A growing global population and the increasing prevalence of diet-related health issues such as“hidden hunger”,obesity,hypertension,and diabetes necessitate a fundamental rethinking of crop design and breeding.Synth...A growing global population and the increasing prevalence of diet-related health issues such as“hidden hunger”,obesity,hypertension,and diabetes necessitate a fundamental rethinking of crop design and breeding.Synthetic metabolic engineering offers a method to modify and redesign metabolic pathways to increase the nutritional value of crops.We summarize recent advances in the biofortification of key nutrients including provitamin A,vitamin C,vitamin B9,iron,zinc,anthocyanins,flavonoids,and unsaturated fatty acids.We discuss the potential of multi-gene stacking,gene editing,enzyme engineering,and artificial intelligence in synthetic metabolic engineering.We propose future research directions and potential solutions centered on leveraging AI-driven systems biology,precision gene editing,enzyme engineering,agrobacterium-mediated genotype-independent transformation,and modular metabolic engineering strategies to develop next-generation nutritionally enhanced super crops and transform global food systems.展开更多
In light of the pressing global challenges of climate change,declining crop resilience,and hidden hunger,it is imperative to overcome the limitations of conventional crop breeding to enhance both the nutritional quali...In light of the pressing global challenges of climate change,declining crop resilience,and hidden hunger,it is imperative to overcome the limitations of conventional crop breeding to enhance both the nutritional quality and stress tolerance of crops.Synthetic metabolic engineering presents innovative strategies for the precision modification and de novo design of metabolic pathways.This approach generally encompasses three essential steps:identifying key metabolites through metabolomics,integrating multi-omics technologies to investigate the synthesis and regulation of these metabolites,and utilizing gene editing or de novo design to modify crop metabolic pathways associated with desirable agronomic traits.This review underscores the vital role of plant metabolite diversity in enhancing crop nutritional quality and stress resilience.Integrated multi-omics analyses facilitate the metabolic engineering by identifying key genes,transporters,and transcription factors that regulate metabolite biosynthesis.Precision modification strategies employ genome editing tools to reprogram endogenous metabolic networks,while de novo design reconstructs metabolic pathways through the introduction of exogenous biological elements—thereby both approaches enable the targeted enhancement of desired traits.These strategies have been effectively implemented in major food crops.However,simultaneously enhancing nutritional quality and stress resilience remains challenging due to inherent trade-offs and resource competition in distinct metabolic pathways within plants.Future research should integrate AI-driven predictive models with multi-omics datasets to decipher dynamic metabolic homeostasis and engineer climate-smart crops that maximize yield while preserving quality and environmental adaptability.展开更多
Cotton production faces significant challenges from insect pests,with chemical pesticide use becoming increasingly limited by resistance and environmental concerns.This study explores the potential use of caffeine,a n...Cotton production faces significant challenges from insect pests,with chemical pesticide use becoming increasingly limited by resistance and environmental concerns.This study explores the potential use of caffeine,a natural plant alkaloid,as an environmentally friendly insect resistance strategy in cotton.Exogenous caffeine application demonstrated potent insecticidal effects against cotton bollworm(Helicoverpa armigera)larvae,with concentrations≥2 mg mL−1 causing near-complete feeding cessation and up to 70%larval mortality.Building on this,we engineered transgenic cotton(Gossypium hirsutum cv.Jin668)for heterologous caffeine biosynthesis by introducing three key N-methyltransferase genes(CaXMT1,CaMXMT1,CaDXMT1)by multiple gene transformation.Transgenic lines expressing all three genes showed remarkable caffeine accumulation(up to 3.59 mg g−1 dry weight),whereas two-gene combinations exhibited wild-type-level production.Feeding preference assays revealed that caffeine-enriched cotton strongly deterred feeding by H.armigera.Non-choice feeding trials demonstrated reduced leaf consumption and reduced larval growth in H.armigera fed on caffeine-producing cotton.The study highlights the effectiveness of synthetic biology approaches using the TGSII-UNiE multigene stacking system,despite challenges in transgene stability.This work advances plant-derived insect resistance research and provides a sustainable framework for reducing chemical pesticide reliance in cotton production,while underscoring unique potential of cotton as a synthetic biology platform for secondary metabolite engineering.展开更多
Betalain,an economically valuable water-soluble natural plant pigment,is prized for its strong antioxidant activity,making it popular as a dietary supplement and a visual marker for plant transformation.However,market...Betalain,an economically valuable water-soluble natural plant pigment,is prized for its strong antioxidant activity,making it popular as a dietary supplement and a visual marker for plant transformation.However,market demand significantly outstrips current production capacity.This study reports the development of an efficient push-and-pull multigene strategy based on polycistronic expression and metabolic flux regulation to enhance betalain biosynthesis in transgenic maize(Zea mays L.)endosperm.We engineered a novel enhanced RUBY(eRUBY)system derived from the original polycistronic RUBY construct(CYP76AD1P2ADODA1P2ADOPA5GT unit,abbreviated CDG)by introducing arogenate dehydrogenase(ADHα)to increase the L-tyrosine substrate supply.All the genes were driven by the endosperm-specific promoter.Fusion of ADHαinto a single polycistronic eRUBY construct(CDGA)produced significantly higher betanin(6.88 mg g−1 dry weight)and isobetanin(1.81 mg g−1 dry weight)levels than in CDG+A,which stacked the ADHαcassette independently with CDG.The high betalain accumulation in CDGA lines(which also exhibited higher transgene copy number)resulted in a 2.85–7.58-fold improvement in endosperm antioxidant capacity compared to WT(versus 2.48–2.80-fold in CDG+A).Importantly,transgenic plants maintained a normal phenotype.Transcriptome and metabolome analyses further indicated that metabolism of phenylalanine,alanine,aspartate,and glutamate contributes to betalain production.Hybridization with sweet corn successfully created a high-sugar eRUBY maize variety.Collectively,these results demonstrate the successful development of a novel maize germplasm with significantly enhanced nutritional value through high betalain accumulation.展开更多
Plants produce a vast array of specialized metabolites that serve as essential defenses against herbivores and pathogens.However,the capacity to produce these compounds differs substantially among plant species and is...Plants produce a vast array of specialized metabolites that serve as essential defenses against herbivores and pathogens.However,the capacity to produce these compounds differs substantially among plant species and is frequently diminished during domestication.Advances in synthetic metabolic engineering enable efficient elucidation and engineering of plant specialized metabolic pathways active in crop pest and pathogen resistance.This review summarizes strategies and workflows for selecting defensive metabolic pathways,identifying candidate biosynthetic genes,and rewiring native or introducing heterologous pathways to enhance crop resistance to pests and pathogens.Strategies include weighted gene co-expression network construction,biosynthetic gene cluster scanning,and metabolite genome-wide association studies for pathway discovery,as well as transcriptional reprogramming,enzyme activity optimization,and transporter deployment for pathway engineering.We further discuss challenges in using synthetic metabolic engineering to enhance crop resistance and highlight the potential of artificial intelligence in addressing them.展开更多
Given the broad applicability of carbazole structural moieties in materials science and medicinal chemistry,significant efforts have been devoted to developing efficient synthetic catalytic methodologies to access thi...Given the broad applicability of carbazole structural moieties in materials science and medicinal chemistry,significant efforts have been devoted to developing efficient synthetic catalytic methodologies to access this valuable scaffold.Catalyzed direct Csp^(2)-H functionalization provides an effective and costefficient approach to synthesizing carbazoles from simple and readily available starting materials,ensuring a promising path characterized by excellent atom and step economy.This review highlights the substantial progress made in the last 10 years in advancing catalytic Csp^(2)-H functionalization techniques for synthesizing carbazoles.展开更多
Synthetic speech detection is an essential task in the field of voice security,aimed at identifying deceptive voice attacks generated by text-to-speech(TTS)systems or voice conversion(VC)systems.In this paper,we propo...Synthetic speech detection is an essential task in the field of voice security,aimed at identifying deceptive voice attacks generated by text-to-speech(TTS)systems or voice conversion(VC)systems.In this paper,we propose a synthetic speech detection model called TFTransformer,which integrates both local and global features to enhance detection capabilities by effectively modeling local and global dependencies.Structurally,the model is divided into two main components:a front-end and a back-end.The front-end of the model uses a combination of SincLayer and two-dimensional(2D)convolution to extract high-level feature maps(HFM)containing local dependency of the input speech signals.The back-end uses time-frequency Transformer module to process these feature maps and further capture global dependency.Furthermore,we propose TFTransformer-SE,which incorporates a channel attention mechanism within the 2D convolutional blocks.This enhancement aims to more effectively capture local dependencies,thereby improving the model’s performance.The experiments were conducted on the ASVspoof 2021 LA dataset,and the results showed that the model achieved an equal error rate(EER)of 3.37%without data augmentation.Additionally,we evaluated the model using the ASVspoof 2019 LA dataset,achieving an EER of 0.84%,also without data augmentation.This demonstrates that combining local and global dependencies in the time-frequency domain can significantly improve detection accuracy.展开更多
A trace analytical method based on solid-phase extraction gas chromatography-tandem mass spectrometry(SPE–GC–MS/MS)was developed for the rapid detection of 256 semi-volatile organic compounds(SVOCs),including 25 pol...A trace analytical method based on solid-phase extraction gas chromatography-tandem mass spectrometry(SPE–GC–MS/MS)was developed for the rapid detection of 256 semi-volatile organic compounds(SVOCs),including 25 polycyclic aromatic hydrocarbons(PAHs),70 polychlorinated biphenyls(PCBs),123 pesticides,20 phthalate esters(PAEs),4 organophosphate esters(OPEs),9 synthetic musks(SMs),and 5 UV filters(UVs)in water.No-tably,this method provided a decent linearity of calibration standards(R^(2)>0.999),excellent method limits of quantification(MLOQs)(0.12–11.41 ng/L),satisfactory matrix spiking recovery rates(60.4%–126%),and high precision(intra-day relative standard deviations(RSDs):1.0%–10.0%,inter-day RSDs:3.0%–15.0%,and inter-week RSDs:3.4%–15.7%),making it suitable for trace-level studies.Statistical analysis revealed that SVOCs with higher volatility exhibited enhanced recovery rates.Validation of the methodology involved analyzing SVOCs in real spring water and river water samples.Twenty-seven SVOCs were detected in spring water and 58 in river water,with an average concentration of 631.73 and 16,095 ng/L,respectively.Among the detected SVOCs,PAEs constituted the predominant proportion.This study underscored the presence of SVOCs contamination specifi-cally within the spring water,although SVOCs concentrations in river water were significantly greater than those found in spring water.In summary,this sensitive method based on SPE–GC–MS/MS was successfully developed and validated for the rapid analysis of a diverse array of 256 SVOCs at trace levels in water,including not only the traditional highly valued PAHs,PCBs,pesticides,and PAEs,but also the emerging OPEs,UVs,and SMs.展开更多
Microbe-based soil inoculants offer a promising approach to sustainable agriculture by reducing reliance on agrochemicals and minimizing environmental damages.The heavy use of chemicals in conventional agriculture pos...Microbe-based soil inoculants offer a promising approach to sustainable agriculture by reducing reliance on agrochemicals and minimizing environmental damages.The heavy use of chemicals in conventional agriculture poses significant challenges to crop production and environmental health.This review explores the integration of microbe-based inoculants,strigolactones(SLs),and nanotechnology to enhance agricultural sustainability.Nanobiofertilizers containing nanoparticles such as Ag,Zn,Fe,ZnO,TiO_(2),SiO_(2),and MgO can provide essential crop protection,while algae species like Chlorella spp.,Arthrospira spp.,and Dunaliella spp.serve as promising biostimulants and biofertilizers.Additionally,plant growth-promoting microorganisms such as Rhizobium,Azotobacter,Azospirillum,Pseudomonas,Bacillus,and Trichoderma,alongside synthetic SLs like GR24,contribute to improving crop yield and stress tolerance.Strigolactone signaling pathways have also been explored for their roles in plant growth and resilience.Recent innovations in biofertilizer research,particularly in genomics,transcriptomics,and metabolomics,have advanced our understanding of plant-microbe interactions.These omics-based technologies help develop tailored biofertilizer formulations suited to specific crops,soils,and environmental conditions.The combination of biofertilizers,nanoparticles,and SLs fosters nutrient uptake,enhances stress tolerance,and promotes overall plant growth.Case studies from various agroecosystems show that biofertilizers can improve soil health,boost crop yields,reduce chemical fertilizer dependency,and lower environmental impacts.With precision farming,biofertilizers offer sustainable solutions to various challenges,including climate change,soil degradation,and food security.This review discusses the mechanisms by which GR24,nanoparticle,and microbe-based biofertilizers benefit plants,emphasizing their potential for sustainable agriculture and future challenges.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
Bone fractures represent a significant global healthcare burden.Although fractures typically heal on their own,some fail to regenerate properly,leading to nonunion,a condition that causes prolonged disability,morbidit...Bone fractures represent a significant global healthcare burden.Although fractures typically heal on their own,some fail to regenerate properly,leading to nonunion,a condition that causes prolonged disability,morbidity,and mortality.The challenge of treating nonunion fractures is further complicated in patients with underlying bone disorders where systemic and local factors impair bone healing.Traditional treatment approaches,including autografts,allografts,xenografts,and synthetic biomaterials,face limitations such as donor site pain,immune rejection,and insufficient mechanical strength,underscoring the need for alternative strategies.Biologic therapies have emerged as promising tools to enhance bone regeneration by leveraging the body’s natural healing processes.This review explores the critical role of conventional and emerging biologics in fracture healing.We categorize biologic therapies into protein-based treatments,gene and transcript therapies,small molecules,peptides,and cell-based therapies,highlighting their mechanisms of action,advantages,and clinical relevance.Finally,we examine the potential applications of biologics in treating fractures associated with bone disorders such as osteoporosis,osteogenesis imperfecta,rickets,osteomalacia,Paget’s disease,and bone tumors.By integrating biologic therapies with existing biomaterial-based strategies,these innovative approaches have the potential to transform clinical management and improve outcomes for patients with difficult-to-heal fractures.展开更多
Source-sink coordination serves as the foundation for improving crop yield.Current research primarily focuses on individual factors,such as increasing the source or expanding the sink,which often leads to disrupted so...Source-sink coordination serves as the foundation for improving crop yield.Current research primarily focuses on individual factors,such as increasing the source or expanding the sink,which often leads to disrupted source-sink balance,causing trade-offs among photosynthesis,yield,and stress response.To address these limitations,we present an integrated synthetic biological framework that synergistically enhances photosynthetic efficiency(source capacity),sink optimization,and abiotic stress tolerance.We developed an editing-overexpression coupling(EOC)vector system enabling simultaneous overexpression of four photosynthesis-enhancing genes(Cyt c6,PsbA,FBPase,OsMGT3),knockout of three yield-limiting genes(GS3,Gn1a,OsAAP5),and self-excision of selection markers,gene-editing modules,and fragment deletion cassettes.Field evaluations of CFMP-gga transgenic lines revealed significant physiological improvements,including 13%–17%increase in photosynthetic rates,improved chlorophyll fluorescence parameters,and increased stomatal conductance.These enhancements translated into remarkable agronomic gains,including 18.7%–22.3%higher grain yield,23.1%–26.1%increased biomass,and improved panicle architecture(increased grain size and grain number per panicle).The engineered lines maintained superior thermotolerance(under 42°C stress)and alkali tolerance(at pH 10)compared to wild-type controls.This study provides a strategy for enhancing crop yield by demonstrating that coordinated multi-gene regulation of source-sink dynamics,coupled with stress resilience engineering,achieves concurrent improvements.展开更多
Crack detection accuracy in computer vision is often constrained by limited annotated datasets.Although Generative Adversarial Networks(GANs)have been applied for data augmentation,they frequently introduce blurs and ...Crack detection accuracy in computer vision is often constrained by limited annotated datasets.Although Generative Adversarial Networks(GANs)have been applied for data augmentation,they frequently introduce blurs and artifacts.To address this challenge,this study leverages Denoising Diffusion Probabilistic Models(DDPMs)to generate high-quality synthetic crack images,enriching the training set with diverse and structurally consistent samples that enhance the crack segmentation.The proposed framework involves a two-stage pipeline:first,DDPMs are used to synthesize high-fidelity crack images that capture fine structural details.Second,these generated samples are combined with real data to train segmentation networks,thereby improving accuracy and robustness in crack detection.Compared with GAN-based approaches,DDPM achieved the best fidelity,with the highest Structural Similarity Index(SSIM)(0.302)and lowest Learned Perceptual Image Patch Similarity(LPIPS)(0.461),producing artifact-free images that preserve fine crack details.To validate its effectiveness,six segmentation models were tested,among which LinkNet consistently achieved the best performance,excelling in both region-level accuracy and structural continuity.Incorporating DDPM-augmented data further enhanced segmentation outcomes,increasing F1 scores by up to 1.1%and IoU by 1.7%,while also improving boundary alignment and skeleton continuity compared with models trained on real images alone.Experiments with varying augmentation ratios showed consistent improvements,with F1 rising from 0.946(no augmentation)to 0.957 and IoU from 0.897 to 0.913 at the highest ratio.These findings demonstrate the effectiveness of diffusion-based augmentation for complex crack detection in structural health monitoring.展开更多
Considering the drastic variations in the surface elevation of the piedmont region in the Bai Cheng West Area,there is no reference point within the Reference Ground Line(RG line)of the starting point of the synthetic...Considering the drastic variations in the surface elevation of the piedmont region in the Bai Cheng West Area,there is no reference point within the Reference Ground Line(RG line)of the starting point of the synthetic seismic records in the process of calibration of the horizon.Through the analysis of the process and properties of the production of the RG line,in the processing of seismic data,it is indicated that the position of the synthetic data of seismic records is not located at the beginning of the RG line.Rather,it must be at the time point of the seismic profile at the elevation of a datum position of the static value of less than the datum plane.Both the RG line and the elevation static correction value line can easily be seen by computerizing the calculated value of the elevation static correction of the datum plane relating to the seismic section and plotting it on the seismic section.To achieve a good calibration with the synthetic seismogram,it is possible to set the starting point of the synthetic seismogram on the elevation static correction value line that is situated at the place of the Common Mid-Point(CMP).In the current paper,a systematic overview of methods and safety procedures for establishing the seismic interpretation work area and horizon calibration in seismic interpretation has been reviewed,which will form an effective guide towards seismic interpretation under the complicated surface conditions in the Bai Cheng west region.展开更多
Accurate identification of water sources is crucial for effective water management and safety in mining operations.However,imbalanced water sample datasets often lead to suboptimal classification accuracy.To address t...Accurate identification of water sources is crucial for effective water management and safety in mining operations.However,imbalanced water sample datasets often lead to suboptimal classification accuracy.To address this challenge,this study proposes a novel water source identification method integrating Synthetic Minority Over-Sampling Technique(SMOTE),Zebra Optimization Algorithm(ZOA),and Light Gradient Boosting Machine(LightGBM).Initially,SMOTE is utilized to synthesize samples for the minority class within the imbalanced dataset,thereby generating a balanced water sample dataset and mitigating class distribution disparities.Subsequently,an efficient water source identification model is constructed by combining ZOA with LightGBM,leveraging the strengths of both algorithms.The model’s performance is validated using a test set and compared with other common classification models.Results demonstrate that SMOTE significantly alleviates class imbalance and enhances the classification accuracy of LightGBM for minority class water samples.ZOA parameter tuning accelerates model convergence and further improves classification accuracy,optimizing the model’s overall performance.In experimental validation,the proposed SMOTE-ZOA-LightGBM model achieved an accuracy of 88.41%and a F1 score of 88.24%,outperforming six other classification models.The method proposed in this paper can accurately identify water source types,effectively addressing the issue of low classification accuracy caused by imbalanced water sample data.It provides reliable technical support and scientific basis for identifying and preventing water inrush sources in mines.展开更多
Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows rais...Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows raises concerns,particularly the risk of subtle distortions or hallucinations that may undermine diagnostic accuracy and weaken trust in AI-assisted decision-making.To address this challenge,we propose a hybrid deep learning framework designed to detect GAN-induced artifacts in medical images,thereby reinforcing the reliability of AI-driven diagnostics.The framework integrates low-level statistical descriptors,including high-frequency residuals and Gray-Level Co-occurrence Matrix(GLCM)texture features,with high-level semantic representations extracted from a pre-trained ResNet18.This dual-stream approach enables detection of both pixel-level anomalies and structural inconsistencies introduced by GAN-based manipulation.We validated the framework on a curated dataset of 10,000 medical images,evenly split between authentic and GAN-generated samples across four modalities:MRI,CT,X-ray,and fundus photography.To improve generalizability to real-world clinical settings,we incorporated domain adaptation strategies such as adversarial training and style transfer,reducing domain shift by 15%.Experimental results demonstrate robust performance,achieving 92.6%accuracy and an F1-score of 0.91 on synthetic test data,and maintaining strong performance on real-world GAN-modified images with 87.3%accuracy and an F1-score of 0.85.Additionally,the model attained an AUC of 0.96 and an average precision of 0.92,outperforming conventional GAN detection pipelines and baseline Convolutional Neural Network(CNN)architectures.These findings establish the proposed framework as an effective and reliable solution for detecting GAN-induced hallucinations in medical imaging,representing an important step toward building trustworthy and clinically deployable AI systems.展开更多
The unique advantage of x-ray ghost imaging(XGI)is its potential in low dose radiology.One of the practical ways to reduce the radiation exposure is to reduce the measurements while remaining sufficient image quality....The unique advantage of x-ray ghost imaging(XGI)is its potential in low dose radiology.One of the practical ways to reduce the radiation exposure is to reduce the measurements while remaining sufficient image quality.Synthetic aperture x-ray ghost imaging(SAXGI)is invented to achieve megapixel XGI with limited measurements,which is expected to implement XGI simultaneously with large field of view and low radiation exposure.In this paper,we experimentally investigate the effect of measurements reduction on the spatial resolution and image quality of SAXGI with standard sample and biomedical specimen.The results with a resolution chart demonstrated that at 360 measurements,SAXGI successfully retrieved the sample image of 1960×1960 pixels with spatial resolution of 4μm.With measurement reduction,the spatial resolution deteriorates but the sparser structures are still discernable.Even with measurements reduced to 10,a spatial resolution of 10μm can still be achieved by SAXGI.A biomedical sample of a fish specimen is employed to evaluate the method and the fish image of 2000×1000 pixels with an SSIM of 0.962 is reconstructed by SAXGI with 770measurements,corresponding to an accumulative exposure reduction of more than 2 times.With the measurements reduced to 10 which corresponds to 1/160 of the accumulative radiation exposure for conventional radiology,bulky structure like the fish skeleton can still be definitely discerned and the SSIM for the reconstructed image still retained 0.9179.Results of this paper demonstrate that measurements reduction is practicable for the radiation exposure reduction of the sample,which implicates that SAXGI with limited measurements is an efficient solution for low dose radiology.展开更多
As we welcome the spring of 2026,we extend our sincere greetings and best wishes to colleagues worldwide in the field of crop science,our partners,and all those committed to sustainable agricultural development!The Ye...As we welcome the spring of 2026,we extend our sincere greetings and best wishes to colleagues worldwide in the field of crop science,our partners,and all those committed to sustainable agricultural development!The Year of the Horse symbolizes endeavor and far-reaching journeys,reflecting our own spirit of continuous exploration and breakthrough innovation on the path of crop science.Here,I extendmysincere appreciation to all our authors and reviewers for their invaluable time,expertise,and dedication,which are instrumental in the success of The Crop Journal,establishing it as a premier platform for the global crop science research community.The Crop Journal publishes its 2026 first issue as a special issue themed“Synthetic Biology for Crop Improvement”,ably vip-edited by four young scientists.The issue provides a comprehensive overview of major advances in the field.In the past few years,crop science has made long strides in metabolic engineering of important pathways in secondary metabolism.The achievements expedite the emergence of synthetic biology as a potent methodology for crop breeding and represent a fundamental paradigm shift from“deciphering crops”to“designing crops”,which is further empowered by artificial intelligence(AI).At this turning point of the New Year,I would like to take this opportunity to provide a brief retrospective and future perspective.展开更多
Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering acti...Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.展开更多
Bathymetric measurement of shallow water is of fundamental importance to coastal environment research and resource management.However,there are still great challenges in estimating water depth using satellite observat...Bathymetric measurement of shallow water is of fundamental importance to coastal environment research and resource management.However,there are still great challenges in estimating water depth using satellite observations in turbid coastal waters.In this paper,we developed a physicsenhanced deep neural network to estimate bathymetry of highly turbid waters of the Changjiang(Yangtze)River estuary from dual-polarized synthetic aperture radar(SAR)images.Sentinel-1A/B SAR images with a spatial resolution of 20 m×22 m were collected and matched with water depth data from nautical charts during 2017-2023.For the input parameters of the model,in addition to the normalized radar backscatter cross section(NRCS)at single polarization and incidence angle,the impacts of both polarimetric characteristics and physical environmental factors on model performance were discussed in detail.Results of feature importance analysis and sensitivity experiments indicate that the polarization ratio and NRCS after removing the influence of background sea surface wind field make significant contributions to the bathymetry retrieval model.The root mean square error(RMSE)of SAR derived water depth decreases from 1.44 to 0.78 m within 0-30-m depth,and the mean relative error(MRE)is reduced from 15.6%to 8.6%.Compared with other machine learning models such as ResNet,XGBoost,and Random Forest,the MRE is reduced by 3.9%,5.7%,and 7.4%,respectively.The spatial distribution of SAR derived water depth also exhibits a high degree of consistency with observations,demonstrating the great potential of the model in estimating the depth of turbid shallow waters.展开更多
基金supported by grants from the Guangxi Science and Technology Major Project(GKAA24206023)the Biological Breeding-National Science and Technology Major Project(2024ZD04077)+2 种基金the National Natural Science Foundation of China(32272120)the National Key Research and Development Program of China(2024YFF1000800)the Guangdong Basic Research Center of Excellence for Precise Breeding of Future Crops Major Project(FCBRCE-202502,FCBRCE-202504).
文摘A growing global population and the increasing prevalence of diet-related health issues such as“hidden hunger”,obesity,hypertension,and diabetes necessitate a fundamental rethinking of crop design and breeding.Synthetic metabolic engineering offers a method to modify and redesign metabolic pathways to increase the nutritional value of crops.We summarize recent advances in the biofortification of key nutrients including provitamin A,vitamin C,vitamin B9,iron,zinc,anthocyanins,flavonoids,and unsaturated fatty acids.We discuss the potential of multi-gene stacking,gene editing,enzyme engineering,and artificial intelligence in synthetic metabolic engineering.We propose future research directions and potential solutions centered on leveraging AI-driven systems biology,precision gene editing,enzyme engineering,agrobacterium-mediated genotype-independent transformation,and modular metabolic engineering strategies to develop next-generation nutritionally enhanced super crops and transform global food systems.
基金supported by the Project of Sanya Yazhou Bay Science and Technology City (SKJC-JYRC-2024-26)the National Natural Science Foundation of China (32460072)+4 种基金Hainan Provincial Natural Science Foundation of China (323RC421)the Hainan Province Science and Technology Special Fund (ZDYF2022XDNY144)the Hainan Provincial Academician Innovation Platform Project (HDYSZX-202004)the Collaborative Innovation Center of Nanfan and High-Efficiency Tropical Agriculture, Hainan University (XTCX2022NYB06)Hainan Postdoctoral Research Grant Project
文摘In light of the pressing global challenges of climate change,declining crop resilience,and hidden hunger,it is imperative to overcome the limitations of conventional crop breeding to enhance both the nutritional quality and stress tolerance of crops.Synthetic metabolic engineering presents innovative strategies for the precision modification and de novo design of metabolic pathways.This approach generally encompasses three essential steps:identifying key metabolites through metabolomics,integrating multi-omics technologies to investigate the synthesis and regulation of these metabolites,and utilizing gene editing or de novo design to modify crop metabolic pathways associated with desirable agronomic traits.This review underscores the vital role of plant metabolite diversity in enhancing crop nutritional quality and stress resilience.Integrated multi-omics analyses facilitate the metabolic engineering by identifying key genes,transporters,and transcription factors that regulate metabolite biosynthesis.Precision modification strategies employ genome editing tools to reprogram endogenous metabolic networks,while de novo design reconstructs metabolic pathways through the introduction of exogenous biological elements—thereby both approaches enable the targeted enhancement of desired traits.These strategies have been effectively implemented in major food crops.However,simultaneously enhancing nutritional quality and stress resilience remains challenging due to inherent trade-offs and resource competition in distinct metabolic pathways within plants.Future research should integrate AI-driven predictive models with multi-omics datasets to decipher dynamic metabolic homeostasis and engineer climate-smart crops that maximize yield while preserving quality and environmental adaptability.
基金supported by the National Natural Science Foundation of China (32325039)
文摘Cotton production faces significant challenges from insect pests,with chemical pesticide use becoming increasingly limited by resistance and environmental concerns.This study explores the potential use of caffeine,a natural plant alkaloid,as an environmentally friendly insect resistance strategy in cotton.Exogenous caffeine application demonstrated potent insecticidal effects against cotton bollworm(Helicoverpa armigera)larvae,with concentrations≥2 mg mL−1 causing near-complete feeding cessation and up to 70%larval mortality.Building on this,we engineered transgenic cotton(Gossypium hirsutum cv.Jin668)for heterologous caffeine biosynthesis by introducing three key N-methyltransferase genes(CaXMT1,CaMXMT1,CaDXMT1)by multiple gene transformation.Transgenic lines expressing all three genes showed remarkable caffeine accumulation(up to 3.59 mg g−1 dry weight),whereas two-gene combinations exhibited wild-type-level production.Feeding preference assays revealed that caffeine-enriched cotton strongly deterred feeding by H.armigera.Non-choice feeding trials demonstrated reduced leaf consumption and reduced larval growth in H.armigera fed on caffeine-producing cotton.The study highlights the effectiveness of synthetic biology approaches using the TGSII-UNiE multigene stacking system,despite challenges in transgene stability.This work advances plant-derived insect resistance research and provides a sustainable framework for reducing chemical pesticide reliance in cotton production,while underscoring unique potential of cotton as a synthetic biology platform for secondary metabolite engineering.
基金supported by grants from the Biological Breeding-National Science and Technology Major Project(2024ZD04077)the Invigorate the Seed Industry of Guangdong Province(2024-NPY-00-044)+3 种基金the National Natural Science Foundation of China(32272120)the Guangxi Science and Technology Major Project(GKAA24206023)the National Key Research and Development Program of China(2024YFF1000800)the Guangdong Basic Research Center of Excellence for Precise Breeding of Future Crops Major Project(FCBRCE-202502,FCBRCE-202504).
文摘Betalain,an economically valuable water-soluble natural plant pigment,is prized for its strong antioxidant activity,making it popular as a dietary supplement and a visual marker for plant transformation.However,market demand significantly outstrips current production capacity.This study reports the development of an efficient push-and-pull multigene strategy based on polycistronic expression and metabolic flux regulation to enhance betalain biosynthesis in transgenic maize(Zea mays L.)endosperm.We engineered a novel enhanced RUBY(eRUBY)system derived from the original polycistronic RUBY construct(CYP76AD1P2ADODA1P2ADOPA5GT unit,abbreviated CDG)by introducing arogenate dehydrogenase(ADHα)to increase the L-tyrosine substrate supply.All the genes were driven by the endosperm-specific promoter.Fusion of ADHαinto a single polycistronic eRUBY construct(CDGA)produced significantly higher betanin(6.88 mg g−1 dry weight)and isobetanin(1.81 mg g−1 dry weight)levels than in CDG+A,which stacked the ADHαcassette independently with CDG.The high betalain accumulation in CDGA lines(which also exhibited higher transgene copy number)resulted in a 2.85–7.58-fold improvement in endosperm antioxidant capacity compared to WT(versus 2.48–2.80-fold in CDG+A).Importantly,transgenic plants maintained a normal phenotype.Transcriptome and metabolome analyses further indicated that metabolism of phenylalanine,alanine,aspartate,and glutamate contributes to betalain production.Hybridization with sweet corn successfully created a high-sugar eRUBY maize variety.Collectively,these results demonstrate the successful development of a novel maize germplasm with significantly enhanced nutritional value through high betalain accumulation.
基金supported by the National Natural Science Foundation of China (32402306)the Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences+1 种基金National Key Research and Development Program of China (2022YFE0203300)the China-Uruguay Joint Laboratory on Soybean Research and Innovation
文摘Plants produce a vast array of specialized metabolites that serve as essential defenses against herbivores and pathogens.However,the capacity to produce these compounds differs substantially among plant species and is frequently diminished during domestication.Advances in synthetic metabolic engineering enable efficient elucidation and engineering of plant specialized metabolic pathways active in crop pest and pathogen resistance.This review summarizes strategies and workflows for selecting defensive metabolic pathways,identifying candidate biosynthetic genes,and rewiring native or introducing heterologous pathways to enhance crop resistance to pests and pathogens.Strategies include weighted gene co-expression network construction,biosynthetic gene cluster scanning,and metabolite genome-wide association studies for pathway discovery,as well as transcriptional reprogramming,enzyme activity optimization,and transporter deployment for pathway engineering.We further discuss challenges in using synthetic metabolic engineering to enhance crop resistance and highlight the potential of artificial intelligence in addressing them.
基金support and funding by the European Union-Next Generation EU under the Italian Ministry of University and Research (MUR) National Innovation Ecosystem (No.ECS00000041-VITALITY and also “Ecosistema TECH4YOU-(Spoke 3-Goal 3.5)MUR is thanked for PRIN-PNRR 2022 project "P2022XKWH7-Circular Waste+3 种基金The University of Perugia is acknowledged for financial support to the university project “Fondo Ricerca di Ateneo,edizione 2022”The National Ph D program in Catalysis coordinated by the University of Perugia is also thankedthe financial supports of key research and development and technology transfer projects of Inner Mongolia Autonomous Region (No.2025KJHZ0008)major special projects of science and technology of Ordos (No.2022EEDSKJZDZX003)。
文摘Given the broad applicability of carbazole structural moieties in materials science and medicinal chemistry,significant efforts have been devoted to developing efficient synthetic catalytic methodologies to access this valuable scaffold.Catalyzed direct Csp^(2)-H functionalization provides an effective and costefficient approach to synthesizing carbazoles from simple and readily available starting materials,ensuring a promising path characterized by excellent atom and step economy.This review highlights the substantial progress made in the last 10 years in advancing catalytic Csp^(2)-H functionalization techniques for synthesizing carbazoles.
基金supported by project ZR2022MF330 supported by Shandong Provincial Natural Science Foundationthe National Natural Science Foundation of China under Grant No.61701286.
文摘Synthetic speech detection is an essential task in the field of voice security,aimed at identifying deceptive voice attacks generated by text-to-speech(TTS)systems or voice conversion(VC)systems.In this paper,we propose a synthetic speech detection model called TFTransformer,which integrates both local and global features to enhance detection capabilities by effectively modeling local and global dependencies.Structurally,the model is divided into two main components:a front-end and a back-end.The front-end of the model uses a combination of SincLayer and two-dimensional(2D)convolution to extract high-level feature maps(HFM)containing local dependency of the input speech signals.The back-end uses time-frequency Transformer module to process these feature maps and further capture global dependency.Furthermore,we propose TFTransformer-SE,which incorporates a channel attention mechanism within the 2D convolutional blocks.This enhancement aims to more effectively capture local dependencies,thereby improving the model’s performance.The experiments were conducted on the ASVspoof 2021 LA dataset,and the results showed that the model achieved an equal error rate(EER)of 3.37%without data augmentation.Additionally,we evaluated the model using the ASVspoof 2019 LA dataset,achieving an EER of 0.84%,also without data augmentation.This demonstrates that combining local and global dependencies in the time-frequency domain can significantly improve detection accuracy.
基金supported by the National Natural Science Foundation of China(No.51939009)Shenzhen Science and Technology Program(Nos.JCYJ20241202125905008 and GXWD20201231165807007-20200810165349001).
文摘A trace analytical method based on solid-phase extraction gas chromatography-tandem mass spectrometry(SPE–GC–MS/MS)was developed for the rapid detection of 256 semi-volatile organic compounds(SVOCs),including 25 polycyclic aromatic hydrocarbons(PAHs),70 polychlorinated biphenyls(PCBs),123 pesticides,20 phthalate esters(PAEs),4 organophosphate esters(OPEs),9 synthetic musks(SMs),and 5 UV filters(UVs)in water.No-tably,this method provided a decent linearity of calibration standards(R^(2)>0.999),excellent method limits of quantification(MLOQs)(0.12–11.41 ng/L),satisfactory matrix spiking recovery rates(60.4%–126%),and high precision(intra-day relative standard deviations(RSDs):1.0%–10.0%,inter-day RSDs:3.0%–15.0%,and inter-week RSDs:3.4%–15.7%),making it suitable for trace-level studies.Statistical analysis revealed that SVOCs with higher volatility exhibited enhanced recovery rates.Validation of the methodology involved analyzing SVOCs in real spring water and river water samples.Twenty-seven SVOCs were detected in spring water and 58 in river water,with an average concentration of 631.73 and 16,095 ng/L,respectively.Among the detected SVOCs,PAEs constituted the predominant proportion.This study underscored the presence of SVOCs contamination specifi-cally within the spring water,although SVOCs concentrations in river water were significantly greater than those found in spring water.In summary,this sensitive method based on SPE–GC–MS/MS was successfully developed and validated for the rapid analysis of a diverse array of 256 SVOCs at trace levels in water,including not only the traditional highly valued PAHs,PCBs,pesticides,and PAEs,but also the emerging OPEs,UVs,and SMs.
基金Siksha‘O’Anusandhan(Deemed to be University),IndiaGraphic Era(Deemed to be University),India+1 种基金Bankura Sammilani College,IndiaRaiganj University,India for their support。
文摘Microbe-based soil inoculants offer a promising approach to sustainable agriculture by reducing reliance on agrochemicals and minimizing environmental damages.The heavy use of chemicals in conventional agriculture poses significant challenges to crop production and environmental health.This review explores the integration of microbe-based inoculants,strigolactones(SLs),and nanotechnology to enhance agricultural sustainability.Nanobiofertilizers containing nanoparticles such as Ag,Zn,Fe,ZnO,TiO_(2),SiO_(2),and MgO can provide essential crop protection,while algae species like Chlorella spp.,Arthrospira spp.,and Dunaliella spp.serve as promising biostimulants and biofertilizers.Additionally,plant growth-promoting microorganisms such as Rhizobium,Azotobacter,Azospirillum,Pseudomonas,Bacillus,and Trichoderma,alongside synthetic SLs like GR24,contribute to improving crop yield and stress tolerance.Strigolactone signaling pathways have also been explored for their roles in plant growth and resilience.Recent innovations in biofertilizer research,particularly in genomics,transcriptomics,and metabolomics,have advanced our understanding of plant-microbe interactions.These omics-based technologies help develop tailored biofertilizer formulations suited to specific crops,soils,and environmental conditions.The combination of biofertilizers,nanoparticles,and SLs fosters nutrient uptake,enhances stress tolerance,and promotes overall plant growth.Case studies from various agroecosystems show that biofertilizers can improve soil health,boost crop yields,reduce chemical fertilizer dependency,and lower environmental impacts.With precision farming,biofertilizers offer sustainable solutions to various challenges,including climate change,soil degradation,and food security.This review discusses the mechanisms by which GR24,nanoparticle,and microbe-based biofertilizers benefit plants,emphasizing their potential for sustainable agriculture and future challenges.
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].
基金performed as part of the cmRNAbone project funded by the European Union’s Horizon 2020 research and innovation program under the Grant Agreement No 874790。
文摘Bone fractures represent a significant global healthcare burden.Although fractures typically heal on their own,some fail to regenerate properly,leading to nonunion,a condition that causes prolonged disability,morbidity,and mortality.The challenge of treating nonunion fractures is further complicated in patients with underlying bone disorders where systemic and local factors impair bone healing.Traditional treatment approaches,including autografts,allografts,xenografts,and synthetic biomaterials,face limitations such as donor site pain,immune rejection,and insufficient mechanical strength,underscoring the need for alternative strategies.Biologic therapies have emerged as promising tools to enhance bone regeneration by leveraging the body’s natural healing processes.This review explores the critical role of conventional and emerging biologics in fracture healing.We categorize biologic therapies into protein-based treatments,gene and transcript therapies,small molecules,peptides,and cell-based therapies,highlighting their mechanisms of action,advantages,and clinical relevance.Finally,we examine the potential applications of biologics in treating fractures associated with bone disorders such as osteoporosis,osteogenesis imperfecta,rickets,osteomalacia,Paget’s disease,and bone tumors.By integrating biologic therapies with existing biomaterial-based strategies,these innovative approaches have the potential to transform clinical management and improve outcomes for patients with difficult-to-heal fractures.
基金the National Key Research and Development Program of China(2020YFA0907600)National Natural Science Foundation of China(31100869)+1 种基金Central Public-interest Scientific Institutions Basal Research Fund for Zhang Zhiguo(Y2025YY06)the Fundamental Research Funds for Central Nonprofit Scientific Institutions for Lu Tiegang,and Cui Xuean.
文摘Source-sink coordination serves as the foundation for improving crop yield.Current research primarily focuses on individual factors,such as increasing the source or expanding the sink,which often leads to disrupted source-sink balance,causing trade-offs among photosynthesis,yield,and stress response.To address these limitations,we present an integrated synthetic biological framework that synergistically enhances photosynthetic efficiency(source capacity),sink optimization,and abiotic stress tolerance.We developed an editing-overexpression coupling(EOC)vector system enabling simultaneous overexpression of four photosynthesis-enhancing genes(Cyt c6,PsbA,FBPase,OsMGT3),knockout of three yield-limiting genes(GS3,Gn1a,OsAAP5),and self-excision of selection markers,gene-editing modules,and fragment deletion cassettes.Field evaluations of CFMP-gga transgenic lines revealed significant physiological improvements,including 13%–17%increase in photosynthetic rates,improved chlorophyll fluorescence parameters,and increased stomatal conductance.These enhancements translated into remarkable agronomic gains,including 18.7%–22.3%higher grain yield,23.1%–26.1%increased biomass,and improved panicle architecture(increased grain size and grain number per panicle).The engineered lines maintained superior thermotolerance(under 42°C stress)and alkali tolerance(at pH 10)compared to wild-type controls.This study provides a strategy for enhancing crop yield by demonstrating that coordinated multi-gene regulation of source-sink dynamics,coupled with stress resilience engineering,achieves concurrent improvements.
基金the National Natural Science Foundation of China(Grant No.:52508343)the Fundamental Research Funds for the Central Universities(Grant No.:B250201004).
文摘Crack detection accuracy in computer vision is often constrained by limited annotated datasets.Although Generative Adversarial Networks(GANs)have been applied for data augmentation,they frequently introduce blurs and artifacts.To address this challenge,this study leverages Denoising Diffusion Probabilistic Models(DDPMs)to generate high-quality synthetic crack images,enriching the training set with diverse and structurally consistent samples that enhance the crack segmentation.The proposed framework involves a two-stage pipeline:first,DDPMs are used to synthesize high-fidelity crack images that capture fine structural details.Second,these generated samples are combined with real data to train segmentation networks,thereby improving accuracy and robustness in crack detection.Compared with GAN-based approaches,DDPM achieved the best fidelity,with the highest Structural Similarity Index(SSIM)(0.302)and lowest Learned Perceptual Image Patch Similarity(LPIPS)(0.461),producing artifact-free images that preserve fine crack details.To validate its effectiveness,six segmentation models were tested,among which LinkNet consistently achieved the best performance,excelling in both region-level accuracy and structural continuity.Incorporating DDPM-augmented data further enhanced segmentation outcomes,increasing F1 scores by up to 1.1%and IoU by 1.7%,while also improving boundary alignment and skeleton continuity compared with models trained on real images alone.Experiments with varying augmentation ratios showed consistent improvements,with F1 rising from 0.946(no augmentation)to 0.957 and IoU from 0.897 to 0.913 at the highest ratio.These findings demonstrate the effectiveness of diffusion-based augmentation for complex crack detection in structural health monitoring.
文摘Considering the drastic variations in the surface elevation of the piedmont region in the Bai Cheng West Area,there is no reference point within the Reference Ground Line(RG line)of the starting point of the synthetic seismic records in the process of calibration of the horizon.Through the analysis of the process and properties of the production of the RG line,in the processing of seismic data,it is indicated that the position of the synthetic data of seismic records is not located at the beginning of the RG line.Rather,it must be at the time point of the seismic profile at the elevation of a datum position of the static value of less than the datum plane.Both the RG line and the elevation static correction value line can easily be seen by computerizing the calculated value of the elevation static correction of the datum plane relating to the seismic section and plotting it on the seismic section.To achieve a good calibration with the synthetic seismogram,it is possible to set the starting point of the synthetic seismogram on the elevation static correction value line that is situated at the place of the Common Mid-Point(CMP).In the current paper,a systematic overview of methods and safety procedures for establishing the seismic interpretation work area and horizon calibration in seismic interpretation has been reviewed,which will form an effective guide towards seismic interpretation under the complicated surface conditions in the Bai Cheng west region.
基金funding from the Natural Science Foundation of Henan Province(252300421852)the State Key Laboratory of Development and Comprehensive Utilization of Coking Coal Resources(41040220201308)+4 种基金the National Natural Science Foundation of China(41972254)the China Postdoctoral Science Foundation(2019M662494)Supported by the Key Scientific Research Projects of Higher Education Institutions of Henan Province(19A170005)the Fundamental Research Funds for the Universities of Henan Province(NSFRF200337,NSFRF200103)Key Research and Development Project of Henan Province(251111322300).
文摘Accurate identification of water sources is crucial for effective water management and safety in mining operations.However,imbalanced water sample datasets often lead to suboptimal classification accuracy.To address this challenge,this study proposes a novel water source identification method integrating Synthetic Minority Over-Sampling Technique(SMOTE),Zebra Optimization Algorithm(ZOA),and Light Gradient Boosting Machine(LightGBM).Initially,SMOTE is utilized to synthesize samples for the minority class within the imbalanced dataset,thereby generating a balanced water sample dataset and mitigating class distribution disparities.Subsequently,an efficient water source identification model is constructed by combining ZOA with LightGBM,leveraging the strengths of both algorithms.The model’s performance is validated using a test set and compared with other common classification models.Results demonstrate that SMOTE significantly alleviates class imbalance and enhances the classification accuracy of LightGBM for minority class water samples.ZOA parameter tuning accelerates model convergence and further improves classification accuracy,optimizing the model’s overall performance.In experimental validation,the proposed SMOTE-ZOA-LightGBM model achieved an accuracy of 88.41%and a F1 score of 88.24%,outperforming six other classification models.The method proposed in this paper can accurately identify water source types,effectively addressing the issue of low classification accuracy caused by imbalanced water sample data.It provides reliable technical support and scientific basis for identifying and preventing water inrush sources in mines.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2601).
文摘Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows raises concerns,particularly the risk of subtle distortions or hallucinations that may undermine diagnostic accuracy and weaken trust in AI-assisted decision-making.To address this challenge,we propose a hybrid deep learning framework designed to detect GAN-induced artifacts in medical images,thereby reinforcing the reliability of AI-driven diagnostics.The framework integrates low-level statistical descriptors,including high-frequency residuals and Gray-Level Co-occurrence Matrix(GLCM)texture features,with high-level semantic representations extracted from a pre-trained ResNet18.This dual-stream approach enables detection of both pixel-level anomalies and structural inconsistencies introduced by GAN-based manipulation.We validated the framework on a curated dataset of 10,000 medical images,evenly split between authentic and GAN-generated samples across four modalities:MRI,CT,X-ray,and fundus photography.To improve generalizability to real-world clinical settings,we incorporated domain adaptation strategies such as adversarial training and style transfer,reducing domain shift by 15%.Experimental results demonstrate robust performance,achieving 92.6%accuracy and an F1-score of 0.91 on synthetic test data,and maintaining strong performance on real-world GAN-modified images with 87.3%accuracy and an F1-score of 0.85.Additionally,the model attained an AUC of 0.96 and an average precision of 0.92,outperforming conventional GAN detection pipelines and baseline Convolutional Neural Network(CNN)architectures.These findings establish the proposed framework as an effective and reliable solution for detecting GAN-induced hallucinations in medical imaging,representing an important step toward building trustworthy and clinically deployable AI systems.
基金Project supported by the National Key Research and Development Program of China(Grant Nos.2022YFA1603601,2021YFF0601203,and 2021YFA1600703)。
文摘The unique advantage of x-ray ghost imaging(XGI)is its potential in low dose radiology.One of the practical ways to reduce the radiation exposure is to reduce the measurements while remaining sufficient image quality.Synthetic aperture x-ray ghost imaging(SAXGI)is invented to achieve megapixel XGI with limited measurements,which is expected to implement XGI simultaneously with large field of view and low radiation exposure.In this paper,we experimentally investigate the effect of measurements reduction on the spatial resolution and image quality of SAXGI with standard sample and biomedical specimen.The results with a resolution chart demonstrated that at 360 measurements,SAXGI successfully retrieved the sample image of 1960×1960 pixels with spatial resolution of 4μm.With measurement reduction,the spatial resolution deteriorates but the sparser structures are still discernable.Even with measurements reduced to 10,a spatial resolution of 10μm can still be achieved by SAXGI.A biomedical sample of a fish specimen is employed to evaluate the method and the fish image of 2000×1000 pixels with an SSIM of 0.962 is reconstructed by SAXGI with 770measurements,corresponding to an accumulative exposure reduction of more than 2 times.With the measurements reduced to 10 which corresponds to 1/160 of the accumulative radiation exposure for conventional radiology,bulky structure like the fish skeleton can still be definitely discerned and the SSIM for the reconstructed image still retained 0.9179.Results of this paper demonstrate that measurements reduction is practicable for the radiation exposure reduction of the sample,which implicates that SAXGI with limited measurements is an efficient solution for low dose radiology.
文摘As we welcome the spring of 2026,we extend our sincere greetings and best wishes to colleagues worldwide in the field of crop science,our partners,and all those committed to sustainable agricultural development!The Year of the Horse symbolizes endeavor and far-reaching journeys,reflecting our own spirit of continuous exploration and breakthrough innovation on the path of crop science.Here,I extendmysincere appreciation to all our authors and reviewers for their invaluable time,expertise,and dedication,which are instrumental in the success of The Crop Journal,establishing it as a premier platform for the global crop science research community.The Crop Journal publishes its 2026 first issue as a special issue themed“Synthetic Biology for Crop Improvement”,ably vip-edited by four young scientists.The issue provides a comprehensive overview of major advances in the field.In the past few years,crop science has made long strides in metabolic engineering of important pathways in secondary metabolism.The achievements expedite the emergence of synthetic biology as a potent methodology for crop breeding and represent a fundamental paradigm shift from“deciphering crops”to“designing crops”,which is further empowered by artificial intelligence(AI).At this turning point of the New Year,I would like to take this opportunity to provide a brief retrospective and future perspective.
基金supported by the National Key R&D Program of China(Grant No.2023YFC3007201)the National Natural Science Foundation of China(Grant No.42377161)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(Grant No.GLAB 2024ZR03).
文摘Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.
基金Supported by the National Natural Science Foundation of China(Nos.T2261149752,41976163,42476172)。
文摘Bathymetric measurement of shallow water is of fundamental importance to coastal environment research and resource management.However,there are still great challenges in estimating water depth using satellite observations in turbid coastal waters.In this paper,we developed a physicsenhanced deep neural network to estimate bathymetry of highly turbid waters of the Changjiang(Yangtze)River estuary from dual-polarized synthetic aperture radar(SAR)images.Sentinel-1A/B SAR images with a spatial resolution of 20 m×22 m were collected and matched with water depth data from nautical charts during 2017-2023.For the input parameters of the model,in addition to the normalized radar backscatter cross section(NRCS)at single polarization and incidence angle,the impacts of both polarimetric characteristics and physical environmental factors on model performance were discussed in detail.Results of feature importance analysis and sensitivity experiments indicate that the polarization ratio and NRCS after removing the influence of background sea surface wind field make significant contributions to the bathymetry retrieval model.The root mean square error(RMSE)of SAR derived water depth decreases from 1.44 to 0.78 m within 0-30-m depth,and the mean relative error(MRE)is reduced from 15.6%to 8.6%.Compared with other machine learning models such as ResNet,XGBoost,and Random Forest,the MRE is reduced by 3.9%,5.7%,and 7.4%,respectively.The spatial distribution of SAR derived water depth also exhibits a high degree of consistency with observations,demonstrating the great potential of the model in estimating the depth of turbid shallow waters.
