A simple and effective polymer fluorescent thermosensitive system was successfully developed based on the synergistic effect of excimer/monomer interconversion of pyrene derivatives and electrostatic interaction betwe...A simple and effective polymer fluorescent thermosensitive system was successfully developed based on the synergistic effect of excimer/monomer interconversion of pyrene derivatives and electrostatic interaction between polyelectrolyte and charged fluorophore. As for the system, the excimer-monomer conversion, thermosensitive behavior and thermo-responsive reversibility were investigated experimentally. Temperature variation and temperature-distribution induced fluorescence changes can be observed directly by naked eyes. Thus, this polymer system holds promise for serving as a fluorescent thermometer.展开更多
A Cu(II)coordination complex(1)with Schiff ligand derived from diaminomaleonitrile was synthesized and characterized,in which the ligand is rigid,planar and conjugated.The complex 1 displays an interesting fluorescent...A Cu(II)coordination complex(1)with Schiff ligand derived from diaminomaleonitrile was synthesized and characterized,in which the ligand is rigid,planar and conjugated.The complex 1 displays an interesting fluorescent property relative to solvents which can be turned-on by CH_(2)Cl_(2) and CHCl_(3) solvent molecules.The mechanism of this selective fluorescence emission has been studied based on the crystal structure and the spectrum analysis.The tuning on and off fluorescence of complex 1 can be controlled by the process of supramolecular aggregation/deag-gregation in different solvents.展开更多
Neurons are highly polarized cells with axons reaching over a meter long in adult humans.To survive and maintain their proper function,neurons depend on specific mechanisms that regulate spatiotemporal signaling and m...Neurons are highly polarized cells with axons reaching over a meter long in adult humans.To survive and maintain their proper function,neurons depend on specific mechanisms that regulate spatiotemporal signaling and metabolic events,which need to be carried out at the right place,time,and intensity.Such mechanisms include axonal transport,local synthesis,and liquid-liquid phase separations.Alterations and malfunctions in these processes are correlated to neurodegenerative diseases such as amyotrophic lateral sclerosis(ALS).展开更多
The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charg...The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charging stations,addressing the unique challenges posed by third-party aggregation platforms.Our approach integrates node equations-based on the parameter identification with a novel deep learning model,xDeepCIN,to detect abnormal data reporting indicative of aggregation attacks.We employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter estimation.The xDeepCIN model,incorporating a Compressed Interaction Network,has the ability to capture complex feature interactions in sparse,high-dimensional charging data.Experimental results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance,with F1-scores increasing by up to 32.3%for specific anomaly types compared to traditional methods,such as wide&deep and DeepFM(Factorization-Machine).Our framework exhibits robust scalability,effectively handling networks ranging from 8 to 85 charging points.Furthermore,we achieve real-time monitoring capabilities,with parameter identification completing within seconds for networks up to 1000 nodes.This research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats,offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.展开更多
As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and use...As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and user privacy concerns within smart grids.However,existing methods struggle with efficiency and security when processing large-scale data.Balancing efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent challenge.This paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data modalities.The approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user privacy.It also explores the application of Boneh Lynn Shacham(BLS)signatures for user authentication.The proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.展开更多
Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective to...Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.展开更多
Parkinson's disease (PD) is a common degenerative disorder that is becoming increasingly prevalent because of the global aging population.The exact cause of the disorder is unknown;however,recent studies have sugg...Parkinson's disease (PD) is a common degenerative disorder that is becoming increasingly prevalent because of the global aging population.The exact cause of the disorder is unknown;however,recent studies have suggested that multiple factors may contribute to its pathogenesis.PD is characterized by a movement disorder that primarily affects motor control;pathologically,the disease is marked by the presence of Lewy bodies (LBs) in the brain.展开更多
Integrating Artificial Intelligence of Things(AIoT)in healthcare offers transformative potential for real-time diagnostics and collaborative learning but presents critical challenges,including privacy preservation,com...Integrating Artificial Intelligence of Things(AIoT)in healthcare offers transformative potential for real-time diagnostics and collaborative learning but presents critical challenges,including privacy preservation,computational efficiency,and regulatory compliance.Traditional approaches,such as differential privacy,homomorphic encryption,and secure multi-party computation,often fail to balance performance and privacy,rendering them unsuitable for resource-constrained healthcare AIoT environments.This paper introduces LMSA(Lightweight Multi-Key Secure Aggregation),a novel framework designed to address these challenges and enable efficient,secure federated learning across distributed healthcare institutions.LMSA incorporates three key innovations:(1)a lightweight multikey management system leveraging Diffie-Hellman key exchange and SHA3-256 hashing,achieving O(n)complexity with AES(Advanced Encryption Standard)-256-level security;(2)a privacy-preserving aggregation protocol employing hardware-accelerated AES-CTR(CounTeR)encryption andmodular arithmetic for securemodel weight combination;and(3)a resource-optimized implementation utilizing AES-NI(New Instructions)instructions and efficient memory management for real-time operations on constrained devices.Experimental evaluations using the National Institutes of Health(NIH)Chest X-ray dataset demonstrate LMSA’s ability to train multi-label thoracic disease prediction models with Vision Transformer(ViT),ResNet-50,and MobileNet architectures across distributed healthcare institutions.Memory usage analysis confirmed minimal overhead,with ViT(327.30 MB),ResNet-50(89.87 MB),and MobileNet(8.63 MB)maintaining stable encryption times across communication rounds.LMSA ensures robust security through hardware acceleration,enabling real-time diagnostics without compromising patient confidentiality or regulatory compliance.Future research aims to optimize LMSA for ultra-low-power devices and validate its scalability in heterogeneous,real-world environments.LMSA represents a foundational advancement for privacy-conscious healthcare AI applications,bridging the gap between privacy and performance.展开更多
Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink.However,many existing data aggregatio...Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink.However,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater environments.Additionally,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole issue.Moreover,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network performance.To address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile UWSNs.The proposed method has four main phases:clustering,CH selection,data aggregation,and re-clustering.During CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy efficiency.In the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving energy.To adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects CHs.Simulation results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.展开更多
Increasing interest has been directed toward the potential of heterogeneous flexible loads to mitigate the challenges associated with the increasing variability and uncertainty of renewable generation.Evaluating the a...Increasing interest has been directed toward the potential of heterogeneous flexible loads to mitigate the challenges associated with the increasing variability and uncertainty of renewable generation.Evaluating the aggregated flexible region of load clusters managed by load aggregators is the crucial basis of power system scheduling for the system operator.This is because the aggregation result affects the qual-ity of the scheduling schemes.A stringent computation based on the Minkowski sum is NP-hard,whereas existing approximation meth-ods that use a special type of polytope exhibit limited adaptability when aggregating heterogeneous loads.This study proposes a stringent internal approximation method based on the convex hull of multiple layers of maximum volume boxes and embeds it into a day-ahead scheduling optimization model.The numerical results indicate that the aggregation accuracy can be improved compared with methods based on one type of special polytope,including boxes,zonotopes,and homothets.Hence,the reliability and economy of the power sys-tem scheduling can be enhanced.展开更多
In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The e...In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.展开更多
The Cyber-Physical Systems (CPS) supported by Wireless Sensor Networks (WSN) helps factories collect data and achieve seamless communication between physical and virtual components. Sensor nodes are energy-constrained...The Cyber-Physical Systems (CPS) supported by Wireless Sensor Networks (WSN) helps factories collect data and achieve seamless communication between physical and virtual components. Sensor nodes are energy-constrained devices. Their energy consumption is typically correlated with the amount of data collection. The purpose of data aggregation is to reduce data transmission, lower energy consumption, and reduce network congestion. For large-scale WSN, data aggregation can greatly improve network efficiency. However, as many heterogeneous data is poured into a specific area at the same time, it sometimes causes data loss and then results in incompleteness and irregularity of production data. This paper proposes an information processing model that encompasses the Energy-Conserving Data Aggregation Algorithm (ECDA) and the Efficient Message Reception Algorithm (EMRA). ECDA is divided into two stages, Energy conservation based on the global cost and Data aggregation based on ant colony optimization. The EMRA comprises the Polling Message Reception Algorithm (PMRA), the Shortest Time Message Reception Algorithm (STMRA), and the Specific Condition Message Reception Algorithm (SCMRA). These algorithms are not only available for the regularity and directionality of sensor information transmission, but also satisfy the different requirements in small factory environments. To compare with the recent HPSO-ILEACH and E-PEGASIS, DCDA can effectively reduce energy consumption. Experimental results show that STMRA consumes 1.3 times the time of SCMRA. Both optimization algorithms exhibit higher time efficiency than PMRA. Furthermore, this paper also evaluates these three algorithms using AHP.展开更多
Digital twin is a novel technology that has achieved significant progress in industrial manufactur-ing systems in recent years.In the digital twin envi-ronment,entities in the virtual space collect data from devices i...Digital twin is a novel technology that has achieved significant progress in industrial manufactur-ing systems in recent years.In the digital twin envi-ronment,entities in the virtual space collect data from devices in the physical space to analyze their states.However,since a lot of devices exist in the physical space,the digital twin system needs to aggregate data from multiple devices at the edge gateway.Homomor-phic integrity and confidentiality protections are two important requirements for this data aggregation pro-cess.Unfortunately,existing homomorphic encryp-tion algorithms do not support integrity protection,and existing homomorphic signing algorithms require all signers to use the same signing key,which is not feasible in the digital twin environment.Moreover,for both integrity and confidentiality protections,the homomorphic signing algorithm must be compatible with the aggregation manner of the homomorphic en-cryption algorithm.To address these issues,this paper designs a novel homomorphic aggregation scheme,which allows multiple devices in the physical space to sign different data using different keys and support in-tegrity and confidentiality protections.Finally,the security of the newly designed scheme is analyzed,and its efficiency is evaluated.Experimental results show that our scheme is feasible for real world applications.展开更多
In contemporary power systems,delving into the flexible regulation potential of demand-side resources is of paramount significance for the efficient operation of power grids.This research puts forward an innovative mu...In contemporary power systems,delving into the flexible regulation potential of demand-side resources is of paramount significance for the efficient operation of power grids.This research puts forward an innovative multivariate flexible load aggregation control approach that takes dynamic demand response into full consideration.In the initial stage,using generalized time-domain aggregation modelling for a wide array of heterogeneous flexible loads,including temperature-controlled loads,electric vehicles,and energy storage devices,a novel calculation method for their maximum adjustable capacities is devised.Distinct from conventional methods,this newly developed approach enables more precise and adaptable quantification of the load-adjusting capabilities,thereby enhancing the accuracy and flexibility of demand-side resource management.Subsequently,an SSA-BiLSTM flexible load classification prediction model is established.This model represents an innovative application in the field,effectively combining the advantages of the Sparrow Search Algorithm(SSA)and the Bidirectional Long-Short-Term Memory(BiLSTM)neural network.Furthermore,a parallel Markov chain is introduced to evaluate the switching state transfer probability of flexible loads accurately.This integration allows for a more refined determination of the maximum response capacity range of the flexible load aggregator,significantly improving the precision of capacity assessment compared to existing methods.Finally,in consonance with the intra-day scheduling plan,a newly developed diffuse filling algorithm is implemented to control the activation times of flexible loads precisely,thus achieving real-time dynamic demand response.Through in-depth case analysis and comprehensive comparative studies,the effectiveness of the proposed method is convincingly validated.With its innovative techniques and enhanced performance,it is demonstrated that this method has the potential to substantially enhance the utilization efficiency of demand-side resources in power systems,providing a novel and effective solution for optimizing power grid operation and demand-side management.展开更多
This paper investigates China's coal price volatility spreaders(CPVSs)from the supply side to locate the volatility source since coal price volatility may destabilize many downstream products'prices or even br...This paper investigates China's coal price volatility spreaders(CPVSs)from the supply side to locate the volatility source since coal price volatility may destabilize many downstream products'prices or even bring uncertainties to macroeconomic output.Especially in the carbon neutrality context,China's coal market is being reconstructed and responding to imbalances between supply and demand;identifying the CPVSs helps alleviate rising market instability and prevent energy-induced system risk.To achieve this objective,we explore causalities among 938 weekly coal prices reported by different coal-producing areas of China from 2006.9.4 to 2021.7.12 using the transfer entropy method.Then,coal price volatility influence is quantified to identify the CPVSs by conjointly using complex network theory and a rank aggregation method.The validity test demonstrates that the proposed hybrid method efficiently identifies the CPVSs as it correlates to many price determinants,e.g.,electricity and coal consumption and generation.The empirical results show that causalities among coal prices changed dramatically in 2016,2018,and 2020,affected by coal decapacity and carbon neutrality policies.Before 2018,coal-producing provinces with strong demand for coal and electricity,e.g.,Jiangxi,Chongqing,and Sichuan,were CPVSs;after 2019,those with comparative advantages in coal supply,e.g.,Gansu and Ningxia,were CPVSs.Overall,the coal market is unstable and sensitive to energy policy and external shocks.Policymakers and market participants are recommended to monitor and manage the CPVSs to improve energy security,avoid policy-induced instability and prevent risks caused by coal price fluctuations.展开更多
In Internet of Vehicles,VehicleInfrastructure-Cloud cooperation supports diverse intelligent driving and intelligent transportation applications.Federated Learning(FL)is the emerging computation paradigm to provide ef...In Internet of Vehicles,VehicleInfrastructure-Cloud cooperation supports diverse intelligent driving and intelligent transportation applications.Federated Learning(FL)is the emerging computation paradigm to provide efficient and privacypreserving collaborative learning.However,in Io V environment,federated learning faces the challenges introduced by high mobility of vehicles and nonIndependently Identically Distribution(non-IID)of data.High mobility causes FL clients quit and the communication offline.The non-IID data leads to slow and unstable convergence of global model and single global model's weak adaptability to clients with different localization characteristics.Accordingly,this paper proposes a personalized aggregation strategy for hierarchical Federated Learning in Io V environment,including Fed SA(Special Asynchronous Federated Learning with Self-adaptive Aggregation)for low-level FL between a Road Side Unit(RSU)and the vehicles within its coverage,and Fed Att(Federated Learning with Attention Mechanism)for high-level FL between a cloud server and multiple RSUs.Agents self-adaptively obtain model aggregation weight based on Advantage Actor-Critic(A2C)algorithm.Experiments show the proposed strategy encourages vehicles to participate in global aggregation,and outperforms existing methods in training performance.展开更多
OBJECTIVE:To investigate the effects of gut microbes regulation of the trimethylamine(TMA)/flavin containing monooxygenase 3(FMO3)/trimethylamine N-oxide(TMAO)pathway on platelet aggregation in acute coronary syndrome...OBJECTIVE:To investigate the effects of gut microbes regulation of the trimethylamine(TMA)/flavin containing monooxygenase 3(FMO3)/trimethylamine N-oxide(TMAO)pathway on platelet aggregation in acute coronary syndrome(ACS)rats and the intervention of Huayu Qutan formula(化瘀祛痰方).METHODS:The ACS rats with syndrome of phlegm and blood stasis rats were established.Platelet,platelet aggregation,platelet activation markers and TMA/FMO3/TMAO pathway were detected.Metagenomics technology was employed to analyze the characteristics of the gut microbiota.RESULTS:Huayu Qutan formula and gut microbes could inhibit high platelet reactivity and regulate the TMA/FMO3/TMAO pathway.The dominant bacteria in ACS rats including but not limited to the major phyla,Firmicutes,Bacteroidetes,Actinobacteria,and Proteobacteria,also including some low abundance phyla,Fusobacteria,Verrucomicrobia,Spirochaetes,and Deferribacteres.The dominant bacteria in the Huayu Qutan formula group were Synergistetes,Deferribacteres,Deferribacteraceae,Faecalibacterium and Mucispirillum.In the Huayu Qutan formula combined with fecal bacteria enema group,the dominant bacteria were Verrucomicrobia,Verrucomicrobiae,Akkermansia and Verrucomicrobium.These gut microbiota were correlated with pathways such as Riboflavin metabolism and Arachidonic acid metabolism.CONCLUSION:Huayu Qutan formula may prevent ACS by modulating gut microbes Synergistetes,Faecalibacterium and Allobaculum,regulating the iron metabolism of Deferribacteres,and driving the TMA/FMO3/TMAO pathway to regulate gut microbiota function,and improving platelet aggregation.Akkermansia may serve as a promising probiotic,which could drive TMA/FMO3/TMAO pathway to regulate Arachidonic acid metabolism to improve platelet aggregation.The findings of this study provide a theoretical basis for the theory of"the heart is connected with the small intestine".展开更多
Finding appropriate flotation reagents to separate copper-nickel sulfide ores from various magnesium silicate gangue minerals has always been a challenge in the mineral processing industry.This study introduced xantha...Finding appropriate flotation reagents to separate copper-nickel sulfide ores from various magnesium silicate gangue minerals has always been a challenge in the mineral processing industry.This study introduced xanthan gum(XG)as a non-toxic and environmentally friendly depressant of talc,olivine,and serpentine.The effects and mechanisms of XG on the aggregation and flotation behavior of talc,olivine and serpentine were investigated by flotation tests,sedimentation tests,IC-FBRM particle size analysis tests,adsorption quantity tests,Fourier transform infrared spectroscopy(FTIR)tests,X-ray photoelectron spectroscopy(XPS)analysis tests and Zeta potential tests.The flotation results indicated that when the three minerals were mixed,XG caused the talc-serpentine aggregation in the solution to shift to olivine-serpentine aggregation,with the remaining XG adsorbing on talc to depress its flotation.In addition,combining XPS and zeta potential tests,the-OH(hydroxyl)groups in XG molecules preferentially adsorbed on Mg sites on the surface of olivine through chemical bonding.The surface potential of olivine significantly shifted to a more negative value,with the negative charge on the olivine surface far exceeding that on the talc surface.This resulted in an increased aggregation effect between positively charged serpentine and negatively charged olivine due to enhanced electrostatic forces.展开更多
Alzheimer’s disease(AD)poses one of the most urgent medical challenges in the 21st century as it affects millions of people.Unfortunately,the etiopathogenesis of AD is not yet fully understood and the current pharmac...Alzheimer’s disease(AD)poses one of the most urgent medical challenges in the 21st century as it affects millions of people.Unfortunately,the etiopathogenesis of AD is not yet fully understood and the current pharmacotherapy options are somewhat limited.Here,we report a novel inhibitor,Compound 44,for targeting cholinesterases,amyloid-β(Aβ)aggregation,and glycogen synthase kinase 3β(GSK-3β)simultaneously with the aim of achieving symptomatic relief and disease modification in AD therapy.We found that Compound 44 had good inhibitory effects on all intended targets with IC_(50)s of submicromolar or better,significant neuroprotective effects in cell models,and beneficial improvement of cognitive deficits in the triple transgenic AD(3×Tg AD)mouse model.Moreover,we showed that Compound 44 acts as an autophagy regulator by inducing nuclear translocation of transcription factor EB through GSK-3βinhibition,enhancing the biogenesis of lysosomes and elevating autophagic flux,thus ameliorating the amyloid burden and tauopathy,as well as mitigating the disease phenotype.Our results suggest that triple-target inhibition via Compound 44 could be a promising strategy that may lead to the development of effective therapeutic approaches for AD.展开更多
Numerous efforts have been devoted to altering the dynamic covalent linkers between the drug structural units in polyprodrugs from the viewpoint of molecular structure;however,the effect of their aggregation states ha...Numerous efforts have been devoted to altering the dynamic covalent linkers between the drug structural units in polyprodrugs from the viewpoint of molecular structure;however,the effect of their aggregation states has not yet been explored.Here,the effect of aggregation states on the in vitro drug release and cytotoxicity was investigated using a pH/glutathione(GSH)co-triggered degradable doxorubicin(DOX)-based polyprodrug(PDOX)as a model,which was synthesized by the facile polymerization of a pH/GSH dual-triggered dimeric prodrug(DDOX_(ss))and 2,2-dimethoxypropane(DMP)by forming acid-labile ketal bond.Owing to the pH/GSH dual-triggered disulfide/α-amide and acid-labile ketal linkers between the DOX structural units,the resultant PDOX exhibited excellent pH/GSH co-triggered DOX release.With a similar diameter,the PDOX-NPs1 nanomedicines via fast precipitation showed faster DOX release than PDOX-NPs2 via slow self-assembly,regardless of their polymerization degree(DP).The effect of aggregation states is expected to be a secondary strategy for a more desired tumor intracellular microenvironment-responsive drug delivery for tumor chemotherapy,in addition to the molecular structures of polyprodrugs as drug self-delivery systems(DSDSs).展开更多
基金financially supported by the Science and Technology Planning Project of Guangdong Province(No.2014A010105009)the National Key Basic Research Program of China(No.2013CB834702)+1 种基金the National Natural Science Foundation of China(Nos.21574044 and 21474031)the Fundamental Research Funds for the Central Universities(No.2015ZY013)
文摘A simple and effective polymer fluorescent thermosensitive system was successfully developed based on the synergistic effect of excimer/monomer interconversion of pyrene derivatives and electrostatic interaction between polyelectrolyte and charged fluorophore. As for the system, the excimer-monomer conversion, thermosensitive behavior and thermo-responsive reversibility were investigated experimentally. Temperature variation and temperature-distribution induced fluorescence changes can be observed directly by naked eyes. Thus, this polymer system holds promise for serving as a fluorescent thermometer.
基金the National Science Council of the People’s Republic of China for supporting this research(Nos.21071018,21271026).
文摘A Cu(II)coordination complex(1)with Schiff ligand derived from diaminomaleonitrile was synthesized and characterized,in which the ligand is rigid,planar and conjugated.The complex 1 displays an interesting fluorescent property relative to solvents which can be turned-on by CH_(2)Cl_(2) and CHCl_(3) solvent molecules.The mechanism of this selective fluorescence emission has been studied based on the crystal structure and the spectrum analysis.The tuning on and off fluorescence of complex 1 can be controlled by the process of supramolecular aggregation/deag-gregation in different solvents.
文摘Neurons are highly polarized cells with axons reaching over a meter long in adult humans.To survive and maintain their proper function,neurons depend on specific mechanisms that regulate spatiotemporal signaling and metabolic events,which need to be carried out at the right place,time,and intensity.Such mechanisms include axonal transport,local synthesis,and liquid-liquid phase separations.Alterations and malfunctions in these processes are correlated to neurodegenerative diseases such as amyotrophic lateral sclerosis(ALS).
基金supported by Jiangsu Provincial Science and Technology Project,grant number J2023124.Jing Guo received this grant,the URLs of sponsors’website is https://kxjst.jiangsu.gov.cn/(accessed on 06 June 2024).
文摘The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charging stations,addressing the unique challenges posed by third-party aggregation platforms.Our approach integrates node equations-based on the parameter identification with a novel deep learning model,xDeepCIN,to detect abnormal data reporting indicative of aggregation attacks.We employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter estimation.The xDeepCIN model,incorporating a Compressed Interaction Network,has the ability to capture complex feature interactions in sparse,high-dimensional charging data.Experimental results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance,with F1-scores increasing by up to 32.3%for specific anomaly types compared to traditional methods,such as wide&deep and DeepFM(Factorization-Machine).Our framework exhibits robust scalability,effectively handling networks ranging from 8 to 85 charging points.Furthermore,we achieve real-time monitoring capabilities,with parameter identification completing within seconds for networks up to 1000 nodes.This research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats,offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.
基金supported by the National Key R&D Program of China(No.2023YFB2703700)the National Natural Science Foundation of China(Nos.U21A20465,62302457,62402444,62172292)+4 种基金the Fundamental Research Funds of Zhejiang Sci-Tech University(Nos.23222092-Y,22222266-Y)the Program for Leading Innovative Research Team of Zhejiang Province(No.2023R01001)the Zhejiang Provincial Natural Science Foundation of China(Nos.LQ24F020008,LQ24F020012)the Foundation of State Key Laboratory of Public Big Data(No.[2022]417)the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(No.2023C01119).
文摘As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and user privacy concerns within smart grids.However,existing methods struggle with efficiency and security when processing large-scale data.Balancing efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent challenge.This paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data modalities.The approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user privacy.It also explores the application of Boneh Lynn Shacham(BLS)signatures for user authentication.The proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.
文摘Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.
基金supported by the Innovation and Technology Commission (ITCPD/17-9)the Hong Kong Research Grants Council,China (GRF16104517)(to KKKC)。
文摘Parkinson's disease (PD) is a common degenerative disorder that is becoming increasingly prevalent because of the global aging population.The exact cause of the disorder is unknown;however,recent studies have suggested that multiple factors may contribute to its pathogenesis.PD is characterized by a movement disorder that primarily affects motor control;pathologically,the disease is marked by the presence of Lewy bodies (LBs) in the brain.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF-2022R1C1C2012463).
文摘Integrating Artificial Intelligence of Things(AIoT)in healthcare offers transformative potential for real-time diagnostics and collaborative learning but presents critical challenges,including privacy preservation,computational efficiency,and regulatory compliance.Traditional approaches,such as differential privacy,homomorphic encryption,and secure multi-party computation,often fail to balance performance and privacy,rendering them unsuitable for resource-constrained healthcare AIoT environments.This paper introduces LMSA(Lightweight Multi-Key Secure Aggregation),a novel framework designed to address these challenges and enable efficient,secure federated learning across distributed healthcare institutions.LMSA incorporates three key innovations:(1)a lightweight multikey management system leveraging Diffie-Hellman key exchange and SHA3-256 hashing,achieving O(n)complexity with AES(Advanced Encryption Standard)-256-level security;(2)a privacy-preserving aggregation protocol employing hardware-accelerated AES-CTR(CounTeR)encryption andmodular arithmetic for securemodel weight combination;and(3)a resource-optimized implementation utilizing AES-NI(New Instructions)instructions and efficient memory management for real-time operations on constrained devices.Experimental evaluations using the National Institutes of Health(NIH)Chest X-ray dataset demonstrate LMSA’s ability to train multi-label thoracic disease prediction models with Vision Transformer(ViT),ResNet-50,and MobileNet architectures across distributed healthcare institutions.Memory usage analysis confirmed minimal overhead,with ViT(327.30 MB),ResNet-50(89.87 MB),and MobileNet(8.63 MB)maintaining stable encryption times across communication rounds.LMSA ensures robust security through hardware acceleration,enabling real-time diagnostics without compromising patient confidentiality or regulatory compliance.Future research aims to optimize LMSA for ultra-low-power devices and validate its scalability in heterogeneous,real-world environments.LMSA represents a foundational advancement for privacy-conscious healthcare AI applications,bridging the gap between privacy and performance.
基金funded by the Deanship of Scientific Research,the Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia under the project(KFU250420).
文摘Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink.However,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater environments.Additionally,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole issue.Moreover,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network performance.To address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile UWSNs.The proposed method has four main phases:clustering,CH selection,data aggregation,and re-clustering.During CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy efficiency.In the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving energy.To adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects CHs.Simulation results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.
基金supported by State Grid science and technology projects“Research on energy and power sup-ply and demand interactive simulation technology for new power system(5100-202257028A-1-1-ZN)”.
文摘Increasing interest has been directed toward the potential of heterogeneous flexible loads to mitigate the challenges associated with the increasing variability and uncertainty of renewable generation.Evaluating the aggregated flexible region of load clusters managed by load aggregators is the crucial basis of power system scheduling for the system operator.This is because the aggregation result affects the qual-ity of the scheduling schemes.A stringent computation based on the Minkowski sum is NP-hard,whereas existing approximation meth-ods that use a special type of polytope exhibit limited adaptability when aggregating heterogeneous loads.This study proposes a stringent internal approximation method based on the convex hull of multiple layers of maximum volume boxes and embeds it into a day-ahead scheduling optimization model.The numerical results indicate that the aggregation accuracy can be improved compared with methods based on one type of special polytope,including boxes,zonotopes,and homothets.Hence,the reliability and economy of the power sys-tem scheduling can be enhanced.
基金supported by the National Natural Science Foundation of China(Nos.12072027,62103052,61603346 and 62103379)the Henan Key Laboratory of General Aviation Technology,China(No.ZHKF-230201)+3 种基金the Funding for the Open Research Project of the Rotor Aerodynamics Key Laboratory,China(No.RAL20200101)the Key Research and Development Program of Henan Province,China(Nos.241111222000 and 241111222900)the Key Science and Technology Program of Henan Province,China(No.232102220067)the Scholarship Funding from the China Scholarship Council(No.202206030079).
文摘In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.
基金Funds for High-Level Talents Programof Xi’an International University(Grant No.XAIU202411).
文摘The Cyber-Physical Systems (CPS) supported by Wireless Sensor Networks (WSN) helps factories collect data and achieve seamless communication between physical and virtual components. Sensor nodes are energy-constrained devices. Their energy consumption is typically correlated with the amount of data collection. The purpose of data aggregation is to reduce data transmission, lower energy consumption, and reduce network congestion. For large-scale WSN, data aggregation can greatly improve network efficiency. However, as many heterogeneous data is poured into a specific area at the same time, it sometimes causes data loss and then results in incompleteness and irregularity of production data. This paper proposes an information processing model that encompasses the Energy-Conserving Data Aggregation Algorithm (ECDA) and the Efficient Message Reception Algorithm (EMRA). ECDA is divided into two stages, Energy conservation based on the global cost and Data aggregation based on ant colony optimization. The EMRA comprises the Polling Message Reception Algorithm (PMRA), the Shortest Time Message Reception Algorithm (STMRA), and the Specific Condition Message Reception Algorithm (SCMRA). These algorithms are not only available for the regularity and directionality of sensor information transmission, but also satisfy the different requirements in small factory environments. To compare with the recent HPSO-ILEACH and E-PEGASIS, DCDA can effectively reduce energy consumption. Experimental results show that STMRA consumes 1.3 times the time of SCMRA. Both optimization algorithms exhibit higher time efficiency than PMRA. Furthermore, this paper also evaluates these three algorithms using AHP.
基金supported by ZTE Industry-University-Institute Cooperation Funds under Grant No.IA20230628015the State Key Laboratory of Particle Detection and Electronics under Grant No.SKLPDE-KF-202314.
文摘Digital twin is a novel technology that has achieved significant progress in industrial manufactur-ing systems in recent years.In the digital twin envi-ronment,entities in the virtual space collect data from devices in the physical space to analyze their states.However,since a lot of devices exist in the physical space,the digital twin system needs to aggregate data from multiple devices at the edge gateway.Homomor-phic integrity and confidentiality protections are two important requirements for this data aggregation pro-cess.Unfortunately,existing homomorphic encryp-tion algorithms do not support integrity protection,and existing homomorphic signing algorithms require all signers to use the same signing key,which is not feasible in the digital twin environment.Moreover,for both integrity and confidentiality protections,the homomorphic signing algorithm must be compatible with the aggregation manner of the homomorphic en-cryption algorithm.To address these issues,this paper designs a novel homomorphic aggregation scheme,which allows multiple devices in the physical space to sign different data using different keys and support in-tegrity and confidentiality protections.Finally,the security of the newly designed scheme is analyzed,and its efficiency is evaluated.Experimental results show that our scheme is feasible for real world applications.
基金the Science and Technology Project of State Grid Shanxi Electric Power Co.,Ltd.,with the project number 52051L240001.
文摘In contemporary power systems,delving into the flexible regulation potential of demand-side resources is of paramount significance for the efficient operation of power grids.This research puts forward an innovative multivariate flexible load aggregation control approach that takes dynamic demand response into full consideration.In the initial stage,using generalized time-domain aggregation modelling for a wide array of heterogeneous flexible loads,including temperature-controlled loads,electric vehicles,and energy storage devices,a novel calculation method for their maximum adjustable capacities is devised.Distinct from conventional methods,this newly developed approach enables more precise and adaptable quantification of the load-adjusting capabilities,thereby enhancing the accuracy and flexibility of demand-side resource management.Subsequently,an SSA-BiLSTM flexible load classification prediction model is established.This model represents an innovative application in the field,effectively combining the advantages of the Sparrow Search Algorithm(SSA)and the Bidirectional Long-Short-Term Memory(BiLSTM)neural network.Furthermore,a parallel Markov chain is introduced to evaluate the switching state transfer probability of flexible loads accurately.This integration allows for a more refined determination of the maximum response capacity range of the flexible load aggregator,significantly improving the precision of capacity assessment compared to existing methods.Finally,in consonance with the intra-day scheduling plan,a newly developed diffuse filling algorithm is implemented to control the activation times of flexible loads precisely,thus achieving real-time dynamic demand response.Through in-depth case analysis and comprehensive comparative studies,the effectiveness of the proposed method is convincingly validated.With its innovative techniques and enhanced performance,it is demonstrated that this method has the potential to substantially enhance the utilization efficiency of demand-side resources in power systems,providing a novel and effective solution for optimizing power grid operation and demand-side management.
基金supported by the National Natural Science Foundation of China(Grant No.72401207 and 42101300)Beijing Municipal Education Commission,China(Grant No.SM202110038001).
文摘This paper investigates China's coal price volatility spreaders(CPVSs)from the supply side to locate the volatility source since coal price volatility may destabilize many downstream products'prices or even bring uncertainties to macroeconomic output.Especially in the carbon neutrality context,China's coal market is being reconstructed and responding to imbalances between supply and demand;identifying the CPVSs helps alleviate rising market instability and prevent energy-induced system risk.To achieve this objective,we explore causalities among 938 weekly coal prices reported by different coal-producing areas of China from 2006.9.4 to 2021.7.12 using the transfer entropy method.Then,coal price volatility influence is quantified to identify the CPVSs by conjointly using complex network theory and a rank aggregation method.The validity test demonstrates that the proposed hybrid method efficiently identifies the CPVSs as it correlates to many price determinants,e.g.,electricity and coal consumption and generation.The empirical results show that causalities among coal prices changed dramatically in 2016,2018,and 2020,affected by coal decapacity and carbon neutrality policies.Before 2018,coal-producing provinces with strong demand for coal and electricity,e.g.,Jiangxi,Chongqing,and Sichuan,were CPVSs;after 2019,those with comparative advantages in coal supply,e.g.,Gansu and Ningxia,were CPVSs.Overall,the coal market is unstable and sensitive to energy policy and external shocks.Policymakers and market participants are recommended to monitor and manage the CPVSs to improve energy security,avoid policy-induced instability and prevent risks caused by coal price fluctuations.
基金supported by the National Natural Science Foundation of China under Grant 61931005Beijing Natural Science Foundation under Grant L202018the Key Laboratory of Internet of Vehicle Technical Innovation and Testing(CAICT),Ministry of Industry and Information Technology under Grant No.KL-2023-001。
文摘In Internet of Vehicles,VehicleInfrastructure-Cloud cooperation supports diverse intelligent driving and intelligent transportation applications.Federated Learning(FL)is the emerging computation paradigm to provide efficient and privacypreserving collaborative learning.However,in Io V environment,federated learning faces the challenges introduced by high mobility of vehicles and nonIndependently Identically Distribution(non-IID)of data.High mobility causes FL clients quit and the communication offline.The non-IID data leads to slow and unstable convergence of global model and single global model's weak adaptability to clients with different localization characteristics.Accordingly,this paper proposes a personalized aggregation strategy for hierarchical Federated Learning in Io V environment,including Fed SA(Special Asynchronous Federated Learning with Self-adaptive Aggregation)for low-level FL between a Road Side Unit(RSU)and the vehicles within its coverage,and Fed Att(Federated Learning with Attention Mechanism)for high-level FL between a cloud server and multiple RSUs.Agents self-adaptively obtain model aggregation weight based on Advantage Actor-Critic(A2C)algorithm.Experiments show the proposed strategy encourages vehicles to participate in global aggregation,and outperforms existing methods in training performance.
基金Supported by National Natural Science Foundation of China Project:based on the Theory of“the Heart is in Harmony with the Small Intestine”to Explore the Influence and Mechanism of Gut Microbes on High Platelet Reactivity of Acute Coronary Syndrome with Phlegm and Blood Stasis Syndrome(No.82104841)Education Department of Liaoning Province Young Science and Technology Talents"Seedling"Project:to Explore the Effect and Mechanism of Huayu Qutan Formula on Platelet Function in Acute Coronary Syndrome Patients with Phlegm and Blood Stasis Syndrome after Percutaneous Coronary Intervention based on Intestinal Microbiome(No.L202039)。
文摘OBJECTIVE:To investigate the effects of gut microbes regulation of the trimethylamine(TMA)/flavin containing monooxygenase 3(FMO3)/trimethylamine N-oxide(TMAO)pathway on platelet aggregation in acute coronary syndrome(ACS)rats and the intervention of Huayu Qutan formula(化瘀祛痰方).METHODS:The ACS rats with syndrome of phlegm and blood stasis rats were established.Platelet,platelet aggregation,platelet activation markers and TMA/FMO3/TMAO pathway were detected.Metagenomics technology was employed to analyze the characteristics of the gut microbiota.RESULTS:Huayu Qutan formula and gut microbes could inhibit high platelet reactivity and regulate the TMA/FMO3/TMAO pathway.The dominant bacteria in ACS rats including but not limited to the major phyla,Firmicutes,Bacteroidetes,Actinobacteria,and Proteobacteria,also including some low abundance phyla,Fusobacteria,Verrucomicrobia,Spirochaetes,and Deferribacteres.The dominant bacteria in the Huayu Qutan formula group were Synergistetes,Deferribacteres,Deferribacteraceae,Faecalibacterium and Mucispirillum.In the Huayu Qutan formula combined with fecal bacteria enema group,the dominant bacteria were Verrucomicrobia,Verrucomicrobiae,Akkermansia and Verrucomicrobium.These gut microbiota were correlated with pathways such as Riboflavin metabolism and Arachidonic acid metabolism.CONCLUSION:Huayu Qutan formula may prevent ACS by modulating gut microbes Synergistetes,Faecalibacterium and Allobaculum,regulating the iron metabolism of Deferribacteres,and driving the TMA/FMO3/TMAO pathway to regulate gut microbiota function,and improving platelet aggregation.Akkermansia may serve as a promising probiotic,which could drive TMA/FMO3/TMAO pathway to regulate Arachidonic acid metabolism to improve platelet aggregation.The findings of this study provide a theoretical basis for the theory of"the heart is connected with the small intestine".
基金Project(52264022)supported by the National Natural Science Foundation of ChinaProject(BGRIMM-KJSKL-2025-17)supported by the Open Foundation of State Key Laboratory of Mineral Processing,China。
文摘Finding appropriate flotation reagents to separate copper-nickel sulfide ores from various magnesium silicate gangue minerals has always been a challenge in the mineral processing industry.This study introduced xanthan gum(XG)as a non-toxic and environmentally friendly depressant of talc,olivine,and serpentine.The effects and mechanisms of XG on the aggregation and flotation behavior of talc,olivine and serpentine were investigated by flotation tests,sedimentation tests,IC-FBRM particle size analysis tests,adsorption quantity tests,Fourier transform infrared spectroscopy(FTIR)tests,X-ray photoelectron spectroscopy(XPS)analysis tests and Zeta potential tests.The flotation results indicated that when the three minerals were mixed,XG caused the talc-serpentine aggregation in the solution to shift to olivine-serpentine aggregation,with the remaining XG adsorbing on talc to depress its flotation.In addition,combining XPS and zeta potential tests,the-OH(hydroxyl)groups in XG molecules preferentially adsorbed on Mg sites on the surface of olivine through chemical bonding.The surface potential of olivine significantly shifted to a more negative value,with the negative charge on the olivine surface far exceeding that on the talc surface.This resulted in an increased aggregation effect between positively charged serpentine and negatively charged olivine due to enhanced electrostatic forces.
基金supported by the Science and Technology Development Fund,Macao SAR(File no.0062/2021/A)the University of Macao(MYRG2022-00171-FHS).
文摘Alzheimer’s disease(AD)poses one of the most urgent medical challenges in the 21st century as it affects millions of people.Unfortunately,the etiopathogenesis of AD is not yet fully understood and the current pharmacotherapy options are somewhat limited.Here,we report a novel inhibitor,Compound 44,for targeting cholinesterases,amyloid-β(Aβ)aggregation,and glycogen synthase kinase 3β(GSK-3β)simultaneously with the aim of achieving symptomatic relief and disease modification in AD therapy.We found that Compound 44 had good inhibitory effects on all intended targets with IC_(50)s of submicromolar or better,significant neuroprotective effects in cell models,and beneficial improvement of cognitive deficits in the triple transgenic AD(3×Tg AD)mouse model.Moreover,we showed that Compound 44 acts as an autophagy regulator by inducing nuclear translocation of transcription factor EB through GSK-3βinhibition,enhancing the biogenesis of lysosomes and elevating autophagic flux,thus ameliorating the amyloid burden and tauopathy,as well as mitigating the disease phenotype.Our results suggest that triple-target inhibition via Compound 44 could be a promising strategy that may lead to the development of effective therapeutic approaches for AD.
文摘Numerous efforts have been devoted to altering the dynamic covalent linkers between the drug structural units in polyprodrugs from the viewpoint of molecular structure;however,the effect of their aggregation states has not yet been explored.Here,the effect of aggregation states on the in vitro drug release and cytotoxicity was investigated using a pH/glutathione(GSH)co-triggered degradable doxorubicin(DOX)-based polyprodrug(PDOX)as a model,which was synthesized by the facile polymerization of a pH/GSH dual-triggered dimeric prodrug(DDOX_(ss))and 2,2-dimethoxypropane(DMP)by forming acid-labile ketal bond.Owing to the pH/GSH dual-triggered disulfide/α-amide and acid-labile ketal linkers between the DOX structural units,the resultant PDOX exhibited excellent pH/GSH co-triggered DOX release.With a similar diameter,the PDOX-NPs1 nanomedicines via fast precipitation showed faster DOX release than PDOX-NPs2 via slow self-assembly,regardless of their polymerization degree(DP).The effect of aggregation states is expected to be a secondary strategy for a more desired tumor intracellular microenvironment-responsive drug delivery for tumor chemotherapy,in addition to the molecular structures of polyprodrugs as drug self-delivery systems(DSDSs).
