Medicinal plants serve as valuable sources of bioactive compounds with critical applications across pharmaceutical,agricultural,and industrial sectors.Compared to chemical synthesis and plant extraction,synthetic biol...Medicinal plants serve as valuable sources of bioactive compounds with critical applications across pharmaceutical,agricultural,and industrial sectors.Compared to chemical synthesis and plant extraction,synthetic biology offers a green,efficient,and sustainable alternative for producing bioactive compounds,which represents a state of art technology.However,this technology still faces several challenges,including overly long metabolic pathways,inadequate catalytic efficiency of key enzymes in the pathway,and incompatibility between gene elements and host cells,leading to low yields of target bioactive compounds.The development and application of regulatory tools in synthetic biology hold great promise for overcoming these obstacles.This review first summarizes the classification and biosynthesis of bioactive compounds based on structural types.Subsequently,recent advancements are outlined in regulation tools and their application in the heterologous production of bioactive compounds.This review aims to establish a foundation for the efficient production of bioactive compounds based on microbial cell factories.This not only has significant practical implications for reducing the resource consumption and environmental impact of traditional production methods,but also highlights the central role of synthetic biology in promoting the sustainable production of bioactive compounds derived from medicinal plants.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
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
Accurate and timely insulator defect detection is crucial for maintaining the reliability and safety of the power supply.However,the development of deep-learning-based insulator defect detection is hindered by the sca...Accurate and timely insulator defect detection is crucial for maintaining the reliability and safety of the power supply.However,the development of deep-learning-based insulator defect detection is hindered by the scarcity of comprehensive,high-quality datasets for insulator defects.To address this gap,the synthetic insulator defect imaging and annotation(SYNTHIDIA)system was proposed.SYNTHIDIA generates synthetic defect images in a 3D virtual environment using domain randomisation,offering a cost-effective and versatile solution for creating diverse and annotated data.Our dataset includes 22,000 images with accurate pixel-level and instance-level annotations,covering broken defect and drop defect types.Through rigorous experiments,SYNTHIDIA demonstrates strong generalisation capabilities to real-world data and provides valuable insights into the impact of various domain factors on model performance.The inclusion of 3D models further supports broader research initiatives.SYNTHIDIA addresses data insufficiency in insulator defect detection and enhances model performance in data-limited scenarios,contributing significantly to the advancement of power inspection.展开更多
Synthetic aperture radar(SAR)aboard SEASAT was first launched in 1978.At the beginning of the 21st century,the Chinese remote sensing community recognized the urgent need to develop domestic SAR capabilities.Unlike sc...Synthetic aperture radar(SAR)aboard SEASAT was first launched in 1978.At the beginning of the 21st century,the Chinese remote sensing community recognized the urgent need to develop domestic SAR capabilities.Unlike scatterometers and al-timeters,space-borne SAR offers high-resolution images of the ocean,regardless of weather conditions or time of day.SAR imagery provides rich information about the sea surface,capturing complicated dynamic processes in the upper layers of the ocean,particular-ly in relation to tropical cyclones.Over the past four decades,the advantages of SAR have been increasingly recognized,leading to notable marine applications,especially in the development of algorithms for retrieving wind and wave data from SAR images.This study reviews the history,progress,and future outlook of SAR-based monitoring of sea surface wind and waves.In particular,the ap-plicability of various SAR wind and wave algorithms is systematically investigated,with a particular focus on their performance un-der extreme sea conditions.展开更多
Plant synthetic biology has emerged as a transformative field in agriculture,offering innovative solutions to enhance food security,provide resilience to climate change,and transition to sustainable farming practices....Plant synthetic biology has emerged as a transformative field in agriculture,offering innovative solutions to enhance food security,provide resilience to climate change,and transition to sustainable farming practices.By integrating advanced genetic tools,computational modeling,and systems biology,researchers can precisely modify plant genomes to enhance traits such as yield,stress tolerance,and nutrient use efficiency.The ability to design plants with specific characteristics tailored to diverse environmental conditions and agricultural needs holds great potential to address global food security challenges.Here,we highlight recent advancements and applications of plant synthetic biology in agriculture,focusing on key areas such as photosynthetic efficiency,nitrogen fixation,drought tolerance,pathogen resistance,nutrient use efficiency,biofortification,climate resilience,microbiology engineering,synthetic plant genomes,and the integration of artificial intelligence with synthetic biology.These innovations aim to maximize resource use efficiency,reduce reliance on external inputs,and mitigate environmental impacts associated with conventional agricultural practices.Despite challenges related to regulatory approval and public acceptance,the integration of synthetic biology in agriculture holds immense promise for creating more resilient and sustainable agricultural systems,contributing to global food security and environmental sustainability.Rigorous multi-field testing of these approaches will undoubtedly be required to ensure reproducibility.展开更多
Synthetic phenolic antioxidants(SPAs)are widely used in diverse industries due to their exceptional antioxidant characteristics.However,human exposure to SPAs may cause health problems.In this study,226 dust samples w...Synthetic phenolic antioxidants(SPAs)are widely used in diverse industries due to their exceptional antioxidant characteristics.However,human exposure to SPAs may cause health problems.In this study,226 dust samples were collected from 10 provinces in China,and six SPAs(three parent SPAs and their three transformation products)were analyzed.The concentrations of6SPAs(the sum of six target compounds)ranged from 15.4 to 3210 ng/g(geometric mean(GM):169 ng/g).The highest concentration of6SPAswas found in Sichuan Province(GM:349 ng/g),which was approximately 4 times higher than that in Hubei Province(81.6 ng/g)(p<0.05).The concentrations of butylated hydroxytoluene(BHT),2,2'-methylene bis(4-methyl-6–tert-butylphenol)(AO2246),2,6-di–tert–butyl–1,4-benzoquinone(BHT-Q),2,6-di–tert–butyl–4-(hydroxymethyl)phenol(BHT-OH),and ∑_(p)-SPAs were substantially higher in dust from urban areas than rural areas(p<0.05).AO2246 concentration in dust from homes(GM:0.400 ng/g)was about 4 times higher than that in workplaces(0.116 ng/g)(p<0.01).Significantly higherp-SPAs concentrations were found in dust from homes(GM:17.5 ng/g)than workplaces(11.4 ng/g)(p<0.01).The estimated daily intakes(EDIs)of ∑_(6)SPAs exposed through dust ingestion were 0.582,0.342,0.197,0.076,and 0.080 ng/kg bw/day in different age groups,and exposed through dermal contact was 0.358,0.252,0.174,0.167,and 0.177 ng/kg bw/day.EDIs showed that the exposure risks of SPAs decreased with age.This is the first work to determine SPAs in dust from10 provinces in China and investigate the spatial distribution of SPAs in those regions.展开更多
Nonperiodic interrupted sampling repeater jamming(ISRJ)against inverse synthetic aperture radar(ISAR)can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation,whi...Nonperiodic interrupted sampling repeater jamming(ISRJ)against inverse synthetic aperture radar(ISAR)can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation,which is obviously dif-ferent from the conventional multi-false-target deception jam-ming.In this paper,a suppression method against this kind of novel jamming is proposed based on inter-pulse energy function and compressed sensing theory.By utilizing the discontinuous property of the jamming in slow time domain,the unjammed pulse is separated using the intra-pulse energy function diffe-rence.Based on this,the two-dimensional orthogonal matching pursuit(2D-OMP)algorithm is proposed.Further,it is proposed to reconstruct the ISAR image with the obtained unjammed pulse sequence.The validity of the proposed method is demon-strated via the Yake-42 plane data simulations.展开更多
An efficient and practical synthetic process for Daprodustat was developed.Starting with N,N'-dicyclohexylcarbodiimide(DCC)and malonic acid,the key intermediate 1,3-dicyclohexylpyrimidine-2,4,6(1H,3H,5H)-trione wa...An efficient and practical synthetic process for Daprodustat was developed.Starting with N,N'-dicyclohexylcarbodiimide(DCC)and malonic acid,the key intermediate 1,3-dicyclohexylpyrimidine-2,4,6(1H,3H,5H)-trione was synthesized via condensation reaction with 91%yield.Subsequent activation of this intermediate by 1,1'-carbonyldiimidazole(CDI),followed by a one-pot reaction with glycine ethyl ester hydrochloride,directly afforded Daprodustat in 92%yield with>99.8%HPLC purity.The process achieved an overall yield of 84%upon validation at 62-gram scale.Structural confirmation of the key intermediate was accomplished through nuclear magnetic resonance(NMR)spectroscopy and high-resolution mass spectrometry(HRMS).Compared with existing methods,this streamlined protocol demonstrates advantages including simplified operation,reduced reaction time,and lower production costs,offering significant potential for industrial-scale synthesis of Daprodustat.展开更多
The use of synthetic biology technology to develop cosmetic ingredients is attracting widespread attention due to its effectiveness,safety,and environmental friendliness.This article explains the concept of synthetic ...The use of synthetic biology technology to develop cosmetic ingredients is attracting widespread attention due to its effectiveness,safety,and environmental friendliness.This article explains the concept of synthetic biology and its key technologies and current status in the production of cosmetic ingredients.It also briefly analyzes the regulatory approaches to synthetic biology-based cosmetic ingredients in different countries and regions,providing guidance for the management of this field in China.The goal is to ensure product safety,enhance consumer trust,and promote the healthy development of the industry.展开更多
There is a growing body of clinical research on the utility of synthetic data derivatives,an emerging research tool in medicine.In nephrology,clinicians can use machine learning and artificial intelligence as powerful...There is a growing body of clinical research on the utility of synthetic data derivatives,an emerging research tool in medicine.In nephrology,clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient privacy.This is especially important given the epidemiology of chronic kidney disease,renal oncology,and hypertension worldwide.However,there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.展开更多
Imbalanced multiclass datasets pose challenges for machine learning algorithms.They often contain minority classes that are important for accurate predictions.However,when the data is sparsely distributed and overlaps...Imbalanced multiclass datasets pose challenges for machine learning algorithms.They often contain minority classes that are important for accurate predictions.However,when the data is sparsely distributed and overlaps with data points fromother classes,it introduces noise.As a result,existing resamplingmethods may fail to preserve the original data patterns,further disrupting data quality and reducingmodel performance.This paper introduces Neighbor Displacement-based Enhanced Synthetic Oversampling(NDESO),a hybridmethod that integrates a data displacement strategy with a resampling technique to achieve data balance.It begins by computing the average distance of noisy data points to their neighbors and adjusting their positions toward the center before applying random oversampling.Extensive evaluations compare 14 alternatives on nine classifiers across synthetic and 20 real-world datasetswith varying imbalance ratios.This evaluation was structured into two distinct test groups.First,the effects of k-neighbor variations and distance metrics are evaluated,followed by a comparison of resampled data distributions against alternatives,and finally,determining the most suitable oversampling technique for data balancing.Second,the overall performance of the NDESO algorithm was assessed,focusing on G-mean and statistical significance.The results demonstrate that our method is robust to a wide range of variations in these parameters and the overall performance achieves an average G-mean score of 0.90,which is among the highest.Additionally,it attains the lowest mean rank of 2.88,indicating statistically significant improvements over existing approaches.This advantage underscores its potential for effectively handling data imbalance in practical scenarios.展开更多
Resilience studies for water distribution systems(WDS)coupled with other interdependent infrastructure systems attract increasing attention from stakeholders and researchers.However,most existing large-scale WDS model...Resilience studies for water distribution systems(WDS)coupled with other interdependent infrastructure systems attract increasing attention from stakeholders and researchers.However,most existing large-scale WDS models are single infrastructure-based without consideration of other infrastructure systems.This is due to a lack of needed information on systems coupling,the structure of the simulator used,and the computation load involved.To address these gaps,this paper presents a synthetic modeling framework for a real-world WDS as coordinating with other infrastructure systems via a building-mediated clustering approach through consideration of physical distance and node capacity.First,the WDS network topology and operation parameters are inferred via bulk open-source information.A building-mediated clustering approach is designed to systematically derive the interdependence between the WDS and the power system similarly created as a case study.Second,a novel linearization method is developed in formulating the WDS model that can relieve computation load while maintaining accuracy.Finally,a disruption-recovery framework is developed to demonstrate the proposed methodology in modelling WDS resilience.The framework is applied to a neighborhood in Queenstown,Singapore,an area of 20.43 km^(2) and 96,000 population.The near-real-time simulations on the coupled system involving 308 nodes and 384 links showcase the effectiveness and application of the proposed synthetic modeling and formulation.展开更多
The earthquake early warning(EEW)system provides advance notice of potentially damaging ground shaking.In EEW,early estimation of magnitude is crucial for timely rescue operations.A set of thirty-four features is extr...The earthquake early warning(EEW)system provides advance notice of potentially damaging ground shaking.In EEW,early estimation of magnitude is crucial for timely rescue operations.A set of thirty-four features is extracted using the primary wave earthquake precursor signal and site-specific information.In Japan's earthquake magnitude dataset,there is a chance of a high imbalance concerning the earthquakes above strong impact.This imbalance causes a high prediction error while training advanced machine learning or deep learning models.In this work,Conditional Tabular Generative Adversarial Networks(CTGAN),a deep machine learning tool,is utilized to learn the characteristics of the first arrival of earthquake P-waves and generate a synthetic dataset based on this information.The result obtained using actual and mixed(synthetic and actual)datasets will be used for training the stacked ensemble magnitude prediction model,MagPred,designed specifically for this study.There are 13295,3989,and1710 records designated for training,testing,and validation.The mean absolute error of the test dataset for single station magnitude detection using early three,four,and five seconds of P wave are 0.41,0.40,and 0.38 MJMA.The study demonstrates that the Generative Adversarial Networks(GANs)can provide a good result for single-station magnitude prediction.The study can be effective where less seismic data is available.The study shows that the machine learning method yields better magnitude detection results compared with the several regression models.The multi-station magnitude prediction study has been conducted on prominent Osaka,Off Fukushima,and Kumamoto earthquakes.Furthermore,to validate the performance of the model,an inter-region study has been performed on the earthquakes of the India or Nepal region.The study demonstrates that GANs can discover effective magnitude estimation compared with non-GAN-based methods.This has a high potential for wide application in earthquake early warning systems.展开更多
Synthetic biology(SynBio)is an emerging field of study with great potential in designing,engineering,and constructing new microbial synthetic cells that do not pre-exist in nature or re-engineering existing cells to a...Synthetic biology(SynBio)is an emerging field of study with great potential in designing,engineering,and constructing new microbial synthetic cells that do not pre-exist in nature or re-engineering existing cells to accomplish industrial purposes.Systems biology seeks to understand biology at multiple dimensions,beginning with the molecular and cellular level and progressing to the tissues and organismal level and characterizes cells as complex information-processing systems.SynBio,on the other hand,toggles further and strives to develop and create its systems from scratch.SynBio is now applied in the development of novel therapeutic drugs for the prevention of human diseases,scale up industrial processes,and accomplish previously unfeasible industrial outcomes.This is made possible through significant breakthroughs in DNA sequencing and synthesis technology,as well as insights gained from synthetic chemistry and systems biology.SynBio technologies have allowed for the introduction of improved and synthetic metabolic functionalities in microorganisms to enable the synthesis of a range of pharmacologically-relevant compounds for pharmaceutical exploration.SynBio applications range from finding new ways to making industrial chemical synthesis processes more sustainable as well as the microbial synthesis of improved therapeutic modalities.Hence,this study underpins several innovations,auspicious potentials,and future directions afforded by SynBio that proposes improved industrial microbial synthesis for pharmaceutical exploration.展开更多
Synthetic phenolic antioxidants(SPAs)and parabens,particularly the high-molecularweight(HMW)SPAs and long alkyl chain(LAC)parabens with higher environmental persistence and toxicities,are widely produced and applied w...Synthetic phenolic antioxidants(SPAs)and parabens,particularly the high-molecularweight(HMW)SPAs and long alkyl chain(LAC)parabens with higher environmental persistence and toxicities,are widely produced and applied worldwide.However,their occurrence and potential risks in aquatic environments remain largely unknown.This work investigated 11 HMW SPAs and 3 LAC parabens in the river and treated industrial wastewater samples along the Yangtze River,the largest river and most important source water in China.For convenience of comparison,6 short alkyl chain(SAC)parabens were also included.In 15 industrial wastewater treatment plant effluents(effluent-IWTPs)and 34 surface water monitoring sections along the river,19 out of 20 target compounds were detected with total concentrations(TCs)from 152.5 to 1955.5 ng/L and 141.3 to 1364.9 ng/L in effluent-IWTPs and surface water,respectively.HMW SPAs 1,3,5-tris-[(3,5-di–tert–butyl–4-hydroxyphenyl)methyl]-1,3,5-triazinane-2,4,6-trione(AO3114)and octadecyl 3-(3,5-di–tert–butyl–4-hydroxyphenyl)propionate(AO1076)were the dominant ones.HMWSPAs bis(3–tert–butyl–4–hydroxy-5-methylphenyl)propionate(AO245)and 4,4'-sulfanediylbis(2–tert–butyl–5-methylphenol)(AO-TBM6)and SAC parabens propylparaben(PrP)and butylparaben(BuP)posed medium to high potential ecological risks in 27 surface water monitoring sections.Additionally,AO-TBM6 also exhibited potential health risks in 2 out of 11 drinking water sources.Though the ecotoxicity data are very limited,the ubiquitous presence of the LAC parabens including hexylparaben(HeP)and octylparaben(OcP)in surface water should be of concern since these compounds usually exhibit stronger estrogen potencies than the SAC ones.Finally,kernel density analysis revealed that regulation of industrial discharges is necessary to mitigate the HMW SPA and paraben contamination.展开更多
Synthetic antioxidants(SAs)are additives used to inhibit the oxidative degradation of materials.Due to their potential toxicity to humans,studies on identifying human exposure pathways of SAs are important.Herein,a br...Synthetic antioxidants(SAs)are additives used to inhibit the oxidative degradation of materials.Due to their potential toxicity to humans,studies on identifying human exposure pathways of SAs are important.Herein,a broad range of SAs were analyzed in hand wipe samples collected before and after basketball,with 37 SAs detected.Playing basketball significantly increases the concentration of SAs in human hands,with a median concentration ofΣSAs increased from 629 ng/wipe before playing basketball to 1.51×103 ng/wipe after playing basketball(p<0.05).Tris(2,4-di-tert-butylphenyl)phosphate(AO168O)was the predominant chemical,with the median concentration enhanced from 310 to 767 ng/wipe.The estimated daily exposure via the dermal exposure pathway of SAs was assessed to be 19.6 ng/kg bw/day after basketball,indicating minimal risks.Handwashing experiments demonstrated that most SAs can be removed from hands with tap water(removal efficiency:19.4−34.0%)and liquid soap(removal efficiency:32.3−81.8%)(p<0.05),while the removal efficiency of AO168O was low,contributing to its high residual levels in human hands.This is the first study to elucidate the dermal exposure to SAs via playing basketball,further indicating the importance of washing hands to reduce SA exposure.展开更多
Amplitude stripes imposed by ionospheric scintillation have been frequently observed in many of the equatorial nighttime acquisitions of the Advanced Land Observing Satellite(ALOS)Phased Array-type L-band Synthetic Ap...Amplitude stripes imposed by ionospheric scintillation have been frequently observed in many of the equatorial nighttime acquisitions of the Advanced Land Observing Satellite(ALOS)Phased Array-type L-band Synthetic Aperture Radar(PALSAR).This type of ionospheric artifact impedes PALSAR interferometric and polarimetric applications,and its formation cause,morphology,and negative influence have been deeply investigated.However,this artifact can provide an alternative opportunity in a positive way for probing and measuring ionosphere scintillation.In this paper,a methodology for measuring ionospheric scintillation parameters from PALSAR images with amplitude stripes is proposed.Firstly,sublook processing is beneficial for recovering the scattered stripes from a single-look complex image;the amplitude stripe pattern is extracted via band-rejection filtering in the frequency domain of the sublook image.Secondly,the amplitude spectrum density function(SDF)is estimated from the amplitude stripe pattern.Thirdly,a fitting scheme for measuring the scintillation strength and spectrum index is conducted between the estimated and theoretical long-wavelength SDFs.In addition,another key parameter,the scintillation index,can be directly measured from the amplitude stripe pattern or indirectly derived from the scintillation strength and spectrum index.The proposed methodology is fully demonstrated on two groups of PALSAR acquisitions in the presence of amplitude stripes.Self-validation is conducted by comparing the measured and derived scintillation index and by comparing the measurements of range lines and azimuth lines.Cross-validation is performed by comparing the PALSAR measurements with in situ Global Position System(GPS)measurements.The processing results demonstrate a powerful capability to robustly measure ionospheric scintillation parameters from space with high spatial resolution.展开更多
The active ingredients found in medicinal plants,which are specialized secondary metabolites accumulated during specific growth stages and localized withinparticular tissues,serve as the foundational material for thei...The active ingredients found in medicinal plants,which are specialized secondary metabolites accumulated during specific growth stages and localized withinparticular tissues,serve as the foundational material for their pharmacological effects.展开更多
We identified the antimony species present in a wide variety of plastic samples by X ray absorption spectroscopy(XAS)at the Sb L_(3)-edge.The samples contained different concentrations of antimony(Sb),ranging from PET...We identified the antimony species present in a wide variety of plastic samples by X ray absorption spectroscopy(XAS)at the Sb L_(3)-edge.The samples contained different concentrations of antimony(Sb),ranging from PET bottles in which Sb compounds are used as catalysts,with concentrations around 300 mg/kg,to electrical equipment in which the element is used as a flame retardant,with concentrations of several tens of thousands of mg/kg.Although the shape of the spectra at the L_(3)-edge is quite similar for all Sb reference materials,we were able to identify antimony glycolate or acetate in PET bottles,bound organic Sb in c-PET trays and senarmontite in electrical materials as themain Sb components.In samples with high Ca content(e.g.,electrical objects,some c-PET food trays and textiles)the Ca Ka emission line interferes with the Sb La line by introducing a high background which reduces the signal-to-noise ratio in the Sb XAS spectrum,resulting in noisy and distorted spectra.The element-resolved map on a PET bottle sample revealed both Sb and Ca hot spots of around 10-20 microns in size,with no correlation.展开更多
基金financial support from National Natural Science Foundation of China(No.32401215 to HS No.2247081930 to HYJ)the non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences(No.2023-I2M-3-015)State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs(No.20240104).
文摘Medicinal plants serve as valuable sources of bioactive compounds with critical applications across pharmaceutical,agricultural,and industrial sectors.Compared to chemical synthesis and plant extraction,synthetic biology offers a green,efficient,and sustainable alternative for producing bioactive compounds,which represents a state of art technology.However,this technology still faces several challenges,including overly long metabolic pathways,inadequate catalytic efficiency of key enzymes in the pathway,and incompatibility between gene elements and host cells,leading to low yields of target bioactive compounds.The development and application of regulatory tools in synthetic biology hold great promise for overcoming these obstacles.This review first summarizes the classification and biosynthesis of bioactive compounds based on structural types.Subsequently,recent advancements are outlined in regulation tools and their application in the heterologous production of bioactive compounds.This review aims to establish a foundation for the efficient production of bioactive compounds based on microbial cell factories.This not only has significant practical implications for reducing the resource consumption and environmental impact of traditional production methods,but also highlights the central role of synthetic biology in promoting the sustainable production of bioactive compounds derived from medicinal plants.
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.
基金supported by Guangdong Power Grid Co.Ltd.Science and Technology Project(GDKJXM20231455).
文摘Accurate and timely insulator defect detection is crucial for maintaining the reliability and safety of the power supply.However,the development of deep-learning-based insulator defect detection is hindered by the scarcity of comprehensive,high-quality datasets for insulator defects.To address this gap,the synthetic insulator defect imaging and annotation(SYNTHIDIA)system was proposed.SYNTHIDIA generates synthetic defect images in a 3D virtual environment using domain randomisation,offering a cost-effective and versatile solution for creating diverse and annotated data.Our dataset includes 22,000 images with accurate pixel-level and instance-level annotations,covering broken defect and drop defect types.Through rigorous experiments,SYNTHIDIA demonstrates strong generalisation capabilities to real-world data and provides valuable insights into the impact of various domain factors on model performance.The inclusion of 3D models further supports broader research initiatives.SYNTHIDIA addresses data insufficiency in insulator defect detection and enhances model performance in data-limited scenarios,contributing significantly to the advancement of power inspection.
基金supported by the National Nat-ural Science Foundation of China(No.42376174)the Natural Science Foundation of Shanghai(No.23ZR 1426900).
文摘Synthetic aperture radar(SAR)aboard SEASAT was first launched in 1978.At the beginning of the 21st century,the Chinese remote sensing community recognized the urgent need to develop domestic SAR capabilities.Unlike scatterometers and al-timeters,space-borne SAR offers high-resolution images of the ocean,regardless of weather conditions or time of day.SAR imagery provides rich information about the sea surface,capturing complicated dynamic processes in the upper layers of the ocean,particular-ly in relation to tropical cyclones.Over the past four decades,the advantages of SAR have been increasingly recognized,leading to notable marine applications,especially in the development of algorithms for retrieving wind and wave data from SAR images.This study reviews the history,progress,and future outlook of SAR-based monitoring of sea surface wind and waves.In particular,the ap-plicability of various SAR wind and wave algorithms is systematically investigated,with a particular focus on their performance un-der extreme sea conditions.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Category B,XDB1090000).
文摘Plant synthetic biology has emerged as a transformative field in agriculture,offering innovative solutions to enhance food security,provide resilience to climate change,and transition to sustainable farming practices.By integrating advanced genetic tools,computational modeling,and systems biology,researchers can precisely modify plant genomes to enhance traits such as yield,stress tolerance,and nutrient use efficiency.The ability to design plants with specific characteristics tailored to diverse environmental conditions and agricultural needs holds great potential to address global food security challenges.Here,we highlight recent advancements and applications of plant synthetic biology in agriculture,focusing on key areas such as photosynthetic efficiency,nitrogen fixation,drought tolerance,pathogen resistance,nutrient use efficiency,biofortification,climate resilience,microbiology engineering,synthetic plant genomes,and the integration of artificial intelligence with synthetic biology.These innovations aim to maximize resource use efficiency,reduce reliance on external inputs,and mitigate environmental impacts associated with conventional agricultural practices.Despite challenges related to regulatory approval and public acceptance,the integration of synthetic biology in agriculture holds immense promise for creating more resilient and sustainable agricultural systems,contributing to global food security and environmental sustainability.Rigorous multi-field testing of these approaches will undoubtedly be required to ensure reproducibility.
基金supported by the National Key Research and Development Program of China(No.2023YFC3706602)the National Natural Science Foundation of China(Nos.22225605 and 22193051)the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB0750200).
文摘Synthetic phenolic antioxidants(SPAs)are widely used in diverse industries due to their exceptional antioxidant characteristics.However,human exposure to SPAs may cause health problems.In this study,226 dust samples were collected from 10 provinces in China,and six SPAs(three parent SPAs and their three transformation products)were analyzed.The concentrations of6SPAs(the sum of six target compounds)ranged from 15.4 to 3210 ng/g(geometric mean(GM):169 ng/g).The highest concentration of6SPAswas found in Sichuan Province(GM:349 ng/g),which was approximately 4 times higher than that in Hubei Province(81.6 ng/g)(p<0.05).The concentrations of butylated hydroxytoluene(BHT),2,2'-methylene bis(4-methyl-6–tert-butylphenol)(AO2246),2,6-di–tert–butyl–1,4-benzoquinone(BHT-Q),2,6-di–tert–butyl–4-(hydroxymethyl)phenol(BHT-OH),and ∑_(p)-SPAs were substantially higher in dust from urban areas than rural areas(p<0.05).AO2246 concentration in dust from homes(GM:0.400 ng/g)was about 4 times higher than that in workplaces(0.116 ng/g)(p<0.01).Significantly higherp-SPAs concentrations were found in dust from homes(GM:17.5 ng/g)than workplaces(11.4 ng/g)(p<0.01).The estimated daily intakes(EDIs)of ∑_(6)SPAs exposed through dust ingestion were 0.582,0.342,0.197,0.076,and 0.080 ng/kg bw/day in different age groups,and exposed through dermal contact was 0.358,0.252,0.174,0.167,and 0.177 ng/kg bw/day.EDIs showed that the exposure risks of SPAs decreased with age.This is the first work to determine SPAs in dust from10 provinces in China and investigate the spatial distribution of SPAs in those regions.
基金supported by the National Natural Science Foundation of China(62001481,61890542,62071475)the Natural Science Foundation of Hunan Province(2022JJ40561)the Research Program of National University of Defense Technology(ZK22-46).
文摘Nonperiodic interrupted sampling repeater jamming(ISRJ)against inverse synthetic aperture radar(ISAR)can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation,which is obviously dif-ferent from the conventional multi-false-target deception jam-ming.In this paper,a suppression method against this kind of novel jamming is proposed based on inter-pulse energy function and compressed sensing theory.By utilizing the discontinuous property of the jamming in slow time domain,the unjammed pulse is separated using the intra-pulse energy function diffe-rence.Based on this,the two-dimensional orthogonal matching pursuit(2D-OMP)algorithm is proposed.Further,it is proposed to reconstruct the ISAR image with the obtained unjammed pulse sequence.The validity of the proposed method is demon-strated via the Yake-42 plane data simulations.
文摘An efficient and practical synthetic process for Daprodustat was developed.Starting with N,N'-dicyclohexylcarbodiimide(DCC)and malonic acid,the key intermediate 1,3-dicyclohexylpyrimidine-2,4,6(1H,3H,5H)-trione was synthesized via condensation reaction with 91%yield.Subsequent activation of this intermediate by 1,1'-carbonyldiimidazole(CDI),followed by a one-pot reaction with glycine ethyl ester hydrochloride,directly afforded Daprodustat in 92%yield with>99.8%HPLC purity.The process achieved an overall yield of 84%upon validation at 62-gram scale.Structural confirmation of the key intermediate was accomplished through nuclear magnetic resonance(NMR)spectroscopy and high-resolution mass spectrometry(HRMS).Compared with existing methods,this streamlined protocol demonstrates advantages including simplified operation,reduced reaction time,and lower production costs,offering significant potential for industrial-scale synthesis of Daprodustat.
文摘The use of synthetic biology technology to develop cosmetic ingredients is attracting widespread attention due to its effectiveness,safety,and environmental friendliness.This article explains the concept of synthetic biology and its key technologies and current status in the production of cosmetic ingredients.It also briefly analyzes the regulatory approaches to synthetic biology-based cosmetic ingredients in different countries and regions,providing guidance for the management of this field in China.The goal is to ensure product safety,enhance consumer trust,and promote the healthy development of the industry.
文摘There is a growing body of clinical research on the utility of synthetic data derivatives,an emerging research tool in medicine.In nephrology,clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient privacy.This is especially important given the epidemiology of chronic kidney disease,renal oncology,and hypertension worldwide.However,there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.
文摘Imbalanced multiclass datasets pose challenges for machine learning algorithms.They often contain minority classes that are important for accurate predictions.However,when the data is sparsely distributed and overlaps with data points fromother classes,it introduces noise.As a result,existing resamplingmethods may fail to preserve the original data patterns,further disrupting data quality and reducingmodel performance.This paper introduces Neighbor Displacement-based Enhanced Synthetic Oversampling(NDESO),a hybridmethod that integrates a data displacement strategy with a resampling technique to achieve data balance.It begins by computing the average distance of noisy data points to their neighbors and adjusting their positions toward the center before applying random oversampling.Extensive evaluations compare 14 alternatives on nine classifiers across synthetic and 20 real-world datasetswith varying imbalance ratios.This evaluation was structured into two distinct test groups.First,the effects of k-neighbor variations and distance metrics are evaluated,followed by a comparison of resampled data distributions against alternatives,and finally,determining the most suitable oversampling technique for data balancing.Second,the overall performance of the NDESO algorithm was assessed,focusing on G-mean and statistical significance.The results demonstrate that our method is robust to a wide range of variations in these parameters and the overall performance achieves an average G-mean score of 0.90,which is among the highest.Additionally,it attains the lowest mean rank of 2.88,indicating statistically significant improvements over existing approaches.This advantage underscores its potential for effectively handling data imbalance in practical scenarios.
文摘Resilience studies for water distribution systems(WDS)coupled with other interdependent infrastructure systems attract increasing attention from stakeholders and researchers.However,most existing large-scale WDS models are single infrastructure-based without consideration of other infrastructure systems.This is due to a lack of needed information on systems coupling,the structure of the simulator used,and the computation load involved.To address these gaps,this paper presents a synthetic modeling framework for a real-world WDS as coordinating with other infrastructure systems via a building-mediated clustering approach through consideration of physical distance and node capacity.First,the WDS network topology and operation parameters are inferred via bulk open-source information.A building-mediated clustering approach is designed to systematically derive the interdependence between the WDS and the power system similarly created as a case study.Second,a novel linearization method is developed in formulating the WDS model that can relieve computation load while maintaining accuracy.Finally,a disruption-recovery framework is developed to demonstrate the proposed methodology in modelling WDS resilience.The framework is applied to a neighborhood in Queenstown,Singapore,an area of 20.43 km^(2) and 96,000 population.The near-real-time simulations on the coupled system involving 308 nodes and 384 links showcase the effectiveness and application of the proposed synthetic modeling and formulation.
基金related to grant PM-31-22-626-414 from the Prime Minister's Research Fellows(PMRF)of the Indian Institute of Technology Roorkee。
文摘The earthquake early warning(EEW)system provides advance notice of potentially damaging ground shaking.In EEW,early estimation of magnitude is crucial for timely rescue operations.A set of thirty-four features is extracted using the primary wave earthquake precursor signal and site-specific information.In Japan's earthquake magnitude dataset,there is a chance of a high imbalance concerning the earthquakes above strong impact.This imbalance causes a high prediction error while training advanced machine learning or deep learning models.In this work,Conditional Tabular Generative Adversarial Networks(CTGAN),a deep machine learning tool,is utilized to learn the characteristics of the first arrival of earthquake P-waves and generate a synthetic dataset based on this information.The result obtained using actual and mixed(synthetic and actual)datasets will be used for training the stacked ensemble magnitude prediction model,MagPred,designed specifically for this study.There are 13295,3989,and1710 records designated for training,testing,and validation.The mean absolute error of the test dataset for single station magnitude detection using early three,four,and five seconds of P wave are 0.41,0.40,and 0.38 MJMA.The study demonstrates that the Generative Adversarial Networks(GANs)can provide a good result for single-station magnitude prediction.The study can be effective where less seismic data is available.The study shows that the machine learning method yields better magnitude detection results compared with the several regression models.The multi-station magnitude prediction study has been conducted on prominent Osaka,Off Fukushima,and Kumamoto earthquakes.Furthermore,to validate the performance of the model,an inter-region study has been performed on the earthquakes of the India or Nepal region.The study demonstrates that GANs can discover effective magnitude estimation compared with non-GAN-based methods.This has a high potential for wide application in earthquake early warning systems.
文摘Synthetic biology(SynBio)is an emerging field of study with great potential in designing,engineering,and constructing new microbial synthetic cells that do not pre-exist in nature or re-engineering existing cells to accomplish industrial purposes.Systems biology seeks to understand biology at multiple dimensions,beginning with the molecular and cellular level and progressing to the tissues and organismal level and characterizes cells as complex information-processing systems.SynBio,on the other hand,toggles further and strives to develop and create its systems from scratch.SynBio is now applied in the development of novel therapeutic drugs for the prevention of human diseases,scale up industrial processes,and accomplish previously unfeasible industrial outcomes.This is made possible through significant breakthroughs in DNA sequencing and synthesis technology,as well as insights gained from synthetic chemistry and systems biology.SynBio technologies have allowed for the introduction of improved and synthetic metabolic functionalities in microorganisms to enable the synthesis of a range of pharmacologically-relevant compounds for pharmaceutical exploration.SynBio applications range from finding new ways to making industrial chemical synthesis processes more sustainable as well as the microbial synthesis of improved therapeutic modalities.Hence,this study underpins several innovations,auspicious potentials,and future directions afforded by SynBio that proposes improved industrial microbial synthesis for pharmaceutical exploration.
基金supported by the National Key Research and Development Program of China(Nos.2021YFC3200801 and 2021YFC3200804).
文摘Synthetic phenolic antioxidants(SPAs)and parabens,particularly the high-molecularweight(HMW)SPAs and long alkyl chain(LAC)parabens with higher environmental persistence and toxicities,are widely produced and applied worldwide.However,their occurrence and potential risks in aquatic environments remain largely unknown.This work investigated 11 HMW SPAs and 3 LAC parabens in the river and treated industrial wastewater samples along the Yangtze River,the largest river and most important source water in China.For convenience of comparison,6 short alkyl chain(SAC)parabens were also included.In 15 industrial wastewater treatment plant effluents(effluent-IWTPs)and 34 surface water monitoring sections along the river,19 out of 20 target compounds were detected with total concentrations(TCs)from 152.5 to 1955.5 ng/L and 141.3 to 1364.9 ng/L in effluent-IWTPs and surface water,respectively.HMW SPAs 1,3,5-tris-[(3,5-di–tert–butyl–4-hydroxyphenyl)methyl]-1,3,5-triazinane-2,4,6-trione(AO3114)and octadecyl 3-(3,5-di–tert–butyl–4-hydroxyphenyl)propionate(AO1076)were the dominant ones.HMWSPAs bis(3–tert–butyl–4–hydroxy-5-methylphenyl)propionate(AO245)and 4,4'-sulfanediylbis(2–tert–butyl–5-methylphenol)(AO-TBM6)and SAC parabens propylparaben(PrP)and butylparaben(BuP)posed medium to high potential ecological risks in 27 surface water monitoring sections.Additionally,AO-TBM6 also exhibited potential health risks in 2 out of 11 drinking water sources.Though the ecotoxicity data are very limited,the ubiquitous presence of the LAC parabens including hexylparaben(HeP)and octylparaben(OcP)in surface water should be of concern since these compounds usually exhibit stronger estrogen potencies than the SAC ones.Finally,kernel density analysis revealed that regulation of industrial discharges is necessary to mitigate the HMW SPA and paraben contamination.
基金supported by the National Natural Science Foundation of China(NSFC 22376119,22206113)Shandong Provincial Natural Science Foundation,China(ZR2023JQ007)Taishan Scholars Project Special Fund(No.tsqn202211039).
文摘Synthetic antioxidants(SAs)are additives used to inhibit the oxidative degradation of materials.Due to their potential toxicity to humans,studies on identifying human exposure pathways of SAs are important.Herein,a broad range of SAs were analyzed in hand wipe samples collected before and after basketball,with 37 SAs detected.Playing basketball significantly increases the concentration of SAs in human hands,with a median concentration ofΣSAs increased from 629 ng/wipe before playing basketball to 1.51×103 ng/wipe after playing basketball(p<0.05).Tris(2,4-di-tert-butylphenyl)phosphate(AO168O)was the predominant chemical,with the median concentration enhanced from 310 to 767 ng/wipe.The estimated daily exposure via the dermal exposure pathway of SAs was assessed to be 19.6 ng/kg bw/day after basketball,indicating minimal risks.Handwashing experiments demonstrated that most SAs can be removed from hands with tap water(removal efficiency:19.4−34.0%)and liquid soap(removal efficiency:32.3−81.8%)(p<0.05),while the removal efficiency of AO168O was low,contributing to its high residual levels in human hands.This is the first study to elucidate the dermal exposure to SAs via playing basketball,further indicating the importance of washing hands to reduce SA exposure.
基金supported partly by the National Natural Science Foundation of China(NSFC)(62101568 and 62371460)the Scientific Research Program of the National University of Defense Technology(ZK21-06)the Taishan Scholars of Shandong Province(ts20190968)。
文摘Amplitude stripes imposed by ionospheric scintillation have been frequently observed in many of the equatorial nighttime acquisitions of the Advanced Land Observing Satellite(ALOS)Phased Array-type L-band Synthetic Aperture Radar(PALSAR).This type of ionospheric artifact impedes PALSAR interferometric and polarimetric applications,and its formation cause,morphology,and negative influence have been deeply investigated.However,this artifact can provide an alternative opportunity in a positive way for probing and measuring ionosphere scintillation.In this paper,a methodology for measuring ionospheric scintillation parameters from PALSAR images with amplitude stripes is proposed.Firstly,sublook processing is beneficial for recovering the scattered stripes from a single-look complex image;the amplitude stripe pattern is extracted via band-rejection filtering in the frequency domain of the sublook image.Secondly,the amplitude spectrum density function(SDF)is estimated from the amplitude stripe pattern.Thirdly,a fitting scheme for measuring the scintillation strength and spectrum index is conducted between the estimated and theoretical long-wavelength SDFs.In addition,another key parameter,the scintillation index,can be directly measured from the amplitude stripe pattern or indirectly derived from the scintillation strength and spectrum index.The proposed methodology is fully demonstrated on two groups of PALSAR acquisitions in the presence of amplitude stripes.Self-validation is conducted by comparing the measured and derived scintillation index and by comparing the measurements of range lines and azimuth lines.Cross-validation is performed by comparing the PALSAR measurements with in situ Global Position System(GPS)measurements.The processing results demonstrate a powerful capability to robustly measure ionospheric scintillation parameters from space with high spatial resolution.
文摘The active ingredients found in medicinal plants,which are specialized secondary metabolites accumulated during specific growth stages and localized withinparticular tissues,serve as the foundational material for their pharmacological effects.
文摘We identified the antimony species present in a wide variety of plastic samples by X ray absorption spectroscopy(XAS)at the Sb L_(3)-edge.The samples contained different concentrations of antimony(Sb),ranging from PET bottles in which Sb compounds are used as catalysts,with concentrations around 300 mg/kg,to electrical equipment in which the element is used as a flame retardant,with concentrations of several tens of thousands of mg/kg.Although the shape of the spectra at the L_(3)-edge is quite similar for all Sb reference materials,we were able to identify antimony glycolate or acetate in PET bottles,bound organic Sb in c-PET trays and senarmontite in electrical materials as themain Sb components.In samples with high Ca content(e.g.,electrical objects,some c-PET food trays and textiles)the Ca Ka emission line interferes with the Sb La line by introducing a high background which reduces the signal-to-noise ratio in the Sb XAS spectrum,resulting in noisy and distorted spectra.The element-resolved map on a PET bottle sample revealed both Sb and Ca hot spots of around 10-20 microns in size,with no correlation.
