Recognition(SER)has received widespread attention as a crucial way for understanding human emotional states.However,the impact of irrelevant information on speech signals and data sparsity limit the development of SER system.To address these issues,this paper proposes a framework that incorporates the Attentive Mask Residual Network(AM-ResNet)and the self-supervised learning model Wav2vec 2.0 to obtain AM-ResNet features and Wav2vec 2.0 features respectively,together with a cross-attention module to interact and fuse these two features.The AM-ResNet branch mainly consists of maximum amplitude difference detection,mask residual block,and an attention mechanism.Among them,the maximum amplitude difference detection and the mask residual block act on the pre-processing and the network,respectively,to reduce the impact of silent frames,and the attention mechanism assigns different weights to unvoiced and voiced speech to reduce redundant emotional information caused by unvoiced speech.In the Wav2vec 2.0 branch,this model is introduced as a feature extractor to obtain general speech features(Wav2vec 2.0 features)through pre-training with a large amount of unlabeled speech data,which can assist the SER task and cope with data sparsity problems.In the cross-attention module,AM-ResNet features and Wav2vec 2.0 features are interacted with and fused to obtain the cross-fused features,which are used to predict the final emotion.Furthermore,multi-label learning is also used to add ambiguous emotion utterances to deal with data limitations.Finally,experimental results illustrate the usefulness and superiority of our proposed framework over existing state-of-the-art approaches.
文摘The increasing demand for radioauthorized applications in the 6G era necessitates enhanced monitoring and management of radio resources,particularly for precise control over the electromagnetic environment.The radio map serves as a crucial tool for describing signal strength distribution within the current electromagnetic environment.However,most existing algorithms rely on sparse measurements of radio strength,disregarding the impact of building information.In this paper,we propose a spectrum cartography(SC)algorithm that eliminates the need for relying on sparse ground-based radio strength measurements by utilizing a satellite network to collect data on buildings and transmitters.Our algorithm leverages Pix2Pix Generative Adversarial Network(GAN)to construct accurate radio maps using transmitter information within real geographical environments.Finally,simulation results demonstrate that our algorithm exhibits superior accuracy compared to previously proposed methods.
基金Tsinghua Precision Medicine Foundation:Tangfukang Plays the Therapeutic Role in Type 2 Diabetes Patients with Qi and Yin Deficiency Syndrome by Regulating the Intestinal Flora Mediated Branched-chain Amino Acids-Phosphatidylinositide 3-Kinases-Protein Kinase B Signaling Pathway(grant number 10001020105)National Natural Science Foundation of China:Tangfukang Plays the Therapeutic Role in Type 2 Diabetes Mellitus by Regulating the Intestinal Flora Mediated Adiponectin-adenosine 5-Monophosphate-activated Protein Kinase-branched-chain Amino Acids Signaling Pathway(grant number 82104812)。
文摘OBJECTIVE:To explore the mechanism of Tangfukang formula(糖复康方,TFK)in treating type 2 diabetes mellitus(T2DM).METHODS:We employed network pharmacology combined with experimental validation to explore the potential mechanism of TFK against T2DM.Initially,we filtered bioactive compounds with the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP)and Symptom Mapping(Sym Map),and gathered targets of TFK and T2DM.Subsequently,we constructed a protein-protein interaction(PPI)network,enriched core targets through Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG),and adopted molecular docking to study the binding mode of compounds and the signaling pathway.Finally,we employed a KKAy mice model to investigate the effect and mechanism of TFK against T2DM.Biochemical assay,histology assay,and Western blot(WB)were used to assess the mechanism.RESULTS:There were 492 bioactive compounds of TFK screened,and 1226 overlapping targets of TFK against T2DM identified.A compound-T2DM-related target network with 997 nodes and 4439 edges was constructed.KEGG enrichment analysis identified some core pathways related to T2DM,including adenosine 5-monophosphate-activated protein kinase(AMPK)signaling pathway.Molecular docking study revealed that compounds of TFK,including citric acid,could bind to the active pocket of AMPK crystal structure with free binding energy of-4.8,-8 and-7.9,respectively.Animal experiments indicated that TFK decreased body weight,fasting blood glucose,fasting serum insulin,homeostasis model of insulin resistance,glycosylated serum protein,total cholesterol,triglyceride,and low-density lipoprotein cholesterol,and improve oral glucose tolerance test results.TFK reduced steatosis in liver tissue,and infiltration of inflammatory cells,and protected liver cells to a certain extent.WB analysis revealed that,TFK upregulated the phosphorylation of AMPK and branchedchainα-ketoacid dehydrogenase proteins.CONCLUSION:TFK has the potential to effectively manage T2DM,possibly by regulating the AMPK signaling pathway.The present study lays a new foundation for the therapeutic application of TFK in the treatment of T2DM.
文摘High-entropy alloy(HEA)offer tunable composition and surface structures,enabling the creation of novel active sites that enhance catalytic performance in renewable energy application.However,the inherent surface complexity and tendency for elemental segregation,which results in discrepancies between bulk and surface compositions,pose challenges for direct investigation via density functional theory.To address this,Monte Carlo simulations combined with molecular dynamics were employed to model surface segregation across a broad range of elements,including Cu,Ag,Au,Pt,Pd,and Al.The analysis revealed a trend in surface segregation propensity following the order Ag>Au>Al>Cu>Pd>Pt.To capture the correlation between surface site characteristics and the free energy of multi-dentate CO_(2)reduction intermediates,a graph neural network was designed,where adsorbates were transformed into pseudo-atoms at their centers of mass.This model achieved mean absolute errors of 0.08–0.15 eV for the free energies of C_(2)intermediates,enabling precise site activity quantification.Results indicated that increasing the concentration of Cu,Ag,and Al significantly boosts activity for CO and C_(2)formation,whereas Au,Pd,and Pt exhibit negative effects.By screening stable composition space,promising HEA bulk compositions for CO,HCOOH,and C_(2)products were predicted,offering superior catalytic activity compared to pure Cu catalysts.
基金supported by the National Natural Science Foundation of China(No.22202033)National College Students Innovation and Entrepreneurship Training Program(No.202310347048)Zhejiang Provincial Training Programs of Innovation and Entrepreneurship for Undergraduates(No.S202310347032)。
文摘Photocatalytic H_(2) evolution from wastewater exhibits fascinating prospects in environment and energy fields.Here,we propose a novel 3D cross-linked g-C_(3)N_(4) network(SCN)assembling with 1D nanowires.This network structure endows SCN with abundant carbon defects,creating a defect energy level and shallow charge trapping centres,which significantly prolongs the photocarrier lifetime,suppresses their recombination and facilitates the mass transfer process during the dye photodegradation.Consequently,in photocatalytic H_(2) evolution coupled with Rhodamine B(RhB)photodegradation under visible light,the H_(2) production rate of SCN is 283μmol h^(-1)g^(-1),accompanying by 97%RhB photodegradation efficiency,much higher than UCN's 31μmol h^(-1)g^(-1)and 64%.In particular,AQY of SCN for H_(2) evolution from RhB solution reaches 23.7%at 380 nm.Furthermore,the calculated transition states demonstrate that the N1 site connected to the defect in SCN has a minimum Gibbs free energy ΔG(H^(*)),indicating that H~+undergoes an H^(+)→H^(*)→H_(2) evolution process.
基金supported by the Liaoning Provincial Education Department Fund,grant number JYTZD2023083.
文摘In dynamic 5G network environments,user mobility and heterogeneous network topologies pose dual challenges to the effort of improving performance of mobile edge caching.Existing studies often overlook the dynamic nature of user locations and the potential of device-to-device(D2D)cooperative caching,limiting the reduction of transmission latency.To address this issue,this paper proposes a joint optimization scheme for edge caching that integrates user mobility prediction with deep reinforcement learning.First,a Transformer-based geolocation prediction model is designed,leveraging multi-head attention mechanisms to capture correlations in historical user trajectories for accurate future location prediction.Then,within a three-tier heterogeneous network,we formulate a latency minimization problem under a D2D cooperative caching architecture and develop a mobility-aware Deep Q-Network(DQN)caching strategy.This strategy takes predicted location information as state input and dynamically adjusts the content distribution across small base stations(SBSs)andmobile users(MUs)to reduce end-to-end delay inmulti-hop content retrieval.Simulation results show that the proposed DQN-based method outperforms other baseline strategies across variousmetrics,achieving a 17.2%reduction in transmission delay compared to DQNmethods withoutmobility integration,thus validating the effectiveness of the joint optimization of location prediction and caching decisions.
文摘In Saharan climates,greenhouses face extreme diurnal temperature fluctuations that generate thermal stress,reduce crop productivity,and hinder sustainable agricultural practices.Passive thermal storage using Phase Change Materials(PCM)is a promising solution to stabilize microclimatic conditions.This study aims to evaluate experimentally and numerically the effectiveness of PCM integration for moderating greenhouse temperature fluctuations under Saharan climatic conditions.Two identical greenhouse prototypes were constructed in Ghardaia,Algeria:a reference greenhouse and a PCM-integrated greenhouse using calcium chloride hexahydrate(CaCl_(2)⋅6H_(2)O).Thermal performance was assessed during a five-day experimental period(7–11May 2025)under severe ambient conditions.To complement this,a Nonlinear Auto-Regressive with eXogenous inputs(NARX)neural network model was developed and trained using a larger dataset(7–25 May 2025)to predict greenhouse thermal dynamics.The PCM greenhouse reduced peak daytime air temperature by an average of 8.14℃and decreased the diurnal temperature amplitude by 53.6%compared to the reference greenhouse.The NARX model achieved high predictive accuracy(R^(2)=0.990,RMSE=0.425℃,MAE=0.223℃,MBE=0.008℃),capturing both sensible and latent heat transfer mechanisms,including PCM melting and solidification.The combined experimental and predictive modeling results confirm the potential of PCM integration as an effective passive thermal regulation strategy for greenhouses in arid regions.This approach enhances microclimatic stability,improves energy efficiency,and supports the sustainability of protected agriculture under extreme climatic conditions.
