Developing an integrated and intelligent approach to securing the ITE(information technology environment)is an emergent and evolving concern for every organization and consumer entity during the last few decades.Major...Developing an integrated and intelligent approach to securing the ITE(information technology environment)is an emergent and evolving concern for every organization and consumer entity during the last few decades.Major topics of concern include“SI”(security intelligence),“D-DA”(data-driven analytics),“PE”(proven expertise),and“R-TD”(real-time defense)capabilities.“DRBTs”(dynamic response behavior types)include“incident response”,“endpoint management”,“threat intelligence”,“network security”,and“fraud protection”.The consumer demand for electricity as essential public access and service is indexed to population growth estimates.Consumer-driven economies continue to add electrical consumption to their grids even though improvements in lower-power consumption and higher design efficiencies are present in new electric-powered products.Dependence on the production of electrical energy has no peer replacement technology and creates a societal vulnerability to targeted public electrical grid interruptions.When access to,or production of,electrical power is interrupted,the result is a“Mass Effect”every consumer feels with equal distribution.Electric grid security falls directly into the levels of authorized,and unauthorized,access via the“IoT”(Internet of Things)concepts,and the“IoM2M”(Internet of Machine-to-Machine)integration.Electrical grid operations that include production and network management augment each other in order to support the demand for electricity every day either in peak or off-peak,thus cybersecurity plays a big role in the protection of such assets at our disposal.With help from AI(artificial intelligence)integrated into the IoT a resilient system can be built to protect the electric grid system nationwide and will be able to detect and preempt Smart Malware attacks.展开更多
Industrial intelligence is a core technology in the upgrading of the production processes and management modes of traditional industries.Motivated by the major development strategies and needs of industrial intellectu...Industrial intelligence is a core technology in the upgrading of the production processes and management modes of traditional industries.Motivated by the major development strategies and needs of industrial intellectualization in China,this study presents an innovative fusion structure that encompasses the theoretical foundation and technological innovation of data analytics and optimization,as well as their application to smart industrial engineering.First,this study describes a general methodology for the fusion of data analytics and optimization.Then,it identifies some data analytics and system optimization technologies to handle key issues in smart manufacturing.Finally,it provides a four-level framework for smart industry based on the theoretical and technological research on the fusion of data analytics and optimization.The framework uses data analytics to perceive and analyze industrial production and logistics processes.It also demonstrates the intelligent capability of planning,scheduling,operation optimization,and optimal control.Data analytics and system optimization technologies are employed in the four-level framework to overcome some critical issues commonly faced by manufacturing,resources and materials,energy,and logistics systems,such as high energy consumption,high costs,low energy efficiency,low resource utilization,and serious environmental pollution.The fusion of data analytics and optimization allows enterprises to enhance the prediction and control of unknown areas and discover hidden knowledge to improve decision-making efficiency。Therefore,industrial intelligence has great importance in China’s industrial upgrading and transformation into a true industrial power.展开更多
Climate research produces a wealth of multivariate data. These data often have a geospatial reference and so it is of interest to show them within their geospatial context. One can consider this configuration as a mul...Climate research produces a wealth of multivariate data. These data often have a geospatial reference and so it is of interest to show them within their geospatial context. One can consider this configuration as a multifield visualization problem, where the geo-space provides the expanse of the field. However, there is a limit on the amount of multivariate information that can be fit within a certain spatial location, and the use of linked multivariate information displays has previously been devised to bridge this gap. In this paper we focus on the interactions in the geographical display, present an implementation that uses Google Earth, and demonstrate it within a tightly linked parallel coordinates display. Several other visual representations, such as pie and bar charts are integrated into the Google Earth display and can be interactively manipulated. Further, we also demonstrate new brushing and visualization techniques for parallel coordinates, such as fixed-window brushing and correlation-enhanced display. We conceived our system with a team of climate researchers, who already made a few important discoveries using it. This demonstrates our system＇s great potential to enable scientific discoveries, possibly also in other domains where data have a geospatial reference.展开更多
Many cities,countries and transport operators around the world are striving to design intelligent transport systems.These systems capture the value of multisource and multiform data related to the functionality and us...Many cities,countries and transport operators around the world are striving to design intelligent transport systems.These systems capture the value of multisource and multiform data related to the functionality and use of transportation infrastructure to better support human mobility,interests,economic activity and lifestyles.They aim to provide services that can enable transportation customers and managers to be better informed and make safer and more efficient use of infrastructure.In developing principles,guidelines,methods and tools to enable synergistic work between humans and computer-generated information,the science of visual analytics continues to expand our understanding of data through effective and interactive visual interfaces.In this paper,we describe an application of visual analytics related to the study of movement and transportation systems.This application documents the use of rapid,2D and 3D web visualisation and data analytics libraries and explores their potential added value to the analysis of big public transport performance data.A novel approach to displaying such data through a generalisable framework visualisation system is demonstrated.This framework recalls over a year’sworth of public transport performance data at a highly granular level in a fast,interactive browser-based environment.Greater Sydney,Australia forms a case study to highlight potential uses of the visualisation of such large,passively-collected data sets as an applied research scenario.In this paper,we argue that such highly visual systems can add data-driven rigour to service planning and longer-term transport decision-making.Furthermore,they enable the sharing of quality of service statistics with various stakeholders and citizens and can showcase improvements in services before and after policy decisions.The paper concludes by making recommendations on the value of this approach in embedding these or similar web-based systems in transport planning practice,performance management,optimisation and understanding of customer experience.展开更多
Regulation of apoptosis represents a key parameter in all living organisms.In this paper,an input-induced logic-gated modular nanocalculator is designed to regulate cancer cell apoptosis by programmatically combining ...Regulation of apoptosis represents a key parameter in all living organisms.In this paper,an input-induced logic-gated modular nanocalculator is designed to regulate cancer cell apoptosis by programmatically combining and connecting logic gate modules with different functions.Via rational design of the various logic gate modules of the nanocalculator,different apoptosis related operations including cancer cell targeting,apoptosis induction,and apoptosis monitoring could be performed.Importantly,each of these logic gate modules could independently perform apoptosis related YES logic operations when ran separately.After combining each YES logic gate module into a logic circuit and connecting it to the GO scaffold to construct a logic-gated nanocalculator,the input-induced logic-gated modular nanocalculator could selectively enter cancer cells and control the drug release to logically apoptosis(output),by performing AND logic gate operations when inputs(nucleolin and H^(+)) were included at the same time.Moreover,evidence suggests that these efficient logical calculations proceed in cancer cell apoptosis regulation without the general limiations of lithography in nanotechnology.As such,this work provides a new vision for the construction of a logic-gated modular nanocalculator with logical calculation proficiency potentially useful in cancer therapy and the regulation of life.展开更多
Vulnerabilities are a known problem in modern Open Source Software(OSS).Most developers often rely on third-party libraries to accelerate feature implementation.However,these libraries may contain vulnerabilities that...Vulnerabilities are a known problem in modern Open Source Software(OSS).Most developers often rely on third-party libraries to accelerate feature implementation.However,these libraries may contain vulnerabilities that attackers can exploit to propagate malicious code,posing security risks to dependent projects.Existing research addresses these challenges through Software Composition Analysis(SCA)for vulnerability detection and remediation.Nevertheless,current solutions may introduce additional issues,such as incompatibilities,dependency conflicts,and additional vulnerabilities.To address this,we propose Vulnerability Scan and Protection(VulnScanPro),a robust solution for detection and remediation vulnerabilities in Java projects.Specifically,VulnScanPro builds a finegrained method graph to identify unreachable methods.The method graph is mapped to the project’s dependency tree,constructing a comprehensive vulnerability propagation graph that identifies unreachable vulnerable APIs and dependencies.Based on this analysis,we propose three solutions for vulnerability remediation:(1)Removing unreachable vulnerable dependencies,thereby resolving security risks and reducing maintenance overhead.(2)Upgrading vulnerable dependencies to the closest non-vulnerable versions,while pinning the versions of transitive dependencies introduced by the vulnerable dependency,in order to mitigate compatibility issues and prevent the introduction of new vulnerabilities.(3)Eliminating unreachable vulnerable APIs,particularly when security patches are either incompatible or absent.Experimental results show that these solutions effectively mitigate vulnerabilities and enhance the overall security of the project.展开更多
Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness a...Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.展开更多
Motor imbalance is a critical failure mode in rotating machinery,potentially causing severe equipment damage if undetected.Traditional vibration-based diagnostic methods rely on direct sensor contact,leading to instal...Motor imbalance is a critical failure mode in rotating machinery,potentially causing severe equipment damage if undetected.Traditional vibration-based diagnostic methods rely on direct sensor contact,leading to installation challenges and measurement artifacts that can compromise accuracy.This study presents a novel radar-based framework for non-contact motor imbalance detection using 24 GHz continuous-wave radar.A dataset of 1802 experimental trials was sourced,covering four imbalance levels(0,10,20,30 g)across varying motor speeds(500–1500 rpm)and load torques(0–3 Nm).Dual-channel in-phase and quadrature radar signals were captured at 10,000 samples per second for 30-s intervals,preserving both amplitude and phase information for analysis.A multi-domain feature extraction methodology captured imbalance signatures in time,frequency,and complex signal domains.From 65 initial features,statistical analysis using Kruskal–Wallis tests identified significant descriptors,and recursive feature elimination with Random Forest reduced the feature set to 20 dimensions,achieving 69%dimensionality reduction without loss of performance.Six machine learning algorithms,Random Forest,Extra Trees Classifier,Extreme Gradient Boosting,Categorical Boosting,Support Vector Machine with radial basis function kernel,and k-Nearest Neighbors were evaluated with grid-search hyperparameter optimization and five-fold cross-validation.The Extra Trees Classifier achieved the best performance with 98.52%test accuracy,98%cross-validation accuracy,and minimal variance,maintaining per-class precision and recall above 97%.Its superior performance is attributed to its randomized split selection and full bootstrapping strategy,which reduce variance and overfitting while effectively capturing the nonlinear feature interactions and non-normal distributions present in the dataset.The model’s average inference time of 70 ms enables near real-time deployment.Comparative analysis demonstrates that the radar-based framework matches or exceeds traditional contact-based methods while eliminating their inherent limitations,providing a robust,scalable,and noninvasive solution for industrial motor condition monitoring,particularly in hazardous or space-constrained environments.展开更多
The problem of fake news detection(FND)is becoming increasingly important in the field of natural language processing(NLP)because of the rapid dissemination of misleading information on the web.Large language models(L...The problem of fake news detection(FND)is becoming increasingly important in the field of natural language processing(NLP)because of the rapid dissemination of misleading information on the web.Large language models(LLMs)such as GPT-4.Zero excels in natural language understanding tasks but can still struggle to distinguish between fact and fiction,particularly when applied in the wild.However,a key challenge of existing FND methods is that they only consider unimodal data(e.g.,images),while more detailed multimodal data(e.g.,user behaviour,temporal dynamics)is neglected,and the latter is crucial for full-context understanding.To overcome these limitations,we introduce M3-FND(Multimodal Misinformation Mitigation for False News Detection),a novel methodological framework that integrates LLMs with multimodal data sources to perform context-aware veracity assessments.Our method proposes a hybrid system that combines image-text alignment,user credibility profiling,and temporal pattern recognition,which is also strengthened through a natural feedback loop that provides real-time feedback for correcting downstream errors.We use contextual reinforcement learning to schedule prompt updating and update the classifier threshold based on the latest multimodal input,which enables the model to better adapt to changing misinformation attack strategies.M3-FND is tested on three diverse datasets,FakeNewsNet,Twitter15,andWeibo,which contain both text and visual socialmedia content.Experiments showthatM3-FND significantly outperforms conventional and LLMbased baselines in terms of accuracy,F1-score,and AUC on all benchmarks.Our results indicate the importance of employing multimodal cues and adaptive learning for effective and timely detection of fake news.展开更多
Composite polymer electrolytes(CPEs)offer a promising solution for all-solid-state lithium-metal batteries(ASSLMBs).However,conventional nanofillers with Lewis-acid-base surfaces make limited contribution to improving...Composite polymer electrolytes(CPEs)offer a promising solution for all-solid-state lithium-metal batteries(ASSLMBs).However,conventional nanofillers with Lewis-acid-base surfaces make limited contribution to improving the overall performance of CPEs due to their difficulty in achieving robust electrochemical and mechanical interfaces simultaneously.Here,by regulating the surface charge characteristics of halloysite nanotube(HNT),we propose a concept of lithium-ion dynamic interface(Li^(+)-DI)engineering in nano-charged CPE(NCCPE).Results show that the surface charge characteristics of HNTs fundamentally change the Li^(+)-DI,and thereof the mechanical and ion-conduction behaviors of the NCCPEs.Particularly,the HNTs with positively charged surface(HNTs+)lead to a higher Li^(+)transference number(0.86)than that of HNTs-(0.73),but a lower toughness(102.13 MJ m^(-3)for HNTs+and 159.69 MJ m^(-3)for HNTs-).Meanwhile,a strong interface compatibilization effect by Li^(+)is observed for especially the HNTs+-involved Li^(+)-DI,which improves the toughness by 2000%compared with the control.Moreover,HNTs+are more effective to weaken the Li^(+)-solvation strength and facilitate the formation of Li F-rich solid-electrolyte interphase of Li metal compared to HNTs-.The resultant Li|NCCPE|LiFePO4cell delivers a capacity of 144.9 m Ah g^(-1)after 400 cycles at 0.5 C and a capacity retention of 78.6%.This study provides deep insights into understanding the roles of surface charges of nanofillers in regulating the mechanical and electrochemical interfaces in ASSLMBs.展开更多
MXene-based smart contact lenses demonstrate a cutting-edge advancement in wearable ophthalmic technology,combining real-time biosensing,therapeutic capabilities,and user comfort in a single platform.These devices tak...MXene-based smart contact lenses demonstrate a cutting-edge advancement in wearable ophthalmic technology,combining real-time biosensing,therapeutic capabilities,and user comfort in a single platform.These devices take the advantage of the exceptional electrical conductivity,mechanical flexibility,and biocompatibility of two-dimensional MXenes to enable noninvasive,tear-based monitoring of key physiological markers such as intraocular pressure and glucose levels.Recent developments focus on the integration of transparent MXene films into the conventional lens materials,allowing multifunctional performance including photothermal therapy,antimicrobial and anti-inflammation protection,and dehydration resistance.These innovations offer promising strategies for ocular disease management and eye protection.In addition to their multifunctionality,improvements in MXene synthesis and device engineering have enhanced the stability,transparency,and wearability of these lenses.Despite these advances,challenges remain in long-term biostability,scalable production,and integration with wireless communication systems.This review summarizes the current progress,key challenges,and future directions of MXene-based smart contact lenses,highlighting their transformative potential in next-generation digital healthcare and ophthalmic care.展开更多
The mechanisms underlying the pathophysiology of ischemic stroke are complex and multifactorial and include excitotoxicity,oxidative stress,inflammatory responses,and blood–brain barrier disruption.While vascular rec...The mechanisms underlying the pathophysiology of ischemic stroke are complex and multifactorial and include excitotoxicity,oxidative stress,inflammatory responses,and blood–brain barrier disruption.While vascular recanalization treatments such as thrombolysis and mechanical thrombectomy have achieved some success,reperfusion injury remains a significant contributor to the exacerbation of brain injury.This emphasizes the need for developing neuroprotective strategies to mitigate this type of injury.The purpose of this review was to examine the application of nanotechnology in the treatment of ischemic stroke,covering research progress in nanoparticlebased drug delivery,targeted therapy,and antioxidant and anti-inflammatory applications.Nanobased drug delivery systems offer several advantages compared to traditional therapies,including enhanced blood–brain barrier penetration,prolonged drug circulation time,improved drug stability,and targeted delivery.For example,inorganic nanoparticles,such as those based on CeO_(2),have been widely studied for their strong antioxidant capabilities.Biomimetic nanoparticles,such as those coated with cell membranes,have garnered significant attention owing to their excellent biocompatibility and targeting abilities.Nanoparticles can be used to deliver a wide range of neuroprotective agents,such as antioxidants(e.g.,edaravone),anti-inflammatory drugs(e.g.,curcumin),and neurotrophic factors.Nanotechnology significantly enhances the efficacy of these drugs while minimizing adverse reactions.Although nanotechnology has demonstrated great potential in animal studies,its clinical application still faces several challenges,including the long-term safety of nanoparticles,the feasibility of large-scale production,quality control,and the ability to predict therapeutic effects in humans.In summary,nanotechnology holds significant promise for the treatment of ischemic stroke.Future research should focus on further exploring the mechanisms of action of nanoparticles,developing multifunctional nanoparticles,and validating their safety and efficacy through rigorous clinical trials.Moreover,interdisciplinary collaboration is essential for advancing the use of nanotechnology in stroke treatment.展开更多
Graphite,encompassing both natural graphite and synthetic graphite,and graphene,have been extensively utilized and investigated as anode materials and additives in lithium-ion batteries(LIBs).In the pursuit of carbon ...Graphite,encompassing both natural graphite and synthetic graphite,and graphene,have been extensively utilized and investigated as anode materials and additives in lithium-ion batteries(LIBs).In the pursuit of carbon neutrality,LIBs are expected to play a pivotal role in reducing CO_(2)emissions by decreasing reliance on fossil fuels and enabling the integration of renewable energy sources.Owing to their technological maturity and exceptional electrochemical performance,the global production of graphite and graphene for LIBs is projected to continue expanding.Over the past decades,numerous researchers have concentrated on reducing the material and energy input whilst optimising the electrochemical performance of graphite and graphene,through novel synthesis methods and various modifications at the laboratory scale.This review provides a comprehensive examination of the manufacturing methods,environmental impact,research progress,and challenges associated with graphite and graphene in LIBs from an industrial perspective,with a particular focus on the carbon footprint of production processes.Additionally,it considers emerging challenges and future development directions of graphite and graphene,offering significant insights for ongoing and future research in the field of green LIBs.展开更多
The increasing production and release of synthetic organic chemicals,including pharmaceuticals,into our envi-ronment has allowed these substances to accumulate in our surface water systems.Current purification technol...The increasing production and release of synthetic organic chemicals,including pharmaceuticals,into our envi-ronment has allowed these substances to accumulate in our surface water systems.Current purification technolo-gies have been unable to eliminate these pollutants,resulting in their ongoing release into aquatic ecosystems.This study focuses on cloperastine(CPS),a cough suppressant and antihistamine medication.The environmental impact of CPS usage has become a concern,mainly due to its increased detection during the COVID-19 pandemic.CPS has been found in wastewater treatment facilities,effluents from senior living residences,river waters,and sewage sludge.However,the photosensitivity of CPS and its photodegradation profile remain largely unknown.This study investigates the photodegradation process of CPS under simulated tertiary treatment conditions using UV photolysis,a method commonly applied in some wastewater treatment plants.Several transformation prod-ucts were identified,evaluating their kinetic profiles using chemometric approaches(i.e.,curve fitting and the hard-soft multivariate curve resolution-alternating least squares(HS-MCR-ALS)algorithm)and calculating the reaction quantum yield.As a result,three different transformation products have been detected and correctly identified.In addition,a comprehensive description of the kinetic pathway involved in the photodegradation process of the CPS drug has been provided,including observed kinetic rate constants.展开更多
Chiral amino acids(AAs)serve as essential building blocks of proteins and play vital physiological roles in living organisms.To achieve accurate,rapid,and high-throughput analysis of chiral AAs,this work proposed a me...Chiral amino acids(AAs)serve as essential building blocks of proteins and play vital physiological roles in living organisms.To achieve accurate,rapid,and high-throughput analysis of chiral AAs,this work proposed a methylbenzyl isocyanate(MBIC)derivatization strategy coupled with ultra-high performance liquid chromatography-mass spectrometry or trapped ion mobility spectrometry-mass spectrometry.The integration of a chiral carbon atom with a rigid urea-based structure can significantly enhance the separation of chiral MBIC-labeled AA enantiomers.This phenomenon can be attributed to the labeled l-AAs allow the carboxyl group to form intramolecular hydrogen bonds with the amino group in the rigid urea-based structure,whereas labeled d-AAs are unable to form such bonds.The method based on MBIC derivatization coupled with ultra-performance liquid chromatography-tandem mass spectrometry achieved simultaneous separation of 19 pairs of chiral AAs using only a C18 column within 30 min,enabling quantitatively detect twelve types of chiral AAs in the serum of healthy humans and Parkinson's patients.The distribution of twenty-four chiral AAs is observed in mouse brain using MBIC labeling-based matrix-assisted laser desorption/ionization-trapped ion mobility spectrometry-mass spectrometry imaging without prior separation.Our work elucidates the principles governing the separation of chiral AAs using derivatization methods,providing valuable guidance for the separation of chiral compounds.展开更多
Photomodulation technology,characterized by its high spatiotemporal resolution,has emerged as a transformative approach for precise,rapid,and noninvasive regulation of intricate cellular signaling networks,offering un...Photomodulation technology,characterized by its high spatiotemporal resolution,has emerged as a transformative approach for precise,rapid,and noninvasive regulation of intricate cellular signaling networks,offering unprecedented opportunities for biomedical research.Nevertheless,the in vivo implementation of wireless photomodulation remains constrained by the inherent limitations of conventional photoresponsive systems.Addressing this challenge,we report a BODIPY-based photocatalyst with exceptional red-light responsiveness(λ>630 nm).Transient spectroscopic studies show that this photocatalyst exhibits the feature with solvent polarity-switching excited state dynamics.In addition,we observed ultralong triplet-state lifetimes(590μs in tetrahydrofuran;862μs in toluene),which is favorable for establishing a far-red-light-driven photocatalytic decaging platform that synergistically integrates the BODIPY photocatalyst with endogenous NADH as the intrinsic electron donor.Crucially,this system demonstrates robust in vivo efficacy,achieving significant tumor growth suppression in murine tumor models through localized prodrug activation.This work not only provides fundamental insights into engineering long-lived triplet states in metal-free organic photocatalysts but also pioneers a biocompatible strategy for spatiotemporally controlled therapeutic interventions,bridging the gap between advanced photochemistry and precision biomedicine.展开更多
Small extracellular vesicles(sEVs)membrane protein profile(sEVpp)is a novel biomarker for cancer,and it can reveal the in-depth phenotype information.The point-of-care testing(POCT)of sEVpp holds great significance fo...Small extracellular vesicles(sEVs)membrane protein profile(sEVpp)is a novel biomarker for cancer,and it can reveal the in-depth phenotype information.The point-of-care testing(POCT)of sEVpp holds great significance for mass screening of cancer,so the cost-effective and simple detection methods of sEVpp are urgently demanded.Herein,we constructed a paper-based multichannel sEVpp POCT device(sEVpp-PAD)enabled by functional DNA probes and metal-organic framework(MOF).The core components are aptamer/MOF-modified paper chips.The modified aptamers can immunocapture the sEV expressing corresponding proteins,while the modified MOF can provide abundant sites for aptamer-modification,reduce the nonspecific protein absorption,and act as reference for ratiometric detection.Simply powered by two syringes,the sEVpp-PAD can efficiently capture sEVs expressing corresponding protein from cell culture media and sera.Furthermore,a detection probe(DP)consisted of CD63 aptamer and G-quadruplex was developed for the colorimetric detection of captured sEVs.Utilizing this device,the sEVpp in various hepatocellular carcinoma cell culture medium and,more importantly,in human sera can be accurately determined,only with$2 device,$0.2 detection reagents and 1.8 h procedure.This simple strategy for sEVpp detection can innovatively promote the POCT and subtyping of cancer based on sEV-related liquid biopsy.展开更多
文摘Developing an integrated and intelligent approach to securing the ITE(information technology environment)is an emergent and evolving concern for every organization and consumer entity during the last few decades.Major topics of concern include“SI”(security intelligence),“D-DA”(data-driven analytics),“PE”(proven expertise),and“R-TD”(real-time defense)capabilities.“DRBTs”(dynamic response behavior types)include“incident response”,“endpoint management”,“threat intelligence”,“network security”,and“fraud protection”.The consumer demand for electricity as essential public access and service is indexed to population growth estimates.Consumer-driven economies continue to add electrical consumption to their grids even though improvements in lower-power consumption and higher design efficiencies are present in new electric-powered products.Dependence on the production of electrical energy has no peer replacement technology and creates a societal vulnerability to targeted public electrical grid interruptions.When access to,or production of,electrical power is interrupted,the result is a“Mass Effect”every consumer feels with equal distribution.Electric grid security falls directly into the levels of authorized,and unauthorized,access via the“IoT”(Internet of Things)concepts,and the“IoM2M”(Internet of Machine-to-Machine)integration.Electrical grid operations that include production and network management augment each other in order to support the demand for electricity every day either in peak or off-peak,thus cybersecurity plays a big role in the protection of such assets at our disposal.With help from AI(artificial intelligence)integrated into the IoT a resilient system can be built to protect the electric grid system nationwide and will be able to detect and preempt Smart Malware attacks.
基金This work is supported by the Major International Joint Research Project of the National Natural Science Foundation of China(Grant No.71520107004)the Major Program of National Natural Science Foundation of China(Grant No.71790614)+1 种基金the Fund for Innovative Research Groups of the National Natural Science Foundation of China(Grant No.71621061)and the 111 Project(Grant No.B16009).
文摘Industrial intelligence is a core technology in the upgrading of the production processes and management modes of traditional industries.Motivated by the major development strategies and needs of industrial intellectualization in China,this study presents an innovative fusion structure that encompasses the theoretical foundation and technological innovation of data analytics and optimization,as well as their application to smart industrial engineering.First,this study describes a general methodology for the fusion of data analytics and optimization.Then,it identifies some data analytics and system optimization technologies to handle key issues in smart manufacturing.Finally,it provides a four-level framework for smart industry based on the theoretical and technological research on the fusion of data analytics and optimization.The framework uses data analytics to perceive and analyze industrial production and logistics processes.It also demonstrates the intelligent capability of planning,scheduling,operation optimization,and optimal control.Data analytics and system optimization technologies are employed in the four-level framework to overcome some critical issues commonly faced by manufacturing,resources and materials,energy,and logistics systems,such as high energy consumption,high costs,low energy efficiency,low resource utilization,and serious environmental pollution.The fusion of data analytics and optimization allows enterprises to enhance the prediction and control of unknown areas and discover hidden knowledge to improve decision-making efficiency。Therefore,industrial intelligence has great importance in China’s industrial upgrading and transformation into a true industrial power.
基金Partial support for this research was provided by the US National Science Foundation (Nos. 1050477, 0959979, and 1117132)by a Brookhaven National Lab LDRD grant+2 种基金by the US Department of Energy (DOE) Office of Basic Energy Sciences, Division of Chemical Sciences, GeosciencesBiosciences and by the IT Consilience Creative Project through the Ministry of Knowledge Economy, Republic of Korea national scientific user facility sponsored by the DOE's OBER at Pacific Northwest National Laboratory (PNNL)PNNL is operated by the US DOE by Battelle Memorial Institute under contract No.DE-AC06-76RL0 1830
文摘Climate research produces a wealth of multivariate data. These data often have a geospatial reference and so it is of interest to show them within their geospatial context. One can consider this configuration as a multifield visualization problem, where the geo-space provides the expanse of the field. However, there is a limit on the amount of multivariate information that can be fit within a certain spatial location, and the use of linked multivariate information displays has previously been devised to bridge this gap. In this paper we focus on the interactions in the geographical display, present an implementation that uses Google Earth, and demonstrate it within a tightly linked parallel coordinates display. Several other visual representations, such as pie and bar charts are integrated into the Google Earth display and can be interactively manipulated. Further, we also demonstrate new brushing and visualization techniques for parallel coordinates, such as fixed-window brushing and correlation-enhanced display. We conceived our system with a team of climate researchers, who already made a few important discoveries using it. This demonstrates our system＇s great potential to enable scientific discoveries, possibly also in other domains where data have a geospatial reference.
文摘Many cities,countries and transport operators around the world are striving to design intelligent transport systems.These systems capture the value of multisource and multiform data related to the functionality and use of transportation infrastructure to better support human mobility,interests,economic activity and lifestyles.They aim to provide services that can enable transportation customers and managers to be better informed and make safer and more efficient use of infrastructure.In developing principles,guidelines,methods and tools to enable synergistic work between humans and computer-generated information,the science of visual analytics continues to expand our understanding of data through effective and interactive visual interfaces.In this paper,we describe an application of visual analytics related to the study of movement and transportation systems.This application documents the use of rapid,2D and 3D web visualisation and data analytics libraries and explores their potential added value to the analysis of big public transport performance data.A novel approach to displaying such data through a generalisable framework visualisation system is demonstrated.This framework recalls over a year’sworth of public transport performance data at a highly granular level in a fast,interactive browser-based environment.Greater Sydney,Australia forms a case study to highlight potential uses of the visualisation of such large,passively-collected data sets as an applied research scenario.In this paper,we argue that such highly visual systems can add data-driven rigour to service planning and longer-term transport decision-making.Furthermore,they enable the sharing of quality of service statistics with various stakeholders and citizens and can showcase improvements in services before and after policy decisions.The paper concludes by making recommendations on the value of this approach in embedding these or similar web-based systems in transport planning practice,performance management,optimisation and understanding of customer experience.
基金financially supported by the National Natural Science Foundation of China (NSFC,Nos.22134005 and 22074124)Chongqing Talents Program for Outstanding Scientists (No.cstc2021ycjh-bgzxm0178)+1 种基金Natural Science Foundation of Chongqing (No.CSTB2022NSCQ-MSX0521)the Chongqing Graduate Student Scientific Research Innovation Project (No.CYB21119)。
文摘Regulation of apoptosis represents a key parameter in all living organisms.In this paper,an input-induced logic-gated modular nanocalculator is designed to regulate cancer cell apoptosis by programmatically combining and connecting logic gate modules with different functions.Via rational design of the various logic gate modules of the nanocalculator,different apoptosis related operations including cancer cell targeting,apoptosis induction,and apoptosis monitoring could be performed.Importantly,each of these logic gate modules could independently perform apoptosis related YES logic operations when ran separately.After combining each YES logic gate module into a logic circuit and connecting it to the GO scaffold to construct a logic-gated nanocalculator,the input-induced logic-gated modular nanocalculator could selectively enter cancer cells and control the drug release to logically apoptosis(output),by performing AND logic gate operations when inputs(nucleolin and H^(+)) were included at the same time.Moreover,evidence suggests that these efficient logical calculations proceed in cancer cell apoptosis regulation without the general limiations of lithography in nanotechnology.As such,this work provides a new vision for the construction of a logic-gated modular nanocalculator with logical calculation proficiency potentially useful in cancer therapy and the regulation of life.
基金supported by the National Natural Science Foundation of China(Grant No.62141210)the Fundamental Research Funds for the Central Universities(Grant No.N2217005)+1 种基金Open Fund of State Key Lab.for Novel Software Technology,Nanjing University(KFKT2021B01)111 Project(B16009).
文摘Vulnerabilities are a known problem in modern Open Source Software(OSS).Most developers often rely on third-party libraries to accelerate feature implementation.However,these libraries may contain vulnerabilities that attackers can exploit to propagate malicious code,posing security risks to dependent projects.Existing research addresses these challenges through Software Composition Analysis(SCA)for vulnerability detection and remediation.Nevertheless,current solutions may introduce additional issues,such as incompatibilities,dependency conflicts,and additional vulnerabilities.To address this,we propose Vulnerability Scan and Protection(VulnScanPro),a robust solution for detection and remediation vulnerabilities in Java projects.Specifically,VulnScanPro builds a finegrained method graph to identify unreachable methods.The method graph is mapped to the project’s dependency tree,constructing a comprehensive vulnerability propagation graph that identifies unreachable vulnerable APIs and dependencies.Based on this analysis,we propose three solutions for vulnerability remediation:(1)Removing unreachable vulnerable dependencies,thereby resolving security risks and reducing maintenance overhead.(2)Upgrading vulnerable dependencies to the closest non-vulnerable versions,while pinning the versions of transitive dependencies introduced by the vulnerable dependency,in order to mitigate compatibility issues and prevent the introduction of new vulnerabilities.(3)Eliminating unreachable vulnerable APIs,particularly when security patches are either incompatible or absent.Experimental results show that these solutions effectively mitigate vulnerabilities and enhance the overall security of the project.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R104)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.
基金funded by Princess Nourah bint Abdulrahman University Researchers Support-ing Project number(PNURSP2026R346)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Motor imbalance is a critical failure mode in rotating machinery,potentially causing severe equipment damage if undetected.Traditional vibration-based diagnostic methods rely on direct sensor contact,leading to installation challenges and measurement artifacts that can compromise accuracy.This study presents a novel radar-based framework for non-contact motor imbalance detection using 24 GHz continuous-wave radar.A dataset of 1802 experimental trials was sourced,covering four imbalance levels(0,10,20,30 g)across varying motor speeds(500–1500 rpm)and load torques(0–3 Nm).Dual-channel in-phase and quadrature radar signals were captured at 10,000 samples per second for 30-s intervals,preserving both amplitude and phase information for analysis.A multi-domain feature extraction methodology captured imbalance signatures in time,frequency,and complex signal domains.From 65 initial features,statistical analysis using Kruskal–Wallis tests identified significant descriptors,and recursive feature elimination with Random Forest reduced the feature set to 20 dimensions,achieving 69%dimensionality reduction without loss of performance.Six machine learning algorithms,Random Forest,Extra Trees Classifier,Extreme Gradient Boosting,Categorical Boosting,Support Vector Machine with radial basis function kernel,and k-Nearest Neighbors were evaluated with grid-search hyperparameter optimization and five-fold cross-validation.The Extra Trees Classifier achieved the best performance with 98.52%test accuracy,98%cross-validation accuracy,and minimal variance,maintaining per-class precision and recall above 97%.Its superior performance is attributed to its randomized split selection and full bootstrapping strategy,which reduce variance and overfitting while effectively capturing the nonlinear feature interactions and non-normal distributions present in the dataset.The model’s average inference time of 70 ms enables near real-time deployment.Comparative analysis demonstrates that the radar-based framework matches or exceeds traditional contact-based methods while eliminating their inherent limitations,providing a robust,scalable,and noninvasive solution for industrial motor condition monitoring,particularly in hazardous or space-constrained environments.
文摘The problem of fake news detection(FND)is becoming increasingly important in the field of natural language processing(NLP)because of the rapid dissemination of misleading information on the web.Large language models(LLMs)such as GPT-4.Zero excels in natural language understanding tasks but can still struggle to distinguish between fact and fiction,particularly when applied in the wild.However,a key challenge of existing FND methods is that they only consider unimodal data(e.g.,images),while more detailed multimodal data(e.g.,user behaviour,temporal dynamics)is neglected,and the latter is crucial for full-context understanding.To overcome these limitations,we introduce M3-FND(Multimodal Misinformation Mitigation for False News Detection),a novel methodological framework that integrates LLMs with multimodal data sources to perform context-aware veracity assessments.Our method proposes a hybrid system that combines image-text alignment,user credibility profiling,and temporal pattern recognition,which is also strengthened through a natural feedback loop that provides real-time feedback for correcting downstream errors.We use contextual reinforcement learning to schedule prompt updating and update the classifier threshold based on the latest multimodal input,which enables the model to better adapt to changing misinformation attack strategies.M3-FND is tested on three diverse datasets,FakeNewsNet,Twitter15,andWeibo,which contain both text and visual socialmedia content.Experiments showthatM3-FND significantly outperforms conventional and LLMbased baselines in terms of accuracy,F1-score,and AUC on all benchmarks.Our results indicate the importance of employing multimodal cues and adaptive learning for effective and timely detection of fake news.
基金the financial support from the National Natural Science Foundation of China(52203123 and 52473248)State Key Laboratory of Polymer Materials Engineering(sklpme2024-2-04)+1 种基金the Fundamental Research Funds for the Central Universitiessponsored by the Double First-Class Construction Funds of Sichuan University。
文摘Composite polymer electrolytes(CPEs)offer a promising solution for all-solid-state lithium-metal batteries(ASSLMBs).However,conventional nanofillers with Lewis-acid-base surfaces make limited contribution to improving the overall performance of CPEs due to their difficulty in achieving robust electrochemical and mechanical interfaces simultaneously.Here,by regulating the surface charge characteristics of halloysite nanotube(HNT),we propose a concept of lithium-ion dynamic interface(Li^(+)-DI)engineering in nano-charged CPE(NCCPE).Results show that the surface charge characteristics of HNTs fundamentally change the Li^(+)-DI,and thereof the mechanical and ion-conduction behaviors of the NCCPEs.Particularly,the HNTs with positively charged surface(HNTs+)lead to a higher Li^(+)transference number(0.86)than that of HNTs-(0.73),but a lower toughness(102.13 MJ m^(-3)for HNTs+and 159.69 MJ m^(-3)for HNTs-).Meanwhile,a strong interface compatibilization effect by Li^(+)is observed for especially the HNTs+-involved Li^(+)-DI,which improves the toughness by 2000%compared with the control.Moreover,HNTs+are more effective to weaken the Li^(+)-solvation strength and facilitate the formation of Li F-rich solid-electrolyte interphase of Li metal compared to HNTs-.The resultant Li|NCCPE|LiFePO4cell delivers a capacity of 144.9 m Ah g^(-1)after 400 cycles at 0.5 C and a capacity retention of 78.6%.This study provides deep insights into understanding the roles of surface charges of nanofillers in regulating the mechanical and electrochemical interfaces in ASSLMBs.
文摘MXene-based smart contact lenses demonstrate a cutting-edge advancement in wearable ophthalmic technology,combining real-time biosensing,therapeutic capabilities,and user comfort in a single platform.These devices take the advantage of the exceptional electrical conductivity,mechanical flexibility,and biocompatibility of two-dimensional MXenes to enable noninvasive,tear-based monitoring of key physiological markers such as intraocular pressure and glucose levels.Recent developments focus on the integration of transparent MXene films into the conventional lens materials,allowing multifunctional performance including photothermal therapy,antimicrobial and anti-inflammation protection,and dehydration resistance.These innovations offer promising strategies for ocular disease management and eye protection.In addition to their multifunctionality,improvements in MXene synthesis and device engineering have enhanced the stability,transparency,and wearability of these lenses.Despite these advances,challenges remain in long-term biostability,scalable production,and integration with wireless communication systems.This review summarizes the current progress,key challenges,and future directions of MXene-based smart contact lenses,highlighting their transformative potential in next-generation digital healthcare and ophthalmic care.
基金supported by the National Natural Science Foundation of China,Nos.82301093(to QC)and 22334004(to HY)the Fuzhou University Fund for Testing Precious Equipment,No.2025T038(to QC)。
文摘The mechanisms underlying the pathophysiology of ischemic stroke are complex and multifactorial and include excitotoxicity,oxidative stress,inflammatory responses,and blood–brain barrier disruption.While vascular recanalization treatments such as thrombolysis and mechanical thrombectomy have achieved some success,reperfusion injury remains a significant contributor to the exacerbation of brain injury.This emphasizes the need for developing neuroprotective strategies to mitigate this type of injury.The purpose of this review was to examine the application of nanotechnology in the treatment of ischemic stroke,covering research progress in nanoparticlebased drug delivery,targeted therapy,and antioxidant and anti-inflammatory applications.Nanobased drug delivery systems offer several advantages compared to traditional therapies,including enhanced blood–brain barrier penetration,prolonged drug circulation time,improved drug stability,and targeted delivery.For example,inorganic nanoparticles,such as those based on CeO_(2),have been widely studied for their strong antioxidant capabilities.Biomimetic nanoparticles,such as those coated with cell membranes,have garnered significant attention owing to their excellent biocompatibility and targeting abilities.Nanoparticles can be used to deliver a wide range of neuroprotective agents,such as antioxidants(e.g.,edaravone),anti-inflammatory drugs(e.g.,curcumin),and neurotrophic factors.Nanotechnology significantly enhances the efficacy of these drugs while minimizing adverse reactions.Although nanotechnology has demonstrated great potential in animal studies,its clinical application still faces several challenges,including the long-term safety of nanoparticles,the feasibility of large-scale production,quality control,and the ability to predict therapeutic effects in humans.In summary,nanotechnology holds significant promise for the treatment of ischemic stroke.Future research should focus on further exploring the mechanisms of action of nanoparticles,developing multifunctional nanoparticles,and validating their safety and efficacy through rigorous clinical trials.Moreover,interdisciplinary collaboration is essential for advancing the use of nanotechnology in stroke treatment.
基金supported by European Union's Horizon Europe,UK Research and Innovation(UKRI).
文摘Graphite,encompassing both natural graphite and synthetic graphite,and graphene,have been extensively utilized and investigated as anode materials and additives in lithium-ion batteries(LIBs).In the pursuit of carbon neutrality,LIBs are expected to play a pivotal role in reducing CO_(2)emissions by decreasing reliance on fossil fuels and enabling the integration of renewable energy sources.Owing to their technological maturity and exceptional electrochemical performance,the global production of graphite and graphene for LIBs is projected to continue expanding.Over the past decades,numerous researchers have concentrated on reducing the material and energy input whilst optimising the electrochemical performance of graphite and graphene,through novel synthesis methods and various modifications at the laboratory scale.This review provides a comprehensive examination of the manufacturing methods,environmental impact,research progress,and challenges associated with graphite and graphene in LIBs from an industrial perspective,with a particular focus on the carbon footprint of production processes.Additionally,it considers emerging challenges and future development directions of graphite and graphene,offering significant insights for ongoing and future research in the field of green LIBs.
基金supported by the grants PID2020-113371RA-C22 and TED2021-130845A-C32,funded by MCIN/AEI/10.13039/501100011033.M.Marín-García,R.González-OlmosC.Gómez-Canela are members of the GESPA group(Grup d’Enginyeria i Simulacióde Processos Ambientals)at IQS-URL,which has been acknowledged as a Consolidated Research Group by the Government of Catalonia(No.2021-SGR-00321)+1 种基金In addition,M.Marín-García has been awarded a public grant for the Investigo Programme,aimed at hiring young job seekers to undertake research and innovation projects under the Recovery,Transformation,and Resilience Plan(PRTR),European Union Next Generation,for the year 2022,through the Government of Catalonia and the Spanish Ministry for Work and Social Economy(No.100045ID16)Ana Belén Cuenca for her support and expertise,which helped to confirm the proposed reaction mechanism involved in the UV photolysis of cloperastine.
文摘The increasing production and release of synthetic organic chemicals,including pharmaceuticals,into our envi-ronment has allowed these substances to accumulate in our surface water systems.Current purification technolo-gies have been unable to eliminate these pollutants,resulting in their ongoing release into aquatic ecosystems.This study focuses on cloperastine(CPS),a cough suppressant and antihistamine medication.The environmental impact of CPS usage has become a concern,mainly due to its increased detection during the COVID-19 pandemic.CPS has been found in wastewater treatment facilities,effluents from senior living residences,river waters,and sewage sludge.However,the photosensitivity of CPS and its photodegradation profile remain largely unknown.This study investigates the photodegradation process of CPS under simulated tertiary treatment conditions using UV photolysis,a method commonly applied in some wastewater treatment plants.Several transformation prod-ucts were identified,evaluating their kinetic profiles using chemometric approaches(i.e.,curve fitting and the hard-soft multivariate curve resolution-alternating least squares(HS-MCR-ALS)algorithm)and calculating the reaction quantum yield.As a result,three different transformation products have been detected and correctly identified.In addition,a comprehensive description of the kinetic pathway involved in the photodegradation process of the CPS drug has been provided,including observed kinetic rate constants.
基金supported by the National Natural Science Foundation of China(Nos.22404023,22274021,and 22036001).
文摘Chiral amino acids(AAs)serve as essential building blocks of proteins and play vital physiological roles in living organisms.To achieve accurate,rapid,and high-throughput analysis of chiral AAs,this work proposed a methylbenzyl isocyanate(MBIC)derivatization strategy coupled with ultra-high performance liquid chromatography-mass spectrometry or trapped ion mobility spectrometry-mass spectrometry.The integration of a chiral carbon atom with a rigid urea-based structure can significantly enhance the separation of chiral MBIC-labeled AA enantiomers.This phenomenon can be attributed to the labeled l-AAs allow the carboxyl group to form intramolecular hydrogen bonds with the amino group in the rigid urea-based structure,whereas labeled d-AAs are unable to form such bonds.The method based on MBIC derivatization coupled with ultra-performance liquid chromatography-tandem mass spectrometry achieved simultaneous separation of 19 pairs of chiral AAs using only a C18 column within 30 min,enabling quantitatively detect twelve types of chiral AAs in the serum of healthy humans and Parkinson's patients.The distribution of twenty-four chiral AAs is observed in mouse brain using MBIC labeling-based matrix-assisted laser desorption/ionization-trapped ion mobility spectrometry-mass spectrometry imaging without prior separation.Our work elucidates the principles governing the separation of chiral AAs using derivatization methods,providing valuable guidance for the separation of chiral compounds.
基金supported by the National Natural Science Foundation of China(NSFC)(013398,22377063)the Research Start-Up Fund of Nankai University and the Haihe Laboratory of Sustainable Chemical Transformations(24HHWCSS00020)。
文摘Photomodulation technology,characterized by its high spatiotemporal resolution,has emerged as a transformative approach for precise,rapid,and noninvasive regulation of intricate cellular signaling networks,offering unprecedented opportunities for biomedical research.Nevertheless,the in vivo implementation of wireless photomodulation remains constrained by the inherent limitations of conventional photoresponsive systems.Addressing this challenge,we report a BODIPY-based photocatalyst with exceptional red-light responsiveness(λ>630 nm).Transient spectroscopic studies show that this photocatalyst exhibits the feature with solvent polarity-switching excited state dynamics.In addition,we observed ultralong triplet-state lifetimes(590μs in tetrahydrofuran;862μs in toluene),which is favorable for establishing a far-red-light-driven photocatalytic decaging platform that synergistically integrates the BODIPY photocatalyst with endogenous NADH as the intrinsic electron donor.Crucially,this system demonstrates robust in vivo efficacy,achieving significant tumor growth suppression in murine tumor models through localized prodrug activation.This work not only provides fundamental insights into engineering long-lived triplet states in metal-free organic photocatalysts but also pioneers a biocompatible strategy for spatiotemporally controlled therapeutic interventions,bridging the gap between advanced photochemistry and precision biomedicine.
基金supported by the National Key Research and Development Program of China(No.2022YFE0201800)the National Natural Science Foundation of China(Nos.22274169 and 22474161)+5 种基金Guangdong Basic and Applied Basic Research Foundation(No.2024A1515030160)the Science and Technology Program of Guangzhou City(No.2023B03J1380)Shenzhen Science and Technology Innovation Commission(No.GJHZ20210705142200001)the Scientific Technology Project of Guangzhou City(No.202103000003)the Guangdong Science and Technology Plan Project(No.2020B1212060077)the Open Research Fund of State Key Laboratory of Analytical Chemistry for Life Science,School of Chemistry and Chemical Engineering,Nanjing University.
文摘Small extracellular vesicles(sEVs)membrane protein profile(sEVpp)is a novel biomarker for cancer,and it can reveal the in-depth phenotype information.The point-of-care testing(POCT)of sEVpp holds great significance for mass screening of cancer,so the cost-effective and simple detection methods of sEVpp are urgently demanded.Herein,we constructed a paper-based multichannel sEVpp POCT device(sEVpp-PAD)enabled by functional DNA probes and metal-organic framework(MOF).The core components are aptamer/MOF-modified paper chips.The modified aptamers can immunocapture the sEV expressing corresponding proteins,while the modified MOF can provide abundant sites for aptamer-modification,reduce the nonspecific protein absorption,and act as reference for ratiometric detection.Simply powered by two syringes,the sEVpp-PAD can efficiently capture sEVs expressing corresponding protein from cell culture media and sera.Furthermore,a detection probe(DP)consisted of CD63 aptamer and G-quadruplex was developed for the colorimetric detection of captured sEVs.Utilizing this device,the sEVpp in various hepatocellular carcinoma cell culture medium and,more importantly,in human sera can be accurately determined,only with$2 device,$0.2 detection reagents and 1.8 h procedure.This simple strategy for sEVpp detection can innovatively promote the POCT and subtyping of cancer based on sEV-related liquid biopsy.
