In this paper our studies about the sequential testing program for predicting and identificating carcinogens, sequential discriminant method and cost- effectiveness analysis are summarized. The analysis of our databas...In this paper our studies about the sequential testing program for predicting and identificating carcinogens, sequential discriminant method and cost- effectiveness analysis are summarized. The analysis of our database of carcinogeniclty and genotoxicity of chemicals demonstrates the uncertainty . of short- term tests ( STTs ) to predict carcinogens and the results of most routine STTs are statistically dependent. We recommend the sequential testing program combining STTs and carclnogenicity assay, the optimal STT batteries, the rules of the sequential discrimination and the preferal choices of STTs tor specific chemical class. For illustrative pmposes the carclnogenicity prediction of several sample chamicals is presented. The results of cost-effectiveness analysis suggest that this program has vast social-economic effectiveness.展开更多
In wireless sensor networks,ensuring communication security via specific emitter identification(SEI)is crucial.However,existing SEI methods are limited to closed-set scenarios and lack the ability to detect unknown de...In wireless sensor networks,ensuring communication security via specific emitter identification(SEI)is crucial.However,existing SEI methods are limited to closed-set scenarios and lack the ability to detect unknown devices and perform classincremental training.This study proposes a class-incremental open-set SEI approach.The open-set SEI model calculates radiofrequency fingerprints(RFFs)prototypes for known signals and employs a self-attention mechanism to enhance their discriminability.Detection thresholds are set through Gaussian fitting for each class.For class-incremental learning,the algorithm freezes the parameters of the previously trained model to initialize the new model.It designs specific losses:the RFFs extraction distribution difference loss and the prototype transformation distribution difference loss,which force the new model to retain old knowledge while learning new knowledge.The training loss enables learning of new class RFFs.Experimental results demonstrate that the open-set SEI model achieves state-of-theart performance and strong noise robustness.Moreover,the class-incremental learning algorithm effectively enables the model to retain old device RFFs knowledge,acquire new device RFFs knowledge,and detect unknown devices simultaneously.展开更多
The genus Actinidia is primarily functionally dioecious,and early sex identification plays a crucial role in improving breeding efficiency and reducing production costs.In this study,the accuracy of three sex-linked m...The genus Actinidia is primarily functionally dioecious,and early sex identification plays a crucial role in improving breeding efficiency and reducing production costs.In this study,the accuracy of three sex-linked molecular markers(SyGI[Shy Girl],FrBy[Friendly Boy],and SmY1)in sex identification was evaluated in various Actinidia species.The selected marker products were subsequently cloned and sequenced in six wild Actinidia species.Ninety-six wild A.chinensis chinensis accessions and 74 A.chinensis deliciosa accessions,most of which were wild,with only one cultivated,were used for comprehensive primer validation.Thirty-three juvenile A.chinensis chinensis hybrid seedlings were used for practical application tests.The results showed that the marker SyGI accurately identified the sex of 20 samples from six Actinidia species and 96 A.chinensis chinensis accessions with 100%reliability.For Actinidia chinensis deliciosa,the identification accuracy reached 98.65%.Sequence analysis revealed that SyGI shared the highest similarity with the male-specific genomic region.Furthermore,SyGI achieved 100%accuracy in identifying the sex of 33 juvenile A.chinensis chinensis individuals.The findings confirm that the SyGI marker possesses high accuracy,strong specificity,and broad applicability,making it a valuable tool for kiwifruit breeding programs.The cloned sequences from wild Actinidia species also provide important references for future research on the mechanisms of sexual evolution and determination.展开更多
Watermelon(Citrullus lanatus) is sensitive to salt stress. For breeding applications, it is of great significance to explore the genetic mechanism underlying salt tolerance in watermelon by analyzing the dehydration r...Watermelon(Citrullus lanatus) is sensitive to salt stress. For breeding applications, it is of great significance to explore the genetic mechanism underlying salt tolerance in watermelon by analyzing the dehydration responsive element-binding(DREB) factor family members.However, they are rarely studied in watermelon. In this study, we identified ClaDREB gene family members in watermelon based on whole genome data;analyzed the physicochemical properties, evolution, and phylogeny;and studied their expression patterns under salt stress in two watermelon varieties with varying salt tolerance. In total, 57 DREB family members were identified in watermelon, and most of them were located in the nucleus. ClaDREBs were divided into six subgroups Ⅰ-Ⅵ. The promoter region of ClaDREBs from subgroup Ⅱ contained many defense-related and stress responsive elements. Among them, ClaDREB14 was significantly upregulated by salt stress and exhibited differential expression in salt-tolerant and salt-sensitive varieties. Moreover, overexpression of ClaDREB14 in watermelon roots significantly improved the salt tolerance of transgenic plants;mainly, it significantly increased the activities of POD, SOD, and CAT and significantly reduced MDA content.However, the results from gene-edited watermelon roots obtained using CRISPR/Cas9 vectors showed the opposite trend. Furthermore, we demonstrated that ClaDREB14 directly binds to the cis-acting element ACCGAC in the promoter region of ClaPOD6 and promotes its expression.Therefore, ClaDREB14 may enhance salt tolerance by increasing the activity of antioxidant enzymes in watermelon roots. This study provided valuable information on the DREB gene family in watermelon and laid the foundation for future functional validation and genetic engineering applications.展开更多
High-throughput single nucleotide polymorphism(SNP) arrays have emerged as essential genotyping tools,significantly accelerating breeding programs and advancing basic research.In this study,a high-throughput 10K SNP g...High-throughput single nucleotide polymorphism(SNP) arrays have emerged as essential genotyping tools,significantly accelerating breeding programs and advancing basic research.In this study,a high-throughput 10K SNP genotyping array for wax gourd was developed using genotyping by target sequencing(GBTS),featuring 10,722 SNPs evenly distributed across all 12 chromosomes,including 278 functional loci associated with key economic traits.To demonstrate its utility,genetic distances among 19 elite inbred lines were calculated from SNP data and correlated with heterosis for single fruit weight.The results revealed that greater genetic distance was associated with higher middle parent heterosis(MPH) for single fruit weight.Furthermore,56 commercial wax gourd cultivars collected from eight regions were selected and genotyped.Population structure analysis,phylogenetic analysis,and principal component analysis(PCA) collectively indicated that these cultivars fall into two major groups.Group I,comprising black or dark green skinned wax gourds,exhibited lower genetic diversity than Group II,which includes green or light green skinned varieties,reflecting shorter genetic distances within Group I.Finally,60 polymorphic SNPs were used to construct DNA fingerprints for distinguishing the 56 cultivars.As the first high-throughput genotyping platform for wax gourd,this SNP array provides an effective and powerful tool for genetic analysis.展开更多
Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely id...Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters.展开更多
The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches ...The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches often suffer from reduced accuracy under dynamically uncertain state-of-charge(SOC)operating ranges and heterogeneous aging stresses.This study presents a unified SOH estimation framework that integrates physics-informed modeling,subspace identification,and Transformer-based learning.A reduced-order model is derived from simplified electrochemical dynamics,providing an interpretable and computationally efficient representation of battery behavior.Subspace identification across a wide SOC and SOH range yields degradation-sensitive features,which the Transformer uses to capture long-range aging dynamics via multi-head self-attention.Experiments on LiFePO4 cells under joint-cell training show consistently accurate SOH estimation,with a maximum error of 1.39%,demonstrating the framework’s effectiveness in decoupling SOC and SOH effects.In cross-cell validation,where training and validation are performed on different cells,the model maintains a maximum error of 2.06%,confirming strong generalization to unseen aging trajectories.Comparative experiments on LiFePO_(4)and public LiCoO_(2)datasets confirm the framework’s cross-chemistry applicability.By extracting low-dimensional,physically interpretable features via subspace identification,the framework significantly reduces training cost while maintaining high SOH estimation accuracy,outperforming conventional data-driven models lacking physical guidance.展开更多
The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermo...The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.展开更多
Selectivity remains a significant challenge for gas sensors. In contrast to conventional gas sensors that depend solely on conductivity to detect gases, we exploited a single NiO-doped SnO_(2) sensor to simultaneously...Selectivity remains a significant challenge for gas sensors. In contrast to conventional gas sensors that depend solely on conductivity to detect gases, we exploited a single NiO-doped SnO_(2) sensor to simultaneously monitor transient changes in both sensor conductivity and temperature. The distinct response profiles of H_(2) and NH_(3) gases were attributed to differences in their redox rates and enthalpy changes during chemical reactions, which provided an opportunity for gas identification using machine learning(ML) algorithms. The test results indicate that preprocessing the extracted calorimetric and chemi-resistive parameters using the principal component analysis(PCA), followed by the application of ML classifiers for identification,enables a 100% accuracy for both target analytes. This work presents a facile gas identification method that enhances chiplevel sensor applications while minimizing the need for complex sensor arrays.展开更多
Background:The medicinal material known as Os Draconis(Longgu)originates from fossilized remains of ancient mammals and is widely used in treating emotional and mental conditions.However,fossil resources are nonrenewa...Background:The medicinal material known as Os Draconis(Longgu)originates from fossilized remains of ancient mammals and is widely used in treating emotional and mental conditions.However,fossil resources are nonrenewable,and clinical demand is increasingly difficult to meet,leading to a proliferation of counterfeit products.During prolonged geological burial,static pressure from the surrounding strata severely compromises the microstructural integrity of osteons in Os Draconis,but Os Draconis still largely retains the structural features of mammalian bone.Methods:Using verified authentic Os Draconis samples over 10,000 years old as a baseline,this study summarizes the ultrastructural characteristics of genuine Os Draconis.Employing electron probe microanalysis and optical polarized light microscopy,we examined 28 batches of authentic Os Draconis and 31 batches of counterfeits to identify their ultrastructural differences.Key points for ultrastructural identification of Os Draconis were compiled,and a new identification approach was proposed based on these differences.Results:Authentic Os Draconis exhibited distinct ultrastructural markers:irregularly shaped osteons with traversing fissures,deformed/displaced Haversian canals,and secondary mineral infill(predominantly calcium carbonate).Counterfeits showed regular osteon arrangements,absent traversal fissures,and homogeneous hydroxyapatite composition.Lab-simulated samples lacked structural degradation features.EPMA confirmed calcium carbonate infill in fossilized Haversian canals,while elemental profiles differentiated lacunae types(void vs.mineral-packed).Conclusion:The study established ultrastructural criteria for authentic Os Draconis identification:osteon deformation,geological fissures penetrating bone units,and heterogenous mineral deposition.These features,unattainable in counterfeits or modern processed bones,provide a cost-effective,accurate identification method.This approach bridges gaps in TCM material standardization and supports quality control for clinical applications.展开更多
Petroleum leakage is a major groundwater contamination source,with chemical composition of water soluble fractions(WSFs)from diverse oil sources significantly impacting groundwater quality and source identification.Th...Petroleum leakage is a major groundwater contamination source,with chemical composition of water soluble fractions(WSFs)from diverse oil sources significantly impacting groundwater quality and source identification.The aim of this study was to assess impact of 15 diverse oils on groundwater quality and environmental forensics based on oil-water equilibrium experiments.Our results indicate that contamination of groundwater by gasoline and naphtha is primarily attributed to volatile hydrocarbons,while pollution from diesel,kerosene,and crude oil is predominantly from non-hydrocarbons.Rapid determination of the extent of non-hydrocarbon pollution in WSFs was achieved through a new quantitative index.Gasoline and naphtha exhibited the highest groundwater contamination potential while kerosene and light crude oils were also likely to cause groundwater contamina-tion.Although volatile hydrocarbons in the WSFs of diesel and jet fuel do not easily exceed current regulatory standards,unregulated non-hydrocarbons may pose a more severe contamination risk to groundwater.Notably,the presence of significant benzene and toluene,hydrogenation and alkylation products(e.g.,C4-C5 alkylben-zenes,alkylindenes,alkyltetralins,and dihydro-indenes),cycloalkanes in WSFs can effectively be utilized for preliminary source identification of light distillates,middle distillates,and crude oils,respectively.展开更多
Industrial robot dynamics lay the foundation for high-precision and high-speed control, and accurate identification of dynamic parameters is essential for precise dynamic calculations. The choice of friction models is...Industrial robot dynamics lay the foundation for high-precision and high-speed control, and accurate identification of dynamic parameters is essential for precise dynamic calculations. The choice of friction models is a critical component in the identification of industrial robot dynamics. Traditional static friction models struggle to capture the hysteresis effects caused by robot joint elasticity and clearances, leading to large torque prediction errors when the joint velocity crosses zero. Due to the presence of hysteresis effects, the joint velocity crosses zero in the forward direction, and the reverse direction will have different friction patterns. Although the hysteresis effects can be modeled as an ordinary differential equation(ODE), it is difficult to determine the ODE structure that achieves both generalization and accuracy to describe the hysteresis effects of the friction model. To address this issue, we propose the neural hysteresis friction(NHF), which uses neural ODE to model the hysteresis effects in a data-driven manner, thereby mitigating the current inadequacies in the study of dynamic friction characteristics. The experiments on a real 6-axis industrial robot demonstrate that our proposed method can accurately model the friction dynamics during directional switching and outperform other modeling methods. Velocity tracking control experiments show that NHF can effectively reduce tracking errors when the velocity crosses zero.展开更多
Simultaneous identification and quantitative detection of phenylenediamine(PDA)isomers,including o-phenylenediamine(OPD),m-phenylenediamine(MPD),and p-phenylenediamine(PPD),are essential for environmental risk assessm...Simultaneous identification and quantitative detection of phenylenediamine(PDA)isomers,including o-phenylenediamine(OPD),m-phenylenediamine(MPD),and p-phenylenediamine(PPD),are essential for environmental risk assessment and human health protection.However,current visual detection methods can only distinguish individual PDA isomers and failed to identify binary or ternary mixtures.Herein,a highly active and ultrastable peroxidase(POD)-like CoPt graphitic nanozyme was used for naked-eye identification and colorimetric/fluorescent(FL)dual-mode quantitative detection of PDA isomers.The CoPt@G nanozyme effectively catalyzed the oxidation of OPD,MPD,PPD,OPD+PPD,OPD+MPD,MPD+PPD and OPD+MPD+PPD into yellow,colorless,lilac,yellow,yellow,wine red and reddish-brown products,respectively,in the presence of H_(2)O_(2).Thus,the MPD,PPD,MPD+PPD and OPD+MPD+PPD were easily identified based on the distinct color of their oxidation products,and the OPD,OPD+PPD,OPD+MPD could be further identified by the additional addition of MPD or PPD.Subsequently,CoPt@G/H_(2)O_(2)-,a 3,3′,5,5′-tetramethylbenzidine(TMB)/CoPt@G/H_(2)O_(2)-,and MPD/CoPt@G/H_(2)O_(2)-enabled colorimetric/FL dual-mode platforms for the quantitative detection of OPD,MPD and PPD were proposed.The experimental results illustrated that the constructed sensing platforms exhibit satisfactory sensitivity,comparable to that reported in previous studies.Finally,the evaluation of PDAs in water samples was realized,yielding satisfactory recoveries.This work expanded the application prospects of nanozymes in assessing environmental risks and protection of human security.展开更多
Schlieren imaging is a widely used technique to visualize the structure of supersonic flow field,which is usually dominated by shock waves.Precise identification of shock waves in schlieren image provides critical ins...Schlieren imaging is a widely used technique to visualize the structure of supersonic flow field,which is usually dominated by shock waves.Precise identification of shock waves in schlieren image provides critical insights for flow diagnostics,especially for supersonic inlet whose performance is highly associated with that of the whole flight.However,conventional shock wave identification methods have limited accuracy in segmenting the shock wave.To overcome the limitation,we proposed an automated shock wave identification method(SW-Segment)that can attain high resolution and automatic shock wave segmentation by integrating correlation-based feature extraction with graph search.We demonstrated the efficacy of SW-Segment via the identification of shock waves in simulatively and experimentally obtained schlieren image.The results proved that SW-Segment showed a shock wave identification accuracy of 95.24%in the numerical schlieren image and an accuracy of 88.33%in the experimental image,clearly demonstrating its reliability.SW-Segment holds broad applicability for shock wave detection in diverse schlieren imaging scenarios,offering robust data support for flow field analysis and supersonic flight design.展开更多
What are the origins,historical development,and lineages of the reincarnation system of Living Buddhas in Tibetan Buddhism?What kind of academic framework is"Han-Tibetan Buddhist Studies"?In an interview wit...What are the origins,historical development,and lineages of the reincarnation system of Living Buddhas in Tibetan Buddhism?What kind of academic framework is"Han-Tibetan Buddhist Studies"?In an interview with this journal,Professor Shen Weirong ofTsinghua University discusses these issues on the basis of his research.展开更多
Root rot is a prevalent soil-borne fungal disease in citrus.Citron C-05(Citrus medica)stands out as a germplasm within Citrus spp.due to its complete resistance to citrus canker and favorable characteristics such as s...Root rot is a prevalent soil-borne fungal disease in citrus.Citron C-05(Citrus medica)stands out as a germplasm within Citrus spp.due to its complete resistance to citrus canker and favorable characteristics such as single embryo and easy rooting.However,Citron C-05 was found to be highly susceptible to root rot during cultivation,with the specific pathogens previously unknown.In this study,four candidate fungal species were isolated from Citron C-05 roots.Sequence analysis of ITS,EF-1a,RPB1,and RPB2 identified two Fusarium solani strains,Rr-2 and Rr-4,as the candidates causing root rot in Citron C-05.Resistance tests showed these two pathogens increased root damage rate from 10.30%to 35.69%in Citron C-05,sour orange(Citrus aurantium),sweet orange(Citrus sinensis)and pummelo(Citrus grandis).F.solani exhibited the weak pathogenicity towards trifoliate orange(Poncirus trifoliata).DAB staining revealed none of reddish-brown precipitation in the four susceptible citrus germplasm after infection with F.solani,while trifoliate orange exhibited significant H2O2 accumulation.Trypan blue staining indicated increased cell death in the four susceptible citrus germplasm following infection with these two pathogens but not in trifoliate orange.These findings provide a comprehensive understanding of citrus root rot and support future research on the mechanisms of root rot resistance in citrus.展开更多
Under the condition of frequent replacement of wind tunnel models,multiple types of wind tunnel models are fixed by a slender support sting with low stiffness damping.When excited by wind load,various models produce r...Under the condition of frequent replacement of wind tunnel models,multiple types of wind tunnel models are fixed by a slender support sting with low stiffness damping.When excited by wind load,various models produce random multi-dimensional vibration with different characteristics,which makes it impossible to obtain accurate and efficient aerodynamic data.Therefore,in order to ensure the reliable and efficient conduction of wind tunnel test,a wind-tunnel-modeladaptive vibration control method is proposed in this paper.First,the split type adaptive vibration suppression structure is designed.Second,the multi-dimensional vibration characteristic characterization method is derived and the vibration characteristic identification method of the system is designed.Then,a vibration state estimation model is established according to the identification results of vibration characteristics,and a multi-actuator cooperative control method based on vibration state estimation is constructed.Finally,a model-adaptive vibration control system is built,and vibration characteristics identification and hammer experiments are carried out for two types of typical models.The results show that the proposed model-adaptive vibration control method increases the equivalent damping ratio of pitch and yaw dimensions of the high-aspect-ratio class model by 8.19 times and 48.81 times,respectively.The equivalent damping ratio of pitch and yaw dimensions of the highslenderness-ratio class model is increased by 16.44 and 5.43 times,respectively.It provides a strong guarantee for the reliable and efficient development of multi-type wind tunnel test tasks.展开更多
Re-entry gliding vehicles exhibit high maneuverability,making trajectory prediction a key factor in the effectiveness of defense systems.To overcome the limited fitting accuracy of existing methods and their poor adap...Re-entry gliding vehicles exhibit high maneuverability,making trajectory prediction a key factor in the effectiveness of defense systems.To overcome the limited fitting accuracy of existing methods and their poor adaptability to maneuver mode mutations,a trajectory prediction method is proposed that integrates online maneuver mode identification with dynamic modeling.Characteristic parameters are extracted from tracking data for parameterized modeling,enabling real-time identification of maneuver modes.In addition,a maneuver detection mechanism based on higher-order cumulants is introduced to detect lateral maneuver mutations and optimize the use of historical data.Simulation results show that the proposed method achieves accurate trajectory prediction during the glide phase and maintains high accuracy under maneuver mutations,significantly enhancing the prediction performance of both three-dimensional trajectories and ground tracks.展开更多
Many spore-forming Bacillus species can cause serious human diseases,because of accidental Bacillusspore infection.Thus,developing an identification strategy with both high sensitivity and specificity is greatly in de...Many spore-forming Bacillus species can cause serious human diseases,because of accidental Bacillusspore infection.Thus,developing an identification strategy with both high sensitivity and specificity is greatly in demand.In this work,we proposed a novel approach named multi-head self-attention mechanism-guided neural network Raman platform to identify living Bacillus spores within a single-cell resolution.The multi-head self-attention mechanism-guided neural network Raman platform was created by combining single-cell Raman spectroscopy,convolutional neural network(CNN),and multi-head self-attention mechanism.To address the limited size of the original spectra dataset,Gaussian noise-based spectra augmentation was employed to increase the number of single-cell Raman spectra datasets for CNN training.Owing to the assistance of both spectra augmentation and multi-head self-attention mechanism,the obtained prediction accuracy of five Bacillus spore species was further improved from 92.29±0.82%to 99.43±0.15%.To figure out the spectra differences covered by the multi-head self-attention mechanism-guided CNN,the relative classification weight from typical Raman bands was visualized via multi-head self-attention mechanism curve.In the process of spectra augmentation from 0 to 1000,the distribution of relative classification weight varied from a discrete state to a more concentrated phase.More importantly,these highlighted four Raman bands(1017,1449,1576,and 1660 cm^(-1))were assigned large weights,showing that the spectra differences in the Raman bands produced the largest contribution to prediction accuracy.It can be foreseen that,our proposed sorting platform has great potential in accurately identifying Bacillus and its related genera species at a single-cell level.展开更多
文摘In this paper our studies about the sequential testing program for predicting and identificating carcinogens, sequential discriminant method and cost- effectiveness analysis are summarized. The analysis of our database of carcinogeniclty and genotoxicity of chemicals demonstrates the uncertainty . of short- term tests ( STTs ) to predict carcinogens and the results of most routine STTs are statistically dependent. We recommend the sequential testing program combining STTs and carclnogenicity assay, the optimal STT batteries, the rules of the sequential discrimination and the preferal choices of STTs tor specific chemical class. For illustrative pmposes the carclnogenicity prediction of several sample chamicals is presented. The results of cost-effectiveness analysis suggest that this program has vast social-economic effectiveness.
基金supported by the National Natural Science Foundation of China(62371465)Taishan Scholar Project of Shandong Province(ts201511020)。
文摘In wireless sensor networks,ensuring communication security via specific emitter identification(SEI)is crucial.However,existing SEI methods are limited to closed-set scenarios and lack the ability to detect unknown devices and perform classincremental training.This study proposes a class-incremental open-set SEI approach.The open-set SEI model calculates radiofrequency fingerprints(RFFs)prototypes for known signals and employs a self-attention mechanism to enhance their discriminability.Detection thresholds are set through Gaussian fitting for each class.For class-incremental learning,the algorithm freezes the parameters of the previously trained model to initialize the new model.It designs specific losses:the RFFs extraction distribution difference loss and the prototype transformation distribution difference loss,which force the new model to retain old knowledge while learning new knowledge.The training loss enables learning of new class RFFs.Experimental results demonstrate that the open-set SEI model achieves state-of-theart performance and strong noise robustness.Moreover,the class-incremental learning algorithm effectively enables the model to retain old device RFFs knowledge,acquire new device RFFs knowledge,and detect unknown devices simultaneously.
基金funded by Sichuan Science and Technology Program,grant numbers 2021YFYZ0010,2023YFH0006,2025YFHZ0295The Basic Research Program of Sichuan Provincial Research Institutes,grant numbers 2024JDKY0001 and 2023JDKY0001.
文摘The genus Actinidia is primarily functionally dioecious,and early sex identification plays a crucial role in improving breeding efficiency and reducing production costs.In this study,the accuracy of three sex-linked molecular markers(SyGI[Shy Girl],FrBy[Friendly Boy],and SmY1)in sex identification was evaluated in various Actinidia species.The selected marker products were subsequently cloned and sequenced in six wild Actinidia species.Ninety-six wild A.chinensis chinensis accessions and 74 A.chinensis deliciosa accessions,most of which were wild,with only one cultivated,were used for comprehensive primer validation.Thirty-three juvenile A.chinensis chinensis hybrid seedlings were used for practical application tests.The results showed that the marker SyGI accurately identified the sex of 20 samples from six Actinidia species and 96 A.chinensis chinensis accessions with 100%reliability.For Actinidia chinensis deliciosa,the identification accuracy reached 98.65%.Sequence analysis revealed that SyGI shared the highest similarity with the male-specific genomic region.Furthermore,SyGI achieved 100%accuracy in identifying the sex of 33 juvenile A.chinensis chinensis individuals.The findings confirm that the SyGI marker possesses high accuracy,strong specificity,and broad applicability,making it a valuable tool for kiwifruit breeding programs.The cloned sequences from wild Actinidia species also provide important references for future research on the mechanisms of sexual evolution and determination.
基金funded by grants fromthe China Agriculture Research System of MOF and MARA(CARS-25)the Key Research and Development Program of Xinjiang Uygur autonomous region(Grant No.2023B02017)+3 种基金the Agricultural Science and Technology Innovation Program(CAAS-ASTIP-2021-ZFRI,CAAS-ASTIP-2024-WRI)the Basic Research Funds of Chinese Academy of Agricultural Sciences(Grant No.1610192023201)Natural Science Foundation of Henan Province(Grant No.252300421694)Joint Research on Agricultural Variety Improvement of Henan Province(Grant No.2022010503).
文摘Watermelon(Citrullus lanatus) is sensitive to salt stress. For breeding applications, it is of great significance to explore the genetic mechanism underlying salt tolerance in watermelon by analyzing the dehydration responsive element-binding(DREB) factor family members.However, they are rarely studied in watermelon. In this study, we identified ClaDREB gene family members in watermelon based on whole genome data;analyzed the physicochemical properties, evolution, and phylogeny;and studied their expression patterns under salt stress in two watermelon varieties with varying salt tolerance. In total, 57 DREB family members were identified in watermelon, and most of them were located in the nucleus. ClaDREBs were divided into six subgroups Ⅰ-Ⅵ. The promoter region of ClaDREBs from subgroup Ⅱ contained many defense-related and stress responsive elements. Among them, ClaDREB14 was significantly upregulated by salt stress and exhibited differential expression in salt-tolerant and salt-sensitive varieties. Moreover, overexpression of ClaDREB14 in watermelon roots significantly improved the salt tolerance of transgenic plants;mainly, it significantly increased the activities of POD, SOD, and CAT and significantly reduced MDA content.However, the results from gene-edited watermelon roots obtained using CRISPR/Cas9 vectors showed the opposite trend. Furthermore, we demonstrated that ClaDREB14 directly binds to the cis-acting element ACCGAC in the promoter region of ClaPOD6 and promotes its expression.Therefore, ClaDREB14 may enhance salt tolerance by increasing the activity of antioxidant enzymes in watermelon roots. This study provided valuable information on the DREB gene family in watermelon and laid the foundation for future functional validation and genetic engineering applications.
基金supported by the Science and Technology Talent Support Project of Hunan Province,China (2022TJ-N15)the Hunan Agricultural Science and Technology Innovation Fund,China (2024CX90 and 2024CX65)the Science and Technology Innovation Program of Hunan Province,China (2021NK1006)。
文摘High-throughput single nucleotide polymorphism(SNP) arrays have emerged as essential genotyping tools,significantly accelerating breeding programs and advancing basic research.In this study,a high-throughput 10K SNP genotyping array for wax gourd was developed using genotyping by target sequencing(GBTS),featuring 10,722 SNPs evenly distributed across all 12 chromosomes,including 278 functional loci associated with key economic traits.To demonstrate its utility,genetic distances among 19 elite inbred lines were calculated from SNP data and correlated with heterosis for single fruit weight.The results revealed that greater genetic distance was associated with higher middle parent heterosis(MPH) for single fruit weight.Furthermore,56 commercial wax gourd cultivars collected from eight regions were selected and genotyped.Population structure analysis,phylogenetic analysis,and principal component analysis(PCA) collectively indicated that these cultivars fall into two major groups.Group I,comprising black or dark green skinned wax gourds,exhibited lower genetic diversity than Group II,which includes green or light green skinned varieties,reflecting shorter genetic distances within Group I.Finally,60 polymorphic SNPs were used to construct DNA fingerprints for distinguishing the 56 cultivars.As the first high-throughput genotyping platform for wax gourd,this SNP array provides an effective and powerful tool for genetic analysis.
基金supported by the National Natural Science Foundation of China(Grant Nos.42130719 and 42177173)the Doctoral Direct Train Project of Chongqing Natural Science Foundation(Grant No.CSTB2023NSCQ-BSX0029).
文摘Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters.
基金supported by the National Natural Science Foundation of China(No.52207228)the Beijing Natural Science Foundation,China(No.3224070)the National Natural Science Foundation of China(No.52077208).
文摘The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches often suffer from reduced accuracy under dynamically uncertain state-of-charge(SOC)operating ranges and heterogeneous aging stresses.This study presents a unified SOH estimation framework that integrates physics-informed modeling,subspace identification,and Transformer-based learning.A reduced-order model is derived from simplified electrochemical dynamics,providing an interpretable and computationally efficient representation of battery behavior.Subspace identification across a wide SOC and SOH range yields degradation-sensitive features,which the Transformer uses to capture long-range aging dynamics via multi-head self-attention.Experiments on LiFePO4 cells under joint-cell training show consistently accurate SOH estimation,with a maximum error of 1.39%,demonstrating the framework’s effectiveness in decoupling SOC and SOH effects.In cross-cell validation,where training and validation are performed on different cells,the model maintains a maximum error of 2.06%,confirming strong generalization to unseen aging trajectories.Comparative experiments on LiFePO_(4)and public LiCoO_(2)datasets confirm the framework’s cross-chemistry applicability.By extracting low-dimensional,physically interpretable features via subspace identification,the framework significantly reduces training cost while maintaining high SOH estimation accuracy,outperforming conventional data-driven models lacking physical guidance.
基金supported by the State Grid Southwest Branch Project“Research on Defect Diagnosis and Early Warning Technology of Relay Protection and Safety Automation Devices Based on Multi-Source Heterogeneous Defect Data”.
文摘The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.
基金supported in part by the National Natural Science Foundation of China (62431018)in part by the Guangzhou Municipal Science and Technology Bureau (SL2023A04J00435)in part by the One Hundred Youth Project of Guangdong University of Technology (263113873)。
文摘Selectivity remains a significant challenge for gas sensors. In contrast to conventional gas sensors that depend solely on conductivity to detect gases, we exploited a single NiO-doped SnO_(2) sensor to simultaneously monitor transient changes in both sensor conductivity and temperature. The distinct response profiles of H_(2) and NH_(3) gases were attributed to differences in their redox rates and enthalpy changes during chemical reactions, which provided an opportunity for gas identification using machine learning(ML) algorithms. The test results indicate that preprocessing the extracted calorimetric and chemi-resistive parameters using the principal component analysis(PCA), followed by the application of ML classifiers for identification,enables a 100% accuracy for both target analytes. This work presents a facile gas identification method that enhances chiplevel sensor applications while minimizing the need for complex sensor arrays.
基金supported by the Scientific and Technological Innovation Project of the China Academy of Chinese Medical Sciences(CI2021A04013)the National Natural Science Foundation of China(82204610)+1 种基金the Qihang Talent Program(L2022046)the Fundamental Research Funds for the Central Public Welfare Research Institutes(ZZ15-YQ-041 and L2021029).
文摘Background:The medicinal material known as Os Draconis(Longgu)originates from fossilized remains of ancient mammals and is widely used in treating emotional and mental conditions.However,fossil resources are nonrenewable,and clinical demand is increasingly difficult to meet,leading to a proliferation of counterfeit products.During prolonged geological burial,static pressure from the surrounding strata severely compromises the microstructural integrity of osteons in Os Draconis,but Os Draconis still largely retains the structural features of mammalian bone.Methods:Using verified authentic Os Draconis samples over 10,000 years old as a baseline,this study summarizes the ultrastructural characteristics of genuine Os Draconis.Employing electron probe microanalysis and optical polarized light microscopy,we examined 28 batches of authentic Os Draconis and 31 batches of counterfeits to identify their ultrastructural differences.Key points for ultrastructural identification of Os Draconis were compiled,and a new identification approach was proposed based on these differences.Results:Authentic Os Draconis exhibited distinct ultrastructural markers:irregularly shaped osteons with traversing fissures,deformed/displaced Haversian canals,and secondary mineral infill(predominantly calcium carbonate).Counterfeits showed regular osteon arrangements,absent traversal fissures,and homogeneous hydroxyapatite composition.Lab-simulated samples lacked structural degradation features.EPMA confirmed calcium carbonate infill in fossilized Haversian canals,while elemental profiles differentiated lacunae types(void vs.mineral-packed).Conclusion:The study established ultrastructural criteria for authentic Os Draconis identification:osteon deformation,geological fissures penetrating bone units,and heterogenous mineral deposition.These features,unattainable in counterfeits or modern processed bones,provide a cost-effective,accurate identification method.This approach bridges gaps in TCM material standardization and supports quality control for clinical applications.
基金supported by the National Science Foundation of China(Nos.42177042,and 42477051)the National Key R&D Program of China(No.2023YFC3708700)the Science Foundation of China University of Petroleum-Beijing(No.2462022QNXZ006).
文摘Petroleum leakage is a major groundwater contamination source,with chemical composition of water soluble fractions(WSFs)from diverse oil sources significantly impacting groundwater quality and source identification.The aim of this study was to assess impact of 15 diverse oils on groundwater quality and environmental forensics based on oil-water equilibrium experiments.Our results indicate that contamination of groundwater by gasoline and naphtha is primarily attributed to volatile hydrocarbons,while pollution from diesel,kerosene,and crude oil is predominantly from non-hydrocarbons.Rapid determination of the extent of non-hydrocarbon pollution in WSFs was achieved through a new quantitative index.Gasoline and naphtha exhibited the highest groundwater contamination potential while kerosene and light crude oils were also likely to cause groundwater contamina-tion.Although volatile hydrocarbons in the WSFs of diesel and jet fuel do not easily exceed current regulatory standards,unregulated non-hydrocarbons may pose a more severe contamination risk to groundwater.Notably,the presence of significant benzene and toluene,hydrogenation and alkylation products(e.g.,C4-C5 alkylben-zenes,alkylindenes,alkyltetralins,and dihydro-indenes),cycloalkanes in WSFs can effectively be utilized for preliminary source identification of light distillates,middle distillates,and crude oils,respectively.
基金supported by the National Natural Science Foundation of China (Grant No.52188102)。
文摘Industrial robot dynamics lay the foundation for high-precision and high-speed control, and accurate identification of dynamic parameters is essential for precise dynamic calculations. The choice of friction models is a critical component in the identification of industrial robot dynamics. Traditional static friction models struggle to capture the hysteresis effects caused by robot joint elasticity and clearances, leading to large torque prediction errors when the joint velocity crosses zero. Due to the presence of hysteresis effects, the joint velocity crosses zero in the forward direction, and the reverse direction will have different friction patterns. Although the hysteresis effects can be modeled as an ordinary differential equation(ODE), it is difficult to determine the ODE structure that achieves both generalization and accuracy to describe the hysteresis effects of the friction model. To address this issue, we propose the neural hysteresis friction(NHF), which uses neural ODE to model the hysteresis effects in a data-driven manner, thereby mitigating the current inadequacies in the study of dynamic friction characteristics. The experiments on a real 6-axis industrial robot demonstrate that our proposed method can accurately model the friction dynamics during directional switching and outperform other modeling methods. Velocity tracking control experiments show that NHF can effectively reduce tracking errors when the velocity crosses zero.
基金supported by the National Key Research and Development Program of China(No.2022YFC2403500)the National Natural Science Foundation of China(No.22225401)+1 种基金the Science and Technology Innovation Program of Hunan Province(No.2020RC4017)the Guizhou Provincial Science and Technology Projects(No.ZK[2023]293).
文摘Simultaneous identification and quantitative detection of phenylenediamine(PDA)isomers,including o-phenylenediamine(OPD),m-phenylenediamine(MPD),and p-phenylenediamine(PPD),are essential for environmental risk assessment and human health protection.However,current visual detection methods can only distinguish individual PDA isomers and failed to identify binary or ternary mixtures.Herein,a highly active and ultrastable peroxidase(POD)-like CoPt graphitic nanozyme was used for naked-eye identification and colorimetric/fluorescent(FL)dual-mode quantitative detection of PDA isomers.The CoPt@G nanozyme effectively catalyzed the oxidation of OPD,MPD,PPD,OPD+PPD,OPD+MPD,MPD+PPD and OPD+MPD+PPD into yellow,colorless,lilac,yellow,yellow,wine red and reddish-brown products,respectively,in the presence of H_(2)O_(2).Thus,the MPD,PPD,MPD+PPD and OPD+MPD+PPD were easily identified based on the distinct color of their oxidation products,and the OPD,OPD+PPD,OPD+MPD could be further identified by the additional addition of MPD or PPD.Subsequently,CoPt@G/H_(2)O_(2)-,a 3,3′,5,5′-tetramethylbenzidine(TMB)/CoPt@G/H_(2)O_(2)-,and MPD/CoPt@G/H_(2)O_(2)-enabled colorimetric/FL dual-mode platforms for the quantitative detection of OPD,MPD and PPD were proposed.The experimental results illustrated that the constructed sensing platforms exhibit satisfactory sensitivity,comparable to that reported in previous studies.Finally,the evaluation of PDAs in water samples was realized,yielding satisfactory recoveries.This work expanded the application prospects of nanozymes in assessing environmental risks and protection of human security.
基金supported by the National Natural Science Foundation of China(Grant Nos.12402336,U20A2070,12025202)the Natural Science Foundation of Jiangsu Province(Grant No.BK20230876)+2 种基金the National High-Level Talent Project(Grant No.YQR23069)the Key Laboratory of Intake and Exhaust Technology,Ministry of Education(Grant No.CEPE2024015)the Key Laboratory of Mechanics and Control for Aerospace Structures(Nanjing University of Aeronautics and Astronautics)(Grant No.MCAS-I-0325K01)。
文摘Schlieren imaging is a widely used technique to visualize the structure of supersonic flow field,which is usually dominated by shock waves.Precise identification of shock waves in schlieren image provides critical insights for flow diagnostics,especially for supersonic inlet whose performance is highly associated with that of the whole flight.However,conventional shock wave identification methods have limited accuracy in segmenting the shock wave.To overcome the limitation,we proposed an automated shock wave identification method(SW-Segment)that can attain high resolution and automatic shock wave segmentation by integrating correlation-based feature extraction with graph search.We demonstrated the efficacy of SW-Segment via the identification of shock waves in simulatively and experimentally obtained schlieren image.The results proved that SW-Segment showed a shock wave identification accuracy of 95.24%in the numerical schlieren image and an accuracy of 88.33%in the experimental image,clearly demonstrating its reliability.SW-Segment holds broad applicability for shock wave detection in diverse schlieren imaging scenarios,offering robust data support for flow field analysis and supersonic flight design.
文摘What are the origins,historical development,and lineages of the reincarnation system of Living Buddhas in Tibetan Buddhism?What kind of academic framework is"Han-Tibetan Buddhist Studies"?In an interview with this journal,Professor Shen Weirong ofTsinghua University discusses these issues on the basis of his research.
基金supported by Joint Funds of the National Natural Science Foundation of China(Grant No.U21A20228).
文摘Root rot is a prevalent soil-borne fungal disease in citrus.Citron C-05(Citrus medica)stands out as a germplasm within Citrus spp.due to its complete resistance to citrus canker and favorable characteristics such as single embryo and easy rooting.However,Citron C-05 was found to be highly susceptible to root rot during cultivation,with the specific pathogens previously unknown.In this study,four candidate fungal species were isolated from Citron C-05 roots.Sequence analysis of ITS,EF-1a,RPB1,and RPB2 identified two Fusarium solani strains,Rr-2 and Rr-4,as the candidates causing root rot in Citron C-05.Resistance tests showed these two pathogens increased root damage rate from 10.30%to 35.69%in Citron C-05,sour orange(Citrus aurantium),sweet orange(Citrus sinensis)and pummelo(Citrus grandis).F.solani exhibited the weak pathogenicity towards trifoliate orange(Poncirus trifoliata).DAB staining revealed none of reddish-brown precipitation in the four susceptible citrus germplasm after infection with F.solani,while trifoliate orange exhibited significant H2O2 accumulation.Trypan blue staining indicated increased cell death in the four susceptible citrus germplasm following infection with these two pathogens but not in trifoliate orange.These findings provide a comprehensive understanding of citrus root rot and support future research on the mechanisms of root rot resistance in citrus.
基金supported in part by the National Natural Science Foundation of China(Nos.52475550,52305095)in part by the Key R&D Project of Liaoning Province,China(No.2023JH2/101800026)。
文摘Under the condition of frequent replacement of wind tunnel models,multiple types of wind tunnel models are fixed by a slender support sting with low stiffness damping.When excited by wind load,various models produce random multi-dimensional vibration with different characteristics,which makes it impossible to obtain accurate and efficient aerodynamic data.Therefore,in order to ensure the reliable and efficient conduction of wind tunnel test,a wind-tunnel-modeladaptive vibration control method is proposed in this paper.First,the split type adaptive vibration suppression structure is designed.Second,the multi-dimensional vibration characteristic characterization method is derived and the vibration characteristic identification method of the system is designed.Then,a vibration state estimation model is established according to the identification results of vibration characteristics,and a multi-actuator cooperative control method based on vibration state estimation is constructed.Finally,a model-adaptive vibration control system is built,and vibration characteristics identification and hammer experiments are carried out for two types of typical models.The results show that the proposed model-adaptive vibration control method increases the equivalent damping ratio of pitch and yaw dimensions of the high-aspect-ratio class model by 8.19 times and 48.81 times,respectively.The equivalent damping ratio of pitch and yaw dimensions of the highslenderness-ratio class model is increased by 16.44 and 5.43 times,respectively.It provides a strong guarantee for the reliable and efficient development of multi-type wind tunnel test tasks.
基金supported by the National Natural Science Foundation of China(12302056)the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(GZC20233445)。
文摘Re-entry gliding vehicles exhibit high maneuverability,making trajectory prediction a key factor in the effectiveness of defense systems.To overcome the limited fitting accuracy of existing methods and their poor adaptability to maneuver mode mutations,a trajectory prediction method is proposed that integrates online maneuver mode identification with dynamic modeling.Characteristic parameters are extracted from tracking data for parameterized modeling,enabling real-time identification of maneuver modes.In addition,a maneuver detection mechanism based on higher-order cumulants is introduced to detect lateral maneuver mutations and optimize the use of historical data.Simulation results show that the proposed method achieves accurate trajectory prediction during the glide phase and maintains high accuracy under maneuver mutations,significantly enhancing the prediction performance of both three-dimensional trajectories and ground tracks.
基金partially supported by the National Natural Science Foundation of China(62075137)the Guangdong Basic and Applied Basic Research Foundation(2023A1515140161)+3 种基金the Guangxi Natural Science Foundation of China(2021JJB 110003)the Dongguan Science and Technology of Social Development Program(20231800936312)the high-level talent program of Dongguan University of Technology(No.221110080)the Sanming Project of Medicine in Shenzhen(No.SZSM202103014).
文摘Many spore-forming Bacillus species can cause serious human diseases,because of accidental Bacillusspore infection.Thus,developing an identification strategy with both high sensitivity and specificity is greatly in demand.In this work,we proposed a novel approach named multi-head self-attention mechanism-guided neural network Raman platform to identify living Bacillus spores within a single-cell resolution.The multi-head self-attention mechanism-guided neural network Raman platform was created by combining single-cell Raman spectroscopy,convolutional neural network(CNN),and multi-head self-attention mechanism.To address the limited size of the original spectra dataset,Gaussian noise-based spectra augmentation was employed to increase the number of single-cell Raman spectra datasets for CNN training.Owing to the assistance of both spectra augmentation and multi-head self-attention mechanism,the obtained prediction accuracy of five Bacillus spore species was further improved from 92.29±0.82%to 99.43±0.15%.To figure out the spectra differences covered by the multi-head self-attention mechanism-guided CNN,the relative classification weight from typical Raman bands was visualized via multi-head self-attention mechanism curve.In the process of spectra augmentation from 0 to 1000,the distribution of relative classification weight varied from a discrete state to a more concentrated phase.More importantly,these highlighted four Raman bands(1017,1449,1576,and 1660 cm^(-1))were assigned large weights,showing that the spectra differences in the Raman bands produced the largest contribution to prediction accuracy.It can be foreseen that,our proposed sorting platform has great potential in accurately identifying Bacillus and its related genera species at a single-cell level.
