采用顶空固相微萃取(headspace solid-phase microextraction,HS-SPME)和液液萃取(liquid-liquid extraction,LLE)结合全二维气相色谱飞行时间质谱(comprehensive two-dimensional gas chromatography time of flight mass spectrometry...采用顶空固相微萃取(headspace solid-phase microextraction,HS-SPME)和液液萃取(liquid-liquid extraction,LLE)结合全二维气相色谱飞行时间质谱(comprehensive two-dimensional gas chromatography time of flight mass spectrometry,GC×GC-TOF-MS)以及香气活度值(odour active value,OAV),对红星二锅头白酒的挥发性成分进行全面解析。研究发现,HS-SPME、LLE分别定性出928、802种挥发性化合物,共计定性出1304种挥发性化合物,共同定性出426种挥发性化合物。基于HS-SPME数据,通过香气数据库筛选出具有香气特征的382种香气化合物,其中酯类相对百分含量占比最高,其次是醇类、酸类和醛类。计算得到了49种香气化合物OAV>1,其中酯类(辛酸乙酯、异戊酸乙酯等)和萜烯类(β-大马酮)对白酒风味的贡献最大,醛类(异戊醛、己醛等)和含硫类(二甲基三硫)其次,醇类(1-辛烯-3-醇)和含氮类(2,3,5-三甲基吡嗪)也有一定风味贡献。该研究丰富了红星二锅头白酒的风味研究,也为下一步生产研究及调控提供了理论和数据支撑。展开更多
This study aimed to investigate the effect of ultrasound-assisted alkaline extraction(UAE)(at 20 kHz and different powers of 0,200,300,400,500 and 600 W for 10 min)on the yield,structure and emulsifying properties of ...This study aimed to investigate the effect of ultrasound-assisted alkaline extraction(UAE)(at 20 kHz and different powers of 0,200,300,400,500 and 600 W for 10 min)on the yield,structure and emulsifying properties of chickpea protein isolate(CPI).Compared with the non-ultrasound group,ultrasound treatment at 400 W resulted in the largest increase in CPI yield,and both the particle size and turbidity decreased with increasing ultrasound power from 0 to 400 W.The scanning electron microscope results showed a uniform structural distribution of CPI.Moreover,itsα-helix content increased,β-sheet content decreased,and total sulfhydryl group content and endogenous fluorescence intensity rose,illustrating that UAE changed the secondary and tertiary structure of CPI.At 400 W,the solubility of the emulsion increased to 63.18%,and the best emulsifying properties were obtained;the emulsifying activity index(EAI)and emulsifying stability index(ESI)increased by 85.42%and 46.78%,respectively.Furthermore,the emulsion droplets formed were smaller and more uniform.In conclusion,proper UAE power conditions increased the extraction yield and protein content of CPI,and effectively improved its structure and emulsifying characteristics.展开更多
The anti-hair loss mechanism of Aquilaria sinensis leaf extract(ASE)has been studied by using metabolomics and network pharmacology.Metabolomics was utilized to comprehensively identify the active constituents of ASE,...The anti-hair loss mechanism of Aquilaria sinensis leaf extract(ASE)has been studied by using metabolomics and network pharmacology.Metabolomics was utilized to comprehensively identify the active constituents of ASE,and the network pharmacology was used to elucidate their anti-hair loss mechanism,which was verified by molecular docking technology.572 active compounds were identified from the ASE by metabolomics methods,where there are 1447 corresponding targets and 492 targets related to hair loss,totaling 88 targets.20 core active substances were identified by constructing a network between common targets and active substances,which include vanillic acid,chorionic acid,caffeic acid and apigenin.The five key targets of TNF,TP53,IL6,PPARG,and EGFR were screened out by the PPI network analysis on 88 common targets.The GO and KEGG pathway enrichment analysis showed that the inflammation,hormone balance,cell growth,proliferation,apoptosis,and oxidative stress are involved.Molecular docking studies have confirmed the high binding affinity between core active compounds and key targets.The drug similarity assessment on these core compounds suggested that they have the potential to be used as potential hair loss treatment drugs.This study elucidates the complex molecular mechanism of ASE in treating hair loss,and provides a reference for the future applications in hair care products.展开更多
Soil DNA extraction,such as microbial community analysis and gene drift detection,is an important basis for multiple analyses in different fields.Nevertheless,the soil DNA extraction methods for field detection are st...Soil DNA extraction,such as microbial community analysis and gene drift detection,is an important basis for multiple analyses in different fields.Nevertheless,the soil DNA extraction methods for field detection are still lacking.This study established a rapid soil DNA extraction(RSDE)method that can be used in field detection.In this method,we first utilized the optimized lysate to isolate DNA from soil and then used a filtration membrane and a DNA adsorption membrane to purify the DNA via the column method.Moreover,we used the pressure from the syringe instead of the conventional centrifugal force of the centrifuge to assist the sample filtration,resulting in very low requirements for this method,with an extraction time of less than 20 min.Furthermore,we demonstrated that the RSDE method was applicable for DNA extraction from different types of soils,with the demand for soil samples as low as 0.1 g and that the amount of obtained DNA was,to some extent,greater than that obtained by a commercial kit.Further analysis revealed that this extracted genomic DNA can be used directly for polymerase chain reaction(PCR)analysis,including ordinary PCR,real-time fluorescent quantitative PCR,and recombinase polymerase amplification(RPA)-CRISPR/Cas12a visual assays.In addition,we demonstrated that this method can be used to extract DNA from residual plant roots in addition to soil microbes,which lays a foundation for the comprehensive analysis of soil plants and microorganisms.In summary,the RSDE method proposed in this study may have wide application prospects.展开更多
Removing copper from nickel electrolysis anode solution has been a major keypoint in the nickel metallurgy industry.In this study,we proposed a novel process flow to promote removing copper from nickel electrolysis an...Removing copper from nickel electrolysis anode solution has been a major keypoint in the nickel metallurgy industry.In this study,we proposed a novel process flow to promote removing copper from nickel electrolysis anode solution.A simulated nickel anode solution was designed,and static and dynamic adsorption experiments were conducted to determine the best of solution pH,adsorption time and temperature,resin dosage and particle size,and stirring speed.The optimal conditions were explored for copper removal from nickel electrolysis anode solution.Based on the optimal experimental conditions and the relevant experimental data,a novel process for copper removal from nickel electrolysis anodes was designed and verified.This novel process of copper removal from nickel electrolysis anodes was confirmed with nickel anolyte solution with nickel 50−60 g/L and copper 0.5 g/L.After finishing the novel process of copper removal,the nickel in the purified nickel anolyte became undetectable and copper concentration was 3 mg/L,the novel process of resin adsorption to remove copper from nickel anode solution through static and dynamic adsorptions has an efficacious copper removal.It is a beneficial supplement to traditional methods.展开更多
Rutin,also known as vitamin P,belongs to the flavonoid class of compounds and is widely present in plants.Its ability to bind with albumin in the blood helps maintain capillary permeability,leading to its extensive us...Rutin,also known as vitamin P,belongs to the flavonoid class of compounds and is widely present in plants.Its ability to bind with albumin in the blood helps maintain capillary permeability,leading to its extensive use for cardiovascular protection.This review aimed to provide insights into the development of rutin raw material industry in China and its future applications in various fields,such as medicine,healthcare,food,and animal husbandry.The study began by comparing rutin quality standards across China,the United States and Europe,outlined the industrial extraction processes of rutin,and examined the biological activity and potential medical applications of rutin.展开更多
With the popularization of social media,public opi-nion information on emergencies spreads rapidly on the Internet,the impact of negative public opinions on an event has become more significant.Based on the organizati...With the popularization of social media,public opi-nion information on emergencies spreads rapidly on the Internet,the impact of negative public opinions on an event has become more significant.Based on the organizational form of public opinion information,the knowledge graph is used to construct the knowledge base of public opinion risk cases on the emer-gency network.The emotion recognition model of negative pub-lic opinion information based on the bi-directional long short-term memory(BiLSTM)network is studied in the model layer design,and a linear discriminant analysis(LDA)topic extraction method combined with association rules is proposed to extract and mine the semantics of negative public opinion topics to real-ize further in-depth analysis of information topics.Focusing on public health emergencies,knowledge acquisition and knowl-edge processing of public opinion information are conducted,and the experimental results show that the knowledge graph framework based on the construction can facilitate in-depth theme evolution analysis of public opinion events,thus demon-strating important research significance for reducing online pub-lic opinion risks.展开更多
Red mud is a solid waste discharged in the process of alumina production,and how to realize the efficient recovery of its iron is an urgent problem to be solved.In this study,the iron extraction test and mechanism stu...Red mud is a solid waste discharged in the process of alumina production,and how to realize the efficient recovery of its iron is an urgent problem to be solved.In this study,the iron extraction test and mechanism study of high iron red mud were carried out under the coupling conditions of multiple physical field(microwave field,gas-solid flow field and temperature field)with biomass as the reducing agent.The test results showed that under the optimal conditions,an iron concentrate with a yield of 78.4%,an iron grade of 59.23%,and a recovery rate of 86.65%was obtained.The analyses of XRD,XPS,TEM,and SEM-EDS showed that during the roasting process,the hematite in the high-iron red mud was completely converted to magnetite,and the biomass produced the reductant that provided the magnetization reaction;A large number of cracks and pores appeared in the surface of the hematite reduction product particles,which helped to induce iron minerals to undergo effective mineral phase transformation.The above study provides ideas for the phase transformation and efficient recovery of iron minerals in red mud.展开更多
[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-base...[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.展开更多
Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emi...Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.展开更多
The current study comprehensively evaluates four different protein extraction methods based on urea,sodium dodecyl sulfate(SDS),anionic surfactants(BT),and total RNA extractor(Trizol),aiming to optimize the sample pre...The current study comprehensively evaluates four different protein extraction methods based on urea,sodium dodecyl sulfate(SDS),anionic surfactants(BT),and total RNA extractor(Trizol),aiming to optimize the sample preparation workflow for mass spectrometry-based proteomics.Using HeLa cells as an example,we found that the method employing the mass spectrometry-compatible surfactant BT reagent significantly reduces the total time consumed for protein extraction and minimizes protein losses during the sample preparation process.Further integrating the four protein extraction methods,we identified over 7000 proteins from HeLa cells without relying on pre-fractionation techniques,and 2990 of them were quantified using label-free quantification.It is worth noting that the BT and SDS methods demonstrate higher efficiency in extracting membrane proteins,while the Urea and Trizol methods are more effective in extracting proteins from nuclear and cytoplasmic fractions.In summary,this study provides a novel solution for deep proteome coverage,particularly in the context of cellular protein extraction,by integrating mass spectrometry-compatible surfactants with traditional extraction methods to effectively enhance protein identification numbers.展开更多
Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition me...Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.展开更多
Background Natural and synthetic plant growth regulators are essential for plant health,likewise these regulators also play a role in increasing organic production productivity and improving quality and yield stabilit...Background Natural and synthetic plant growth regulators are essential for plant health,likewise these regulators also play a role in increasing organic production productivity and improving quality and yield stability.In the present study,we have evaluated the effects of foliar applied plant growth regulators,i.e.,moringa leaf extract(MLE)and mepiquat chloride(MC)alone and in combination MC and MLE on the conventional cotton cultivar(CIM 573)and transgenic one(CIM 598).The growth regulators were applied at the start of bloom,45 and 90 days after blooming.Results The application of MC and MLE at 90 days after blooming significantly improved the relative growth rate,net assimilation rate,the number of bolls per plant,and seed cotton yield.Likewise,the combined application of MLE and MC at 90 days after blooming significantly boosted the nitrogen uptake in locules,as well as the phosphorus and potassium uptake in the leaves of both cotton cultivars.The application of MLE alone has considerably improved the nitrogen uptake in leaves,and phosphorus and potassium contents in locules of Bt and conventional cotton cultivars.Similarly,Bt cotton treated with MLE at 90 days after blooming produced significantly higher ginning out turn and oil contents.Treatment in combination(MLE+MC)at 90 days after blooming produced considerably higher micronaire value,fiber strength,and staple length in conventional cultivar.Conclusion The natural growth enhancer,MLE is a rich source of minerals and zeatin,improving the nutrient absorption and quality of cotton fiber in both conventional and Bt cotton cultivars.展开更多
As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decompos...As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decomposition of the input space by DiPLS,there are false alarms in the actual industrial process during fault detection.To address the above problems,a dynamic modeling method based on autoregressive-dynamic inner total PLS(AR-DiTPLS)is proposed.The method first uses the regression relation matrix to decompose the input space orthogonally,which reduces useless information for the predic-tion output in the quality-related dynamic subspace.Then,a vector autoregressive model(VAR)is constructed for the predic-tion score to separate dynamic information and static informa-tion.Based on the VAR model,appropriate statistical indicators are further constructed for online monitoring,which reduces the occurrence of false alarms.The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system.展开更多
In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) ba...In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.展开更多
In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of ea...In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.展开更多
In order to optimize the ultrasonic extraction technique for the total flavonoid of leaf yellows plus, the contents of 21 leaf yellows plus total flavonoid from four regions in Heilongjiang Province were comparatively...In order to optimize the ultrasonic extraction technique for the total flavonoid of leaf yellows plus, the contents of 21 leaf yellows plus total flavonoid from four regions in Heilongjiang Province were comparatively analyzed. The ultrasonic extraction technology was optimized by Box-Behnken response surface method, and the total flavonoid content of 21 kinds of Acanthopanax senticosus(Rupr. et Maxim.) Harms from different producing areas were analyzed by cluster analysis. The optimal process conditions were determined as ultrasonic time 30 min, solid-liquid ratio 1 : 12 and ultrasonic power 250 W, and the average yield of the total flavonoid was 1.453 mg·g^ (-1). By optimizing the ultrasonic-assisted extraction method, the total flavonoid content from different producing areas was compared in the experiment, which provided certain data support for the optimization of the extraction process in the future and laid a certain theoretical foundation for the quality analysis of Chinese medicinal materials.展开更多
A GC-MS method for the determination of 27 organochlorine pesticides and 15 Pyrethroid pesticides in animal food is established.The method was based on Gel Permeation chromatography combined with solid-phase extractio...A GC-MS method for the determination of 27 organochlorine pesticides and 15 Pyrethroid pesticides in animal food is established.The method was based on Gel Permeation chromatography combined with solid-phase extraction for sample preparation.Rapid qualitative and quantitative analyses was carried out by gas chromatography-mass spectrometry under the selective-ion monitoring mode.The experimental results showed that the correlation coefficients were better than 0.99,the recoveries for spiked standards were 70%-104%,the relative standard deviations were 2.1%-15.9%.展开更多
文摘采用顶空固相微萃取(headspace solid-phase microextraction,HS-SPME)和液液萃取(liquid-liquid extraction,LLE)结合全二维气相色谱飞行时间质谱(comprehensive two-dimensional gas chromatography time of flight mass spectrometry,GC×GC-TOF-MS)以及香气活度值(odour active value,OAV),对红星二锅头白酒的挥发性成分进行全面解析。研究发现,HS-SPME、LLE分别定性出928、802种挥发性化合物,共计定性出1304种挥发性化合物,共同定性出426种挥发性化合物。基于HS-SPME数据,通过香气数据库筛选出具有香气特征的382种香气化合物,其中酯类相对百分含量占比最高,其次是醇类、酸类和醛类。计算得到了49种香气化合物OAV>1,其中酯类(辛酸乙酯、异戊酸乙酯等)和萜烯类(β-大马酮)对白酒风味的贡献最大,醛类(异戊醛、己醛等)和含硫类(二甲基三硫)其次,醇类(1-辛烯-3-醇)和含氮类(2,3,5-三甲基吡嗪)也有一定风味贡献。该研究丰富了红星二锅头白酒的风味研究,也为下一步生产研究及调控提供了理论和数据支撑。
文摘This study aimed to investigate the effect of ultrasound-assisted alkaline extraction(UAE)(at 20 kHz and different powers of 0,200,300,400,500 and 600 W for 10 min)on the yield,structure and emulsifying properties of chickpea protein isolate(CPI).Compared with the non-ultrasound group,ultrasound treatment at 400 W resulted in the largest increase in CPI yield,and both the particle size and turbidity decreased with increasing ultrasound power from 0 to 400 W.The scanning electron microscope results showed a uniform structural distribution of CPI.Moreover,itsα-helix content increased,β-sheet content decreased,and total sulfhydryl group content and endogenous fluorescence intensity rose,illustrating that UAE changed the secondary and tertiary structure of CPI.At 400 W,the solubility of the emulsion increased to 63.18%,and the best emulsifying properties were obtained;the emulsifying activity index(EAI)and emulsifying stability index(ESI)increased by 85.42%and 46.78%,respectively.Furthermore,the emulsion droplets formed were smaller and more uniform.In conclusion,proper UAE power conditions increased the extraction yield and protein content of CPI,and effectively improved its structure and emulsifying characteristics.
文摘The anti-hair loss mechanism of Aquilaria sinensis leaf extract(ASE)has been studied by using metabolomics and network pharmacology.Metabolomics was utilized to comprehensively identify the active constituents of ASE,and the network pharmacology was used to elucidate their anti-hair loss mechanism,which was verified by molecular docking technology.572 active compounds were identified from the ASE by metabolomics methods,where there are 1447 corresponding targets and 492 targets related to hair loss,totaling 88 targets.20 core active substances were identified by constructing a network between common targets and active substances,which include vanillic acid,chorionic acid,caffeic acid and apigenin.The five key targets of TNF,TP53,IL6,PPARG,and EGFR were screened out by the PPI network analysis on 88 common targets.The GO and KEGG pathway enrichment analysis showed that the inflammation,hormone balance,cell growth,proliferation,apoptosis,and oxidative stress are involved.Molecular docking studies have confirmed the high binding affinity between core active compounds and key targets.The drug similarity assessment on these core compounds suggested that they have the potential to be used as potential hair loss treatment drugs.This study elucidates the complex molecular mechanism of ASE in treating hair loss,and provides a reference for the future applications in hair care products.
基金the Experimental Technology Research Project of Zhejiang University(SYB202138)National Natural Science Foundation of China(32000195).
文摘Soil DNA extraction,such as microbial community analysis and gene drift detection,is an important basis for multiple analyses in different fields.Nevertheless,the soil DNA extraction methods for field detection are still lacking.This study established a rapid soil DNA extraction(RSDE)method that can be used in field detection.In this method,we first utilized the optimized lysate to isolate DNA from soil and then used a filtration membrane and a DNA adsorption membrane to purify the DNA via the column method.Moreover,we used the pressure from the syringe instead of the conventional centrifugal force of the centrifuge to assist the sample filtration,resulting in very low requirements for this method,with an extraction time of less than 20 min.Furthermore,we demonstrated that the RSDE method was applicable for DNA extraction from different types of soils,with the demand for soil samples as low as 0.1 g and that the amount of obtained DNA was,to some extent,greater than that obtained by a commercial kit.Further analysis revealed that this extracted genomic DNA can be used directly for polymerase chain reaction(PCR)analysis,including ordinary PCR,real-time fluorescent quantitative PCR,and recombinase polymerase amplification(RPA)-CRISPR/Cas12a visual assays.In addition,we demonstrated that this method can be used to extract DNA from residual plant roots in addition to soil microbes,which lays a foundation for the comprehensive analysis of soil plants and microorganisms.In summary,the RSDE method proposed in this study may have wide application prospects.
基金Project(2019yff0216502)supported by the National Key Research&Development Plan of Ministry of Science and Technology of ChinaProject(2021SK1020-4)supported by the Major Science and Technological Innovation Project of Hunan Province,China。
文摘Removing copper from nickel electrolysis anode solution has been a major keypoint in the nickel metallurgy industry.In this study,we proposed a novel process flow to promote removing copper from nickel electrolysis anode solution.A simulated nickel anode solution was designed,and static and dynamic adsorption experiments were conducted to determine the best of solution pH,adsorption time and temperature,resin dosage and particle size,and stirring speed.The optimal conditions were explored for copper removal from nickel electrolysis anode solution.Based on the optimal experimental conditions and the relevant experimental data,a novel process for copper removal from nickel electrolysis anodes was designed and verified.This novel process of copper removal from nickel electrolysis anodes was confirmed with nickel anolyte solution with nickel 50−60 g/L and copper 0.5 g/L.After finishing the novel process of copper removal,the nickel in the purified nickel anolyte became undetectable and copper concentration was 3 mg/L,the novel process of resin adsorption to remove copper from nickel anode solution through static and dynamic adsorptions has an efficacious copper removal.It is a beneficial supplement to traditional methods.
基金Supported by the Scientific and Technological Research Project Foundation of Henan Provincial Scientific and Technological Department(242102310530,242102310119,252102310498)the Zhumadian Science and Technology Innovation Youth Special Project(QNZX202413)+1 种基金the Henan Provincial Medical Science and Technology Tackling Program(LHGJ20241009,LHGJ20241006)the Key Scientific Research Project of Higher Education Institutions in Henan Province(25B180015,25B230006)。
文摘Rutin,also known as vitamin P,belongs to the flavonoid class of compounds and is widely present in plants.Its ability to bind with albumin in the blood helps maintain capillary permeability,leading to its extensive use for cardiovascular protection.This review aimed to provide insights into the development of rutin raw material industry in China and its future applications in various fields,such as medicine,healthcare,food,and animal husbandry.The study began by comparing rutin quality standards across China,the United States and Europe,outlined the industrial extraction processes of rutin,and examined the biological activity and potential medical applications of rutin.
基金supported by the National Social Science Foundation Major Project(22&ZD135)the National Social Science Fund National Emergency Management System Construction Research Project(20VYJ061).
文摘With the popularization of social media,public opi-nion information on emergencies spreads rapidly on the Internet,the impact of negative public opinions on an event has become more significant.Based on the organizational form of public opinion information,the knowledge graph is used to construct the knowledge base of public opinion risk cases on the emer-gency network.The emotion recognition model of negative pub-lic opinion information based on the bi-directional long short-term memory(BiLSTM)network is studied in the model layer design,and a linear discriminant analysis(LDA)topic extraction method combined with association rules is proposed to extract and mine the semantics of negative public opinion topics to real-ize further in-depth analysis of information topics.Focusing on public health emergencies,knowledge acquisition and knowl-edge processing of public opinion information are conducted,and the experimental results show that the knowledge graph framework based on the construction can facilitate in-depth theme evolution analysis of public opinion events,thus demon-strating important research significance for reducing online pub-lic opinion risks.
基金Project(MMCS2023OF02)supported by the Open Foundation of State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structures,ChinaProject(AA23073018)supported by the Guangxi Science and Technology,ChinaProject(2024M751861)supported by the China Postdoctoral Science Foundation。
文摘Red mud is a solid waste discharged in the process of alumina production,and how to realize the efficient recovery of its iron is an urgent problem to be solved.In this study,the iron extraction test and mechanism study of high iron red mud were carried out under the coupling conditions of multiple physical field(microwave field,gas-solid flow field and temperature field)with biomass as the reducing agent.The test results showed that under the optimal conditions,an iron concentrate with a yield of 78.4%,an iron grade of 59.23%,and a recovery rate of 86.65%was obtained.The analyses of XRD,XPS,TEM,and SEM-EDS showed that during the roasting process,the hematite in the high-iron red mud was completely converted to magnetite,and the biomass produced the reductant that provided the magnetization reaction;A large number of cracks and pores appeared in the surface of the hematite reduction product particles,which helped to induce iron minerals to undergo effective mineral phase transformation.The above study provides ideas for the phase transformation and efficient recovery of iron minerals in red mud.
文摘[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.
基金supported by the National Natural Science Foundation of China(62061003)Sichuan Science and Technology Program(2021YFG0192)the Research Foundation of the Civil Aviation Flight University of China(ZJ2020-04,J2020-033)。
文摘Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.
文摘The current study comprehensively evaluates four different protein extraction methods based on urea,sodium dodecyl sulfate(SDS),anionic surfactants(BT),and total RNA extractor(Trizol),aiming to optimize the sample preparation workflow for mass spectrometry-based proteomics.Using HeLa cells as an example,we found that the method employing the mass spectrometry-compatible surfactant BT reagent significantly reduces the total time consumed for protein extraction and minimizes protein losses during the sample preparation process.Further integrating the four protein extraction methods,we identified over 7000 proteins from HeLa cells without relying on pre-fractionation techniques,and 2990 of them were quantified using label-free quantification.It is worth noting that the BT and SDS methods demonstrate higher efficiency in extracting membrane proteins,while the Urea and Trizol methods are more effective in extracting proteins from nuclear and cytoplasmic fractions.In summary,this study provides a novel solution for deep proteome coverage,particularly in the context of cellular protein extraction,by integrating mass spectrometry-compatible surfactants with traditional extraction methods to effectively enhance protein identification numbers.
基金supported by the National Natural Science Foundation of China (Project No.72301293)。
文摘Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.
文摘Background Natural and synthetic plant growth regulators are essential for plant health,likewise these regulators also play a role in increasing organic production productivity and improving quality and yield stability.In the present study,we have evaluated the effects of foliar applied plant growth regulators,i.e.,moringa leaf extract(MLE)and mepiquat chloride(MC)alone and in combination MC and MLE on the conventional cotton cultivar(CIM 573)and transgenic one(CIM 598).The growth regulators were applied at the start of bloom,45 and 90 days after blooming.Results The application of MC and MLE at 90 days after blooming significantly improved the relative growth rate,net assimilation rate,the number of bolls per plant,and seed cotton yield.Likewise,the combined application of MLE and MC at 90 days after blooming significantly boosted the nitrogen uptake in locules,as well as the phosphorus and potassium uptake in the leaves of both cotton cultivars.The application of MLE alone has considerably improved the nitrogen uptake in leaves,and phosphorus and potassium contents in locules of Bt and conventional cotton cultivars.Similarly,Bt cotton treated with MLE at 90 days after blooming produced significantly higher ginning out turn and oil contents.Treatment in combination(MLE+MC)at 90 days after blooming produced considerably higher micronaire value,fiber strength,and staple length in conventional cultivar.Conclusion The natural growth enhancer,MLE is a rich source of minerals and zeatin,improving the nutrient absorption and quality of cotton fiber in both conventional and Bt cotton cultivars.
基金supported by the National Natural Science Foundation of China(62273354,61673387,61833016).
文摘As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decomposition of the input space by DiPLS,there are false alarms in the actual industrial process during fault detection.To address the above problems,a dynamic modeling method based on autoregressive-dynamic inner total PLS(AR-DiTPLS)is proposed.The method first uses the regression relation matrix to decompose the input space orthogonally,which reduces useless information for the predic-tion output in the quality-related dynamic subspace.Then,a vector autoregressive model(VAR)is constructed for the predic-tion score to separate dynamic information and static informa-tion.Based on the VAR model,appropriate statistical indicators are further constructed for online monitoring,which reduces the occurrence of false alarms.The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system.
基金supported by the National Natural Science Foundation of China (62271255,61871218)the Fundamental Research Funds for the Central University (3082019NC2019002)+1 种基金the Aeronautical Science Foundation (ASFC-201920007002)the Program of Remote Sensing Intelligent Monitoring and Emergency Services for Regional Security Elements。
文摘In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.
文摘In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.
基金Supported by the Breeding Techniques for New Varieties of Acanthopanax senticosus(CZKYF2022-1-B023)。
文摘In order to optimize the ultrasonic extraction technique for the total flavonoid of leaf yellows plus, the contents of 21 leaf yellows plus total flavonoid from four regions in Heilongjiang Province were comparatively analyzed. The ultrasonic extraction technology was optimized by Box-Behnken response surface method, and the total flavonoid content of 21 kinds of Acanthopanax senticosus(Rupr. et Maxim.) Harms from different producing areas were analyzed by cluster analysis. The optimal process conditions were determined as ultrasonic time 30 min, solid-liquid ratio 1 : 12 and ultrasonic power 250 W, and the average yield of the total flavonoid was 1.453 mg·g^ (-1). By optimizing the ultrasonic-assisted extraction method, the total flavonoid content from different producing areas was compared in the experiment, which provided certain data support for the optimization of the extraction process in the future and laid a certain theoretical foundation for the quality analysis of Chinese medicinal materials.
文摘A GC-MS method for the determination of 27 organochlorine pesticides and 15 Pyrethroid pesticides in animal food is established.The method was based on Gel Permeation chromatography combined with solid-phase extraction for sample preparation.Rapid qualitative and quantitative analyses was carried out by gas chromatography-mass spectrometry under the selective-ion monitoring mode.The experimental results showed that the correlation coefficients were better than 0.99,the recoveries for spiked standards were 70%-104%,the relative standard deviations were 2.1%-15.9%.
