A method for correlating thermal light over a wide spectral range is proposed.A multi-wavelength pseudothermal source,prepared by projecting laser beams of multiple wavelengths(650 nm,635 nm,532 nm,and 473 nm)onto a m...A method for correlating thermal light over a wide spectral range is proposed.A multi-wavelength pseudothermal source,prepared by projecting laser beams of multiple wavelengths(650 nm,635 nm,532 nm,and 473 nm)onto a moving thin ground glass plate,is employed in a double-slit interference experiment.The ground glass plate induces random phase differences between light beams of different wavelengths passing through it.This initial random phase difference significantly influences the high-order intensity correlation functions of multi-wavelength thermal beams.Experimentally,second-order correlated interference patterns,including subwavelength interference,of pseudothermal beams with different wavelengths are observed in the intensity correlation measurements.This method facilitates applications of correlated thermal photons in quantum information processing and quantum imaging.展开更多
Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in comp...Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in complex backgrounds,small target objects,and limited training data,leading to poor recognition.Fine-grained images exhibit“small inter-class differences,”and while second-order feature encoding enhances discrimination,it often requires dual Convolutional Neural Networks(CNN),increasing training time and complexity.This study proposes a model integrating discriminative region localization and efficient second-order feature encoding.By ranking feature map channels via a fully connected layer,it selects high-importance channels to generate an enhanced map,accurately locating discriminative regions.Cropping and erasing augmentations further refine recognition.To improve efficiency,a novel second-order feature encoding module generates an attention map from the fourth convolutional group of Residual Network 50 layers(ResNet-50)and multiplies it with features from the fifth group,producing second-order features while reducing dimensionality and training time.Experiments on Caltech-University of California,San Diego Birds-200-2011(CUB-200-2011),Stanford Car,and Fine-Grained Visual Classification of Aircraft(FGVC Aircraft)datasets show state-of-the-art accuracy of 88.9%,94.7%,and 93.3%,respectively.展开更多
Machado-Joseph disease,or spinocerebellar ataxia type 3(SCA3),represents the most common autosomal dominant cerebellar ataxia worldwide.Despite its progressive and debilitating nature,disease-modifying therapies remai...Machado-Joseph disease,or spinocerebellar ataxia type 3(SCA3),represents the most common autosomal dominant cerebellar ataxia worldwide.Despite its progressive and debilitating nature,disease-modifying therapies remain elusive.Repetitive transcranial magnetic stimulation(rTMS)has emerged as a promising non-invasive intervention;however,its clinical application has been hindered by inconsistent protocols and a lack of mechanistic understanding.A recent landmark study published in Brain Stimulation by Chen et al.addressed these challenges by combining a high-dose intermittent theta-burst stimulation(iTBS)protocol with concurrent transcranial magnetic stimulation-electroencephalography(TMS-EEG).This commentary provides an in-depth analysis of their findings,highlighting the restoration of cerebello-cortical inhibition(CBI)as a key therapeutic mechanism.Furthermore,we discuss the broader implications of this work,proposing that future translational research should integrate accelerated iTBS(aiTBS)paradigms,cortical response measurements(CRM),and individualized neuro-navigation to establish a new era of precision neuromodulation for ataxia.展开更多
Chilo suppressalis(Walker)is one of the most important rice pests worldwide,posing a significant challenge to effective control.To develop a precision-timed,eco-friendly management strategy,overwintering population in...Chilo suppressalis(Walker)is one of the most important rice pests worldwide,posing a significant challenge to effective control.To develop a precision-timed,eco-friendly management strategy,overwintering population investigation and dynamic monitoring of C.suppressalis populations were conducted in the Meishan region of Sichuan,China,from 2023 to 2024.The optimal timing for insecticide application was estimated,followed by field trials evaluating the efficacy of different insecticides.Results demonstrated that the peak emergence of first-generation adults typically occurred in early July(under the environmental conditions of the Meishan region),with the ambient humidity below 75%and temperature around 29◦C.Pesticide efficacy trials show that insecticide combinations exhibited superior control.Notably,a combined treatment of emamectin benzoate⋅methoxyfenozide+chlorantraniliprole achieved the highest control efficacy(90.05%)and a corresponding yield of 12,491.55 kg/ha.All tested treatments were determined to be safe for rice growth.Furthermore,this optimized strategy resulted in notable economic benefits,including a 50%reduction in pesticide usage and cost savings of 4796.15 CNY compared to conventional practices.This study provides valuable insights into sustainable rice production and pest management and,for the first time,proposes a precision application time window based on intelligent monitoring.展开更多
Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing can...Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.展开更多
With the continuous advancement of bionanomaterial technology,the design and fabrication strategies of biomimetic nanocarriers have undergone significant strategic transformations and innovations.This article systemat...With the continuous advancement of bionanomaterial technology,the design and fabrication strategies of biomimetic nanocarriers have undergone significant strategic transformations and innovations.This article systematically reviews the evolution from single-cell membrane nanovesicles to hybrid cell membrane nanovesicles integrating multiple cell membranes,culminating in cell membrane hybrid lipid nanoparticles(CM-LNPs)combining natural cell membranes or membrane proteins with engineered synthetic phospholipids.This technological progression enables the synergistic retention of multicellular biological functions and the incorporation of advantageous synthetic material properties,such as enhanced engineering flexibility and surface modifiability.Additionally,the article discusses the advantages and limitations of traditional extrusion and ultrasonication methods in the preparation of cell membrane nanovesicles,highlights the benefits and development prospects of novel microfluidic techniques in the preparation of CM-LNPs,and explores the future application prospects and challenges of CM-LNPs in the biomedical field.展开更多
1.Introduction Crop breeding is transitioning to engineering by synthetic biology.Conventional breeding,constrained by limited genetic variation and lengthy development cycles,cannot meet the challenges of micronutrie...1.Introduction Crop breeding is transitioning to engineering by synthetic biology.Conventional breeding,constrained by limited genetic variation and lengthy development cycles,cannot meet the challenges of micronutrient malnutrition and yield reductions from climate change with sufficient speed or precision[1].Consequently,agriculture is transitioning from selection-based breeding to designbased engineering.Synthetic biology enables the precision modification of metabolic pathways and the construction of novel trait combinations[1,2].This special issue,Synthetic Biology for Crop Improvement,brings together 26 articles that showcase the field’s transition from laboratory curiosity to field-validated agricultural technology.The collection spans 13 plant species,from staple grains and major industrial crops to horticultural and medicinal plants,demonstrating the universal applicability of metabolic engineering.These studies reveal maturation toward field readiness:independent groups achieving reproducible results in identical pathways,greenhouse concepts advancing to multi-season field trials,and engineered traits delivering measurable agronomic value.This progression answers the central question in crop synthetic biology,shifting the paradigm from asking“can it work?”to demonstrating“how it works,and here are the yields”.This transformation is grounded in understanding and manipulating plant metabolism at molecular resolution[3].展开更多
High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging ...High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).展开更多
Doping is essential for modulating semiconductor conductivity,forming p-n junctions,and reducing contact resistance[1].Notably,as organic semiconductors(OSCs)advance toward high performance,flexibility,and miniaturiza...Doping is essential for modulating semiconductor conductivity,forming p-n junctions,and reducing contact resistance[1].Notably,as organic semiconductors(OSCs)advance toward high performance,flexibility,and miniaturization,achieving precise regionally selective doping becomes critical for building complex,highly integrated devices[2].In inorganic semiconductors(e.g.,silicon),sub-100-nanometer regional doping is achievable through photolithography and ion implantation—techniques foundational to modern complementary metaloxide-semiconductor(CMOS)technology[3].展开更多
Acute respiratory distress syndrome(ARDS)is a life-threatening condition that is characterized by high mortality rates and limited therapeutic options.Notably,Zhang et al demonstrated that CD146+mesenchymal stromal ce...Acute respiratory distress syndrome(ARDS)is a life-threatening condition that is characterized by high mortality rates and limited therapeutic options.Notably,Zhang et al demonstrated that CD146+mesenchymal stromal cells(MSCs)exhibited greater therapeutic efficacy than CD146-MSCs.These cells enhance epithelial repair through nuclear factor kappa B/cyclooxygenase-2-associated paracrine signaling and secretion of pro-angiogenic factors.We concur that MSCs hold significant promise for ARDS treatment;however,the heterogeneity of cell products is a translational barrier.Phenotype-aware strategies,such as CD146 enrichment,standardized potency assays,and extracellular vesicle profiling,are essential for improving the consistency of these studies.Further-more,advanced preclinical models,such as lung-on-a-chip systems,may provide more predictive insights into the therapeutic mechanisms.This article underscores the importance of CD146+MSCs in ARDS,emphasizes the need for precision in defining cell products,and discusses how integrating subset selection into translational pipelines could enhance the clinical impact of MSC-based therapies.展开更多
A discretization precision control method based on the second order osculating surface is proposed. The discretization precision of 3 D solid is controlled according to the error between the discrete solid surface a...A discretization precision control method based on the second order osculating surface is proposed. The discretization precision of 3 D solid is controlled according to the error between the discrete solid surface and its second order osculating surface. The global maximal error has been gotten after analyzing all the extremums of the error function. It can be used in controlling and optimizing the discretization precision of 3 D solid in computer 3 D modeling and NC milling path generation.展开更多
The management of breast cancer,one of the most common and heterogeneous malignancies,has transformed with the advent of precision medicine.This review explores current developments in genetic profiling,molecular diag...The management of breast cancer,one of the most common and heterogeneous malignancies,has transformed with the advent of precision medicine.This review explores current developments in genetic profiling,molecular diagnostics,and targeted therapies that have revolutionized breast cancer treatment.Key innovations,such as cyclin-dependent kinases 4/6(CDK4/6)inhibitors,antibodydrug conjugates(ADCs),and immune checkpoint inhibitors(ICIs),have improved outcomes for hormone receptor-positive(HR+),HER2-positive(HER2+),and triple-negative breast cancer(TNBC)subtypes remarkably.Additionally,emerging treatments,such as PI3K inhibitors,poly(ADP-ribose)polymerase(PARP)inhibitors,and m RNA-based therapies,offer new avenues for targeting specific genetic mutations and improving treatment response,particularly in difficult-to-treat breast cancer subtypes.The integration of liquid biopsy technologies provides a non-invasive approach for real-time monitoring of tumor evolution and treatment response,thus enabling dynamic adjustments to therapy.Molecular imaging and artificial intelligence(AI)are increasingly crucial in enhancing diagnostic precision,personalizing treatment plans,and predicting therapeutic outcomes.As precision medicine continues to evolve,it has the potential to significantly improve survival rates,decrease recurrence,and enhance quality of life for patients with breast cancer.By combining cutting-edge diagnostics,personalized therapies,and emerging treatments,precision medicine can transform breast cancer care by offering more effective,individualized,and less invasive treatment options.展开更多
In the dynamic landscape of modern healthcare and precision medicine,the digital revolution is reshaping medical industries at an unprecedented pace,and traditional Chinese medicine(TCM)is no exception[1-4].The paper...In the dynamic landscape of modern healthcare and precision medicine,the digital revolution is reshaping medical industries at an unprecedented pace,and traditional Chinese medicine(TCM)is no exception[1-4].The paper“From digits towards digitization:the past,present,and future of traditional Chinese medicine”by Academician&TCM National Master Qi WANG(王琦).展开更多
Organoids are three-dimensional stem cell-derived models that offer a more physiologically relevant representation of tumor biology compared to traditional two-dimensional cell cultures or animal models.Organoids pres...Organoids are three-dimensional stem cell-derived models that offer a more physiologically relevant representation of tumor biology compared to traditional two-dimensional cell cultures or animal models.Organoids preserve the complex tissue architecture and cellular diversity of human cancers,enabling more accurate predictions of tumor growth,metastasis,and drug responses.Integration with microfluidic platforms,such as organ-on-a-chip systems,further enhances the ability to model tumor-environment interactions in real-time.Organoids facilitate in-depth exploration of tumor heterogeneity,molecular mechanisms,and the development of personalized treatment strategies when coupled with multi-omics technologies.Organoids provide a platform for investigating tumor-immune cell interactions,which aid in the design and testing of immune-based therapies and vaccines.Taken together,these features position organoids as a transformative tool in advancing cancer research and precision medicine.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
Zenith Tropospheric Delay(ZTD)is an important factor that restricts the high-precision positioning of global navigation satellite system(GNSS),and it is of great significance in establishing a real-time and highprecis...Zenith Tropospheric Delay(ZTD)is an important factor that restricts the high-precision positioning of global navigation satellite system(GNSS),and it is of great significance in establishing a real-time and highprecision ZTD model.However,existing ZTD models only consider the impact of linear terms on ZTD estimation,whereas the nonlinear factors have rarely been investigated before and thus become the focus of this study.A real-time and high-precision ZTD model for large height difference area is proposed by considering the linear and nonlinear characteristics of ZTD spatiotemporal variations and is called the realtime linear and nonlinearity ZTD(RLNZ)model.This model uses the ZTD estimated from the Global Pressure and Temperature 3(GPT3)model as the initial value.The linear impacts of periodic term and height on the estimation of ZTD difference between GNSS and GPT3 model are first considered.In addition,nonlinear factors such as geographical location and time are further used to fit the remaining nonlinear ZTD residuals using the general regression neural network method.Finally,the RLNZ-derived ZTD is obtained at an arbitrary location.The western United States,with height difference ranging from-500 to 4000 m,is selected,and the hourly ZTD of 484 GNSS stations provided by the Nevada Geodetic Laboratory(NGL)and the data of 9 radiosonde(RS)stations in the year 2021 are used.Experiment results show that a better performance of ZTD estimation can be retrieved from the proposed RLNZ model when compared with the GPT3 model.Statistical results show the averaged root mean square(RMS),Bias,and mean absolute error(MAE)of ZTD from GPT3 and RLNZ models are 33.7/0.8/25.7 mm and 22.6/0.1/17.4 mm,respectively.The average improvement rate of the RLNZ model is 33% when compared to the GPT3 model.Finally,the application of the proposed RLNZ model in simulated real-time Precise Point Positioning(PPP)indicates that the accuracy of PPP in N,E and U components is improved by 8%,2%,and 6% when compared with that from the GPT3-based PPP.Meanwhile,the convergence time in N and U components is improved by 23% and 7%,respectively.Such results verify the superiority of the proposed RLNZ model in retrieving realtime ZTD maps for GNSS positioning and navigation applications.展开更多
Artificial intelligence(AI)and machine learning(ML)are transforming spine care by addressing diagnostics,treatment planning,and rehabilitation challenges.This study highlights advancements in precision medicine for sp...Artificial intelligence(AI)and machine learning(ML)are transforming spine care by addressing diagnostics,treatment planning,and rehabilitation challenges.This study highlights advancements in precision medicine for spinal pathologies,leveraging AI and ML to enhance diagnostic accuracy through deep learning algorithms,enabling faster and more accurate detection of abnormalities.AIpowered robotics and surgical navigation systems improve implant placement precision and reduce complications in complex spine surgeries.Wearable devices and virtual platforms,designed with AI,offer personalized,adaptive therapies that improve treatment adherence and recovery outcomes.AI also enables preventive interventions by assessing spine condition risks early.Despite progress,challenges remain,including limited healthcare datasets,algorithmic biases,ethical concerns,and integration into existing systems.Interdisciplinary collaboration and explainable AI frameworks are essential to unlock AI’s full potential in spine care.Future developments include multimodal AI systems integrating imaging,clinical,and genetic data for holistic treatment approaches.AI and ML promise significant improvements in diagnostic accuracy,treatment personalization,service accessibility,and cost efficiency,paving the way for more streamlined and effective spine care,ultimately enhancing patient outcomes.展开更多
The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus...The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.展开更多
The parachute deployment conditions during the terminal entry phase in Mars landing missions exhibit critical impact on landing precision.In this article,aiming at the requirements of safe parachute deployment and acc...The parachute deployment conditions during the terminal entry phase in Mars landing missions exhibit critical impact on landing precision.In this article,aiming at the requirements of safe parachute deployment and accurate landing,a multidimensional parachute deployment box for determining deployment condition during Mars landing was proposed.First,an extremerange optimization model was established,synthesizing the dynamics and constraints of both parachute descent and powered descent phases.Then,on the basis of the two-dimensional altitude-velocity deployment box,a multi-dimensional parachute deployment box characterized by altitude,velocity,flight-path angle,and extreme range was constructed through the integration of extreme range information.Furthermore,an evaluation index for landing precision was formulated and a deployment control logic was proposed for minimizing landing deviation.Finally,the proposed deployment box was simulated in a Mars landing mission.The results demonstrate that the proposed box effectively satisfies safe deployment and landing precision demands,eliminating the range-to-go error at the terminal of the entry phase.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.62105278 and 11674273)the Natural Science Foundation of Shandong Province(Grant No.ZR2023MA015)。
文摘A method for correlating thermal light over a wide spectral range is proposed.A multi-wavelength pseudothermal source,prepared by projecting laser beams of multiple wavelengths(650 nm,635 nm,532 nm,and 473 nm)onto a moving thin ground glass plate,is employed in a double-slit interference experiment.The ground glass plate induces random phase differences between light beams of different wavelengths passing through it.This initial random phase difference significantly influences the high-order intensity correlation functions of multi-wavelength thermal beams.Experimentally,second-order correlated interference patterns,including subwavelength interference,of pseudothermal beams with different wavelengths are observed in the intensity correlation measurements.This method facilitates applications of correlated thermal photons in quantum information processing and quantum imaging.
基金supported,in part,by the National Nature Science Foundation of China under Grant 62272236,62376128 and 62306139the Natural Science Foundation of Jiangsu Province under Grant BK20201136,BK20191401.
文摘Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in complex backgrounds,small target objects,and limited training data,leading to poor recognition.Fine-grained images exhibit“small inter-class differences,”and while second-order feature encoding enhances discrimination,it often requires dual Convolutional Neural Networks(CNN),increasing training time and complexity.This study proposes a model integrating discriminative region localization and efficient second-order feature encoding.By ranking feature map channels via a fully connected layer,it selects high-importance channels to generate an enhanced map,accurately locating discriminative regions.Cropping and erasing augmentations further refine recognition.To improve efficiency,a novel second-order feature encoding module generates an attention map from the fourth convolutional group of Residual Network 50 layers(ResNet-50)and multiplies it with features from the fifth group,producing second-order features while reducing dimensionality and training time.Experiments on Caltech-University of California,San Diego Birds-200-2011(CUB-200-2011),Stanford Car,and Fine-Grained Visual Classification of Aircraft(FGVC Aircraft)datasets show state-of-the-art accuracy of 88.9%,94.7%,and 93.3%,respectively.
基金supported by grants from the Open Research Fund of the Zhejiang Key Laboratory of Precision Psychiatry(2025A2)the Natural Science Foundation of Zhejiang Province(LY23C090002)。
文摘Machado-Joseph disease,or spinocerebellar ataxia type 3(SCA3),represents the most common autosomal dominant cerebellar ataxia worldwide.Despite its progressive and debilitating nature,disease-modifying therapies remain elusive.Repetitive transcranial magnetic stimulation(rTMS)has emerged as a promising non-invasive intervention;however,its clinical application has been hindered by inconsistent protocols and a lack of mechanistic understanding.A recent landmark study published in Brain Stimulation by Chen et al.addressed these challenges by combining a high-dose intermittent theta-burst stimulation(iTBS)protocol with concurrent transcranial magnetic stimulation-electroencephalography(TMS-EEG).This commentary provides an in-depth analysis of their findings,highlighting the restoration of cerebello-cortical inhibition(CBI)as a key therapeutic mechanism.Furthermore,we discuss the broader implications of this work,proposing that future translational research should integrate accelerated iTBS(aiTBS)paradigms,cortical response measurements(CRM),and individualized neuro-navigation to establish a new era of precision neuromodulation for ataxia.
基金funded by the National Key R&D Project‘Innovation and Integration of Key Technologies for Integration of Agricultural Machinery and Agronomy in Weak Links of Hybrid Mid-season Rice in Hilly Areas of Southwest China’(2023YFD2301901).
文摘Chilo suppressalis(Walker)is one of the most important rice pests worldwide,posing a significant challenge to effective control.To develop a precision-timed,eco-friendly management strategy,overwintering population investigation and dynamic monitoring of C.suppressalis populations were conducted in the Meishan region of Sichuan,China,from 2023 to 2024.The optimal timing for insecticide application was estimated,followed by field trials evaluating the efficacy of different insecticides.Results demonstrated that the peak emergence of first-generation adults typically occurred in early July(under the environmental conditions of the Meishan region),with the ambient humidity below 75%and temperature around 29◦C.Pesticide efficacy trials show that insecticide combinations exhibited superior control.Notably,a combined treatment of emamectin benzoate⋅methoxyfenozide+chlorantraniliprole achieved the highest control efficacy(90.05%)and a corresponding yield of 12,491.55 kg/ha.All tested treatments were determined to be safe for rice growth.Furthermore,this optimized strategy resulted in notable economic benefits,including a 50%reduction in pesticide usage and cost savings of 4796.15 CNY compared to conventional practices.This study provides valuable insights into sustainable rice production and pest management and,for the first time,proposes a precision application time window based on intelligent monitoring.
文摘Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.
基金financially supported by the National Natural Science Foundation of China(32371475)。
文摘With the continuous advancement of bionanomaterial technology,the design and fabrication strategies of biomimetic nanocarriers have undergone significant strategic transformations and innovations.This article systematically reviews the evolution from single-cell membrane nanovesicles to hybrid cell membrane nanovesicles integrating multiple cell membranes,culminating in cell membrane hybrid lipid nanoparticles(CM-LNPs)combining natural cell membranes or membrane proteins with engineered synthetic phospholipids.This technological progression enables the synergistic retention of multicellular biological functions and the incorporation of advantageous synthetic material properties,such as enhanced engineering flexibility and surface modifiability.Additionally,the article discusses the advantages and limitations of traditional extrusion and ultrasonication methods in the preparation of cell membrane nanovesicles,highlights the benefits and development prospects of novel microfluidic techniques in the preparation of CM-LNPs,and explores the future application prospects and challenges of CM-LNPs in the biomedical field.
文摘1.Introduction Crop breeding is transitioning to engineering by synthetic biology.Conventional breeding,constrained by limited genetic variation and lengthy development cycles,cannot meet the challenges of micronutrient malnutrition and yield reductions from climate change with sufficient speed or precision[1].Consequently,agriculture is transitioning from selection-based breeding to designbased engineering.Synthetic biology enables the precision modification of metabolic pathways and the construction of novel trait combinations[1,2].This special issue,Synthetic Biology for Crop Improvement,brings together 26 articles that showcase the field’s transition from laboratory curiosity to field-validated agricultural technology.The collection spans 13 plant species,from staple grains and major industrial crops to horticultural and medicinal plants,demonstrating the universal applicability of metabolic engineering.These studies reveal maturation toward field readiness:independent groups achieving reproducible results in identical pathways,greenhouse concepts advancing to multi-season field trials,and engineered traits delivering measurable agronomic value.This progression answers the central question in crop synthetic biology,shifting the paradigm from asking“can it work?”to demonstrating“how it works,and here are the yields”.This transformation is grounded in understanding and manipulating plant metabolism at molecular resolution[3].
文摘High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).
文摘Doping is essential for modulating semiconductor conductivity,forming p-n junctions,and reducing contact resistance[1].Notably,as organic semiconductors(OSCs)advance toward high performance,flexibility,and miniaturization,achieving precise regionally selective doping becomes critical for building complex,highly integrated devices[2].In inorganic semiconductors(e.g.,silicon),sub-100-nanometer regional doping is achievable through photolithography and ion implantation—techniques foundational to modern complementary metaloxide-semiconductor(CMOS)technology[3].
基金the Scientific and Technological Research Council of Türkiye(TÜBİTAK)Under the International Postdoctoral Research Fellowship Program(2219),No.1059B192400980the National Postdoctoral Research Fellowship Program(2218),No.122C158.
文摘Acute respiratory distress syndrome(ARDS)is a life-threatening condition that is characterized by high mortality rates and limited therapeutic options.Notably,Zhang et al demonstrated that CD146+mesenchymal stromal cells(MSCs)exhibited greater therapeutic efficacy than CD146-MSCs.These cells enhance epithelial repair through nuclear factor kappa B/cyclooxygenase-2-associated paracrine signaling and secretion of pro-angiogenic factors.We concur that MSCs hold significant promise for ARDS treatment;however,the heterogeneity of cell products is a translational barrier.Phenotype-aware strategies,such as CD146 enrichment,standardized potency assays,and extracellular vesicle profiling,are essential for improving the consistency of these studies.Further-more,advanced preclinical models,such as lung-on-a-chip systems,may provide more predictive insights into the therapeutic mechanisms.This article underscores the importance of CD146+MSCs in ARDS,emphasizes the need for precision in defining cell products,and discusses how integrating subset selection into translational pipelines could enhance the clinical impact of MSC-based therapies.
文摘A discretization precision control method based on the second order osculating surface is proposed. The discretization precision of 3 D solid is controlled according to the error between the discrete solid surface and its second order osculating surface. The global maximal error has been gotten after analyzing all the extremums of the error function. It can be used in controlling and optimizing the discretization precision of 3 D solid in computer 3 D modeling and NC milling path generation.
基金supported by grants from the National Natural Science Foundation of China(Grant Nos.82103614 and 32171363)Natural Science Foundation of Fujian Province of China(Grant No.2021J05007)+4 种基金funding from the start-up fund for Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast CancerXiamen’s Key Laboratory of Precision Medicine for Endocrine-Related Cancersstart-up and supporting funds from the Third Affiliated Hospital of Kunming Medical University,Yunnan Cancer Hospital for Guo-Jun Zhang and Jing-Wen BaiKey Research and development program for social development of Yunnan Science and Technology Department(Grant No.202403AC100014-2)horizontal project funding from the Third Affiliated Hospital of Kunming Medical University(Grant Nos.20233160A0866 and 20243160A0511)。
文摘The management of breast cancer,one of the most common and heterogeneous malignancies,has transformed with the advent of precision medicine.This review explores current developments in genetic profiling,molecular diagnostics,and targeted therapies that have revolutionized breast cancer treatment.Key innovations,such as cyclin-dependent kinases 4/6(CDK4/6)inhibitors,antibodydrug conjugates(ADCs),and immune checkpoint inhibitors(ICIs),have improved outcomes for hormone receptor-positive(HR+),HER2-positive(HER2+),and triple-negative breast cancer(TNBC)subtypes remarkably.Additionally,emerging treatments,such as PI3K inhibitors,poly(ADP-ribose)polymerase(PARP)inhibitors,and m RNA-based therapies,offer new avenues for targeting specific genetic mutations and improving treatment response,particularly in difficult-to-treat breast cancer subtypes.The integration of liquid biopsy technologies provides a non-invasive approach for real-time monitoring of tumor evolution and treatment response,thus enabling dynamic adjustments to therapy.Molecular imaging and artificial intelligence(AI)are increasingly crucial in enhancing diagnostic precision,personalizing treatment plans,and predicting therapeutic outcomes.As precision medicine continues to evolve,it has the potential to significantly improve survival rates,decrease recurrence,and enhance quality of life for patients with breast cancer.By combining cutting-edge diagnostics,personalized therapies,and emerging treatments,precision medicine can transform breast cancer care by offering more effective,individualized,and less invasive treatment options.
文摘In the dynamic landscape of modern healthcare and precision medicine,the digital revolution is reshaping medical industries at an unprecedented pace,and traditional Chinese medicine(TCM)is no exception[1-4].The paper“From digits towards digitization:the past,present,and future of traditional Chinese medicine”by Academician&TCM National Master Qi WANG(王琦).
基金supported by the Chinese Academy of Medical Sciences(Grant No.2021RU002)Beijing Natural Science Foundation(Grant No.Z240013)+2 种基金National Natural Science Foundation of China(Grant Nos.82450111,82388102,82373416,and 92259303)Beijing Research Ward Excellence Program(Grant Nos.BRWEP2024W034080200 and BRWEP2024W034080204)Peking University People’s Hospital Research and Development Funds(Grant No.RZG2024-02).
文摘Organoids are three-dimensional stem cell-derived models that offer a more physiologically relevant representation of tumor biology compared to traditional two-dimensional cell cultures or animal models.Organoids preserve the complex tissue architecture and cellular diversity of human cancers,enabling more accurate predictions of tumor growth,metastasis,and drug responses.Integration with microfluidic platforms,such as organ-on-a-chip systems,further enhances the ability to model tumor-environment interactions in real-time.Organoids facilitate in-depth exploration of tumor heterogeneity,molecular mechanisms,and the development of personalized treatment strategies when coupled with multi-omics technologies.Organoids provide a platform for investigating tumor-immune cell interactions,which aid in the design and testing of immune-based therapies and vaccines.Taken together,these features position organoids as a transformative tool in advancing cancer research and precision medicine.
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 the National Natural Science Foundation of China(42274039)Shaanxi Provincial Innovation Capacity Support Plan Project(2023KJXX-050)+2 种基金The Open Grants of the State Key Laboratory of Severe Weather(2023LASW-B18)Scientific and technological research projects for major issues in military medicine and aviation medicine(2022ZZXM012)Local special scientific research plan project of Shaanxi Provincial Department of Education(22JE012)。
文摘Zenith Tropospheric Delay(ZTD)is an important factor that restricts the high-precision positioning of global navigation satellite system(GNSS),and it is of great significance in establishing a real-time and highprecision ZTD model.However,existing ZTD models only consider the impact of linear terms on ZTD estimation,whereas the nonlinear factors have rarely been investigated before and thus become the focus of this study.A real-time and high-precision ZTD model for large height difference area is proposed by considering the linear and nonlinear characteristics of ZTD spatiotemporal variations and is called the realtime linear and nonlinearity ZTD(RLNZ)model.This model uses the ZTD estimated from the Global Pressure and Temperature 3(GPT3)model as the initial value.The linear impacts of periodic term and height on the estimation of ZTD difference between GNSS and GPT3 model are first considered.In addition,nonlinear factors such as geographical location and time are further used to fit the remaining nonlinear ZTD residuals using the general regression neural network method.Finally,the RLNZ-derived ZTD is obtained at an arbitrary location.The western United States,with height difference ranging from-500 to 4000 m,is selected,and the hourly ZTD of 484 GNSS stations provided by the Nevada Geodetic Laboratory(NGL)and the data of 9 radiosonde(RS)stations in the year 2021 are used.Experiment results show that a better performance of ZTD estimation can be retrieved from the proposed RLNZ model when compared with the GPT3 model.Statistical results show the averaged root mean square(RMS),Bias,and mean absolute error(MAE)of ZTD from GPT3 and RLNZ models are 33.7/0.8/25.7 mm and 22.6/0.1/17.4 mm,respectively.The average improvement rate of the RLNZ model is 33% when compared to the GPT3 model.Finally,the application of the proposed RLNZ model in simulated real-time Precise Point Positioning(PPP)indicates that the accuracy of PPP in N,E and U components is improved by 8%,2%,and 6% when compared with that from the GPT3-based PPP.Meanwhile,the convergence time in N and U components is improved by 23% and 7%,respectively.Such results verify the superiority of the proposed RLNZ model in retrieving realtime ZTD maps for GNSS positioning and navigation applications.
文摘Artificial intelligence(AI)and machine learning(ML)are transforming spine care by addressing diagnostics,treatment planning,and rehabilitation challenges.This study highlights advancements in precision medicine for spinal pathologies,leveraging AI and ML to enhance diagnostic accuracy through deep learning algorithms,enabling faster and more accurate detection of abnormalities.AIpowered robotics and surgical navigation systems improve implant placement precision and reduce complications in complex spine surgeries.Wearable devices and virtual platforms,designed with AI,offer personalized,adaptive therapies that improve treatment adherence and recovery outcomes.AI also enables preventive interventions by assessing spine condition risks early.Despite progress,challenges remain,including limited healthcare datasets,algorithmic biases,ethical concerns,and integration into existing systems.Interdisciplinary collaboration and explainable AI frameworks are essential to unlock AI’s full potential in spine care.Future developments include multimodal AI systems integrating imaging,clinical,and genetic data for holistic treatment approaches.AI and ML promise significant improvements in diagnostic accuracy,treatment personalization,service accessibility,and cost efficiency,paving the way for more streamlined and effective spine care,ultimately enhancing patient outcomes.
基金Projects(U22B2084,52275483,52075142)supported by the National Natural Science Foundation of ChinaProject(2023ZY01050)supported by the Ministry of Industry and Information Technology High Quality Development,China。
文摘The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.
基金Supported by the National Natural Science Foundation of China(62073034)。
文摘The parachute deployment conditions during the terminal entry phase in Mars landing missions exhibit critical impact on landing precision.In this article,aiming at the requirements of safe parachute deployment and accurate landing,a multidimensional parachute deployment box for determining deployment condition during Mars landing was proposed.First,an extremerange optimization model was established,synthesizing the dynamics and constraints of both parachute descent and powered descent phases.Then,on the basis of the two-dimensional altitude-velocity deployment box,a multi-dimensional parachute deployment box characterized by altitude,velocity,flight-path angle,and extreme range was constructed through the integration of extreme range information.Furthermore,an evaluation index for landing precision was formulated and a deployment control logic was proposed for minimizing landing deviation.Finally,the proposed deployment box was simulated in a Mars landing mission.The results demonstrate that the proposed box effectively satisfies safe deployment and landing precision demands,eliminating the range-to-go error at the terminal of the entry phase.
