The cure rate for chronic neurodegenerative diseases remains low,creating an urgent need for improved intervention methods.Recent studies have shown that enhancing mitochondrial function can mitigate the effects of th...The cure rate for chronic neurodegenerative diseases remains low,creating an urgent need for improved intervention methods.Recent studies have shown that enhancing mitochondrial function can mitigate the effects of these diseases.This paper comprehensively reviews the relationship between mitochondrial dysfunction and chronic neurodegenerative diseases,aiming to uncover the potential use of targeted mitochondrial interventions as viable therapeutic options.We detail five targeted mitochondrial intervention strategies for chronic neurodegenerative diseases that act by promoting mitophagy,inhibiting mitochondrial fission,enhancing mitochondrial biogenesis,applying mitochondria-targeting antioxidants,and transplanting mitochondria.Each method has unique advantages and potential limitations,making them suitable for various therapeutic situations.Therapies that promote mitophagy or inhibit mitochondrial fission could be particularly effective in slowing disease progression,especially in the early stages.In contrast,those that enhance mitochondrial biogenesis and apply mitochondria-targeting antioxidants may offer great benefits during the middle stages of the disease by improving cellular antioxidant capacity and energy metabolism.Mitochondrial transplantation,while still experimental,holds great promise for restoring the function of damaged cells.Future research should focus on exploring the mechanisms and effects of these intervention strategies,particularly regarding their safety and efficacy in clinical settings.Additionally,the development of innovative mitochondria-targeting approaches,such as gene editing and nanotechnology,may provide new solutions for treating chronic neurodegenerative diseases.Implementing combined therapeutic strategies that integrate multiple intervention methods could also enhance treatment outcomes.展开更多
This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mi...This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering.展开更多
This study compares the relative efficacy of the continuation task and the model-as-feedbackwriting (MAFW) task in EFL writing development. Ninety intermediate-level Chinese EFL learnerswere randomly assigned to a con...This study compares the relative efficacy of the continuation task and the model-as-feedbackwriting (MAFW) task in EFL writing development. Ninety intermediate-level Chinese EFL learnerswere randomly assigned to a continuation group, a MAFW group, and a control group, each with30 learners. A pretest and a posttest were used to gauge L2 writing development. Results showedthat the continuation task outperformed the MAFW task not only in enhancing the overall qualityof L2 writing, but also in promoting the quality of three components of L2 writing, namely, content,organization, and language. The finding has important implications for L2 writing teaching andlearning.展开更多
Considering the impact of terminal impact time constraints and the state information of maneuvering targets on the guidance accuracy in multi-UAV cooperative guidance,this paper proposes an impact time cooperative con...Considering the impact of terminal impact time constraints and the state information of maneuvering targets on the guidance accuracy in multi-UAV cooperative guidance,this paper proposes an impact time cooperative control guidance law(ITCCG)that combines the optimal error dynamics with an improved adaptive cubature Kalman filter(IACKF)algorithm.First,a terminal impact time feedback term is introduced into proportional navigation guidance based on the relative virtual guidance model,and terminal time control is achieved through optimal error dynamics.Then,the Huber loss function is used to reduce the impact of measurement outliers,and the diagonal decomposition is applied to address the issue of non-positive definite matrices that cannot undergo Cholesky decomposition.Finally,the ITCCG and IACKF algorithms combined achieve multi-UAV time-cooperated guidance based on maneuvering target state estimation.Simulation results show that the proposed algorithm effectively reduces the target state estimation error and achieves cooperative guidance within the desired time frame.展开更多
The prolonged and intricate history of oncological treatments has transitioned significantly since the introduction of chemotherapy.Substantial therapeutic benefits in cancer therapy have been achieved by the integrat...The prolonged and intricate history of oncological treatments has transitioned significantly since the introduction of chemotherapy.Substantial therapeutic benefits in cancer therapy have been achieved by the integration of conventional treatments with molecular biosciences and omics technologies.Human epidermal growth factor receptor,hormone receptors,and angiogenesis factors are among the established therapies in tumor reduction and managing side effects.Novel targeted therapies like KRAS G12C,Claudin-18 isoform 2(CLDN18.2),Trophoblast cell-surface antigen 2(TROP2),and epigenetic regulators emphasize their promise in advancing precision medicine.However,in many cases,the resistance mechanisms associated with these interventions render them ineffective in carrying out their functions.The purpose of this review is to provide a comprehensive and up-to-date examination of both established and emerging drug targets and mechanisms of treatment resistance in oncology.This review seeks to elucidate recent advancements,address persisting challenges,and explore opportunities for innovative developments in cancer target research.Additionally,it explores the growing role of artificial intelligence in reshaping cancer drug discovery and development frameworks as potential avenues for future research.In conclusion,innovative approaches in oncology,supported by pharmacological research,ongoing clinical trials,molecular biosciences,and artificial intelligence,are poised to significantly transform cancer treatment.展开更多
Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.Howev...Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.展开更多
The iterative continuation task(ICT)requires English as a foreign language(EFL)learners to read a segment and write a continuation that aligns with the preceding segment of an English novel with successive turns,offer...The iterative continuation task(ICT)requires English as a foreign language(EFL)learners to read a segment and write a continuation that aligns with the preceding segment of an English novel with successive turns,offering exposure to diverse grammatical structures and opportunities for contextualized usage.Given the importance of integrating technology into second language(L2)writing and the critical role that grammar plays in L2 writing development,automated written corrective feedback provided by Grammarly has gained significant attention.This study investigates the impact of Grammarly on grammar learning strategies,grammar grit,and grammar competence among EFL college students engaged in ICT.This study employed a mixed-methods sequential exploratory design;56 participants were divided into an experimental group(n=28),receiving Grammarly feedback for ICT,and a control group(n=28),completing ICT without Grammarly feedback.Quantitative results revealed that both groups showed improvements in L2 grammar learning strategies,grit and competence.For the experimental group,significant differences were observed across all variables of L2 grammar learning strategies,grit,and competence between pre-and post-tests.For the control group,significant differences were only observed in the affective dimension of grammar learning strategies,Consistency of Interest(COI)of grammar grit,and grammar competence.However,the control group presented a significantly higher improvement in grammar competence.Qualitative analysis showed both positive and negative perceptions of Grammarly.The pedagogical implications of integrating Grammarly and ICT for L2 grammar development are discussed.展开更多
Naphthalene,anthracene and pyridone endoperoxides are known to thermally release singlet oxygen.However,in the cycloreversion reaction,singlet oxygen is produced stoichiometrically;therefore,multiple singlet oxygen re...Naphthalene,anthracene and pyridone endoperoxides are known to thermally release singlet oxygen.However,in the cycloreversion reaction,singlet oxygen is produced stoichiometrically;therefore,multiple singlet oxygen releasing modules are expected to be very useful in inducing apoptosis of cancer cells.Herein,we present a potential therapeutic agent presenting three-pyridone endoperoxide modules and a mitochondria targeting group.Compared to previously reported pyridone-based monofunctional endoperoxides,the triple endoperoxide is highly effective as evidenced by assays and fluorescence microscopy.展开更多
In recent years,proteolysis-targeting chimeras(PROTACs)have gained widespread attention as an emerging therapeutic approach.PROTACs are bifunctional molecules composed of a target protein-binding ligand,an E3 ubiquiti...In recent years,proteolysis-targeting chimeras(PROTACs)have gained widespread attention as an emerging therapeutic approach.PROTACs are bifunctional molecules composed of a target protein-binding ligand,an E3 ubiquitin ligase ligand,and a linker connecting these ligands.By harnessing the cell’s intrinsic ubiquitin-proteasome system(UPS),they promote the ubiquitination of specific target proteins,leading to their degradation and therapeutic effects.PROTACs show exceptional promise in targeting conventional“undruggable”targets compared to traditional small-molecule inhibitors.This review provides an overview of PROTACs,including their molecular mechanism of action,therapeutic benefits,development history,key design aspects,current research and development challenges,and future trends in nextgeneration PROTAC technology.展开更多
Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-through...Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-throughput sequencing technology have become prominent in biomedical research,and they reveal molecular aspects of cancer diagnosis and therapy.Despite the development of advanced sequencing technology,the presence of high-dimensionality in multi-omics data makes it challenging to interpret the data.Methods:In this study,we introduce RankXLAN,an explainable ensemble-based multi-omics framework that integrates feature selection(FS),ensemble learning,bioinformatics,and in-silico validation for robust biomarker detection,potential therapeutic drug-repurposing candidates’identification,and classification of SC.To enhance the interpretability of the model,we incorporated explainable artificial intelligence(SHapley Additive exPlanations analysis),as well as accuracy,precision,F1-score,recall,cross-validation,specificity,likelihood ratio(LR)+,LR−,and Youden index results.Results:The experimental results showed that the top four FS algorithms achieved improved results when applied to the ensemble learning classification model.The proposed ensemble model produced an area under the curve(AUC)score of 0.994 for gene expression,0.97 for methylation,and 0.96 for miRNA expression data.Through the integration of bioinformatics and ML approach of the transcriptomic and epigenomic multi-omics dataset,we identified potential marker genes,namely,UBE2D2,HPCAL4,IGHA1,DPT,and FN3K.In-silico molecular docking revealed a strong binding affinity between ANKRD13C and the FDA-approved drug Everolimus(binding affinity−10.1 kcal/mol),identifying ANKRD13C as a potential therapeutic drug-repurposing target for SC.Conclusion:The proposed framework RankXLAN outperforms other existing frameworks for serum biomarker identification,therapeutic target identification,and SC classification with multi-omics datasets.展开更多
In scenarios where ground-based cloud computing infrastructure is unavailable,unmanned aerial vehicles(UAVs)act as mobile edge computing(MEC)servers to provide on-demand computation services for ground terminals.To ad...In scenarios where ground-based cloud computing infrastructure is unavailable,unmanned aerial vehicles(UAVs)act as mobile edge computing(MEC)servers to provide on-demand computation services for ground terminals.To address the challenge of jointly optimizing task scheduling and UAV trajectory under limited resources and high mobility of UAVs,this paper presents PER-MATD3,a multi-agent deep reinforcement learning algorithm with prioritized experience replay(PER)into the Centralized Training with Decentralized Execution(CTDE)framework.Specifically,PER-MATD3 enables each agent to learn a decentralized policy using only local observations during execution,while leveraging a shared replay buffer with prioritized sampling and centralized critic during training to accelerate convergence and improve sample efficiency.Simulation results show that PER-MATD3 reduces average task latency by up to 23%,improves energy efficiency by 21%,and enhances service coverage compared to state-of-the-art baselines,demonstrating its effectiveness and practicality in scenarios without terrestrial networks.展开更多
The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous c...The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks.Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay.In this network,the task processed at fog nodes reduces transmission delay.Still,it increases energy consumption,while routing tasks to the cloud server saves energy at the cost of higher communication delay.Moreover,the order in which offloaded tasks are executed affects the system’s efficiency.For instance,executing lower-priority tasks before higher-priority jobs can disturb the reliability and stability of the system.Therefore,an efficient strategy of optimal computation offloading and task scheduling is required for operational efficacy.In this paper,we introduced a multi-objective and enhanced version of Cheeta Optimizer(CO),namely(MoECO),to jointly optimize the computation offloading and task scheduling in cloud-fog networks to minimize two competing objectives,i.e.,energy consumption and communication delay.MoECO first assigns tasks to the optimal computational nodes and then the allocated tasks are scheduled for processing based on the task priority.The mathematical modelling of CO needs improvement in computation time and convergence speed.Therefore,MoECO is proposed to increase the search capability of agents by controlling the search strategy based on a leader’s location.The adaptive step length operator is adjusted to diversify the solution and thus improves the exploration phase,i.e.,global search strategy.Consequently,this prevents the algorithm from getting trapped in the local optimal solution.Moreover,the interaction factor during the exploitation phase is also adjusted based on the location of the prey instead of the adjacent Cheetah.This increases the exploitation capability of agents,i.e.,local search capability.Furthermore,MoECO employs a multi-objective Pareto-optimal front to simultaneously minimize designated objectives.Comprehensive simulations in MATLAB demonstrate that the proposed algorithm obtains multiple solutions via a Pareto-optimal front and achieves an efficient trade-off between optimization objectives compared to baseline methods.展开更多
Triple-negative breast cancer(TNBC)presents significant diagnostic and therapeutic challenges due to the lack of targeted treatments,rapid progression,high recurrence and metastasis rates,and overall poorer prognosis....Triple-negative breast cancer(TNBC)presents significant diagnostic and therapeutic challenges due to the lack of targeted treatments,rapid progression,high recurrence and metastasis rates,and overall poorer prognosis.Herein,the targeted theranostic platform of cysteine-modified gold nanodots-sulfhydrated luteinizing hormone releasing hormone(CGN-SLR)nanosystem was designed for target recognition and precise dual-mode imaging-guided photothermal therapy(PTT)against TNBC.On the one hand,the CGN-SLR nanosystem can serve as an ideal targeting fluorescent probe and computed tomography(CT)enhancer to facilitate the accurate diagnosis and surgical guidance of TNBC.On the other hand,the CGN-SLR nanosystem with great targeting and PTT ability can significantly inhibit the growth of TNBC,without causing harm to normal tissues and healthy organs.It provides an effective strategy for the diagnosis and treatment of TNBC through the rational design of multifunctional nanoplatform with target recognition,multiple imaging guidance/monitoring,and high-efficiency PTT.展开更多
We read with great interest Deng et al.’s study 1 comparing sextant(6-core)and 12-core systematic biopsy in theMRI-targeted era,which valuably challenges the“more cores=higher accuracy”dogma by proposing a precisio...We read with great interest Deng et al.’s study 1 comparing sextant(6-core)and 12-core systematic biopsy in theMRI-targeted era,which valuably challenges the“more cores=higher accuracy”dogma by proposing a precision sampling strategy based on prostate cancer’s spatial distribution,aligning with personalized diagnosis trends.展开更多
Metabolic reprogramming involving branched-chain amino acids(BCAAs)—leucine,isoleucine,and valine—is increasingly recognized as pivotal in cancer progression,metastasis,and immune modulation.This review comprehensiv...Metabolic reprogramming involving branched-chain amino acids(BCAAs)—leucine,isoleucine,and valine—is increasingly recognized as pivotal in cancer progression,metastasis,and immune modulation.This review comprehensively explores how cancer cells rewire BCAA metabolism to enhance proliferation,survival,and therapy resistance.Tumors manipulate BCAA uptake and catabolism via high expression of transporters like L-type amino acid transporter 1(LAT1)and enzymes including branched chain amino acid transaminase 1(BCAT1),branched chain amino acid transaminase 2(BCAT2),branched-chain alpha-keto acid dehydrogenase(BCKDH),and branched chain alpha-keto acid dehydrogenase kinase(BCKDK).These alterations sustain energy production,biosynthesis,redox homeostasis,and oncogenic signaling(especially mammalian target of rapamycin complex 1[mTORC1]).Crucially,tumor-driven BCAA depletion also shapes an immunosuppressive microenvironment,impairing anti-tumor immunity by limiting essential nutrients for T cells and natural killer(NK)cells.Innovative therapeutic strategies targeting BCAA pathways—ranging from selective small-molecule inhibitors(e.g.,LAT1 and BCAT1/2)to dietary modulation—have shown promising preclinical and early clinical efficacy,highlighting their potential to exploit metabolic vulnerabilities in cancer cells while bolstering immune responses.By integrating multi-omics data and precision targeting approaches,this review underscores the translational significance of BCAA metabolic reprogramming,positioning it as a novel frontier in cancer treatment.展开更多
Delivery carriers serve as a highly efficient approach for precision nutrition and medicine;however,artificial delivery carriers are prone to triggering the immune response and have the disadvantages of poor stability...Delivery carriers serve as a highly efficient approach for precision nutrition and medicine;however,artificial delivery carriers are prone to triggering the immune response and have the disadvantages of poor stability and low bioavailability.Extracellular vesicles(EVs),nucleus-free biological particles composed of phospholipid bilayers secreted by living cells,are a new generation of targeted delivery carriers.In recent years,an increasing number of species have been reported to contain EVs.Among them,food-derived extracellular vesicles(FDEVs)show outstanding comprehensive properties.FDEVs are considered to have great application potential due to their wide range of sources,high yields,absence of human pathogenic pathogens,and ethical concerns.In this review,the preparation,nomenclature,physicochemical characteristics,and preservation methods of FDEVs are discussed,as well as their potential protein markers,bioactivities,and applications as novel targeted delivery carriers of FDEVs from animals,plants,and microorganisms.We also summarized the adverse consequences of FDEVs in current studies,and put forward the problems and challenges in the process of FDEVs research and commercialization.In short,the importance of FDEVs has been highlighted,and FDEVs have good application prospects as a new class of targeted delivery carriers.The current problems should be paid attention to and actively solved.展开更多
Oncology Research Editorial Office Published:19 January 2026 The published article titled“miR-202 Promotes Cell Apoptosis in Esophageal Squamous Cell Carcinoma by Targeting HSF2”has been retracted from Oncology Rese...Oncology Research Editorial Office Published:19 January 2026 The published article titled“miR-202 Promotes Cell Apoptosis in Esophageal Squamous Cell Carcinoma by Targeting HSF2”has been retracted from Oncology Research,Vol.25,No.2,2017,pp.215-223.DOI:10.3727/096504016X14732772150541 URL:https://www.techscience.com/or/v25n2/56800.展开更多
Food-grade biopolymers and nanotechnology have been increasingly used to revolutionize the delivery of bioactive compounds by enhancing stability,bioavailability,and controlled release.Within the scope of nanoencapsul...Food-grade biopolymers and nanotechnology have been increasingly used to revolutionize the delivery of bioactive compounds by enhancing stability,bioavailability,and controlled release.Within the scope of nanoencapsulation systems,this review explores food-derived polymers such as vicilin,zein,gluten,cruciferin,inulin,and others.These biopolymers are ideal since they encapsulate numerous functional compounds,such as vitamins,probiotics,essential oils,and polyphenols,because they are biocompatible,amphiphilic,and biodegradable.The specific physical and chemical properties of each polymer,extraction procedures,and nanoencapsulation techniques applied therein(e.g.,ionic gelation and spray drying)are described.The review highlights advanced targeting systems like pH-sensitive,magnetic delivery.Additional applications include those in synergistic nutraceutical systems,oral administration of vaccination,and intelligent food packaging.All these findings demonstrate that food polymers are increasingly more viable as functional nanocarriers by way of increasing bioactive delivery and the shifting requirements of personalized and health-based dietary regimes.展开更多
Computing electrostatic interaction on non-cooperative targets with unknown meshes is crucial for electrostatic-based space on-orbit services.Although meshes for electrostatic interaction computations can be reconstru...Computing electrostatic interaction on non-cooperative targets with unknown meshes is crucial for electrostatic-based space on-orbit services.Although meshes for electrostatic interaction computations can be reconstructed from point clouds,they are usually too dense,leading to high computational costs.This paper presents an optimization method for converting dense meshes into optimal meshes,enabling fast and accurate computation of the electrostatic interaction by point clouds.First,the dense mesh reconstructed from point clouds is simplified into a coarse mesh using local operators.Second,the simplified mesh is refined by an iterative strategy that integrates a lightweight method of moments and an impedance matrix inheritance technique,ultimately yielding an optimal mesh for computing the electrostatic interaction.Simulation results show that our method effectively optimizes dense meshes,making electrostatic interaction computations using point clouds approximately 63.4 times more efficient than the previous method.展开更多
基金partly supported by the Yan’an University Qin Chuanyuan“Scientist+Engineer”Team Special Fund,No.2023KXJ-012(to YL)Yan’an University Transformation of Scientific and Technological Achievements Fund,No.2023CGZH-001(to YL)+2 种基金College Students Innovation and Entrepreneurship Training Program,Nos.D2023158,202410719056(to XS,JM)Yan’an University Production and Cultivation Project,No.CXY202001(to YL)Kweichow Moutai Hospital Research and Talent Development Fund Project,No.MTyk2022-25(to XO)。
文摘The cure rate for chronic neurodegenerative diseases remains low,creating an urgent need for improved intervention methods.Recent studies have shown that enhancing mitochondrial function can mitigate the effects of these diseases.This paper comprehensively reviews the relationship between mitochondrial dysfunction and chronic neurodegenerative diseases,aiming to uncover the potential use of targeted mitochondrial interventions as viable therapeutic options.We detail five targeted mitochondrial intervention strategies for chronic neurodegenerative diseases that act by promoting mitophagy,inhibiting mitochondrial fission,enhancing mitochondrial biogenesis,applying mitochondria-targeting antioxidants,and transplanting mitochondria.Each method has unique advantages and potential limitations,making them suitable for various therapeutic situations.Therapies that promote mitophagy or inhibit mitochondrial fission could be particularly effective in slowing disease progression,especially in the early stages.In contrast,those that enhance mitochondrial biogenesis and apply mitochondria-targeting antioxidants may offer great benefits during the middle stages of the disease by improving cellular antioxidant capacity and energy metabolism.Mitochondrial transplantation,while still experimental,holds great promise for restoring the function of damaged cells.Future research should focus on exploring the mechanisms and effects of these intervention strategies,particularly regarding their safety and efficacy in clinical settings.Additionally,the development of innovative mitochondria-targeting approaches,such as gene editing and nanotechnology,may provide new solutions for treating chronic neurodegenerative diseases.Implementing combined therapeutic strategies that integrate multiple intervention methods could also enhance treatment outcomes.
基金supported by the National Natural Science Foundation of China(No.12372045)the National Key Research and the Development Program of China(Nos.2023YFC2205900,2023YFC2205901)。
文摘This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering.
文摘This study compares the relative efficacy of the continuation task and the model-as-feedbackwriting (MAFW) task in EFL writing development. Ninety intermediate-level Chinese EFL learnerswere randomly assigned to a continuation group, a MAFW group, and a control group, each with30 learners. A pretest and a posttest were used to gauge L2 writing development. Results showedthat the continuation task outperformed the MAFW task not only in enhancing the overall qualityof L2 writing, but also in promoting the quality of three components of L2 writing, namely, content,organization, and language. The finding has important implications for L2 writing teaching andlearning.
基金supported by the Fundamental Research Funds for the Central Universities of China(FRF-TP-24-058A)with additional support from the National Key Laboratory of Helicopter Aeromechanics(2024-ZSJ-LB-02-02).
文摘Considering the impact of terminal impact time constraints and the state information of maneuvering targets on the guidance accuracy in multi-UAV cooperative guidance,this paper proposes an impact time cooperative control guidance law(ITCCG)that combines the optimal error dynamics with an improved adaptive cubature Kalman filter(IACKF)algorithm.First,a terminal impact time feedback term is introduced into proportional navigation guidance based on the relative virtual guidance model,and terminal time control is achieved through optimal error dynamics.Then,the Huber loss function is used to reduce the impact of measurement outliers,and the diagonal decomposition is applied to address the issue of non-positive definite matrices that cannot undergo Cholesky decomposition.Finally,the ITCCG and IACKF algorithms combined achieve multi-UAV time-cooperated guidance based on maneuvering target state estimation.Simulation results show that the proposed algorithm effectively reduces the target state estimation error and achieves cooperative guidance within the desired time frame.
文摘The prolonged and intricate history of oncological treatments has transitioned significantly since the introduction of chemotherapy.Substantial therapeutic benefits in cancer therapy have been achieved by the integration of conventional treatments with molecular biosciences and omics technologies.Human epidermal growth factor receptor,hormone receptors,and angiogenesis factors are among the established therapies in tumor reduction and managing side effects.Novel targeted therapies like KRAS G12C,Claudin-18 isoform 2(CLDN18.2),Trophoblast cell-surface antigen 2(TROP2),and epigenetic regulators emphasize their promise in advancing precision medicine.However,in many cases,the resistance mechanisms associated with these interventions render them ineffective in carrying out their functions.The purpose of this review is to provide a comprehensive and up-to-date examination of both established and emerging drug targets and mechanisms of treatment resistance in oncology.This review seeks to elucidate recent advancements,address persisting challenges,and explore opportunities for innovative developments in cancer target research.Additionally,it explores the growing role of artificial intelligence in reshaping cancer drug discovery and development frameworks as potential avenues for future research.In conclusion,innovative approaches in oncology,supported by pharmacological research,ongoing clinical trials,molecular biosciences,and artificial intelligence,are poised to significantly transform cancer treatment.
文摘Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.
文摘The iterative continuation task(ICT)requires English as a foreign language(EFL)learners to read a segment and write a continuation that aligns with the preceding segment of an English novel with successive turns,offering exposure to diverse grammatical structures and opportunities for contextualized usage.Given the importance of integrating technology into second language(L2)writing and the critical role that grammar plays in L2 writing development,automated written corrective feedback provided by Grammarly has gained significant attention.This study investigates the impact of Grammarly on grammar learning strategies,grammar grit,and grammar competence among EFL college students engaged in ICT.This study employed a mixed-methods sequential exploratory design;56 participants were divided into an experimental group(n=28),receiving Grammarly feedback for ICT,and a control group(n=28),completing ICT without Grammarly feedback.Quantitative results revealed that both groups showed improvements in L2 grammar learning strategies,grit and competence.For the experimental group,significant differences were observed across all variables of L2 grammar learning strategies,grit,and competence between pre-and post-tests.For the control group,significant differences were only observed in the affective dimension of grammar learning strategies,Consistency of Interest(COI)of grammar grit,and grammar competence.However,the control group presented a significantly higher improvement in grammar competence.Qualitative analysis showed both positive and negative perceptions of Grammarly.The pedagogical implications of integrating Grammarly and ICT for L2 grammar development are discussed.
基金supported by the National Natural Science Foundation of China(22007008,22178048).
文摘Naphthalene,anthracene and pyridone endoperoxides are known to thermally release singlet oxygen.However,in the cycloreversion reaction,singlet oxygen is produced stoichiometrically;therefore,multiple singlet oxygen releasing modules are expected to be very useful in inducing apoptosis of cancer cells.Herein,we present a potential therapeutic agent presenting three-pyridone endoperoxide modules and a mitochondria targeting group.Compared to previously reported pyridone-based monofunctional endoperoxides,the triple endoperoxide is highly effective as evidenced by assays and fluorescence microscopy.
文摘In recent years,proteolysis-targeting chimeras(PROTACs)have gained widespread attention as an emerging therapeutic approach.PROTACs are bifunctional molecules composed of a target protein-binding ligand,an E3 ubiquitin ligase ligand,and a linker connecting these ligands.By harnessing the cell’s intrinsic ubiquitin-proteasome system(UPS),they promote the ubiquitination of specific target proteins,leading to their degradation and therapeutic effects.PROTACs show exceptional promise in targeting conventional“undruggable”targets compared to traditional small-molecule inhibitors.This review provides an overview of PROTACs,including their molecular mechanism of action,therapeutic benefits,development history,key design aspects,current research and development challenges,and future trends in nextgeneration PROTAC technology.
基金the Deanship of Research and Graduate Studies at King Khalid University,KSA,for funding this work through the Large Research Project under grant number RGP2/164/46.
文摘Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-throughput sequencing technology have become prominent in biomedical research,and they reveal molecular aspects of cancer diagnosis and therapy.Despite the development of advanced sequencing technology,the presence of high-dimensionality in multi-omics data makes it challenging to interpret the data.Methods:In this study,we introduce RankXLAN,an explainable ensemble-based multi-omics framework that integrates feature selection(FS),ensemble learning,bioinformatics,and in-silico validation for robust biomarker detection,potential therapeutic drug-repurposing candidates’identification,and classification of SC.To enhance the interpretability of the model,we incorporated explainable artificial intelligence(SHapley Additive exPlanations analysis),as well as accuracy,precision,F1-score,recall,cross-validation,specificity,likelihood ratio(LR)+,LR−,and Youden index results.Results:The experimental results showed that the top four FS algorithms achieved improved results when applied to the ensemble learning classification model.The proposed ensemble model produced an area under the curve(AUC)score of 0.994 for gene expression,0.97 for methylation,and 0.96 for miRNA expression data.Through the integration of bioinformatics and ML approach of the transcriptomic and epigenomic multi-omics dataset,we identified potential marker genes,namely,UBE2D2,HPCAL4,IGHA1,DPT,and FN3K.In-silico molecular docking revealed a strong binding affinity between ANKRD13C and the FDA-approved drug Everolimus(binding affinity−10.1 kcal/mol),identifying ANKRD13C as a potential therapeutic drug-repurposing target for SC.Conclusion:The proposed framework RankXLAN outperforms other existing frameworks for serum biomarker identification,therapeutic target identification,and SC classification with multi-omics datasets.
基金supported by the National Natural Science Foundation of China under Grant No.61701100.
文摘In scenarios where ground-based cloud computing infrastructure is unavailable,unmanned aerial vehicles(UAVs)act as mobile edge computing(MEC)servers to provide on-demand computation services for ground terminals.To address the challenge of jointly optimizing task scheduling and UAV trajectory under limited resources and high mobility of UAVs,this paper presents PER-MATD3,a multi-agent deep reinforcement learning algorithm with prioritized experience replay(PER)into the Centralized Training with Decentralized Execution(CTDE)framework.Specifically,PER-MATD3 enables each agent to learn a decentralized policy using only local observations during execution,while leveraging a shared replay buffer with prioritized sampling and centralized critic during training to accelerate convergence and improve sample efficiency.Simulation results show that PER-MATD3 reduces average task latency by up to 23%,improves energy efficiency by 21%,and enhances service coverage compared to state-of-the-art baselines,demonstrating its effectiveness and practicality in scenarios without terrestrial networks.
基金appreciation to the Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R384)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks.Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay.In this network,the task processed at fog nodes reduces transmission delay.Still,it increases energy consumption,while routing tasks to the cloud server saves energy at the cost of higher communication delay.Moreover,the order in which offloaded tasks are executed affects the system’s efficiency.For instance,executing lower-priority tasks before higher-priority jobs can disturb the reliability and stability of the system.Therefore,an efficient strategy of optimal computation offloading and task scheduling is required for operational efficacy.In this paper,we introduced a multi-objective and enhanced version of Cheeta Optimizer(CO),namely(MoECO),to jointly optimize the computation offloading and task scheduling in cloud-fog networks to minimize two competing objectives,i.e.,energy consumption and communication delay.MoECO first assigns tasks to the optimal computational nodes and then the allocated tasks are scheduled for processing based on the task priority.The mathematical modelling of CO needs improvement in computation time and convergence speed.Therefore,MoECO is proposed to increase the search capability of agents by controlling the search strategy based on a leader’s location.The adaptive step length operator is adjusted to diversify the solution and thus improves the exploration phase,i.e.,global search strategy.Consequently,this prevents the algorithm from getting trapped in the local optimal solution.Moreover,the interaction factor during the exploitation phase is also adjusted based on the location of the prey instead of the adjacent Cheetah.This increases the exploitation capability of agents,i.e.,local search capability.Furthermore,MoECO employs a multi-objective Pareto-optimal front to simultaneously minimize designated objectives.Comprehensive simulations in MATLAB demonstrate that the proposed algorithm obtains multiple solutions via a Pareto-optimal front and achieves an efficient trade-off between optimization objectives compared to baseline methods.
基金supported by the Natural Science Foundation of Jilin Province(No.SKL202302002).
文摘Triple-negative breast cancer(TNBC)presents significant diagnostic and therapeutic challenges due to the lack of targeted treatments,rapid progression,high recurrence and metastasis rates,and overall poorer prognosis.Herein,the targeted theranostic platform of cysteine-modified gold nanodots-sulfhydrated luteinizing hormone releasing hormone(CGN-SLR)nanosystem was designed for target recognition and precise dual-mode imaging-guided photothermal therapy(PTT)against TNBC.On the one hand,the CGN-SLR nanosystem can serve as an ideal targeting fluorescent probe and computed tomography(CT)enhancer to facilitate the accurate diagnosis and surgical guidance of TNBC.On the other hand,the CGN-SLR nanosystem with great targeting and PTT ability can significantly inhibit the growth of TNBC,without causing harm to normal tissues and healthy organs.It provides an effective strategy for the diagnosis and treatment of TNBC through the rational design of multifunctional nanoplatform with target recognition,multiple imaging guidance/monitoring,and high-efficiency PTT.
文摘We read with great interest Deng et al.’s study 1 comparing sextant(6-core)and 12-core systematic biopsy in theMRI-targeted era,which valuably challenges the“more cores=higher accuracy”dogma by proposing a precision sampling strategy based on prostate cancer’s spatial distribution,aligning with personalized diagnosis trends.
基金supported by a grant from the Dalian Science and Technology Innovation Fund Program(No.2024JJ13PT070)United Foundation for Dalian Institute of Chemical Physics,Chinese Academy of Sciences and the Second Hospital of Dalian Medical University(No.DMU-2&DICP UN202410)Dalian Life and Health Field Guidance Program Project(No.2024ZDJH01PT084).
文摘Metabolic reprogramming involving branched-chain amino acids(BCAAs)—leucine,isoleucine,and valine—is increasingly recognized as pivotal in cancer progression,metastasis,and immune modulation.This review comprehensively explores how cancer cells rewire BCAA metabolism to enhance proliferation,survival,and therapy resistance.Tumors manipulate BCAA uptake and catabolism via high expression of transporters like L-type amino acid transporter 1(LAT1)and enzymes including branched chain amino acid transaminase 1(BCAT1),branched chain amino acid transaminase 2(BCAT2),branched-chain alpha-keto acid dehydrogenase(BCKDH),and branched chain alpha-keto acid dehydrogenase kinase(BCKDK).These alterations sustain energy production,biosynthesis,redox homeostasis,and oncogenic signaling(especially mammalian target of rapamycin complex 1[mTORC1]).Crucially,tumor-driven BCAA depletion also shapes an immunosuppressive microenvironment,impairing anti-tumor immunity by limiting essential nutrients for T cells and natural killer(NK)cells.Innovative therapeutic strategies targeting BCAA pathways—ranging from selective small-molecule inhibitors(e.g.,LAT1 and BCAT1/2)to dietary modulation—have shown promising preclinical and early clinical efficacy,highlighting their potential to exploit metabolic vulnerabilities in cancer cells while bolstering immune responses.By integrating multi-omics data and precision targeting approaches,this review underscores the translational significance of BCAA metabolic reprogramming,positioning it as a novel frontier in cancer treatment.
基金supported by the National Natural Science Foundation of China(82373277).
文摘Delivery carriers serve as a highly efficient approach for precision nutrition and medicine;however,artificial delivery carriers are prone to triggering the immune response and have the disadvantages of poor stability and low bioavailability.Extracellular vesicles(EVs),nucleus-free biological particles composed of phospholipid bilayers secreted by living cells,are a new generation of targeted delivery carriers.In recent years,an increasing number of species have been reported to contain EVs.Among them,food-derived extracellular vesicles(FDEVs)show outstanding comprehensive properties.FDEVs are considered to have great application potential due to their wide range of sources,high yields,absence of human pathogenic pathogens,and ethical concerns.In this review,the preparation,nomenclature,physicochemical characteristics,and preservation methods of FDEVs are discussed,as well as their potential protein markers,bioactivities,and applications as novel targeted delivery carriers of FDEVs from animals,plants,and microorganisms.We also summarized the adverse consequences of FDEVs in current studies,and put forward the problems and challenges in the process of FDEVs research and commercialization.In short,the importance of FDEVs has been highlighted,and FDEVs have good application prospects as a new class of targeted delivery carriers.The current problems should be paid attention to and actively solved.
文摘Oncology Research Editorial Office Published:19 January 2026 The published article titled“miR-202 Promotes Cell Apoptosis in Esophageal Squamous Cell Carcinoma by Targeting HSF2”has been retracted from Oncology Research,Vol.25,No.2,2017,pp.215-223.DOI:10.3727/096504016X14732772150541 URL:https://www.techscience.com/or/v25n2/56800.
文摘Food-grade biopolymers and nanotechnology have been increasingly used to revolutionize the delivery of bioactive compounds by enhancing stability,bioavailability,and controlled release.Within the scope of nanoencapsulation systems,this review explores food-derived polymers such as vicilin,zein,gluten,cruciferin,inulin,and others.These biopolymers are ideal since they encapsulate numerous functional compounds,such as vitamins,probiotics,essential oils,and polyphenols,because they are biocompatible,amphiphilic,and biodegradable.The specific physical and chemical properties of each polymer,extraction procedures,and nanoencapsulation techniques applied therein(e.g.,ionic gelation and spray drying)are described.The review highlights advanced targeting systems like pH-sensitive,magnetic delivery.Additional applications include those in synergistic nutraceutical systems,oral administration of vaccination,and intelligent food packaging.All these findings demonstrate that food polymers are increasingly more viable as functional nanocarriers by way of increasing bioactive delivery and the shifting requirements of personalized and health-based dietary regimes.
基金supported by the National Natural Science Foundation of China(No.62003269).
文摘Computing electrostatic interaction on non-cooperative targets with unknown meshes is crucial for electrostatic-based space on-orbit services.Although meshes for electrostatic interaction computations can be reconstructed from point clouds,they are usually too dense,leading to high computational costs.This paper presents an optimization method for converting dense meshes into optimal meshes,enabling fast and accurate computation of the electrostatic interaction by point clouds.First,the dense mesh reconstructed from point clouds is simplified into a coarse mesh using local operators.Second,the simplified mesh is refined by an iterative strategy that integrates a lightweight method of moments and an impedance matrix inheritance technique,ultimately yielding an optimal mesh for computing the electrostatic interaction.Simulation results show that our method effectively optimizes dense meshes,making electrostatic interaction computations using point clouds approximately 63.4 times more efficient than the previous method.
