Carcinogenesis describes the process through which normal cells transform into malignant cells(cancer).There were an estimated 18.1 million new cases of cancer(all cancers combined excluding non-melanoma skin cancer)w...Carcinogenesis describes the process through which normal cells transform into malignant cells(cancer).There were an estimated 18.1 million new cases of cancer(all cancers combined excluding non-melanoma skin cancer)worldwide in 2020:8.8 million(48%)in females and 9.3 million(52%)in males,giving a male:female ratio of 10:9.5.It may be initiated by the action of biological,physical,or chemical agents that cause a non-lethal,permanent,DNA error on the cell with a consequence of altered cell metabolism.This altered cell metabolism include the Warburg effect,altered lipid and amino acid metabolism and production of various metabolites.It also results in unique metabolic dependencies that,in some cases,can be targeted with precision medicine and nutrition,including drugs that selectively target metabolic enzymes.Metabolic fingerprinting has been applied to the study of carcinogenesis and is particularly helpful in early diagnosis,staging and choice of treatment,thus improving health outcomes.This technology could therefore be harnessed effectively while combining with other omics technologies.展开更多
Radio frequency fingerprinting(RFF)is a remarkable lightweight authentication scheme to support rapid and scalable identification in the internet of things(IoT)systems.Deep learning(DL)is a critical enabler of RFF ide...Radio frequency fingerprinting(RFF)is a remarkable lightweight authentication scheme to support rapid and scalable identification in the internet of things(IoT)systems.Deep learning(DL)is a critical enabler of RFF identification by leveraging the hardware-level features.However,traditional supervised learning methods require huge labeled training samples.Therefore,how to establish a highperformance supervised learning model with few labels under practical application is still challenging.To address this issue,we in this paper propose a novel RFF semi-supervised learning(RFFSSL)model which can obtain a better performance with few meta labels.Specifically,the proposed RFFSSL model is constituted by a teacher-student network,in which the student network learns from the pseudo label predicted by the teacher.Then,the output of the student model will be exploited to improve the performance of teacher among the labeled data.Furthermore,a comprehensive evaluation on the accuracy is conducted.We derive about 50 GB real long-term evolution(LTE)mobile phone’s raw signal datasets,which is used to evaluate various models.Experimental results demonstrate that the proposed RFFSSL scheme can achieve up to 97%experimental testing accuracy over a noisy environment only with 10%labeled samples when training samples equal to 2700.展开更多
Herbal medicines are popular natural medicines that have been used for decades.The use of alternative medicines continues to expand rapidly across the world.The World Health Organization suggests that quality assessme...Herbal medicines are popular natural medicines that have been used for decades.The use of alternative medicines continues to expand rapidly across the world.The World Health Organization suggests that quality assessment of natural medicines is essential for any therapeutic or health care applications,as their therapeutic potential varies between different geographic origins,plant species,and varieties.Classification of herbal medicines based on a limited number of secondary metabolites is not an ideal approach.Their quality should be considered based on a complete metabolic profile,as their pharmacological activity is not due to a few specific secondary metabolites but rather a larger group of bioactive compounds.A holistic and integrative approach using rapid and nondestructive analytical strategies for the screening of herbal medicines is required for robust characterization.In this study,a rapid and effective quality assessment system for geographical traceability,species,and variety-specific authenticity of the widely used natural medicines turmeric,Ocimum,and Withania somnifera was investigated using Fourier transform near-infrared(FT-NIR)spectroscopy-based metabolic fingerprinting.Four different geographical origins of turmeric,five different Ocimum species,and three different varieties of roots and leaves of Withania somnifera were studied with the aid of machine learning approaches.Extremely good discrimination(R^(2)>0.98,Q^(2)>0.97,and accuracy=1.0)with sensitivity and specificity of 100%was achieved using this metabolic fingerprinting strategy.Our study demonstrated that FT-NIR-based rapid metabolic fingerprinting can be used as a robust analytical method to authenticate several important medicinal herbs.展开更多
The rapid growth of modern vehicles with advanced technologies requires strong security to ensure customer safety.One key system that needs protection is the passive key entry system(PKES).To prevent attacks aimed at ...The rapid growth of modern vehicles with advanced technologies requires strong security to ensure customer safety.One key system that needs protection is the passive key entry system(PKES).To prevent attacks aimed at defeating the PKES,we propose a novel radio frequency(RF)fingerprinting method.Our method extracts the cepstral coefficient feature as a fingerprint of a radio frequency signal.This feature is then analyzed using a convolutional neural network(CNN)for device identification.In evaluation,we conducted experiments to determine the effectiveness of different cepstral coefficient features and the convolutional neural network-based model.Our experimental results revealed that the Gammatone Frequency Cepstral Coefficient(GFCC)was the most compelling feature compared to Mel-Frequency Cepstral Coefficient(MFCC),Inverse Mel-Frequency Cepstral Coefficient(IMFCC),Linear-Frequency Cepstral Coefficient(LFCC),and Bark-Frequency Cepstral Coefficient(BFCC).Additionally,we experimented with evaluating the effectiveness of our method in comparison to existing approaches that are similar to ours.展开更多
With over 10 million points of genetic variation from person to person, every individual’s genome is unique and provides a highly reliable form of identification. This is because the genetic code is specific to each ...With over 10 million points of genetic variation from person to person, every individual’s genome is unique and provides a highly reliable form of identification. This is because the genetic code is specific to each individual and does not change over time. Genetic information has been used to identify individuals in a variety of contexts, such as criminal investigations, paternity tests, and medical research. In this study, each individual’s genetic makeup has been formatted to create a secure, unique code that incorporates various elements, such as species, gender, and the genetic identification code itself. The combinations of markers required for this code have been derived from common single nucleotide polymorphisms (SNPs), points of variation found in the human genome. The final output is in the form of a 24 numerical code with each number having three possible combinations. The custom code can then be utilized to create various modes of identification on the decentralized blockchain network as well as personalized services and products that offer users a novel way to uniquely identify themselves in ways that were not possible before.展开更多
RAPD (Randomly Amplified Polymorphic DNA) analysis was performed with filaments of 15 Porphyra lines representing four important groups (P. yezoensis, P. haitanensis, P. katadai var. Hemiphylla and P. digospermatangia...RAPD (Randomly Amplified Polymorphic DNA) analysis was performed with filaments of 15 Porphyra lines representing four important groups (P. yezoensis, P. haitanensis, P. katadai var. Hemiphylla and P. digospermatangia). Eight stable and repeatable RAPD bands amplified with two primers, OPN-02 and OPJ-18, were selected for the construction of DNA fingerprinting. The RAPD results were scored based on the presence or absence of each of the 8 bands and then converted to computer language expressed with two digitals, 1 and 0, which represented the presence (numbered as 1) or absence (numbered as 0) of each band, respectively. Based on these results, a model DNA fingerprint and a computerized DNA fingerprint were constructed. In the constructed DNA fingerprint, each Porphyra line has its unique fingerprinting pattern and can be easily distinguished from each other. Later, a software, named as PhGI, was designed based on this DNA fingerprinting. It can be used in practical Porphyra line identification.展开更多
Simple sequence repeat(SSR) markers have been shown to be a powerful tool for varieties identification in plants.However,SSR fingerprinting of sweetpotato varieties has been a little reported.In this study,a total of ...Simple sequence repeat(SSR) markers have been shown to be a powerful tool for varieties identification in plants.However,SSR fingerprinting of sweetpotato varieties has been a little reported.In this study,a total of 1 294 SSR primer pairs,including 1 215 genomic-SSR and 79 expressed sequence tag(EST)-SSR primer pairs,were screened with sweetpotato varieties Zhengshu 20 and Luoxushu 8 and their 2 F1 individuals randomly sampled,and 273 and 38 of them generated polymorphic bands,respectively.Four genomic-SSR and 3 EST-SSR primer pairs,which showed good polymorphism,were selected to amplify 203 sweetpotato varieties and gave a total of 172 bands,85(49.42%) of which were polymorphic.All of the 203 sweetpotato varieties showed unique fingerprint patterns,indicating the utility of SSR markers in variety identification of this crop.Polymorphism information content(PIC) ranged from 0.5824 to 0.9322 with an average of 0.8176.SSR-based genetic distances varied from 0.0118 to 0.6353 with an average of 0.3100 among these varieties.Thus,these sweetpotato varieties exhibited high levels of genetic similarity and had distinct fingerprint profiles.The SSR fingerprints of the 203 sweetpotato varieties have been successfully constructed.The highly polymorphic SSR primer pairs developed in this study have the potential to be used as core primer pairs for variety identification,genetic diversity assessment and linkage map construction in sweetpotato and other plants.展开更多
Chinese chestnut is an important nut tree around the world.Although the types of Chinese chestnut resources are abundant,resource utilization and protection of chestnut accessions are still very limited.Here,we finger...Chinese chestnut is an important nut tree around the world.Although the types of Chinese chestnut resources are abundant,resource utilization and protection of chestnut accessions are still very limited.Here,we fingerprinted and determined the genetic relationships and core collections of Chinese chestnuts using 18 fluorescently labeled SSR markers generated from 146 chestnut accessions.Our analyses showed that these markers from the tested accessions are highly polymorphic,with an average allele number(N_(a))and polymorphic information content(PIC)of 8.100 and 0.622 per locus,respectively.Using these strongly distinguishing markers,we successfully constructed unique fingerprints for 146 chestnut accessions and selected seven of the SSR markers as core markers to rapidly distinguish different accessions.Our exploration of the genetic relationships among the five cultivar groups indicated that Chinese chestnut accessions are divided into three regional type groups:group I(North China(NC)and Northwest China(NWC)cultivar groups),group II(middle and lower reaches of the Yangtze River(MLY)cultivar group)and group III(Southeast China(SEC)and Southwest China(SWC)cultivar groups).Finally,we selected 45 core collection members which represent the most genetic diversity of Chinese chestnut accessions.This study provides valuable information for identifying chestnut accessions and understanding the phylogenetic relationships among cultivar groups,which can serve as the basis for efficient breeding in the future.展开更多
High performance liquid chromatographic(HPLC) fingerprints of Cassia seed,a traditional Chinese medicine(TCM),were developed by means of the chromatograms at two wavelengths of 238 and 282 nm.Then,the two data sets we...High performance liquid chromatographic(HPLC) fingerprints of Cassia seed,a traditional Chinese medicine(TCM),were developed by means of the chromatograms at two wavelengths of 238 and 282 nm.Then,the two data sets were combined into one matrix.The application of principal component analysis(PCA) for this data matrix showed that the samples were clustered into four groups in accordance with the plant sources and preparation procedures.Furthermore,partial least squares(PLS),back propagation artificial neu...展开更多
Chromatographic fingerprinting has been perceived as an essential tool for assessing quality and chemical equivalence of traditional Chinese medicine.However,this pattern-oriented approach still has some weak points i...Chromatographic fingerprinting has been perceived as an essential tool for assessing quality and chemical equivalence of traditional Chinese medicine.However,this pattern-oriented approach still has some weak points in terms of chemical coverage and robustness.In this work,we proposed a multiple reaction monitoring(MRM)-based fingerprinting method in which approximately 100 constituents were simultaneously detected for quality assessment.The derivative MRM approach was employed to rapidly design MRM transitions independent of chemical standards,based on which the large-scale fingerprinting method was efficiently established.This approach was exemplified on QiShenYiQi Pill(QSYQ),a traditional Chinese medicine-derived drug product,and its robustness was systematically evaluated by four indices:clustering analysis by principal component analysis,similarity analysis by the congruence coefficient,the number of separated peaks,and the peak area proportion of separated peaks.Compared with conventional ultraviolet-based fingerprints,the MRM fingerprints provided not only better discriminatory capacity for the tested normal/abnormal QSYQ samples,but also higher robustness under different chromatographic conditions(i.e.,flow rate,apparent pH,column temperature,and column).The result also showed for such large-scale fingerprints including a large number of peaks,the angle cosine measure after min-max normalization was more suitable for setting a decision criterion than the unnormalized algorithm.This proof-of-concept application gives evidence that combining MRM technique with proper similarity analysis metrices can provide a highly sensitive,robust and comprehensive analytical approach for quality assessment of traditional Chinese medicine.展开更多
Variety identification plays an important role in protecting the intellectual property of varieties,ensuring seed quality,and encouraging breeding innovation.Currently,morphological evaluation in the field,such as dis...Variety identification plays an important role in protecting the intellectual property of varieties,ensuring seed quality,and encouraging breeding innovation.Currently,morphological evaluation in the field,such as distinctness,uniformity,and stability(DUS)testing,and DNA fingerprinting in the laboratory using molecular markers are two dominant methods used for variety identification.Few studies have compared the results of these approaches,and the relationship between the two methods is obscure.In this study,134 dominant cucumber varieties were evaluated using 50 DUS testing traits and genotyped by 40 single nucleotide polymorphisms(SNPs).The 40 SNPs were developed in our previous study and arewell suited for variety identification.In the DUS testing,significant positive or negative correlations among 50 DUS traits were observed,and 20 core traits,including 15 fruit traits,were further selected to increase field inspection efficiency.This suggested that fruit shape plays an important role in variety identification.The ratio of fruit length/diameter was themost important trait,explaining 9.2%of the phenotypic variation.In the DNA fingerprinting test,the 40 SNPs were highly polymorphic and could distinguish all of the 134 cucumber varieties,and 14 core SNPs were selected to improve the identification rate.Interestingly,the population structure analysis of 134 cucumber varieties by phenotypic data in the DUS test was in accordance with the genotypic data from the DNA fingerprinting,indicating that all varieties could be divided into the same four subgroups:European type,North China type,South China type,and hybrids of the North China and South China types.Moreover,linear correlativity of distinguishment for each pair of varieties was observed between the DUS test and the DNA fingerprinting.These results indicated that these two methods have good application in future research,especially for the scaled-up analysis of hundreds of varieties.展开更多
Objective: In this study, one of the objectives was to investigate the total flavonoid contents of Fupenzi (R. chingii Hu.) obtained from different regions of China and to evaluate their anatioxidant activities. And t...Objective: In this study, one of the objectives was to investigate the total flavonoid contents of Fupenzi (R. chingii Hu.) obtained from different regions of China and to evaluate their anatioxidant activities. And the second objective of this study was to develop a validated HPLC method for chromatographic fingerprints of the samples extracts of Fupenzi. Method: The total flavonoid contents were determined by a colorimetric method and the antioxidant activity was determined spectrophotometrically by DPPH and ABTS radical scavenging assays. The chromatographic fingerprint was developed by high-performance liquid chromatography coupled with diode array detection for the control of Fupenzi. Results: A significant correlation between antioxidant activity and the total flavonoid content was observed for the DPPH assay (r2 = 0.758, ρ = 0.004) and the ABTS assay (r2 = 0.788, ρ = 0.002). Under the optimized chromatographic conditions, the validated method was successfully applied to assessment of chemical fingerprinting of 12 batches of FPZ collected from different regions of China. Comparisons of the chromatograms showed that 15 characteristic peaks could be selected as markers for identification and evaluation of Fupenzi. In addition, the proposed method was also successfully applied to simultaneous determination of five compounds (including puerarin, rutin, hyperin, quercetin and kaempferol) in these samples. Conclusions: The qualitative and quantitative analysis described in this paper could be used for identification and evaluation of Fupenzi.展开更多
A Wi-Fi fingerprinting localization approach has attracted increasing attention in recent years due to the ubiquity of Access Point( AP). However,typical fingerprinting localization methods fail to resist accidental e...A Wi-Fi fingerprinting localization approach has attracted increasing attention in recent years due to the ubiquity of Access Point( AP). However,typical fingerprinting localization methods fail to resist accidental environmental changes,such as AP movement. In order to address this problem,a robust fingerprinting indoor localization method is initiated. In the offline phase,three attributes of Received Signal Strength Indication( RSSI) —average,standard deviation and AP's response rate—are computed to prepare for the subsequent computation. In this way,the underlying location-relevant information can be captured comprehensively. Then in the online phase, a three-step voting scheme-based decision mechanism is demonstrated, detecting and eliminating the part of AP where the signals measured are severely distorted by AP 's movement. In the following localization step,in order to achieve accuracy and efficiency simultaneously,a novel fingerprinting localization algorithm is applied. Bhattacharyya distance is utilized to measure the RSSI distribution distance,thus realizing the optimization of MAximum Overlapping algorithm( MAO). Finally,experimental results are displayed,which demonstrate the effectiveness of our proposed methods in eliminating outliers and attaining relatively higher localization accuracy.展开更多
The main cultivated varieties in the world belong to the species of upland cotton(Gossypium hirsutum L.),and their genetic background is very narrow.However,the wild species and races in
In this paper, a method to fingerprint digital images is proposed, and different watermarked copies with different identification string are made. After determining the number of the customers and the length of the wa...In this paper, a method to fingerprint digital images is proposed, and different watermarked copies with different identification string are made. After determining the number of the customers and the length of the watermark string, this method chooses some values inside the digital image using a characteristic function, and adds watermarks to these values in a way that can protect the product against the attacks happened by comparing two fingerprinted copies.The watermarks are a string of binary numbers -1s and 1s. Every customer will be distinguished by a series of 1s and -1s generated by a pseudo-random generator. The owner of the image can determine the number of customers and the length of the string as well as this method will add another watermarking values to watermark string to protect the product.展开更多
Due to the inability of the Global Positioning System(GPS)signals to penetrate through surfaces like roofs,walls,and other objects in indoor environments,numerous alternative methods for user positioning have been pre...Due to the inability of the Global Positioning System(GPS)signals to penetrate through surfaces like roofs,walls,and other objects in indoor environments,numerous alternative methods for user positioning have been presented.Amongst those,the Wi-Fi fingerprinting method has gained considerable interest in Indoor Positioning Systems(IPS)as the need for lineof-sight measurements is minimal,and it achieves better efficiency in even complex indoor environments.Offline and online are the two phases of the fingerprinting method.Many researchers have highlighted the problems in the offline phase as it deals with huge datasets and validation of Fingerprints without pre-processing of data becomes a concern.Machine learning is used for the model training in the offline phase while the locations are estimated in the online phase.Many researchers have considered the concerns in the offline phase as it deals with huge datasets and validation of Fingerprints becomes an issue.Machine learning algorithms are a natural solution for winnowing through large datasets and determining the significant fragments of information for localization,creating precise models to predict an indoor location.Large training sets are a key for obtaining better results in machine learning problems.Therefore,an existing WLAN fingerprinting-based multistory building location database has been used with 21049 samples including 19938 training and 1111 testing samples.The proposed model consists of mean and median filtering as pre-processing techniques applied to the database for enhancing the accuracy by mitigating the impact of environmental dispersion and investigated machine learning algorithms(kNN,WkNN,FSkNN,and SVM)for estimating the location.The proposed SVM with median filtering algorithm gives a reduced mean positioning error of 0.7959 m and an improved efficiency of 92.84%as compared to all variants of the proposed method for 108703 m^(2) area.展开更多
This paper considers outdoor fingerprinting localization in LTE cellular Networks,which can localize non-cooperative user equipment(UE)that is unwilling to provide Global Positioning System(GPS)information.We propose ...This paper considers outdoor fingerprinting localization in LTE cellular Networks,which can localize non-cooperative user equipment(UE)that is unwilling to provide Global Positioning System(GPS)information.We propose a low-cost fingerprinting localization scheme that can improve the localization accuracy while reducing the computational complexity.Firstly,a data filtering strategy is employed to filter the fingerprints which are far from the target UE by using the Cell-ID,Timing Advance(TA)and eNodeB environment information,and the distribution of TA difference is analyzed to guide how to use TA rationally in the filtering strategy.Then,improved Weighted K Nearest Neighbors(WKNN)are implemented on the filtered fingerprints to give the final location prediction,and the WKNN is improved by removing the fingerprints that are still far away from the most of the K neighbors.Experiment results show that the performance is improved by the proposed localization scheme,and positioning errors corresponding to Cumulative Distribution Function(CDF)equaling to 67% and 95% are declined to 50 m and 150 m.展开更多
In order to reduce deleterious effect on environment,human health and facilities caused by original sulfides, more attention should be paid to biodesulfurization studying for fossil fuels. In this work, eight isolates...In order to reduce deleterious effect on environment,human health and facilities caused by original sulfides, more attention should be paid to biodesulfurization studying for fossil fuels. In this work, eight isolates were characterized by several DNA-based methods such as BOX element polymerase chain reaction( BOX-PCR), enterobacterial repetitive intergenic consensus( ERIC)-PCR and random amplification of polymorphic DNA( RAPD)-PCR. The desulfurization performance was determined by micro-coulometric method,Gibb's assay and barium sulfate test. It was found out that ERIC-PCR displays a much higher inter-strain heterogeneity compared with using BOX. The length of the primer didnot play the most important role in bacterial classification. The combination of the analysis of repetitive-sequence-based polymerase chain reaction ngerprinting and 16 S r DNA was able to provide more effective way in the separation and identification of bacteria.According to the analysis of 16 S r DNA,the more efficient desulfurization strain should belong to Klebsiella variicola.展开更多
Development of fingerprints based on DNA markers is necessary for proper identification and standardization of plant species. These techniques are widely used to develop an unquestionable method of plant identificatio...Development of fingerprints based on DNA markers is necessary for proper identification and standardization of plant species. These techniques are widely used to develop an unquestionable method of plant identification to protect the patents and quality control for industry. In this study, fifteen commercially important medicinal plants of Pakistan were collected from botanical garden of Qarshi Industries (Pvt.) Ltd, Pakistan. The objective was to optimize the extraction of genomic DNA for use in a PCR-based random amplified polymorphic DNA marker approach. The initial protocol used 60 decamers to amplify scorable amplicons;only nine markers produced significant bands in genomic DNA of medicinal plants. These markers generated 51 bands ranging between 250 and 1600 bp. The most important property of genomic markers is polymorphism to enable specific identification;all the used markers showed 100% polymorphism across 15 different plants. Further, six decamers amplified specific bands to reliably identify 8 species. The amplified bands were arranged in a binary matrix and analyzed by DNAMAN version 5.2.2 statistical software. A homology tree was constructed using binary data for nine markers, and four major clusters/clades were observed. The Rose, Mentha and Stevia accessions had shown clear clustering and grouped in major clusters/clads I, II and III respectively. Sixty decamers amplified 51 polymorphic loci in the genomes of 15 commercially valuable accessions. Moreover clear phylogenetic construction was observed in the generation of homolog tree. This protocol could therefore be useful to provide a baseline to authenticate, identify and perform phylogenetic analysis of important medicinal plants used in the Pakistani herbal medicine industry.展开更多
In recent years, indoor localization becomes more and more essential in our daily life thanks to its interesting applications that cover all domains including security, tourism. Unfortunately, the existing outdoor loc...In recent years, indoor localization becomes more and more essential in our daily life thanks to its interesting applications that cover all domains including security, tourism. Unfortunately, the existing outdoor localization systems fails in indoor environment, which has motivated researchers to develop new localization systems that challenge the indoor environments. In our work, we propose a 3D fingerprinting-based localization system that estimates a source position using acoustic signals. The latter has the advantage of being used in almost roaming devices. No dedicated infrastructure is necessary and the existing infrastructures can then be reused for indoor purposes. The proposed system has been evaluated in experimental tests in an area of dimensions 1.5 m * 1.5 m * 2 m when four microphones were placed at known positions and an artificial fan is turned on. Results show that turbulence affects the precision of estimating the source position by 7% for an accuracy of 8.5 cm.展开更多
文摘Carcinogenesis describes the process through which normal cells transform into malignant cells(cancer).There were an estimated 18.1 million new cases of cancer(all cancers combined excluding non-melanoma skin cancer)worldwide in 2020:8.8 million(48%)in females and 9.3 million(52%)in males,giving a male:female ratio of 10:9.5.It may be initiated by the action of biological,physical,or chemical agents that cause a non-lethal,permanent,DNA error on the cell with a consequence of altered cell metabolism.This altered cell metabolism include the Warburg effect,altered lipid and amino acid metabolism and production of various metabolites.It also results in unique metabolic dependencies that,in some cases,can be targeted with precision medicine and nutrition,including drugs that selectively target metabolic enzymes.Metabolic fingerprinting has been applied to the study of carcinogenesis and is particularly helpful in early diagnosis,staging and choice of treatment,thus improving health outcomes.This technology could therefore be harnessed effectively while combining with other omics technologies.
基金supported by Innovation Talents Promotion Program of Shaanxi Province,China(No.2021TD08)。
文摘Radio frequency fingerprinting(RFF)is a remarkable lightweight authentication scheme to support rapid and scalable identification in the internet of things(IoT)systems.Deep learning(DL)is a critical enabler of RFF identification by leveraging the hardware-level features.However,traditional supervised learning methods require huge labeled training samples.Therefore,how to establish a highperformance supervised learning model with few labels under practical application is still challenging.To address this issue,we in this paper propose a novel RFF semi-supervised learning(RFFSSL)model which can obtain a better performance with few meta labels.Specifically,the proposed RFFSSL model is constituted by a teacher-student network,in which the student network learns from the pseudo label predicted by the teacher.Then,the output of the student model will be exploited to improve the performance of teacher among the labeled data.Furthermore,a comprehensive evaluation on the accuracy is conducted.We derive about 50 GB real long-term evolution(LTE)mobile phone’s raw signal datasets,which is used to evaluate various models.Experimental results demonstrate that the proposed RFFSSL scheme can achieve up to 97%experimental testing accuracy over a noisy environment only with 10%labeled samples when training samples equal to 2700.
基金Department of Science and Technology-SERB-SRG research grant(Grant No.:SRG/2021/000750-G)and Department of Biotechnology for Ramalingaswami grant(Grant No.:BT/RLF/Re-entry/21/2020)Director,Prabodh Kumar Trivedi,of CSIR-CIMAP for providing infrastructure,facility,and funding support from CSIR,India(Grant Nos.:FC2020-23/NMITLI/TLP0001&TLP0002)We acknowledge Dr.Ritu Trivedi(CSIR-CDRI Lucknow,India)for support and Dr.Abolie Girme and Dr.Lal Hingorani(Pharmanza herbal Pvt.Ltd,India)for providing Withania somnifera samples.We acknowledge Dr.Neerja Tiwari for FT-NIR access,Ms.Manju Yadav and Ms.Namita Gupta for HPLC access,and Ms.Anju Yadav for GC-MS access.Authors would like to thank Aroma mission HCP-0007,India for funding support.Prof.Christopher T.Elliott would like to thank Bualuang ASEAN Chair Professor Fund,UK and Queen's University Belfast Fund,UK.
文摘Herbal medicines are popular natural medicines that have been used for decades.The use of alternative medicines continues to expand rapidly across the world.The World Health Organization suggests that quality assessment of natural medicines is essential for any therapeutic or health care applications,as their therapeutic potential varies between different geographic origins,plant species,and varieties.Classification of herbal medicines based on a limited number of secondary metabolites is not an ideal approach.Their quality should be considered based on a complete metabolic profile,as their pharmacological activity is not due to a few specific secondary metabolites but rather a larger group of bioactive compounds.A holistic and integrative approach using rapid and nondestructive analytical strategies for the screening of herbal medicines is required for robust characterization.In this study,a rapid and effective quality assessment system for geographical traceability,species,and variety-specific authenticity of the widely used natural medicines turmeric,Ocimum,and Withania somnifera was investigated using Fourier transform near-infrared(FT-NIR)spectroscopy-based metabolic fingerprinting.Four different geographical origins of turmeric,five different Ocimum species,and three different varieties of roots and leaves of Withania somnifera were studied with the aid of machine learning approaches.Extremely good discrimination(R^(2)>0.98,Q^(2)>0.97,and accuracy=1.0)with sensitivity and specificity of 100%was achieved using this metabolic fingerprinting strategy.Our study demonstrated that FT-NIR-based rapid metabolic fingerprinting can be used as a robust analytical method to authenticate several important medicinal herbs.
基金supported by Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea Government(MIST)(No.2022-0-01022,Development of Collection and Integrated Analysis Methods of Automotive Inter/Intra System Artifacts through Construction of Event-Based Experimental System).
文摘The rapid growth of modern vehicles with advanced technologies requires strong security to ensure customer safety.One key system that needs protection is the passive key entry system(PKES).To prevent attacks aimed at defeating the PKES,we propose a novel radio frequency(RF)fingerprinting method.Our method extracts the cepstral coefficient feature as a fingerprint of a radio frequency signal.This feature is then analyzed using a convolutional neural network(CNN)for device identification.In evaluation,we conducted experiments to determine the effectiveness of different cepstral coefficient features and the convolutional neural network-based model.Our experimental results revealed that the Gammatone Frequency Cepstral Coefficient(GFCC)was the most compelling feature compared to Mel-Frequency Cepstral Coefficient(MFCC),Inverse Mel-Frequency Cepstral Coefficient(IMFCC),Linear-Frequency Cepstral Coefficient(LFCC),and Bark-Frequency Cepstral Coefficient(BFCC).Additionally,we experimented with evaluating the effectiveness of our method in comparison to existing approaches that are similar to ours.
文摘With over 10 million points of genetic variation from person to person, every individual’s genome is unique and provides a highly reliable form of identification. This is because the genetic code is specific to each individual and does not change over time. Genetic information has been used to identify individuals in a variety of contexts, such as criminal investigations, paternity tests, and medical research. In this study, each individual’s genetic makeup has been formatted to create a secure, unique code that incorporates various elements, such as species, gender, and the genetic identification code itself. The combinations of markers required for this code have been derived from common single nucleotide polymorphisms (SNPs), points of variation found in the human genome. The final output is in the form of a 24 numerical code with each number having three possible combinations. The custom code can then be utilized to create various modes of identification on the decentralized blockchain network as well as personalized services and products that offer users a novel way to uniquely identify themselves in ways that were not possible before.
基金This study was supported by China Ocean "863" ProjectNational Natural Science Foundation of China.
文摘RAPD (Randomly Amplified Polymorphic DNA) analysis was performed with filaments of 15 Porphyra lines representing four important groups (P. yezoensis, P. haitanensis, P. katadai var. Hemiphylla and P. digospermatangia). Eight stable and repeatable RAPD bands amplified with two primers, OPN-02 and OPJ-18, were selected for the construction of DNA fingerprinting. The RAPD results were scored based on the presence or absence of each of the 8 bands and then converted to computer language expressed with two digitals, 1 and 0, which represented the presence (numbered as 1) or absence (numbered as 0) of each band, respectively. Based on these results, a model DNA fingerprint and a computerized DNA fingerprint were constructed. In the constructed DNA fingerprint, each Porphyra line has its unique fingerprinting pattern and can be easily distinguished from each other. Later, a software, named as PhGI, was designed based on this DNA fingerprinting. It can be used in practical Porphyra line identification.
基金supported by the earmarked fund for the China Agriculture Research System (CARS-11)the National Natural Science Foundation of China (31461143017)the Science andTechnology Planning Project of Guangdong Province,China (2015B020202008)
文摘Simple sequence repeat(SSR) markers have been shown to be a powerful tool for varieties identification in plants.However,SSR fingerprinting of sweetpotato varieties has been a little reported.In this study,a total of 1 294 SSR primer pairs,including 1 215 genomic-SSR and 79 expressed sequence tag(EST)-SSR primer pairs,were screened with sweetpotato varieties Zhengshu 20 and Luoxushu 8 and their 2 F1 individuals randomly sampled,and 273 and 38 of them generated polymorphic bands,respectively.Four genomic-SSR and 3 EST-SSR primer pairs,which showed good polymorphism,were selected to amplify 203 sweetpotato varieties and gave a total of 172 bands,85(49.42%) of which were polymorphic.All of the 203 sweetpotato varieties showed unique fingerprint patterns,indicating the utility of SSR markers in variety identification of this crop.Polymorphism information content(PIC) ranged from 0.5824 to 0.9322 with an average of 0.8176.SSR-based genetic distances varied from 0.0118 to 0.6353 with an average of 0.3100 among these varieties.Thus,these sweetpotato varieties exhibited high levels of genetic similarity and had distinct fingerprint profiles.The SSR fingerprints of the 203 sweetpotato varieties have been successfully constructed.The highly polymorphic SSR primer pairs developed in this study have the potential to be used as core primer pairs for variety identification,genetic diversity assessment and linkage map construction in sweetpotato and other plants.
基金the Project of Construction of Innovative Teams and Teacher Career Development for Universities and Colleges under Beijing Municipality,China(IDHT20180509)the National Key Research&Development Program of China(2018YFD1000605)the Opening Project of Beijing Key Laboratory of New Technology in Agricultural Application,China(kf2018024)。
文摘Chinese chestnut is an important nut tree around the world.Although the types of Chinese chestnut resources are abundant,resource utilization and protection of chestnut accessions are still very limited.Here,we fingerprinted and determined the genetic relationships and core collections of Chinese chestnuts using 18 fluorescently labeled SSR markers generated from 146 chestnut accessions.Our analyses showed that these markers from the tested accessions are highly polymorphic,with an average allele number(N_(a))and polymorphic information content(PIC)of 8.100 and 0.622 per locus,respectively.Using these strongly distinguishing markers,we successfully constructed unique fingerprints for 146 chestnut accessions and selected seven of the SSR markers as core markers to rapidly distinguish different accessions.Our exploration of the genetic relationships among the five cultivar groups indicated that Chinese chestnut accessions are divided into three regional type groups:group I(North China(NC)and Northwest China(NWC)cultivar groups),group II(middle and lower reaches of the Yangtze River(MLY)cultivar group)and group III(Southeast China(SEC)and Southwest China(SWC)cultivar groups).Finally,we selected 45 core collection members which represent the most genetic diversity of Chinese chestnut accessions.This study provides valuable information for identifying chestnut accessions and understanding the phylogenetic relationships among cultivar groups,which can serve as the basis for efficient breeding in the future.
基金the financial support for this study by the National Natural Science Foundation of China(No.NSFC20562009)the Jiangxi Province Natural Science Foundation(No.JXNSF0620041)the State Key Laboratory of Food Science and Technology of Nanchang University(Nos.SKLF-MB200807 and SKLF-TS200819)
文摘High performance liquid chromatographic(HPLC) fingerprints of Cassia seed,a traditional Chinese medicine(TCM),were developed by means of the chromatograms at two wavelengths of 238 and 282 nm.Then,the two data sets were combined into one matrix.The application of principal component analysis(PCA) for this data matrix showed that the samples were clustered into four groups in accordance with the plant sources and preparation procedures.Furthermore,partial least squares(PLS),back propagation artificial neu...
基金financially supported by the National Natural Science Foundation of China(Grant No.81803714)the Fundamental Research Funds for the Central Universities(Grant No.2019QNA7041).
文摘Chromatographic fingerprinting has been perceived as an essential tool for assessing quality and chemical equivalence of traditional Chinese medicine.However,this pattern-oriented approach still has some weak points in terms of chemical coverage and robustness.In this work,we proposed a multiple reaction monitoring(MRM)-based fingerprinting method in which approximately 100 constituents were simultaneously detected for quality assessment.The derivative MRM approach was employed to rapidly design MRM transitions independent of chemical standards,based on which the large-scale fingerprinting method was efficiently established.This approach was exemplified on QiShenYiQi Pill(QSYQ),a traditional Chinese medicine-derived drug product,and its robustness was systematically evaluated by four indices:clustering analysis by principal component analysis,similarity analysis by the congruence coefficient,the number of separated peaks,and the peak area proportion of separated peaks.Compared with conventional ultraviolet-based fingerprints,the MRM fingerprints provided not only better discriminatory capacity for the tested normal/abnormal QSYQ samples,but also higher robustness under different chromatographic conditions(i.e.,flow rate,apparent pH,column temperature,and column).The result also showed for such large-scale fingerprints including a large number of peaks,the angle cosine measure after min-max normalization was more suitable for setting a decision criterion than the unnormalized algorithm.This proof-of-concept application gives evidence that combining MRM technique with proper similarity analysis metrices can provide a highly sensitive,robust and comprehensive analytical approach for quality assessment of traditional Chinese medicine.
基金supported by the National Natural Science Foundation of China(Grant No.31972432)Beijing Academy of Agricultural and Forestry Sciences,China(Grant Nos.QNJJ20190901,KJCX20200113,JKZX202207),Young Top Talents of the National High-level Talents Special Support Program.
文摘Variety identification plays an important role in protecting the intellectual property of varieties,ensuring seed quality,and encouraging breeding innovation.Currently,morphological evaluation in the field,such as distinctness,uniformity,and stability(DUS)testing,and DNA fingerprinting in the laboratory using molecular markers are two dominant methods used for variety identification.Few studies have compared the results of these approaches,and the relationship between the two methods is obscure.In this study,134 dominant cucumber varieties were evaluated using 50 DUS testing traits and genotyped by 40 single nucleotide polymorphisms(SNPs).The 40 SNPs were developed in our previous study and arewell suited for variety identification.In the DUS testing,significant positive or negative correlations among 50 DUS traits were observed,and 20 core traits,including 15 fruit traits,were further selected to increase field inspection efficiency.This suggested that fruit shape plays an important role in variety identification.The ratio of fruit length/diameter was themost important trait,explaining 9.2%of the phenotypic variation.In the DNA fingerprinting test,the 40 SNPs were highly polymorphic and could distinguish all of the 134 cucumber varieties,and 14 core SNPs were selected to improve the identification rate.Interestingly,the population structure analysis of 134 cucumber varieties by phenotypic data in the DUS test was in accordance with the genotypic data from the DNA fingerprinting,indicating that all varieties could be divided into the same four subgroups:European type,North China type,South China type,and hybrids of the North China and South China types.Moreover,linear correlativity of distinguishment for each pair of varieties was observed between the DUS test and the DNA fingerprinting.These results indicated that these two methods have good application in future research,especially for the scaled-up analysis of hundreds of varieties.
文摘Objective: In this study, one of the objectives was to investigate the total flavonoid contents of Fupenzi (R. chingii Hu.) obtained from different regions of China and to evaluate their anatioxidant activities. And the second objective of this study was to develop a validated HPLC method for chromatographic fingerprints of the samples extracts of Fupenzi. Method: The total flavonoid contents were determined by a colorimetric method and the antioxidant activity was determined spectrophotometrically by DPPH and ABTS radical scavenging assays. The chromatographic fingerprint was developed by high-performance liquid chromatography coupled with diode array detection for the control of Fupenzi. Results: A significant correlation between antioxidant activity and the total flavonoid content was observed for the DPPH assay (r2 = 0.758, ρ = 0.004) and the ABTS assay (r2 = 0.788, ρ = 0.002). Under the optimized chromatographic conditions, the validated method was successfully applied to assessment of chemical fingerprinting of 12 batches of FPZ collected from different regions of China. Comparisons of the chromatograms showed that 15 characteristic peaks could be selected as markers for identification and evaluation of Fupenzi. In addition, the proposed method was also successfully applied to simultaneous determination of five compounds (including puerarin, rutin, hyperin, quercetin and kaempferol) in these samples. Conclusions: The qualitative and quantitative analysis described in this paper could be used for identification and evaluation of Fupenzi.
基金Sponsored by the National High Technology Research and Development Program of China(Grant No.2014AA123103)
文摘A Wi-Fi fingerprinting localization approach has attracted increasing attention in recent years due to the ubiquity of Access Point( AP). However,typical fingerprinting localization methods fail to resist accidental environmental changes,such as AP movement. In order to address this problem,a robust fingerprinting indoor localization method is initiated. In the offline phase,three attributes of Received Signal Strength Indication( RSSI) —average,standard deviation and AP's response rate—are computed to prepare for the subsequent computation. In this way,the underlying location-relevant information can be captured comprehensively. Then in the online phase, a three-step voting scheme-based decision mechanism is demonstrated, detecting and eliminating the part of AP where the signals measured are severely distorted by AP 's movement. In the following localization step,in order to achieve accuracy and efficiency simultaneously,a novel fingerprinting localization algorithm is applied. Bhattacharyya distance is utilized to measure the RSSI distribution distance,thus realizing the optimization of MAximum Overlapping algorithm( MAO). Finally,experimental results are displayed,which demonstrate the effectiveness of our proposed methods in eliminating outliers and attaining relatively higher localization accuracy.
文摘The main cultivated varieties in the world belong to the species of upland cotton(Gossypium hirsutum L.),and their genetic background is very narrow.However,the wild species and races in
基金Supported by the National Natural Science Foundation(No.69882002,69772035)National "863" Programme(863-ZT05-2)
文摘In this paper, a method to fingerprint digital images is proposed, and different watermarked copies with different identification string are made. After determining the number of the customers and the length of the watermark string, this method chooses some values inside the digital image using a characteristic function, and adds watermarks to these values in a way that can protect the product against the attacks happened by comparing two fingerprinted copies.The watermarks are a string of binary numbers -1s and 1s. Every customer will be distinguished by a series of 1s and -1s generated by a pseudo-random generator. The owner of the image can determine the number of customers and the length of the string as well as this method will add another watermarking values to watermark string to protect the product.
基金The authors extend their appreciation to the National University of Sciences and Technology for funding this work through the Researchers Supporting Grant,National University of Sciences and Technology,Islamabad,Pakistan.
文摘Due to the inability of the Global Positioning System(GPS)signals to penetrate through surfaces like roofs,walls,and other objects in indoor environments,numerous alternative methods for user positioning have been presented.Amongst those,the Wi-Fi fingerprinting method has gained considerable interest in Indoor Positioning Systems(IPS)as the need for lineof-sight measurements is minimal,and it achieves better efficiency in even complex indoor environments.Offline and online are the two phases of the fingerprinting method.Many researchers have highlighted the problems in the offline phase as it deals with huge datasets and validation of Fingerprints without pre-processing of data becomes a concern.Machine learning is used for the model training in the offline phase while the locations are estimated in the online phase.Many researchers have considered the concerns in the offline phase as it deals with huge datasets and validation of Fingerprints becomes an issue.Machine learning algorithms are a natural solution for winnowing through large datasets and determining the significant fragments of information for localization,creating precise models to predict an indoor location.Large training sets are a key for obtaining better results in machine learning problems.Therefore,an existing WLAN fingerprinting-based multistory building location database has been used with 21049 samples including 19938 training and 1111 testing samples.The proposed model consists of mean and median filtering as pre-processing techniques applied to the database for enhancing the accuracy by mitigating the impact of environmental dispersion and investigated machine learning algorithms(kNN,WkNN,FSkNN,and SVM)for estimating the location.The proposed SVM with median filtering algorithm gives a reduced mean positioning error of 0.7959 m and an improved efficiency of 92.84%as compared to all variants of the proposed method for 108703 m^(2) area.
基金supported by the ZTE Industry-Academia-Research Cooperation Funds under Grant No.20160722-01
文摘This paper considers outdoor fingerprinting localization in LTE cellular Networks,which can localize non-cooperative user equipment(UE)that is unwilling to provide Global Positioning System(GPS)information.We propose a low-cost fingerprinting localization scheme that can improve the localization accuracy while reducing the computational complexity.Firstly,a data filtering strategy is employed to filter the fingerprints which are far from the target UE by using the Cell-ID,Timing Advance(TA)and eNodeB environment information,and the distribution of TA difference is analyzed to guide how to use TA rationally in the filtering strategy.Then,improved Weighted K Nearest Neighbors(WKNN)are implemented on the filtered fingerprints to give the final location prediction,and the WKNN is improved by removing the fingerprints that are still far away from the most of the K neighbors.Experiment results show that the performance is improved by the proposed localization scheme,and positioning errors corresponding to Cumulative Distribution Function(CDF)equaling to 67% and 95% are declined to 50 m and 150 m.
基金National Natural Science Foundations of China(Nos.41302079,21176145)Project of Shandong Province Higher Educational Science and Technology Program,China(No.J13LD54)
文摘In order to reduce deleterious effect on environment,human health and facilities caused by original sulfides, more attention should be paid to biodesulfurization studying for fossil fuels. In this work, eight isolates were characterized by several DNA-based methods such as BOX element polymerase chain reaction( BOX-PCR), enterobacterial repetitive intergenic consensus( ERIC)-PCR and random amplification of polymorphic DNA( RAPD)-PCR. The desulfurization performance was determined by micro-coulometric method,Gibb's assay and barium sulfate test. It was found out that ERIC-PCR displays a much higher inter-strain heterogeneity compared with using BOX. The length of the primer didnot play the most important role in bacterial classification. The combination of the analysis of repetitive-sequence-based polymerase chain reaction ngerprinting and 16 S r DNA was able to provide more effective way in the separation and identification of bacteria.According to the analysis of 16 S r DNA,the more efficient desulfurization strain should belong to Klebsiella variicola.
文摘Development of fingerprints based on DNA markers is necessary for proper identification and standardization of plant species. These techniques are widely used to develop an unquestionable method of plant identification to protect the patents and quality control for industry. In this study, fifteen commercially important medicinal plants of Pakistan were collected from botanical garden of Qarshi Industries (Pvt.) Ltd, Pakistan. The objective was to optimize the extraction of genomic DNA for use in a PCR-based random amplified polymorphic DNA marker approach. The initial protocol used 60 decamers to amplify scorable amplicons;only nine markers produced significant bands in genomic DNA of medicinal plants. These markers generated 51 bands ranging between 250 and 1600 bp. The most important property of genomic markers is polymorphism to enable specific identification;all the used markers showed 100% polymorphism across 15 different plants. Further, six decamers amplified specific bands to reliably identify 8 species. The amplified bands were arranged in a binary matrix and analyzed by DNAMAN version 5.2.2 statistical software. A homology tree was constructed using binary data for nine markers, and four major clusters/clades were observed. The Rose, Mentha and Stevia accessions had shown clear clustering and grouped in major clusters/clads I, II and III respectively. Sixty decamers amplified 51 polymorphic loci in the genomes of 15 commercially valuable accessions. Moreover clear phylogenetic construction was observed in the generation of homolog tree. This protocol could therefore be useful to provide a baseline to authenticate, identify and perform phylogenetic analysis of important medicinal plants used in the Pakistani herbal medicine industry.
文摘In recent years, indoor localization becomes more and more essential in our daily life thanks to its interesting applications that cover all domains including security, tourism. Unfortunately, the existing outdoor localization systems fails in indoor environment, which has motivated researchers to develop new localization systems that challenge the indoor environments. In our work, we propose a 3D fingerprinting-based localization system that estimates a source position using acoustic signals. The latter has the advantage of being used in almost roaming devices. No dedicated infrastructure is necessary and the existing infrastructures can then be reused for indoor purposes. The proposed system has been evaluated in experimental tests in an area of dimensions 1.5 m * 1.5 m * 2 m when four microphones were placed at known positions and an artificial fan is turned on. Results show that turbulence affects the precision of estimating the source position by 7% for an accuracy of 8.5 cm.
