BACKGROUND As a well-known fact to the public,gestational diabetes mellitus(GDM)could bring serious risks for both pregnant women and infants.During this important investigation into the linkage between GDM patients a...BACKGROUND As a well-known fact to the public,gestational diabetes mellitus(GDM)could bring serious risks for both pregnant women and infants.During this important investigation into the linkage between GDM patients and their altered expression in the serum,proteomics techniques were deployed to detect the differentially expressed proteins(DEPs)of in the serum of GDM patients to further explore its pathogenesis,and find out possible biomarkers to forecast GDM occurrence.METHODS Subjects were divided into GDM and normal control groups according to the IADPSG diagnostic criteria.Serum samples were randomly selected from four cases in each group at 24-28 wk of gestation,and the blood samples were identified by applying iTRAQ technology combined with liquid chromatography-tandem mass spectrometry.Key proteins and signaling pathways associated with GDM were identified by bioinformatics analysis,and the expression of key proteins in serum from 12 wk to 16 wk of gestation was further verified using enzyme-linked immunosorbent assay (ELISA).RESULTS Forty-seven proteins were significantly differentially expressed by analyzing the serum samples between the GDMgravidas as well as the healthy ones. Among them, 31 proteins were found to be upregulated notably and the rest16 proteins were downregulated remarkably. Bioinformatic data report revealed abnormal expression of proteinsassociated with lipid metabolism, coagulation cascade activation, complement system and inflammatory responsein the GDM group. ELISA results showed that the contents of RBP4, as well as ANGPTL8, increased in the serumof GDM gravidas compared with the healthy ones, and this change was found to initiate from 12 wk to 16 wk ofgestation.CONCLUSION GDM symptoms may involve abnormalities in lipid metabolism, coagulation cascade activation, complementsystem and inflammatory response. RBP4 and ANGPTL8 are expected to be early predictors of GDM.展开更多
In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the articles.Besides,it uses co-occurrence techniq...In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the articles.Besides,it uses co-occurrence techniques and tries to combine nodes’textual content for modelling.They still do not,however,directly simulate many interactions in network learning.In order to address these issues,we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling recommendations.Specifically,we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles in the citation network.Then,the Commuting Matrix for massive node pair paths is used to improve computational performance.Finally,the two commonalities mentioned above are combined with the interaction paper labels based on the additivity of Poisson distribution.In addition,we also consider solving the model’s parameters by applying variational inference.Experimental results demonstrate that our suggested framework agrees and significantly outperforms the state-of-the-art baseline on two real datasets by efficiently merging the three relational data.Based on the Area Under Curve(AUC)and Mean Average Precision(MAP)analysis,the performance of the suggested task is evaluated,and it is demonstrated to have a greater solving efficiency than current techniques.展开更多
When the radio frequency identification(RFID)system inventories multiple tags,the recognition rate will be seriously affected due to collisions.Based on the existing dynamic frame slotted Aloha(DFSA)algorithm,a sub-fr...When the radio frequency identification(RFID)system inventories multiple tags,the recognition rate will be seriously affected due to collisions.Based on the existing dynamic frame slotted Aloha(DFSA)algorithm,a sub-frame observation and cyclic redundancy check(CRC)grouping combined dynamic framed slotted Aloha(SUBF-CGDFSA)algorithm is proposed.The algorithm combines the precise estimation method of the quantity of large-scale tags,the large-scale tags grouping mechanism based on CRC pseudo-randomcharacteristics,and the Aloha anti-collision optimization mechanism based on sub-frame observation.By grouping tags and sequentially identifying themwithin subframes,it accurately estimates the number of remaining tags and optimizes frame length accordingly to improve efficiency in large-scale RFID systems.Simulation outcomes demonstrate that this proposed algorithmcan effectively break through the system throughput bottleneck of 36.8%,which is up to 30%higher than the existing DFSA standard scheme,and has more significant advantages,which is suitable for application in largescale RFID tags scenarios.展开更多
Background:The aim of this study was to investigate the influence of marking meth-ods on the outcomes of body composition analysis and provide guidance for the se-lection of marking methods in mouse body composition a...Background:The aim of this study was to investigate the influence of marking meth-ods on the outcomes of body composition analysis and provide guidance for the se-lection of marking methods in mouse body composition analysis.Methods:Male C57BL/6J mice aged 6 weeks were randomly assigned for pre-and post-ear tagging measurements.The body composition of the mice was measured using a small animal body composition analyzer,which provided measurements of the mass of fat,lean,and free fluid.Then,the mass of fat,lean and free fluid to body weight ratio was gained.Further data analysis was conducted to obtain the range and coeffi-cient of variation in body composition measurements for each mouse.The distribution of fat and lean tissue in the mice was also analyzed by comparing the fat-to-lean ratio.Results:(1)The mass of all body composition components in the ear tagging group was significantly lower than that in the control group.(2)There was a significant in-crease in the range and coefficient of variation of body composition measurements between the ear tagging group and the control group.(3)The fat-to-lean ratio in the ear tagging group was significantly lower than that in the control group.Conclusions:Ear tagging significantly lowered the results of body composition analy-sis in mice and higher the results of measurement error.Therefore,ear tagging should be avoided as much as possible when conducting body composition analysis experi-ments in mice.展开更多
Chinese named entity recognition(CNER)has received widespread attention as an important task of Chinese information extraction.Most previous research has focused on individually studying flat CNER,overlapped CNER,or d...Chinese named entity recognition(CNER)has received widespread attention as an important task of Chinese information extraction.Most previous research has focused on individually studying flat CNER,overlapped CNER,or discontinuous CNER.However,a unified CNER is often needed in real-world scenarios.Recent studies have shown that grid tagging-based methods based on character-pair relationship classification hold great potential for achieving unified NER.Nevertheless,how to enrich Chinese character-pair grid representations and capture deeper dependencies between character pairs to improve entity recognition performance remains an unresolved challenge.In this study,we enhance the character-pair grid representation by incorporating both local and global information.Significantly,we introduce a new approach by considering the character-pair grid representation matrix as a specialized image,converting the classification of character-pair relationships into a pixel-level semantic segmentation task.We devise a U-shaped network to extract multi-scale and deeper semantic information from the grid image,allowing for a more comprehensive understanding of associative features between character pairs.This approach leads to improved accuracy in predicting their relationships,ultimately enhancing entity recognition performance.We conducted experiments on two public CNER datasets in the biomedical domain,namely CMeEE-V2 and Diakg.The results demonstrate the effectiveness of our approach,which achieves F1-score improvements of 7.29 percentage points and 1.64 percentage points compared to the current state-of-the-art(SOTA)models,respectively.展开更多
This study was to explore the functional mechanism of rare earth regulating soybean leaves and the characteristics and functions of differentially expressed proteins under the regulation of rare earth. In this study, ...This study was to explore the functional mechanism of rare earth regulating soybean leaves and the characteristics and functions of differentially expressed proteins under the regulation of rare earth. In this study, Dongnong 42 was used as material, and 30 mg·L^(-1) CeCl_(3) solution was sprayed on soybean leaves at the seedling stage. Tandem mass tag(TMT) quantitative proteomics technique and bioinformatics analysis were used to identify soybean leaf proteins. A total of 8 510 proteins were identified, and 127 differentially expressed proteins(DEPs) in response to rare earth cerium regulation were identified, among which 64 were upregulated and 63 were down-regulated. The gene ontology(GO) annotation indicated that DEPs were mainly involved in metabolic process, cellular process, response to stimulus, biological regulation, and response to a stimulus;DEPs in cell module categories were mainly involved in cells, cell part, organelle, membrane, membrane part, organelle par, and protein-containing complex;DEPs in molecular functional categories were mainly involved in catalytic activity, binding and antioxidant activity. Kyoto encyclopedia of genes and genomes(KEGG) pathway significantly enriched starch and sucrose metabolism, glycolysis/gluconeogenesis, galactose metabolism, pentose phosphate pathway, and MAPK signaling pathway-plant. These DEPs were mainly involved in photosynthesis, glucose metabolism and stress response. Forty-six differential protein interaction networks were identified by protein interaction network analysis. This experiment provided a reference for studies of the mechanism of rare earth cerium regulating soybean leaf function from the proteomic perspective.展开更多
Proteomics is a powerful tool that can be used to elucidate the underlying mechanisms of diseases and identify new biomarkers.Therefore,it may also be helpful for understanding the detailed pathological mechanism of t...Proteomics is a powerful tool that can be used to elucidate the underlying mechanisms of diseases and identify new biomarkers.Therefore,it may also be helpful for understanding the detailed pathological mechanism of traumatic brain injury(TBI).In this study,we performed Tandem Mass Tag-based quantitative analysis of cortical proteome profiles in a mouse model of TBI.Our results showed that there were 302 differentially expressed proteins in TBI mice compared with normal mice 7 days after injury.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses showed that these differentially expressed proteins were predominantly involved in inflammatory responses,including complement and coagulation cascades,as well as chemokine signaling pathways.Subsequent transcription factor analysis revealed that the inflammation-related transcription factors NF-κB1,RelA,IRF1,STAT1,and Spi1 play pivotal roles in the secondary injury that occurs after TBI,which further corroborates the functional enrichment for inflammatory factors.Our results suggest that inflammation-related proteins and inflammatory responses are promising targets for the treatment of TBI.展开更多
Radio Frequency Identification(RFID)technology has been widely used to identify missing items.In many applications,rapidly pinpointing key tags that are attached to favorable or valuable items is critical.To realize t...Radio Frequency Identification(RFID)technology has been widely used to identify missing items.In many applications,rapidly pinpointing key tags that are attached to favorable or valuable items is critical.To realize this goal,interference from ordinary tags should be avoided,while key tags should be efficiently verified.Despite many previous studies,how to rapidly and dynamically filter out ordinary tags when the ratio of ordinary tags changes has not been addressed.Moreover,how to efficiently verify missing key tags in groups rather than one by one has not been explored,especially with varying missing rates.In this paper,we propose an Efficient and Robust missing Key tag Identification(ERKI)protocol that consists of a filtering mechanism and a verification mechanism.Specifically,the filtering mechanism adopts the Bloom filter to quickly filter out ordinary tags and uses the labeling vector to optimize the Bloom filter's performance when the key tag ratio is high.Furthermore,the verification mechanism can dynamically verify key tags according to the missing rates,in which an appropriate number of key tags is mapped to a slot and verified at once.Moreover,we theoretically analyze the parameters of the ERKI protocol to minimize its execution time.Extensive numerical results show that ERKI can accelerate the execution time by more than 2.14compared with state-of-the-art solutions.展开更多
Milk thistle(Silybum marianum)is a crucial medicinal plant containing a large amount of oil.In the study,the changes in storage oil during seed germination and seedling transition from heterotrophic phases were invest...Milk thistle(Silybum marianum)is a crucial medicinal plant containing a large amount of oil.In the study,the changes in storage oil during seed germination and seedling transition from heterotrophic phases were investigated.The results showed that seed oil decreased from 19.53%to 0.88%on the 7th day of seedling development.Oil hydrolysis continued until the 4th day of germination with a low slope,but then increased the use of oils in seed germination end seedling growth metabolism.The results indicated that the quantitative changes in fatty acids,presented at lower amount,were relatively higher than dominant fatty acids.There were decreasing phenolic content in the developing seedlings,but overall,lowest level of total phenolic content can be attributed to the control(30.52 mg⋅100 g⋅Oil^(-1)).In contrast,the maximum peroxide value(2.58 meq⋅kg Oil^(-1))in the developing seedling was observed on the last day of the experiment.The results showed that there was a significant correlation between saturated fatty acid,unsaturated fatty acid,and lipase activity.However,the correlation between lipase activity and polyunsaturated fatty acids was significantly higher than between lipase activity and monounsaturated fatty acids(R^(2)=90%and R^(2)=77%,respectively).Therefore,the lipolysis process acts selectively in milk thistle oils.According to the results,C12:0 exhibits a greater impact on the early seedling growth rather than on the germination process and is one of the determining factors in the transition from heterotroph to autotroph.Also,it can be a marker for TAGs breakdown.展开更多
Optical fibers are typically used in telecommunications services for data transmission,where the use of fiber tags is essential to distinguish between the different transmission fibers or channels and thus ensure the ...Optical fibers are typically used in telecommunications services for data transmission,where the use of fiber tags is essential to distinguish between the different transmission fibers or channels and thus ensure the working functionality of the communication system.Traditional physical entity marking methods for fiber labeling are bulky,easily confused,and,most importantly,the label information can be accessed easily by all potential users.This work proposes an encrypted optical fiber tag based on an encoded fiber Bragg grating(FBG)array that is fabricated using a point-by-point femtosecond laser pulse chain inscription method.Gratings with different resonant wavelengths and reflectivities are realized by adjusting the grating period and the refractive index modulations.It is demonstrated that a binary data sequence carried by a fiber tag can be inscribed into the fiber core in the form of an FBG array,and the tag data can be encrypted through appropriate design of the spatial distributions of the FBGs with various reflection wavelengths and reflectivities.The proposed fiber tag technology can be used for applications in port identification,encrypted data storage,and transmission in fiber networks.展开更多
High-frequency(HF)and ultrahigh-frequency(UHF)dual-band radio frequency identification(RFID)tags with both near-field and farfield communication can meet different application scenarios.However,it is time-consuming to...High-frequency(HF)and ultrahigh-frequency(UHF)dual-band radio frequency identification(RFID)tags with both near-field and farfield communication can meet different application scenarios.However,it is time-consuming to calculate the return loss of a UHF antenna in a dualband tag antenna using electromagnetic(EM)simulators.To overcome this,the present work proposes a model of a multi-scale convolutional neural network stacked with long and short-term memory(MSCNN-LSTM)for predicting the return loss of UHF antennas instead of EM simulators.In the proposed MSCNN-LSTM,the MSCNN has three branches,which include three convolution layers with different kernel sizes and numbers.Therefore,MSCNN can extract fine-grain localized information of the antenna and overall features.The LSTM can effectively learn the EM characteristics of different structures of the antenna to improve the prediction accuracy of the model.Experimental results show that the mean absolute error(0.0073),mean square error(0.00032),and root mean square error(0.01814)of theMSCNNLSTM are better than those of other prediction methods.In predicting the return loss of 100UHFantennas,compared with the simulation time of 4800 s for High Frequency Structure Simulator(HFSS),MSCNN-LSTM takes only 0.927519 s under the premise of ensuring prediction accuracy,significantly reducing the calculation time,which provides a basis for the rapid design of HF-UHF RFID tag antenna.ThenMSCNN-LSTM is used to determine the dimensions of the UHF antenna quickly.The return loss of the designed dualband RFID tag antenna is−58.76 and−22.63 dB at 13.56 and 915 MHz,respectively,achieving the desired goal.展开更多
基金This study was reviewed and approved by the Maternal and child health hospital of Hubei Province(Approval No.20201025).
文摘BACKGROUND As a well-known fact to the public,gestational diabetes mellitus(GDM)could bring serious risks for both pregnant women and infants.During this important investigation into the linkage between GDM patients and their altered expression in the serum,proteomics techniques were deployed to detect the differentially expressed proteins(DEPs)of in the serum of GDM patients to further explore its pathogenesis,and find out possible biomarkers to forecast GDM occurrence.METHODS Subjects were divided into GDM and normal control groups according to the IADPSG diagnostic criteria.Serum samples were randomly selected from four cases in each group at 24-28 wk of gestation,and the blood samples were identified by applying iTRAQ technology combined with liquid chromatography-tandem mass spectrometry.Key proteins and signaling pathways associated with GDM were identified by bioinformatics analysis,and the expression of key proteins in serum from 12 wk to 16 wk of gestation was further verified using enzyme-linked immunosorbent assay (ELISA).RESULTS Forty-seven proteins were significantly differentially expressed by analyzing the serum samples between the GDMgravidas as well as the healthy ones. Among them, 31 proteins were found to be upregulated notably and the rest16 proteins were downregulated remarkably. Bioinformatic data report revealed abnormal expression of proteinsassociated with lipid metabolism, coagulation cascade activation, complement system and inflammatory responsein the GDM group. ELISA results showed that the contents of RBP4, as well as ANGPTL8, increased in the serumof GDM gravidas compared with the healthy ones, and this change was found to initiate from 12 wk to 16 wk ofgestation.CONCLUSION GDM symptoms may involve abnormalities in lipid metabolism, coagulation cascade activation, complementsystem and inflammatory response. RBP4 and ANGPTL8 are expected to be early predictors of GDM.
基金supported by the National Natural Science Foundation of China(No.62271274).
文摘In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the articles.Besides,it uses co-occurrence techniques and tries to combine nodes’textual content for modelling.They still do not,however,directly simulate many interactions in network learning.In order to address these issues,we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling recommendations.Specifically,we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles in the citation network.Then,the Commuting Matrix for massive node pair paths is used to improve computational performance.Finally,the two commonalities mentioned above are combined with the interaction paper labels based on the additivity of Poisson distribution.In addition,we also consider solving the model’s parameters by applying variational inference.Experimental results demonstrate that our suggested framework agrees and significantly outperforms the state-of-the-art baseline on two real datasets by efficiently merging the three relational data.Based on the Area Under Curve(AUC)and Mean Average Precision(MAP)analysis,the performance of the suggested task is evaluated,and it is demonstrated to have a greater solving efficiency than current techniques.
基金supported in part by National Natural Science Foundation of China(U22B2004,62371106)in part by the Joint Project of China Mobile Research Institute&X-NET(Project Number:2022H002)+6 种基金in part by the Pre-Research Project(31513070501)in part by National Key R&D Program(2018AAA0103203)in part by Guangdong Provincial Research and Development Plan in Key Areas(2019B010141001)in part by Sichuan Provincial Science and Technology Planning Program of China(2022YFG0230,2023YFG0040)in part by the Fundamental Enhancement Program Technology Area Fund(2021-JCJQ-JJ-0667)in part by the Joint Fund of ZF and Ministry of Education(8091B022126)in part by Innovation Ability Construction Project for Sichuan Provincial Engineering Research Center of Communication Technology for Intelligent IoT(2303-510109-04-03-318020).
文摘When the radio frequency identification(RFID)system inventories multiple tags,the recognition rate will be seriously affected due to collisions.Based on the existing dynamic frame slotted Aloha(DFSA)algorithm,a sub-frame observation and cyclic redundancy check(CRC)grouping combined dynamic framed slotted Aloha(SUBF-CGDFSA)algorithm is proposed.The algorithm combines the precise estimation method of the quantity of large-scale tags,the large-scale tags grouping mechanism based on CRC pseudo-randomcharacteristics,and the Aloha anti-collision optimization mechanism based on sub-frame observation.By grouping tags and sequentially identifying themwithin subframes,it accurately estimates the number of remaining tags and optimizes frame length accordingly to improve efficiency in large-scale RFID systems.Simulation outcomes demonstrate that this proposed algorithmcan effectively break through the system throughput bottleneck of 36.8%,which is up to 30%higher than the existing DFSA standard scheme,and has more significant advantages,which is suitable for application in largescale RFID tags scenarios.
文摘Background:The aim of this study was to investigate the influence of marking meth-ods on the outcomes of body composition analysis and provide guidance for the se-lection of marking methods in mouse body composition analysis.Methods:Male C57BL/6J mice aged 6 weeks were randomly assigned for pre-and post-ear tagging measurements.The body composition of the mice was measured using a small animal body composition analyzer,which provided measurements of the mass of fat,lean,and free fluid.Then,the mass of fat,lean and free fluid to body weight ratio was gained.Further data analysis was conducted to obtain the range and coeffi-cient of variation in body composition measurements for each mouse.The distribution of fat and lean tissue in the mice was also analyzed by comparing the fat-to-lean ratio.Results:(1)The mass of all body composition components in the ear tagging group was significantly lower than that in the control group.(2)There was a significant in-crease in the range and coefficient of variation of body composition measurements between the ear tagging group and the control group.(3)The fat-to-lean ratio in the ear tagging group was significantly lower than that in the control group.Conclusions:Ear tagging significantly lowered the results of body composition analy-sis in mice and higher the results of measurement error.Therefore,ear tagging should be avoided as much as possible when conducting body composition analysis experi-ments in mice.
基金supported by Yunnan Provincial Major Science and Technology Special Plan Projects(Grant Nos.202202AD080003,202202AE090008,202202AD080004,202302AD080003)National Natural Science Foundation of China(Grant Nos.U21B2027,62266027,62266028,62266025)Yunnan Province Young and Middle-Aged Academic and Technical Leaders Reserve Talent Program(Grant No.202305AC160063).
文摘Chinese named entity recognition(CNER)has received widespread attention as an important task of Chinese information extraction.Most previous research has focused on individually studying flat CNER,overlapped CNER,or discontinuous CNER.However,a unified CNER is often needed in real-world scenarios.Recent studies have shown that grid tagging-based methods based on character-pair relationship classification hold great potential for achieving unified NER.Nevertheless,how to enrich Chinese character-pair grid representations and capture deeper dependencies between character pairs to improve entity recognition performance remains an unresolved challenge.In this study,we enhance the character-pair grid representation by incorporating both local and global information.Significantly,we introduce a new approach by considering the character-pair grid representation matrix as a specialized image,converting the classification of character-pair relationships into a pixel-level semantic segmentation task.We devise a U-shaped network to extract multi-scale and deeper semantic information from the grid image,allowing for a more comprehensive understanding of associative features between character pairs.This approach leads to improved accuracy in predicting their relationships,ultimately enhancing entity recognition performance.We conducted experiments on two public CNER datasets in the biomedical domain,namely CMeEE-V2 and Diakg.The results demonstrate the effectiveness of our approach,which achieves F1-score improvements of 7.29 percentage points and 1.64 percentage points compared to the current state-of-the-art(SOTA)models,respectively.
基金Supported by the National Natural Science Foundation of China(31471440)。
文摘This study was to explore the functional mechanism of rare earth regulating soybean leaves and the characteristics and functions of differentially expressed proteins under the regulation of rare earth. In this study, Dongnong 42 was used as material, and 30 mg·L^(-1) CeCl_(3) solution was sprayed on soybean leaves at the seedling stage. Tandem mass tag(TMT) quantitative proteomics technique and bioinformatics analysis were used to identify soybean leaf proteins. A total of 8 510 proteins were identified, and 127 differentially expressed proteins(DEPs) in response to rare earth cerium regulation were identified, among which 64 were upregulated and 63 were down-regulated. The gene ontology(GO) annotation indicated that DEPs were mainly involved in metabolic process, cellular process, response to stimulus, biological regulation, and response to a stimulus;DEPs in cell module categories were mainly involved in cells, cell part, organelle, membrane, membrane part, organelle par, and protein-containing complex;DEPs in molecular functional categories were mainly involved in catalytic activity, binding and antioxidant activity. Kyoto encyclopedia of genes and genomes(KEGG) pathway significantly enriched starch and sucrose metabolism, glycolysis/gluconeogenesis, galactose metabolism, pentose phosphate pathway, and MAPK signaling pathway-plant. These DEPs were mainly involved in photosynthesis, glucose metabolism and stress response. Forty-six differential protein interaction networks were identified by protein interaction network analysis. This experiment provided a reference for studies of the mechanism of rare earth cerium regulating soybean leaf function from the proteomic perspective.
基金supported by the National Natural Science Foundation of China,No. 81771327a grant for the Platform Construction of Basic Research and Clinical Translation of Nervous System Injury,China,No. PXM2020_026280_000002 (both to BYL)
文摘Proteomics is a powerful tool that can be used to elucidate the underlying mechanisms of diseases and identify new biomarkers.Therefore,it may also be helpful for understanding the detailed pathological mechanism of traumatic brain injury(TBI).In this study,we performed Tandem Mass Tag-based quantitative analysis of cortical proteome profiles in a mouse model of TBI.Our results showed that there were 302 differentially expressed proteins in TBI mice compared with normal mice 7 days after injury.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses showed that these differentially expressed proteins were predominantly involved in inflammatory responses,including complement and coagulation cascades,as well as chemokine signaling pathways.Subsequent transcription factor analysis revealed that the inflammation-related transcription factors NF-κB1,RelA,IRF1,STAT1,and Spi1 play pivotal roles in the secondary injury that occurs after TBI,which further corroborates the functional enrichment for inflammatory factors.Our results suggest that inflammation-related proteins and inflammatory responses are promising targets for the treatment of TBI.
基金This work was supported in part by the National Natural Science Foundation of China under project contracts No.61971113 and 61901095in part by National Key R&D Program under project contract No.2018AAA0103203+5 种基金in part by Guangdong Provincial Research and Development Plan in Key Areas under project contract No.2019B010141001 and 2019B010142001in part by Sichuan Provincial Science and Technology Planning Program under project contracts No.2020YFG0039,No.2021YFG0013 and No.2021YFH0133in part by Ministry of Education China Mobile Fund Program under project contract No.MCM20180104in part by Yibin Science and Technology Program-Key Projects under project contract No.2018ZSF001 and 2019GY001in part by Central University Business Fee Program under project contract No.A03019023801224the Central Universities under Grant ZYGX2019Z022.
文摘Radio Frequency Identification(RFID)technology has been widely used to identify missing items.In many applications,rapidly pinpointing key tags that are attached to favorable or valuable items is critical.To realize this goal,interference from ordinary tags should be avoided,while key tags should be efficiently verified.Despite many previous studies,how to rapidly and dynamically filter out ordinary tags when the ratio of ordinary tags changes has not been addressed.Moreover,how to efficiently verify missing key tags in groups rather than one by one has not been explored,especially with varying missing rates.In this paper,we propose an Efficient and Robust missing Key tag Identification(ERKI)protocol that consists of a filtering mechanism and a verification mechanism.Specifically,the filtering mechanism adopts the Bloom filter to quickly filter out ordinary tags and uses the labeling vector to optimize the Bloom filter's performance when the key tag ratio is high.Furthermore,the verification mechanism can dynamically verify key tags according to the missing rates,in which an appropriate number of key tags is mapped to a slot and verified at once.Moreover,we theoretically analyze the parameters of the ERKI protocol to minimize its execution time.Extensive numerical results show that ERKI can accelerate the execution time by more than 2.14compared with state-of-the-art solutions.
基金financially supported by the University of Torbat Heydarieh.
文摘Milk thistle(Silybum marianum)is a crucial medicinal plant containing a large amount of oil.In the study,the changes in storage oil during seed germination and seedling transition from heterotrophic phases were investigated.The results showed that seed oil decreased from 19.53%to 0.88%on the 7th day of seedling development.Oil hydrolysis continued until the 4th day of germination with a low slope,but then increased the use of oils in seed germination end seedling growth metabolism.The results indicated that the quantitative changes in fatty acids,presented at lower amount,were relatively higher than dominant fatty acids.There were decreasing phenolic content in the developing seedlings,but overall,lowest level of total phenolic content can be attributed to the control(30.52 mg⋅100 g⋅Oil^(-1)).In contrast,the maximum peroxide value(2.58 meq⋅kg Oil^(-1))in the developing seedling was observed on the last day of the experiment.The results showed that there was a significant correlation between saturated fatty acid,unsaturated fatty acid,and lipase activity.However,the correlation between lipase activity and polyunsaturated fatty acids was significantly higher than between lipase activity and monounsaturated fatty acids(R^(2)=90%and R^(2)=77%,respectively).Therefore,the lipolysis process acts selectively in milk thistle oils.According to the results,C12:0 exhibits a greater impact on the early seedling growth rather than on the germination process and is one of the determining factors in the transition from heterotroph to autotroph.Also,it can be a marker for TAGs breakdown.
基金supported by the National Natural Science Foundation of China(62122057,62075136,62105217,62205221,62205222)the Basic and Applied Basic Research Foundation of Guangdong Province(2022B1515120061)Shenzhen Science and Technology Program(Shenzhen Key Laboratory of Ultrafast Laser Micro/Nano Manufacturing ZDSYS20220606100405013,RCYX20200714114524139,JCYJ20200109114001806)。
文摘Optical fibers are typically used in telecommunications services for data transmission,where the use of fiber tags is essential to distinguish between the different transmission fibers or channels and thus ensure the working functionality of the communication system.Traditional physical entity marking methods for fiber labeling are bulky,easily confused,and,most importantly,the label information can be accessed easily by all potential users.This work proposes an encrypted optical fiber tag based on an encoded fiber Bragg grating(FBG)array that is fabricated using a point-by-point femtosecond laser pulse chain inscription method.Gratings with different resonant wavelengths and reflectivities are realized by adjusting the grating period and the refractive index modulations.It is demonstrated that a binary data sequence carried by a fiber tag can be inscribed into the fiber core in the form of an FBG array,and the tag data can be encrypted through appropriate design of the spatial distributions of the FBGs with various reflection wavelengths and reflectivities.The proposed fiber tag technology can be used for applications in port identification,encrypted data storage,and transmission in fiber networks.
基金The research work is carried out under the Beijing Natural Science Foundation-Beijing Education Commission Joint Project(KZ202210015020)Discipline Construction and Postgraduate Education Project of BIGC(No.21090122005)BIGC Project(Ee202204).
文摘High-frequency(HF)and ultrahigh-frequency(UHF)dual-band radio frequency identification(RFID)tags with both near-field and farfield communication can meet different application scenarios.However,it is time-consuming to calculate the return loss of a UHF antenna in a dualband tag antenna using electromagnetic(EM)simulators.To overcome this,the present work proposes a model of a multi-scale convolutional neural network stacked with long and short-term memory(MSCNN-LSTM)for predicting the return loss of UHF antennas instead of EM simulators.In the proposed MSCNN-LSTM,the MSCNN has three branches,which include three convolution layers with different kernel sizes and numbers.Therefore,MSCNN can extract fine-grain localized information of the antenna and overall features.The LSTM can effectively learn the EM characteristics of different structures of the antenna to improve the prediction accuracy of the model.Experimental results show that the mean absolute error(0.0073),mean square error(0.00032),and root mean square error(0.01814)of theMSCNNLSTM are better than those of other prediction methods.In predicting the return loss of 100UHFantennas,compared with the simulation time of 4800 s for High Frequency Structure Simulator(HFSS),MSCNN-LSTM takes only 0.927519 s under the premise of ensuring prediction accuracy,significantly reducing the calculation time,which provides a basis for the rapid design of HF-UHF RFID tag antenna.ThenMSCNN-LSTM is used to determine the dimensions of the UHF antenna quickly.The return loss of the designed dualband RFID tag antenna is−58.76 and−22.63 dB at 13.56 and 915 MHz,respectively,achieving the desired goal.
