Neuromorphic devices have garnered significant attention as potential building blocks for energy-efficient hardware systems owing to their capacity to emulate the computational efficiency of the brain.In this regard,r...Neuromorphic devices have garnered significant attention as potential building blocks for energy-efficient hardware systems owing to their capacity to emulate the computational efficiency of the brain.In this regard,reservoir computing(RC)framework,which leverages straightforward training methods and efficient temporal signal processing,has emerged as a promising scheme.While various physical reservoir devices,including ferroelectric,optoelectronic,and memristor-based systems,have been demonstrated,many still face challenges related to compatibility with mainstream complementary metal oxide semiconductor(CMOS)integration processes.This study introduced a silicon-based schottky barrier metal-oxide-semiconductor field effect transistor(SB-MOSFET),which was fabricated under low thermal budget and compatible with back-end-of-line(BEOL).The device demonstrated short-term memory characteristics,facilitated by the modulation of schottky barriers and charge trapping.Utilizing these characteristics,a RC system for temporal data processing was constructed,and its performance was validated in a 5×4 digital classification task,achieving an accuracy exceeding 98%after 50 training epochs.Furthermore,the system successfully processed temporal signal in waveform classification and prediction tasks using time-division multiplexing.Overall,the SB-MOSFET's high compatibility with CMOS technology provides substantial advantages for large-scale integration,enabling the development of energy-efficient reservoir computing hardware.展开更多
Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single ...Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single target”presents significant challenges due to its holistic approach.Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks,overcoming the limitations of reductionist research models and showing considerable value in TCM research.Recent integration of network target computational and experimental methods with artificial intelligence(AI)and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology.The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles.This review,centered on network targets,examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships,alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae,syndromes,and toxicity.Looking forward,network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics,potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.展开更多
Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug devel...Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug development has become a new trend,particularly in predicting drug-target associations.To address the challenge of drug-target prediction,AI-driven models have emerged as powerful tools,offering innovative solutions by effectively extracting features from complex biological data,accurately modeling molecular interactions,and precisely predicting potential drug-target outcomes.Traditional machine learning(ML),network-based,and advanced deep learning architectures such as convolutional neural networks(CNNs),graph convolutional networks(GCNs),and transformers play a pivotal role.This review systematically compiles and evaluates AI algorithms for drug-and drug combination-target predictions,highlighting their theoretical frameworks,strengths,and limitations.CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions.GCNs provide deep insights into molecular interactions via relational data,whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences.Network-based models offer a systematic perspective by integrating diverse data sources,and traditional ML efficiently handles large datasets to improve overall predictive accuracy.Collectively,these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy.This review summarizes the application of AI in drug development,particularly in drug-target prediction,and offers recommendations on models and algorithms for researchers engaged in biomedical research.It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.展开更多
The research and development of new traditional Chinese medicine(TCM)drugs have progressively established a novel system founded on the integration of TCM theory,human experience,and clinical trials(termed the“Three ...The research and development of new traditional Chinese medicine(TCM)drugs have progressively established a novel system founded on the integration of TCM theory,human experience,and clinical trials(termed the“Three Combinations”).However,considering TCM's distinctive features of“syndrome differentiation and treatment”and“multicomponent formulations and complex mechanisms”,current TCM drug development faces challenges such as insufficient understanding of the material basis and the overall mechanism of action and an incomplete evidence chain system.Moreover,significant obstacles persist in gathering human experience data,evaluating clinical efficacy,and controlling the quality of active ingredients,which impede the innovation process in TCM drug development.Network pharmacology,centered on the“network targets”theory,transcends the limitations of the conventional“single target”reductionist research model.It emphasizes the comprehensive effects of disease or syndrome biological networks as targets to elucidate the overall regulatory mechanism of TCM prescriptions.This approach aligns with the holistic perspective of TCM,offering a novel method consistent with TCM's holistic view for investigating the complex mechanisms of TCM and developing new TCM drugs.It is internationally recognized as a“next-generation drug research model”.To advance the research of new tools,methods,and standards for TCM evaluation and to overcome fundamental,critical,and cutting-edge technical challenges in TCM regulation,this consensus aims to explore the characteristics,progress,challenges,applicable pathways,and specific applications of network pharmacology as a new theory,method,and tool in TCM drug development.The goal is to enhance the quality of TCM drug research and development and accelerate the efficiency of developing new TCM products.展开更多
The flexoelectric effect refers to the electromechanical coupling between electric polarization and mechanical strain gradient.It universally exists in a variety of materials in any space group,such as liquid crystals...The flexoelectric effect refers to the electromechanical coupling between electric polarization and mechanical strain gradient.It universally exists in a variety of materials in any space group,such as liquid crystals,dielectrics,biological materials,and semiconductors.Because of its unique size effect,nanoscale flexoelectricity has shown novel phenomena and promising applications in electronics,optronics,mechatronics,and photovoltaics.In this review,we provide a succinct report on the discovery and development of the flexoelectric effect,focusing on flexoelectric materials and related applications.Finally,we discuss recent flexoelectric research progress and still‐unsolved problems.展开更多
In recent years,the rapid development of artificial intelligence has driven the widespread deployment of visual systems in complex environments such as autonomous driving,security surveillance,and medical diagnosis.Ho...In recent years,the rapid development of artificial intelligence has driven the widespread deployment of visual systems in complex environments such as autonomous driving,security surveillance,and medical diagnosis.However,existing image sensors—such as CMOS and CCD devices—intrinsically suffer from the limitation of fixed spectral response.Especially in environments with strong glare,haze,or dust,external spectral conditions often severely mismatch the device's design range,leading to significant degradation in image quality and a sharp drop in target recognition accuracy.While algorithmic post-processing(such as color bias correction or background suppression)can mitigate these issues,algorithm approaches typically introduce computational latency and increased energy consumption,making them unsuitable for edge computing or high-speed scenarios.展开更多
Many applications for locating a radio signal source employ Global Navigation Satellite System(GNSS)to obtain a sensor’s position.By using GNSS,a sensor can also synchronize with other sensors.For a sensor that is eq...Many applications for locating a radio signal source employ Global Navigation Satellite System(GNSS)to obtain a sensor’s position.By using GNSS,a sensor can also synchronize with other sensors.For a sensor that is equipped with a GNSS receiver,it can be independent and is readily to be loaded on a flexible platform,such as an unmanned aerial vehicle(UAV).In this paper,we consider using such sensors and timeof-arrival(TOA)techniques to locate a radio signal source,and analyze the performance limit of source localization.Besides the performance analysis,this paper provides the geometric interpretation of the performance limit,which can illustrate how a sensor contributes to the source localization accuracy.The performance analysis and the geometric interpretation together give important insights into how to make better use of GNSS receiver for passive localization.Another contribution is we propose a modified closedform solution for this localization problem.Compared with previous literature,this solution takes both sensor position and synchronization uncertainty into account,and it does not need proper initial guess of source position and is computationally efficient.Our simulation results validate the efficiency of this solution.展开更多
Multimedia semantic communication has been receiving increasing attention due to its significant enhancement of communication efficiency.Semantic coding,which is oriented towards extracting and encoding the key semant...Multimedia semantic communication has been receiving increasing attention due to its significant enhancement of communication efficiency.Semantic coding,which is oriented towards extracting and encoding the key semantics of video for transmission,is a key aspect in the framework of multimedia semantic communication.In this paper,we propose a facial video semantic coding method with low bitrate based on the temporal continuity of video semantics.At the sender’s end,we selectively transmit facial keypoints and deformation information,allocating distinct bitrates to different keypoints across frames.Compressive techniques involving sampling and quantization are employed to reduce the bitrate while retaining facial key semantic information.At the receiver’s end,a GAN-based generative network is utilized for reconstruction,effectively mitigating block artifacts and buffering problems present in traditional codec algorithms under low bitrates.The performance of the proposed approach is validated on multiple datasets,such as VoxCeleb and TalkingHead-1kH,employing metrics such as LPIPS,DISTS,and AKD for assessment.Experimental results demonstrate significant advantages over traditional codec methods,achieving up to approximately 10-fold bitrate reduction in prolonged,stable head pose scenarios across diverse conversational video settings.展开更多
In recent years, significant research efforts have been made to optimize the lithography processes. Liu et al.[1](Nat.Commun, 2024, https://doi.org/10.1038/s41467-024-46743-5)pioneered a new multi-photon lithography t...In recent years, significant research efforts have been made to optimize the lithography processes. Liu et al.[1](Nat.Commun, 2024, https://doi.org/10.1038/s41467-024-46743-5)pioneered a new multi-photon lithography technology in which light field and matter are co-confined, significantly exceeding the limitations of traditional lithography technology. In this news and views, we introduce this work to readers.展开更多
Block copolymer(BCP) nanolithography offers potential beyond traditional photolithographic limits, yet reliably producing low-defect, perpendicular domains remains challenging. We introduce a microenvironmentdriven is...Block copolymer(BCP) nanolithography offers potential beyond traditional photolithographic limits, yet reliably producing low-defect, perpendicular domains remains challenging. We introduce a microenvironmentdriven isothermal annealing method for directed self-assembly of BCP thin films. By annealing films at stable temperature in a quasi-sealed, inert-gas chamber, our approach promotes highly uniform perpendicular lamellar nanopatterns over large areas, effectively mitigating environmental fluctuations and emulating solvent-vapor annealing without solvent exposure. Resulting BCP structures demonstrate enhanced spatial coherence and notably low defect density. Furthermore, we successfully transfer these nanopatterns into precise metal nano-line arrays,confirming the method's capability for high-fidelity pattern replication. This scalable, solvent-free technique provides a robust, reliable route for high-resolution nanopatterning in advanced semiconductor manufacturing.展开更多
Photothermoelectric(PTE)photodetectors with selfpowered and uncooled advantages have attracted much interest due to the wide application prospects in the military and civilian fields.However,traditional PTE photodetec...Photothermoelectric(PTE)photodetectors with selfpowered and uncooled advantages have attracted much interest due to the wide application prospects in the military and civilian fields.However,traditional PTE photodetectors lack of mechanical flexibility and cannot operate independently without the test instrument.Herein,we present a flexible PTE photodetector capable of dual-mode output,combining electrical and optical signal generation for enhanced functionality.Using solution processing,high-quality MXene thin films are assembled on asymmetric electrodes as the photosensitive layer.The geometrically asymmetric electrode design significantly enhances the responsivity,achieving 0.33 m A W^(-1)under infrared illumination,twice that of the symmetrical configuration.This improvement stems from optimized photothermal conversion and an expanded temperature gradient.The PTE device maintains stable performance after 300 bending cycles,demonstrating excellent flexibility.A new energy conversion pathway has been established by coupling the photothermal conversion of MXene with thermochromic composite materials,leading to a real-time visualization of invisible infrared radiation.Leveraging this functionality,we demonstrate the first human-machine collaborative infrared imaging system,wherein the dual-mode photodetector arrays synchronously generate human-readable pattern and machine-readable pattern.Our study not only provides a new solution for functional integration of flexible photodetectors,but also sets a new benchmark for human-machine collaborative optoelectronics.展开更多
Background:Skin photoaging is a physiological or pathological process caused by multiple factors.Developing anti-skin photoaging drugs is a hot topic in cosmetology research fields.The purpose of this study was to exp...Background:Skin photoaging is a physiological or pathological process caused by multiple factors.Developing anti-skin photoaging drugs is a hot topic in cosmetology research fields.The purpose of this study was to explore the therapeutic effect of Dendrobium officinale(D.officinale)on skin aging.Methods:The ingredients of D.officinale were detected by UHPLC-Q-TOF/MS.The targets of D.officinale were screened by Swiss Target Prediction database.GeneCards,NCBI,and OMIM databases were utilized to find out the targets associated with skin photoaging.Overlapping targets of D.officinale and skin photoaging were obtained by Venn analysis.The ingredient-disease target network and protein-protein interaction network were constructed by using the STRING database and Cytoscape software.The key compounds and hub genes were obtained by analyzing networks.The DAVID database was applied for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis of the overlapping targets.Autodock Vina software was used to simulate molecular docking and the results were visualized using Pymol.Finally,the skin photoaging models of cells and mice were established to validate the results predicted by network pharmacology.Results:In D.officinale,a total of 59 compounds and 595 targets were detected,of which 59 proteins were intersectional with skin photoaging targets.The top 10 active ingredients(Dendrophenol,Herbacetin,Lyoniresinol,Trans-ferulaldehyde,Naringenin,and so on)and 8 hub genes(AKT1,TNF,VEGFA,MAPK3,CASP3,MMP9,CTNNB1,and EGFR)were identified.All the key active compounds could bind well with core protein targets(binding energy<-5 kcal/mol).The potential therapeutic targets were related to the response to reactive oxygen species,collagen catabolic process,extracellular matrix organization,and apoptotic process,mainly.We also found that D.officinale could enhance the cell viability and activity of anti-oxidases,reduce reactive oxygen species and MMP9 levels,and stable mitochondrial membrane potential.Furthermore,D.officinale could alleviate skin photoaging injury and reduce malondialdehyde level in mice.Conclusion:D.officinale alleviated skin photoaging via regulating oxidative stress,apoptosis,and collagen catabolic process.展开更多
Correction to:Nano-Micro Letters(2025)17:191 https://doi.org/10.1007/s40820-025-01702-7 Following the publication of the original article[1],the authors reported an error in Fig.3(b),and the figure legend was reversed...Correction to:Nano-Micro Letters(2025)17:191 https://doi.org/10.1007/s40820-025-01702-7 Following the publication of the original article[1],the authors reported an error in Fig.3(b),and the figure legend was reversed.The correct Fig.3 has been provided in this orrection.展开更多
Ethanol(EtOH)is a common trigger for gastric mucosal diseases,and mitigating oxidative stress is essential for attenuating gastric mucosal damage.Capsaicin(CAP)has been identified as a potential agent to counteract ox...Ethanol(EtOH)is a common trigger for gastric mucosal diseases,and mitigating oxidative stress is essential for attenuating gastric mucosal damage.Capsaicin(CAP)has been identified as a potential agent to counteract oxidative damage in the gastric mucosa;however,its precise mechanism remains unclear.This study demonstrates that CAP alleviates EtOH-induced gastric mucosal injuries through two primary pathways:by suppressing the chemokine receptor 4(CCR4)/Src/p47phox axis,thereby reducing oxidative stress,and by inhibiting the phosphorylation and nuclear translocation of nuclear factor-κB p65(NF-κB)p65,resulting in diminished inflammatory responses.These findings elucidate the mechanistic pathways of CAP and provide a theoretical foundation for its potential therapeutic application in the treatment of gastric mucosal injuries.展开更多
基金supported in part by the Chinese Academy of Sciences(No.XDA0330302)NSFC program(No.22127901)。
文摘Neuromorphic devices have garnered significant attention as potential building blocks for energy-efficient hardware systems owing to their capacity to emulate the computational efficiency of the brain.In this regard,reservoir computing(RC)framework,which leverages straightforward training methods and efficient temporal signal processing,has emerged as a promising scheme.While various physical reservoir devices,including ferroelectric,optoelectronic,and memristor-based systems,have been demonstrated,many still face challenges related to compatibility with mainstream complementary metal oxide semiconductor(CMOS)integration processes.This study introduced a silicon-based schottky barrier metal-oxide-semiconductor field effect transistor(SB-MOSFET),which was fabricated under low thermal budget and compatible with back-end-of-line(BEOL).The device demonstrated short-term memory characteristics,facilitated by the modulation of schottky barriers and charge trapping.Utilizing these characteristics,a RC system for temporal data processing was constructed,and its performance was validated in a 5×4 digital classification task,achieving an accuracy exceeding 98%after 50 training epochs.Furthermore,the system successfully processed temporal signal in waveform classification and prediction tasks using time-division multiplexing.Overall,the SB-MOSFET's high compatibility with CMOS technology provides substantial advantages for large-scale integration,enabling the development of energy-efficient reservoir computing hardware.
文摘Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single target”presents significant challenges due to its holistic approach.Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks,overcoming the limitations of reductionist research models and showing considerable value in TCM research.Recent integration of network target computational and experimental methods with artificial intelligence(AI)and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology.The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles.This review,centered on network targets,examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships,alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae,syndromes,and toxicity.Looking forward,network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics,potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.
基金supported by grants from the National Natural Science Foundation of China(Grant No.:T2341008)Intelligent and Precise Research on TCM for Spleen and Stomach Diseases(20233930063).
文摘Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug development has become a new trend,particularly in predicting drug-target associations.To address the challenge of drug-target prediction,AI-driven models have emerged as powerful tools,offering innovative solutions by effectively extracting features from complex biological data,accurately modeling molecular interactions,and precisely predicting potential drug-target outcomes.Traditional machine learning(ML),network-based,and advanced deep learning architectures such as convolutional neural networks(CNNs),graph convolutional networks(GCNs),and transformers play a pivotal role.This review systematically compiles and evaluates AI algorithms for drug-and drug combination-target predictions,highlighting their theoretical frameworks,strengths,and limitations.CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions.GCNs provide deep insights into molecular interactions via relational data,whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences.Network-based models offer a systematic perspective by integrating diverse data sources,and traditional ML efficiently handles large datasets to improve overall predictive accuracy.Collectively,these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy.This review summarizes the application of AI in drug development,particularly in drug-target prediction,and offers recommendations on models and algorithms for researchers engaged in biomedical research.It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.
基金supported by the National Medical Products Administration Commissioned Research Project (No.20211440216)the National Administration of Traditional Chinese Medicine Science and Technology Project (No.GZY-KJS-2024-03)+3 种基金the State Key Laboratory of Drug Regulatory Science Project (No.2023SKLDRS0104)the Basic Research Program Natural Science Fund-Frontier Leading Technology Basic Research Special Project of Jiangsu Province (No.BK20232014)the Programs Foundation for Leading Talents in National Administration of Traditional Chinese Medicine of China“Qihuang scholars”Projectthe Tianjin Administration for Market Regulation Science and Technology Key Projects (No.2022-W35)。
文摘The research and development of new traditional Chinese medicine(TCM)drugs have progressively established a novel system founded on the integration of TCM theory,human experience,and clinical trials(termed the“Three Combinations”).However,considering TCM's distinctive features of“syndrome differentiation and treatment”and“multicomponent formulations and complex mechanisms”,current TCM drug development faces challenges such as insufficient understanding of the material basis and the overall mechanism of action and an incomplete evidence chain system.Moreover,significant obstacles persist in gathering human experience data,evaluating clinical efficacy,and controlling the quality of active ingredients,which impede the innovation process in TCM drug development.Network pharmacology,centered on the“network targets”theory,transcends the limitations of the conventional“single target”reductionist research model.It emphasizes the comprehensive effects of disease or syndrome biological networks as targets to elucidate the overall regulatory mechanism of TCM prescriptions.This approach aligns with the holistic perspective of TCM,offering a novel method consistent with TCM's holistic view for investigating the complex mechanisms of TCM and developing new TCM drugs.It is internationally recognized as a“next-generation drug research model”.To advance the research of new tools,methods,and standards for TCM evaluation and to overcome fundamental,critical,and cutting-edge technical challenges in TCM regulation,this consensus aims to explore the characteristics,progress,challenges,applicable pathways,and specific applications of network pharmacology as a new theory,method,and tool in TCM drug development.The goal is to enhance the quality of TCM drug research and development and accelerate the efficiency of developing new TCM products.
基金support of the National Natural Science Foundation of China(Grant Nos.52192611,51872031,61904013,and 62405157)China Postdoctoral Science Foundation(Nos.2023M741890 and GZC20231215)the Fundamental Research Funds for the Central Universities.
文摘The flexoelectric effect refers to the electromechanical coupling between electric polarization and mechanical strain gradient.It universally exists in a variety of materials in any space group,such as liquid crystals,dielectrics,biological materials,and semiconductors.Because of its unique size effect,nanoscale flexoelectricity has shown novel phenomena and promising applications in electronics,optronics,mechatronics,and photovoltaics.In this review,we provide a succinct report on the discovery and development of the flexoelectric effect,focusing on flexoelectric materials and related applications.Finally,we discuss recent flexoelectric research progress and still‐unsolved problems.
基金supported in part by STI 2030-Major Projects(2022ZD0209200)in part by National Natural Science Foundation of China(62374099)+2 种基金in part by Beijing Natural Science Foundation−Xiaomi Innovation Joint Fund(L233009)Beijing Natural Science Foundation(L248104)in part by Independent Research Program of School of Integrated Circuits,Tsinghua University,in part by Tsinghua University Fuzhou Data Technology Joint Research Institute.
文摘In recent years,the rapid development of artificial intelligence has driven the widespread deployment of visual systems in complex environments such as autonomous driving,security surveillance,and medical diagnosis.However,existing image sensors—such as CMOS and CCD devices—intrinsically suffer from the limitation of fixed spectral response.Especially in environments with strong glare,haze,or dust,external spectral conditions often severely mismatch the device's design range,leading to significant degradation in image quality and a sharp drop in target recognition accuracy.While algorithmic post-processing(such as color bias correction or background suppression)can mitigate these issues,algorithm approaches typically introduce computational latency and increased energy consumption,making them unsuitable for edge computing or high-speed scenarios.
基金supported by the National Natural Science Foundation of China(Grant No.61973181)Tsinghua University Initiative Scientific Research Program(Grant No.2018Z05JZY004).
文摘Many applications for locating a radio signal source employ Global Navigation Satellite System(GNSS)to obtain a sensor’s position.By using GNSS,a sensor can also synchronize with other sensors.For a sensor that is equipped with a GNSS receiver,it can be independent and is readily to be loaded on a flexible platform,such as an unmanned aerial vehicle(UAV).In this paper,we consider using such sensors and timeof-arrival(TOA)techniques to locate a radio signal source,and analyze the performance limit of source localization.Besides the performance analysis,this paper provides the geometric interpretation of the performance limit,which can illustrate how a sensor contributes to the source localization accuracy.The performance analysis and the geometric interpretation together give important insights into how to make better use of GNSS receiver for passive localization.Another contribution is we propose a modified closedform solution for this localization problem.Compared with previous literature,this solution takes both sensor position and synchronization uncertainty into account,and it does not need proper initial guess of source position and is computationally efficient.Our simulation results validate the efficiency of this solution.
基金supported by the National Natural Science Foundation of China (Nos. NSFC 61925105, 62322109, 62171257 and U22B2001)the Xplorer Prize in Information and Electronics technologiesthe Tsinghua University (Department of Electronic Engineering)-Nantong Research Institute for Advanced Communication Technologies Joint Research Center for Space, Air, Ground and Sea Cooperative Communication Network Technology
文摘Multimedia semantic communication has been receiving increasing attention due to its significant enhancement of communication efficiency.Semantic coding,which is oriented towards extracting and encoding the key semantics of video for transmission,is a key aspect in the framework of multimedia semantic communication.In this paper,we propose a facial video semantic coding method with low bitrate based on the temporal continuity of video semantics.At the sender’s end,we selectively transmit facial keypoints and deformation information,allocating distinct bitrates to different keypoints across frames.Compressive techniques involving sampling and quantization are employed to reduce the bitrate while retaining facial key semantic information.At the receiver’s end,a GAN-based generative network is utilized for reconstruction,effectively mitigating block artifacts and buffering problems present in traditional codec algorithms under low bitrates.The performance of the proposed approach is validated on multiple datasets,such as VoxCeleb and TalkingHead-1kH,employing metrics such as LPIPS,DISTS,and AKD for assessment.Experimental results demonstrate significant advantages over traditional codec methods,achieving up to approximately 10-fold bitrate reduction in prolonged,stable head pose scenarios across diverse conversational video settings.
基金supported by Xishan-Tsinghua University Industry University Research Deep Integration Special Projectby Beijing Natural Science Foundation–Xiaomi Innovation Joint Fund (Grant No. L233009)by National Natural Science Foundation of China under Grant No. 62374099。
文摘In recent years, significant research efforts have been made to optimize the lithography processes. Liu et al.[1](Nat.Commun, 2024, https://doi.org/10.1038/s41467-024-46743-5)pioneered a new multi-photon lithography technology in which light field and matter are co-confined, significantly exceeding the limitations of traditional lithography technology. In this news and views, we introduce this work to readers.
基金supported by the National Natural Science Foundation of China (Grant Nos.U20A20168 and 62404120)the National Key R&D Program (Grant No.2022YFB3204100)+2 种基金the Postdoctoral Fellowship Program of CPSF (Grant Nos.GZB20240335 and GZC20231216)the China Postdoctoral Science Foundation (Grant No.2025T180151)the Initiative Scientific Research Program of the School of Integrated Circuits,Tsinghua University。
文摘Block copolymer(BCP) nanolithography offers potential beyond traditional photolithographic limits, yet reliably producing low-defect, perpendicular domains remains challenging. We introduce a microenvironmentdriven isothermal annealing method for directed self-assembly of BCP thin films. By annealing films at stable temperature in a quasi-sealed, inert-gas chamber, our approach promotes highly uniform perpendicular lamellar nanopatterns over large areas, effectively mitigating environmental fluctuations and emulating solvent-vapor annealing without solvent exposure. Resulting BCP structures demonstrate enhanced spatial coherence and notably low defect density. Furthermore, we successfully transfer these nanopatterns into precise metal nano-line arrays,confirming the method's capability for high-fidelity pattern replication. This scalable, solvent-free technique provides a robust, reliable route for high-resolution nanopatterning in advanced semiconductor manufacturing.
基金supported by the Fundamental Research Funds for the Central Universities(xxj022019009)。
文摘Photothermoelectric(PTE)photodetectors with selfpowered and uncooled advantages have attracted much interest due to the wide application prospects in the military and civilian fields.However,traditional PTE photodetectors lack of mechanical flexibility and cannot operate independently without the test instrument.Herein,we present a flexible PTE photodetector capable of dual-mode output,combining electrical and optical signal generation for enhanced functionality.Using solution processing,high-quality MXene thin films are assembled on asymmetric electrodes as the photosensitive layer.The geometrically asymmetric electrode design significantly enhances the responsivity,achieving 0.33 m A W^(-1)under infrared illumination,twice that of the symmetrical configuration.This improvement stems from optimized photothermal conversion and an expanded temperature gradient.The PTE device maintains stable performance after 300 bending cycles,demonstrating excellent flexibility.A new energy conversion pathway has been established by coupling the photothermal conversion of MXene with thermochromic composite materials,leading to a real-time visualization of invisible infrared radiation.Leveraging this functionality,we demonstrate the first human-machine collaborative infrared imaging system,wherein the dual-mode photodetector arrays synchronously generate human-readable pattern and machine-readable pattern.Our study not only provides a new solution for functional integration of flexible photodetectors,but also sets a new benchmark for human-machine collaborative optoelectronics.
文摘Background:Skin photoaging is a physiological or pathological process caused by multiple factors.Developing anti-skin photoaging drugs is a hot topic in cosmetology research fields.The purpose of this study was to explore the therapeutic effect of Dendrobium officinale(D.officinale)on skin aging.Methods:The ingredients of D.officinale were detected by UHPLC-Q-TOF/MS.The targets of D.officinale were screened by Swiss Target Prediction database.GeneCards,NCBI,and OMIM databases were utilized to find out the targets associated with skin photoaging.Overlapping targets of D.officinale and skin photoaging were obtained by Venn analysis.The ingredient-disease target network and protein-protein interaction network were constructed by using the STRING database and Cytoscape software.The key compounds and hub genes were obtained by analyzing networks.The DAVID database was applied for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis of the overlapping targets.Autodock Vina software was used to simulate molecular docking and the results were visualized using Pymol.Finally,the skin photoaging models of cells and mice were established to validate the results predicted by network pharmacology.Results:In D.officinale,a total of 59 compounds and 595 targets were detected,of which 59 proteins were intersectional with skin photoaging targets.The top 10 active ingredients(Dendrophenol,Herbacetin,Lyoniresinol,Trans-ferulaldehyde,Naringenin,and so on)and 8 hub genes(AKT1,TNF,VEGFA,MAPK3,CASP3,MMP9,CTNNB1,and EGFR)were identified.All the key active compounds could bind well with core protein targets(binding energy<-5 kcal/mol).The potential therapeutic targets were related to the response to reactive oxygen species,collagen catabolic process,extracellular matrix organization,and apoptotic process,mainly.We also found that D.officinale could enhance the cell viability and activity of anti-oxidases,reduce reactive oxygen species and MMP9 levels,and stable mitochondrial membrane potential.Furthermore,D.officinale could alleviate skin photoaging injury and reduce malondialdehyde level in mice.Conclusion:D.officinale alleviated skin photoaging via regulating oxidative stress,apoptosis,and collagen catabolic process.
基金supported in part by STI 2030-Major Projects under Grant 2022ZD0209200in part by Beijing Natural Science Foundation-Xiaomi Innovation Joint Fund (L233009)+4 种基金in part by National Natural Science Foundation of China under Grant No. 62374099in part by the Tsinghua-Toyota Joint Research Fundin part by the Daikin Tsinghua Union Programin part by Independent Research Program of School of Integrated Circuits,Tsinghua Universitysponsored by CIE-Tencent Robotics X Rhino-Bird Focused Research Program
文摘Correction to:Nano-Micro Letters(2025)17:191 https://doi.org/10.1007/s40820-025-01702-7 Following the publication of the original article[1],the authors reported an error in Fig.3(b),and the figure legend was reversed.The correct Fig.3 has been provided in this orrection.
基金supported by the High-level Talent Research Start-up Project Funding of Henan Academy of Sciences (No.252028037)。
文摘Ethanol(EtOH)is a common trigger for gastric mucosal diseases,and mitigating oxidative stress is essential for attenuating gastric mucosal damage.Capsaicin(CAP)has been identified as a potential agent to counteract oxidative damage in the gastric mucosa;however,its precise mechanism remains unclear.This study demonstrates that CAP alleviates EtOH-induced gastric mucosal injuries through two primary pathways:by suppressing the chemokine receptor 4(CCR4)/Src/p47phox axis,thereby reducing oxidative stress,and by inhibiting the phosphorylation and nuclear translocation of nuclear factor-κB p65(NF-κB)p65,resulting in diminished inflammatory responses.These findings elucidate the mechanistic pathways of CAP and provide a theoretical foundation for its potential therapeutic application in the treatment of gastric mucosal injuries.
