Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technol...Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technology overcomes the limitations of traditional single-organ models,providing a novel platform for investigating complex disease mechanisms and evaluating drug efficacy and toxicity.Although it demonstrates broad application prospects,its development still faces critical bottlenecks,including inadequate physiological coupling between organs,short functional maintenance durations,and limited real-time monitoring capabilities.Contemporary research is advancing along three key directions,including functional coupling,sensor integration,and full-process automation systems,to propel the technology toward enhanced levels of physiological relevance and predictive accuracy.展开更多
Since the first design of tactile sensors was proposed by Harmon in 1982,tactile sensors have evolved through four key phases:industrial applications(1980s,basic pressure detection),miniaturization via MEMS(1990s),fle...Since the first design of tactile sensors was proposed by Harmon in 1982,tactile sensors have evolved through four key phases:industrial applications(1980s,basic pressure detection),miniaturization via MEMS(1990s),flexible electronics(2010s,stretchable materials),and intelligent systems(2020s-present,AI-driven multimodal sensing).With the innovation of material,processing techniques,and multimodal fusion of stimuli,the application of tactile sensors has been continuously expanding to a diversity of areas,including but not limited to medical care,aerospace,sports and intelligent robots.Currently,researchers are dedicated to develop tactile sensors with emerging mechanisms and structures,pursuing high-sensitivity,high-resolution,and multimodal characteristics and further constructing tactile systems which imitate and approach the performance of human organs.However,challenges in the combination between the theoretical research and the practical applications are still significant.There is a lack of comprehensive understanding in the state of the art of such knowledge transferring from academic work to technical products.Scaled-up production of laboratory materials faces fatal challenges like high costs,small scale,and inconsistent quality.Ambient factors,such as temperature,humidity,and electromagnetic interference,also impair signal reliability.Moreover,tactile sensors must operate across a wide pressure range(0.1 k Pa to several or even dozens of MPa)to meet diverse application needs.Meanwhile,the existing algorithms,data models and sensing systems commonly reveal insufficient precision as well as undesired robustness in data processing,and there is a realistic gap between the designed and the demanded system response speed.In this review,oriented by the design requirements of intelligent tactile sensing systems,we summarize the common sensing mechanisms,inspired structures,key performance,and optimizing strategies,followed by a brief overview of the recent advances in the perspectives of system integration and algorithm implementation,and the possible roadmap of future development of tactile sensors,providing a forward-looking as well as critical discussions in the future industrial applications of flexible tactile sensors.展开更多
As silicon-based transistors face fundamental scaling limits,the search for breakthrough alternatives has led to innovations in 3D architectures,heterogeneous integration,and sub-3 nm semiconductor body thicknesses.Ho...As silicon-based transistors face fundamental scaling limits,the search for breakthrough alternatives has led to innovations in 3D architectures,heterogeneous integration,and sub-3 nm semiconductor body thicknesses.However,the true effectiveness of these advancements lies in the seamless integration of alternative semiconductors tailored for next-generation transistors.In this review,we highlight key advances that enhance both scalability and switching performance by leveraging emerging semiconductor materials.Among the most promising candidates are 2D van der Waals semiconductors,Mott insulators,and amorphous oxide semiconductors,which offer not only unique electrical properties but also low-power operation and high carrier mobility.Additionally,we explore the synergistic interactions between these novel semiconductors and advanced gate dielectrics,including high-K materials,ferroelectrics,and atomically thin hexagonal boron nitride layers.Beyond introducing these novel material configurations,we address critical challenges such as leakage current and long-term device reliability,which become increasingly crucial as transistors scale down to atomic dimensions.Through concrete examples showcasing the potential of these materials in transistors,we provide key insights into overcoming fundamental obstacles—such as device reliability,scaling down limitations,and extended applications in artificial intelligence—ultimately paving the way for the development of future transistor technologies.展开更多
China is carving out a distinctive development path which features urban-rural integration.This approach has not only yielded tangible results domestically but also drawn the attention of other countries.
Flexible electronics face critical challenges in achieving monolithic three-dimensional(3D)integration,including material compatibility,structural stability,and scalable fabrication methods.Inspired by the tactile sen...Flexible electronics face critical challenges in achieving monolithic three-dimensional(3D)integration,including material compatibility,structural stability,and scalable fabrication methods.Inspired by the tactile sensing mechanism of the human skin,we have developed a flexible monolithic 3D-integrated tactile sensing system based on a holey MXene paste,where each vertical one-body unit simultaneously functions as a microsupercapacitor and pressure sensor.The in-plane mesopores of MXene significantly improve ion accessibility,mitigate the self-stacking of nanosheets,and allow the holey MXene to multifunctionally act as a sensing material,an active electrode,and a conductive interconnect,thus drastically reducing the interface mismatch and enhancing the mechanical robustness.Furthermore,we fabricate a large-scale device using a blade-coating and stamping method,which demonstrates excellent mechanical flexibility,low-power consumption,rapid response,and stable long-term operation.As a proof-of-concept application,we integrate our sensing array into a smart access control system,leveraging deep learning to accurately identify users based on their unique pressing behaviors.This study provides a promising approach for designing highly integrated,intelligent,and flexible electronic systems for advanced human-computer interactions and personalized electronics.展开更多
The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities...The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.展开更多
Exogenous neural stem cell transplantation has become one of the most promising treatment methods for chronic stroke.Recent studies have shown that most ischemia-reperfusion model rats recover spontaneously after inju...Exogenous neural stem cell transplantation has become one of the most promising treatment methods for chronic stroke.Recent studies have shown that most ischemia-reperfusion model rats recover spontaneously after injury,which limits the ability to observe long-term behavioral recovery.Here,we used a severe stroke rat model with 150 minutes of ischemia,which produced severe behavioral deficiencies that persisted at 12 weeks,to study the therapeutic effect of neural stem cells on neural restoration in chronic stroke.Our study showed that stroke model rats treated with human neural stem cells had long-term sustained recovery of motor function,reduced infarction volume,long-term human neural stem cell survival,and improved local inflammatory environment and angiogenesis.We also demonstrated that transplanted human neural stem cells differentiated into mature neurons in vivo,formed stable functional synaptic connections with host neurons,and exhibited the electrophysiological properties of functional mature neurons,indicating that they replaced the damaged host neurons.The findings showed that human fetal-derived neural stem cells had long-term effects for neurological recovery in a model of severe stroke,which suggests that human neural stem cells-based therapy may be effective for repairing damaged neural circuits in stroke patients.展开更多
We read with great interest the study by Zhang et al on Yiyi Fuzi Baijiang powder(YFB),which exemplifies the power of modern methods to validate traditional Chinese medicine(TCM).The key insight is that YFB doesn’t m...We read with great interest the study by Zhang et al on Yiyi Fuzi Baijiang powder(YFB),which exemplifies the power of modern methods to validate traditional Chinese medicine(TCM).The key insight is that YFB doesn’t merely alter“good”or“bad”bacteria but restores the gut microbiota’s holistic equilibrium.This is powerfully shown by its paradoxical reduction of anaerobic probiotics like Bifidobacterium,rectifying the diseased,hypoxic environment,causing their aberrant overgrowth.This challenges the conventional probiotic paradigm and underscores a core TCM principle:Herbal formulas treat disease by restoring the body’s overall functional balance.Future research should focus on the interplay between herbal components,intestinal oxygen,and microbial metabolites to further unravel this sophisticated dialogue.展开更多
Understanding the neural underpinning of human gait and balance is one of the most pertinent challenges for 21st-century translational neuroscience due to the profound impact that falls and mobility disturbances have ...Understanding the neural underpinning of human gait and balance is one of the most pertinent challenges for 21st-century translational neuroscience due to the profound impact that falls and mobility disturbances have on our aging population.Posture and gait control does not happen automatically,as previously believed,but rather requires continuous involvement of central nervous mechanisms.To effectively exert control over the body,the brain must integrate multiple streams of sensory information,including visual,vestibular,and somatosensory signals.The mechanisms which underpin the integration of these multisensory signals are the principal topic of the present work.Existing multisensory integration theories focus on how failure of cognitive processes thought to be involved in multisensory integration leads to falls in older adults.Insufficient emphasis,however,has been placed on specific contributions of individual sensory modalities to multisensory integration processes and cross-modal interactions that occur between the sensory modalities in relation to gait and balance.In the present work,we review the contributions of somatosensory,visual,and vestibular modalities,along with their multisensory intersections to gait and balance in older adults and patients with Parkinson’s disease.We also review evidence of vestibular contributions to multisensory temporal binding windows,previously shown to be highly pertinent to fall risk in older adults.Lastly,we relate multisensory vestibular mechanisms to potential neural substrates,both at the level of neurobiology(concerning positron emission tomography imaging)and at the level of electrophysiology(concerning electroencephalography).We hope that this integrative review,drawing influence across multiple subdisciplines of neuroscience,paves the way for novel research directions and therapeutic neuromodulatory approaches,to improve the lives of older adults and patients with neurodegenerative diseases.展开更多
Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
Digitization is the inevitable path for the natural development of traditional Chinese medicine(TCM)in the context of the Fourth Industrial Revolution.The goal of TCM digitization is to generate intelligence from numb...Digitization is the inevitable path for the natural development of traditional Chinese medicine(TCM)in the context of the Fourth Industrial Revolution.The goal of TCM digitization is to generate intelligence from numbers.Originating from the reasoning paradigm of Xiangshu(象数,image-number)or phenotype-numerology thinking,TCM came with a deep correlation of clinical observations with digits and laid a strong theoretical basis for digitization.The digitization of TCM should start from the clinical aspect,solve the problem of electronic medical records,achieve standardization and informatization,and on this basis,form a TCM knowledge base through knowledge-building.This process depends on the combined efforts of multiple disciplines such as medicine,mathematics,and engineering to achieve the digitization and intelligent transformation of TCM.This era calls for TCM to break down barriers,embrace opportunities,and move towards digitization.However,during the transformation,it should maintain its essence,avoid simplistic conversions,be guided by scientific value,leverage cutting-edge technologies,and enhance the depth and breadth of the interpretation of TCM connotations.The digitization of TCM will also improve its service capabilities,create an innovative digitally-intelligent TCM service platform,and contribute to the development of“Healthy China”initiatives with wisdom and solutions.展开更多
Plant synthetic biology has emerged as a transformative field in agriculture,offering innovative solutions to enhance food security,provide resilience to climate change,and transition to sustainable farming practices....Plant synthetic biology has emerged as a transformative field in agriculture,offering innovative solutions to enhance food security,provide resilience to climate change,and transition to sustainable farming practices.By integrating advanced genetic tools,computational modeling,and systems biology,researchers can precisely modify plant genomes to enhance traits such as yield,stress tolerance,and nutrient use efficiency.The ability to design plants with specific characteristics tailored to diverse environmental conditions and agricultural needs holds great potential to address global food security challenges.Here,we highlight recent advancements and applications of plant synthetic biology in agriculture,focusing on key areas such as photosynthetic efficiency,nitrogen fixation,drought tolerance,pathogen resistance,nutrient use efficiency,biofortification,climate resilience,microbiology engineering,synthetic plant genomes,and the integration of artificial intelligence with synthetic biology.These innovations aim to maximize resource use efficiency,reduce reliance on external inputs,and mitigate environmental impacts associated with conventional agricultural practices.Despite challenges related to regulatory approval and public acceptance,the integration of synthetic biology in agriculture holds immense promise for creating more resilient and sustainable agricultural systems,contributing to global food security and environmental sustainability.Rigorous multi-field testing of these approaches will undoubtedly be required to ensure reproducibility.展开更多
Artificial intelligence(AI)is driving a paradigm shift in gastroenterology and hepa-tology by delivering cutting-edge tools for disease screening,diagnosis,treatment,and prognostic management.Through deep learning,rad...Artificial intelligence(AI)is driving a paradigm shift in gastroenterology and hepa-tology by delivering cutting-edge tools for disease screening,diagnosis,treatment,and prognostic management.Through deep learning,radiomics,and multimodal data integration,AI has achieved diagnostic parity with expert cli-nicians in endoscopic image analysis(e.g.,early gastric cancer detection,colorectal polyp identification)and non-invasive assessment of liver pathologies(e.g.,fibrosis staging,fatty liver typing)while demonstrating utility in personalized care scenarios such as predicting hepatocellular carcinoma recurrence and opti-mizing inflammatory bowel disease treatment responses.Despite these advance-ments challenges persist including limited model generalization due to frag-mented datasets,algorithmic limitations in rare conditions(e.g.,pediatric liver diseases)caused by insufficient training data,and unresolved ethical issues related to bias,accountability,and patient privacy.Mitigation strategies involve constructing standardized multicenter databases,validating AI tools through prospective trials,leveraging federated learning to address data scarcity,and de-veloping interpretable systems(e.g.,attention heatmap visualization)to enhance clinical trust.Integrating generative AI,digital twin technologies,and establishing unified ethical/regulatory frameworks will accelerate AI adoption in primary care and foster equitable healthcare access while interdisciplinary collaboration and evidence-based implementation remain critical for realizing AI’s potential to redefine precision care for digestive disorders,improve global health outcomes,and reshape healthcare equity.展开更多
The integrated optical true time delay phased array antenna system has the advantages of high bandwidth,small size,low loss and strong antiinterference capability,etc.The high integration of the optically controlled p...The integrated optical true time delay phased array antenna system has the advantages of high bandwidth,small size,low loss and strong antiinterference capability,etc.The high integration of the optically controlled phased array antenna system is a necessary trend for the future development of the phased array,and it is also a major focus and difficulty in the current research of integrated microwave photonics.This paper firstly introduces the basic principle and development history of optical true time delay phased array antenna system based on microwave photonics,and briefly introduces the main implementation methods and integration platform of optical true time delay.Then,the application and development prospect of optical true time delay technology in beam control of phased array antenna system are mainly presented.Finally,according to the current research progress,the possible research directions of integrated optically controlled phased array antenna systems in the future are proposed.展开更多
The accelerating global energy transition,driven by climate imperatives and technological advancements,demands fundamen-tal transformations in power systems.Smart grids,characterized by cyber-physical integration,dist...The accelerating global energy transition,driven by climate imperatives and technological advancements,demands fundamen-tal transformations in power systems.Smart grids,characterized by cyber-physical integration,distributed renewable resources,and data-driven intelligence,have emerged as the backbone of this evolution.This convergence,however,introduces unprecedented complexities in resilience,security,stability,and market operation.This special issue presents five pivotal studies addressing these interconnected challenges,offering novel methodologies and insights to advance the efficiency,resilience,and sustainability of modern power systems.展开更多
The rapid growth of artificial intelligence has accelerated data generation,which increasingly exposes the limitations faced by traditional computational architectures,particularly in terms of energy consumption and d...The rapid growth of artificial intelligence has accelerated data generation,which increasingly exposes the limitations faced by traditional computational architectures,particularly in terms of energy consumption and data latency.In contrast,data-centric computing that integrates processing and storage has the potential of reducing latency and energy usage.Organic optoelectronic synaptic transistors have emerged as one type of promising devices to implement the data-centric com-puting paradigm owing to their superiority of flexibility,low cost,and large-area fabrication.However,sophisticated functions including vector-matrix multiplication that a single device can achieve are limited.Thus,the fabrication and utilization of organic optoelectronic synaptic transistor arrays(OOSTAs)are imperative.Here,we summarize the recent advances in OOSTAs.Various strategies for manufacturing OOSTAs are introduced,including coating and casting,physical vapor deposition,printing,and photolithography.Furthermore,innovative applications of the OOSTA system integration are discussed,including neuromor-phic visual systems and neuromorphic computing systems.At last,challenges and future perspectives of utilizing OOSTAs in real-world applications are discussed.展开更多
Due to complex geological structures and a narrow safe mud density window,offshore fractured formations frequently encounter severe lost circulation(LC)during drilling,significantly hindering oil and gas exploration a...Due to complex geological structures and a narrow safe mud density window,offshore fractured formations frequently encounter severe lost circulation(LC)during drilling,significantly hindering oil and gas exploration and development.Predicting LC risks enables the targeted implementation of mitigation strategies,thereby reducing the frequency of such incidents.To address the limitations of existing 3D geomechanical modeling in predicting LC,such as arbitrary factor selection,subjective weight assignment,and the inability to achieve pre-drilling prediction along the entire well section,an improved prediction method is proposed.This method integrates multi-source data and incorporates three LC-related sensitivity factors:fracture characteristics,rock brittleness,and in-situ stress conditions.A quantitative risk assessment model for LC is developed by combining the subjective analytic hierarchy process with the objective entropy weight method(EWM)to assign weights.Subsequently,3D geomechanical modeling is applied to identify regional risk zones,enabling digital visualization for pre-drilling risk prediction.The developed 3D LC risk prediction model was validated using actual LC incidents from drilled wells.Results were generally consistent with field-identified LC zones,with an average relative error of 19.08%,confirming its reliability.This method provides practical guidance for mitigating potential LC risks and optimizing drilling program designs in fractured formations.展开更多
Multimodal deep learning has emerged as a key paradigm in contemporary medical diagnostics,advancing precision medicine by enabling integration and learning from diverse data sources.The exponential growth of high-dim...Multimodal deep learning has emerged as a key paradigm in contemporary medical diagnostics,advancing precision medicine by enabling integration and learning from diverse data sources.The exponential growth of high-dimensional healthcare data,encompassing genomic,transcriptomic,and other omics profiles,as well as radiological imaging and histopathological slides,makes this approach increasingly important because,when examined separately,these data sources only offer a fragmented picture of intricate disease processes.Multimodal deep learning leverages the complementary properties of multiple data modalities to enable more accurate prognostic modeling,more robust disease characterization,and improved treatment decision-making.This review provides a comprehensive overview of the current state of multimodal deep learning approaches in medical diagnosis.We classify and examine important application domains,such as(1)radiology,where automated report generation and lesion detection are facilitated by image-text integration;(2)histopathology,where fusion models improve tumor classification and grading;and(3)multi-omics,where molecular subtypes and latent biomarkers are revealed through cross-modal learning.We provide an overview of representative research,methodological advancements,and clinical consequences for each domain.Additionally,we critically analyzed the fundamental issues preventing wider adoption,including computational complexity(particularly in training scalable,multi-branch networks),data heterogeneity(resulting from modality-specific noise,resolution variations,and inconsistent annotations),and the challenge of maintaining significant cross-modal correlations during fusion.These problems impede interpretability,which is crucial for clinical trust and use,in addition to performance and generalizability.Lastly,we outline important areas for future research,including the development of standardized protocols for harmonizing data,the creation of lightweight and interpretable fusion architectures,the integration of real-time clinical decision support systems,and the promotion of cooperation for federated multimodal learning.Our goal is to provide researchers and clinicians with a concise overview of the field’s present state,enduring constraints,and exciting directions for further research through this review.展开更多
基金supported by the Shenzhen Medical Research Fund(Grant No.A2303049)Guangdong Basic and Applied Basic Research(Grant No.2023A1515010647)+1 种基金National Natural Science Foundation of China(Grant No.22004135)Shenzhen Science and Technology Program(Grant No.RCBS20210706092409020,GXWD20201231165807008,20200824162253002).
文摘Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technology overcomes the limitations of traditional single-organ models,providing a novel platform for investigating complex disease mechanisms and evaluating drug efficacy and toxicity.Although it demonstrates broad application prospects,its development still faces critical bottlenecks,including inadequate physiological coupling between organs,short functional maintenance durations,and limited real-time monitoring capabilities.Contemporary research is advancing along three key directions,including functional coupling,sensor integration,and full-process automation systems,to propel the technology toward enhanced levels of physiological relevance and predictive accuracy.
基金the financial support of the National Natural Science Foundation of China(NO.52173028)。
文摘Since the first design of tactile sensors was proposed by Harmon in 1982,tactile sensors have evolved through four key phases:industrial applications(1980s,basic pressure detection),miniaturization via MEMS(1990s),flexible electronics(2010s,stretchable materials),and intelligent systems(2020s-present,AI-driven multimodal sensing).With the innovation of material,processing techniques,and multimodal fusion of stimuli,the application of tactile sensors has been continuously expanding to a diversity of areas,including but not limited to medical care,aerospace,sports and intelligent robots.Currently,researchers are dedicated to develop tactile sensors with emerging mechanisms and structures,pursuing high-sensitivity,high-resolution,and multimodal characteristics and further constructing tactile systems which imitate and approach the performance of human organs.However,challenges in the combination between the theoretical research and the practical applications are still significant.There is a lack of comprehensive understanding in the state of the art of such knowledge transferring from academic work to technical products.Scaled-up production of laboratory materials faces fatal challenges like high costs,small scale,and inconsistent quality.Ambient factors,such as temperature,humidity,and electromagnetic interference,also impair signal reliability.Moreover,tactile sensors must operate across a wide pressure range(0.1 k Pa to several or even dozens of MPa)to meet diverse application needs.Meanwhile,the existing algorithms,data models and sensing systems commonly reveal insufficient precision as well as undesired robustness in data processing,and there is a realistic gap between the designed and the demanded system response speed.In this review,oriented by the design requirements of intelligent tactile sensing systems,we summarize the common sensing mechanisms,inspired structures,key performance,and optimizing strategies,followed by a brief overview of the recent advances in the perspectives of system integration and algorithm implementation,and the possible roadmap of future development of tactile sensors,providing a forward-looking as well as critical discussions in the future industrial applications of flexible tactile sensors.
基金supported by the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(MSIT),South Korea(RS-2024-00421181)financially supported in part by National R&D Program(2021M3H4A3A02086430)through NRF(National Research Foundation of Korea)funded by Ministry of Science and ICT+2 种基金the National Research Council of Science&Technology(NST)grant by the Korea government(MSIT)(No.GTL25021-210)The Inter-University Semiconductor Research Center,Institute of Engineering Research,and Soft Foundry Institute at Seoul National University provided research facilities for this workhe grant by the National Research Foundation of Korea(NSF)supported by the Korea government(MIST)(RS-2025-16903034)。
文摘As silicon-based transistors face fundamental scaling limits,the search for breakthrough alternatives has led to innovations in 3D architectures,heterogeneous integration,and sub-3 nm semiconductor body thicknesses.However,the true effectiveness of these advancements lies in the seamless integration of alternative semiconductors tailored for next-generation transistors.In this review,we highlight key advances that enhance both scalability and switching performance by leveraging emerging semiconductor materials.Among the most promising candidates are 2D van der Waals semiconductors,Mott insulators,and amorphous oxide semiconductors,which offer not only unique electrical properties but also low-power operation and high carrier mobility.Additionally,we explore the synergistic interactions between these novel semiconductors and advanced gate dielectrics,including high-K materials,ferroelectrics,and atomically thin hexagonal boron nitride layers.Beyond introducing these novel material configurations,we address critical challenges such as leakage current and long-term device reliability,which become increasingly crucial as transistors scale down to atomic dimensions.Through concrete examples showcasing the potential of these materials in transistors,we provide key insights into overcoming fundamental obstacles—such as device reliability,scaling down limitations,and extended applications in artificial intelligence—ultimately paving the way for the development of future transistor technologies.
文摘China is carving out a distinctive development path which features urban-rural integration.This approach has not only yielded tangible results domestically but also drawn the attention of other countries.
基金supported by the National Natural Science Foundation of China(52272177,12204010)the Foundation for the Introduction of High-Level Talents of Anhui University(S020118002/097)+1 种基金the University Synergy Innovation Program of Anhui Province(GXXT-2023-066)the Scientific Research Project of Anhui Provincial Higher Education Institution(2023AH040008)。
文摘Flexible electronics face critical challenges in achieving monolithic three-dimensional(3D)integration,including material compatibility,structural stability,and scalable fabrication methods.Inspired by the tactile sensing mechanism of the human skin,we have developed a flexible monolithic 3D-integrated tactile sensing system based on a holey MXene paste,where each vertical one-body unit simultaneously functions as a microsupercapacitor and pressure sensor.The in-plane mesopores of MXene significantly improve ion accessibility,mitigate the self-stacking of nanosheets,and allow the holey MXene to multifunctionally act as a sensing material,an active electrode,and a conductive interconnect,thus drastically reducing the interface mismatch and enhancing the mechanical robustness.Furthermore,we fabricate a large-scale device using a blade-coating and stamping method,which demonstrates excellent mechanical flexibility,low-power consumption,rapid response,and stable long-term operation.As a proof-of-concept application,we integrate our sensing array into a smart access control system,leveraging deep learning to accurately identify users based on their unique pressing behaviors.This study provides a promising approach for designing highly integrated,intelligent,and flexible electronic systems for advanced human-computer interactions and personalized electronics.
文摘The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.
文摘Exogenous neural stem cell transplantation has become one of the most promising treatment methods for chronic stroke.Recent studies have shown that most ischemia-reperfusion model rats recover spontaneously after injury,which limits the ability to observe long-term behavioral recovery.Here,we used a severe stroke rat model with 150 minutes of ischemia,which produced severe behavioral deficiencies that persisted at 12 weeks,to study the therapeutic effect of neural stem cells on neural restoration in chronic stroke.Our study showed that stroke model rats treated with human neural stem cells had long-term sustained recovery of motor function,reduced infarction volume,long-term human neural stem cell survival,and improved local inflammatory environment and angiogenesis.We also demonstrated that transplanted human neural stem cells differentiated into mature neurons in vivo,formed stable functional synaptic connections with host neurons,and exhibited the electrophysiological properties of functional mature neurons,indicating that they replaced the damaged host neurons.The findings showed that human fetal-derived neural stem cells had long-term effects for neurological recovery in a model of severe stroke,which suggests that human neural stem cells-based therapy may be effective for repairing damaged neural circuits in stroke patients.
文摘We read with great interest the study by Zhang et al on Yiyi Fuzi Baijiang powder(YFB),which exemplifies the power of modern methods to validate traditional Chinese medicine(TCM).The key insight is that YFB doesn’t merely alter“good”or“bad”bacteria but restores the gut microbiota’s holistic equilibrium.This is powerfully shown by its paradoxical reduction of anaerobic probiotics like Bifidobacterium,rectifying the diseased,hypoxic environment,causing their aberrant overgrowth.This challenges the conventional probiotic paradigm and underscores a core TCM principle:Herbal formulas treat disease by restoring the body’s overall functional balance.Future research should focus on the interplay between herbal components,intestinal oxygen,and microbial metabolites to further unravel this sophisticated dialogue.
文摘Understanding the neural underpinning of human gait and balance is one of the most pertinent challenges for 21st-century translational neuroscience due to the profound impact that falls and mobility disturbances have on our aging population.Posture and gait control does not happen automatically,as previously believed,but rather requires continuous involvement of central nervous mechanisms.To effectively exert control over the body,the brain must integrate multiple streams of sensory information,including visual,vestibular,and somatosensory signals.The mechanisms which underpin the integration of these multisensory signals are the principal topic of the present work.Existing multisensory integration theories focus on how failure of cognitive processes thought to be involved in multisensory integration leads to falls in older adults.Insufficient emphasis,however,has been placed on specific contributions of individual sensory modalities to multisensory integration processes and cross-modal interactions that occur between the sensory modalities in relation to gait and balance.In the present work,we review the contributions of somatosensory,visual,and vestibular modalities,along with their multisensory intersections to gait and balance in older adults and patients with Parkinson’s disease.We also review evidence of vestibular contributions to multisensory temporal binding windows,previously shown to be highly pertinent to fall risk in older adults.Lastly,we relate multisensory vestibular mechanisms to potential neural substrates,both at the level of neurobiology(concerning positron emission tomography imaging)and at the level of electrophysiology(concerning electroencephalography).We hope that this integrative review,drawing influence across multiple subdisciplines of neuroscience,paves the way for novel research directions and therapeutic neuromodulatory approaches,to improve the lives of older adults and patients with neurodegenerative diseases.
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
基金Special Funds of the National Natural Science Foundation of China(T2341006).
文摘Digitization is the inevitable path for the natural development of traditional Chinese medicine(TCM)in the context of the Fourth Industrial Revolution.The goal of TCM digitization is to generate intelligence from numbers.Originating from the reasoning paradigm of Xiangshu(象数,image-number)or phenotype-numerology thinking,TCM came with a deep correlation of clinical observations with digits and laid a strong theoretical basis for digitization.The digitization of TCM should start from the clinical aspect,solve the problem of electronic medical records,achieve standardization and informatization,and on this basis,form a TCM knowledge base through knowledge-building.This process depends on the combined efforts of multiple disciplines such as medicine,mathematics,and engineering to achieve the digitization and intelligent transformation of TCM.This era calls for TCM to break down barriers,embrace opportunities,and move towards digitization.However,during the transformation,it should maintain its essence,avoid simplistic conversions,be guided by scientific value,leverage cutting-edge technologies,and enhance the depth and breadth of the interpretation of TCM connotations.The digitization of TCM will also improve its service capabilities,create an innovative digitally-intelligent TCM service platform,and contribute to the development of“Healthy China”initiatives with wisdom and solutions.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Category B,XDB1090000).
文摘Plant synthetic biology has emerged as a transformative field in agriculture,offering innovative solutions to enhance food security,provide resilience to climate change,and transition to sustainable farming practices.By integrating advanced genetic tools,computational modeling,and systems biology,researchers can precisely modify plant genomes to enhance traits such as yield,stress tolerance,and nutrient use efficiency.The ability to design plants with specific characteristics tailored to diverse environmental conditions and agricultural needs holds great potential to address global food security challenges.Here,we highlight recent advancements and applications of plant synthetic biology in agriculture,focusing on key areas such as photosynthetic efficiency,nitrogen fixation,drought tolerance,pathogen resistance,nutrient use efficiency,biofortification,climate resilience,microbiology engineering,synthetic plant genomes,and the integration of artificial intelligence with synthetic biology.These innovations aim to maximize resource use efficiency,reduce reliance on external inputs,and mitigate environmental impacts associated with conventional agricultural practices.Despite challenges related to regulatory approval and public acceptance,the integration of synthetic biology in agriculture holds immense promise for creating more resilient and sustainable agricultural systems,contributing to global food security and environmental sustainability.Rigorous multi-field testing of these approaches will undoubtedly be required to ensure reproducibility.
基金Supported by the Natural Science Foundation of Jilin Province,No.YDZJ202401182ZYTSJilin Provincial Key Laboratory of Precision Infectious Diseases,No.20200601011JCJilin Provincial Engineering Laboratory of Precision Prevention and Control for Common Diseases,Jilin Province Development and Reform Commission,No.2022C036.
文摘Artificial intelligence(AI)is driving a paradigm shift in gastroenterology and hepa-tology by delivering cutting-edge tools for disease screening,diagnosis,treatment,and prognostic management.Through deep learning,radiomics,and multimodal data integration,AI has achieved diagnostic parity with expert cli-nicians in endoscopic image analysis(e.g.,early gastric cancer detection,colorectal polyp identification)and non-invasive assessment of liver pathologies(e.g.,fibrosis staging,fatty liver typing)while demonstrating utility in personalized care scenarios such as predicting hepatocellular carcinoma recurrence and opti-mizing inflammatory bowel disease treatment responses.Despite these advance-ments challenges persist including limited model generalization due to frag-mented datasets,algorithmic limitations in rare conditions(e.g.,pediatric liver diseases)caused by insufficient training data,and unresolved ethical issues related to bias,accountability,and patient privacy.Mitigation strategies involve constructing standardized multicenter databases,validating AI tools through prospective trials,leveraging federated learning to address data scarcity,and de-veloping interpretable systems(e.g.,attention heatmap visualization)to enhance clinical trust.Integrating generative AI,digital twin technologies,and establishing unified ethical/regulatory frameworks will accelerate AI adoption in primary care and foster equitable healthcare access while interdisciplinary collaboration and evidence-based implementation remain critical for realizing AI’s potential to redefine precision care for digestive disorders,improve global health outcomes,and reshape healthcare equity.
基金supported by Fund of State Key Laboratory of IPOC(BUPT)(No.IPOC2021ZT16),China.
文摘The integrated optical true time delay phased array antenna system has the advantages of high bandwidth,small size,low loss and strong antiinterference capability,etc.The high integration of the optically controlled phased array antenna system is a necessary trend for the future development of the phased array,and it is also a major focus and difficulty in the current research of integrated microwave photonics.This paper firstly introduces the basic principle and development history of optical true time delay phased array antenna system based on microwave photonics,and briefly introduces the main implementation methods and integration platform of optical true time delay.Then,the application and development prospect of optical true time delay technology in beam control of phased array antenna system are mainly presented.Finally,according to the current research progress,the possible research directions of integrated optically controlled phased array antenna systems in the future are proposed.
文摘The accelerating global energy transition,driven by climate imperatives and technological advancements,demands fundamen-tal transformations in power systems.Smart grids,characterized by cyber-physical integration,distributed renewable resources,and data-driven intelligence,have emerged as the backbone of this evolution.This convergence,however,introduces unprecedented complexities in resilience,security,stability,and market operation.This special issue presents five pivotal studies addressing these interconnected challenges,offering novel methodologies and insights to advance the efficiency,resilience,and sustainability of modern power systems.
基金supported by the National Key Research and Development Program of China(2021YFA1101303)the National Natural Science Foundation of China(62374115)the Innovation Program of Shanghai Municipal Education Commission(2021-01-07-00-07-E00096).
文摘The rapid growth of artificial intelligence has accelerated data generation,which increasingly exposes the limitations faced by traditional computational architectures,particularly in terms of energy consumption and data latency.In contrast,data-centric computing that integrates processing and storage has the potential of reducing latency and energy usage.Organic optoelectronic synaptic transistors have emerged as one type of promising devices to implement the data-centric com-puting paradigm owing to their superiority of flexibility,low cost,and large-area fabrication.However,sophisticated functions including vector-matrix multiplication that a single device can achieve are limited.Thus,the fabrication and utilization of organic optoelectronic synaptic transistor arrays(OOSTAs)are imperative.Here,we summarize the recent advances in OOSTAs.Various strategies for manufacturing OOSTAs are introduced,including coating and casting,physical vapor deposition,printing,and photolithography.Furthermore,innovative applications of the OOSTA system integration are discussed,including neuromor-phic visual systems and neuromorphic computing systems.At last,challenges and future perspectives of utilizing OOSTAs in real-world applications are discussed.
基金supported by the National Natural Science Foundation of China(No.52074312)the CNPC Science and Technology Innovation Foundation(No.2021DQ02-0505)+1 种基金the Open Fund Project of the National Key Laboratory for the Enrichment Mechanism and Efficient Development of Shale Oil and Gas(No.36650000-24-ZC0609-0006)the Major Science and Technology Project of Karamay City(No.20232023zdzx0003).
文摘Due to complex geological structures and a narrow safe mud density window,offshore fractured formations frequently encounter severe lost circulation(LC)during drilling,significantly hindering oil and gas exploration and development.Predicting LC risks enables the targeted implementation of mitigation strategies,thereby reducing the frequency of such incidents.To address the limitations of existing 3D geomechanical modeling in predicting LC,such as arbitrary factor selection,subjective weight assignment,and the inability to achieve pre-drilling prediction along the entire well section,an improved prediction method is proposed.This method integrates multi-source data and incorporates three LC-related sensitivity factors:fracture characteristics,rock brittleness,and in-situ stress conditions.A quantitative risk assessment model for LC is developed by combining the subjective analytic hierarchy process with the objective entropy weight method(EWM)to assign weights.Subsequently,3D geomechanical modeling is applied to identify regional risk zones,enabling digital visualization for pre-drilling risk prediction.The developed 3D LC risk prediction model was validated using actual LC incidents from drilled wells.Results were generally consistent with field-identified LC zones,with an average relative error of 19.08%,confirming its reliability.This method provides practical guidance for mitigating potential LC risks and optimizing drilling program designs in fractured formations.
文摘Multimodal deep learning has emerged as a key paradigm in contemporary medical diagnostics,advancing precision medicine by enabling integration and learning from diverse data sources.The exponential growth of high-dimensional healthcare data,encompassing genomic,transcriptomic,and other omics profiles,as well as radiological imaging and histopathological slides,makes this approach increasingly important because,when examined separately,these data sources only offer a fragmented picture of intricate disease processes.Multimodal deep learning leverages the complementary properties of multiple data modalities to enable more accurate prognostic modeling,more robust disease characterization,and improved treatment decision-making.This review provides a comprehensive overview of the current state of multimodal deep learning approaches in medical diagnosis.We classify and examine important application domains,such as(1)radiology,where automated report generation and lesion detection are facilitated by image-text integration;(2)histopathology,where fusion models improve tumor classification and grading;and(3)multi-omics,where molecular subtypes and latent biomarkers are revealed through cross-modal learning.We provide an overview of representative research,methodological advancements,and clinical consequences for each domain.Additionally,we critically analyzed the fundamental issues preventing wider adoption,including computational complexity(particularly in training scalable,multi-branch networks),data heterogeneity(resulting from modality-specific noise,resolution variations,and inconsistent annotations),and the challenge of maintaining significant cross-modal correlations during fusion.These problems impede interpretability,which is crucial for clinical trust and use,in addition to performance and generalizability.Lastly,we outline important areas for future research,including the development of standardized protocols for harmonizing data,the creation of lightweight and interpretable fusion architectures,the integration of real-time clinical decision support systems,and the promotion of cooperation for federated multimodal learning.Our goal is to provide researchers and clinicians with a concise overview of the field’s present state,enduring constraints,and exciting directions for further research through this review.
