The genome tagging project(GTP)plays a pivotal role in addressing a critical gap in the understanding of protein functions.Within this framework,we successfully generated a human influenza hemagglutinin-tagged sperm-s...The genome tagging project(GTP)plays a pivotal role in addressing a critical gap in the understanding of protein functions.Within this framework,we successfully generated a human influenza hemagglutinin-tagged sperm-specific protein 411(HA-tagged Ssp411)mouse model.This model is instrumental in probing the expression and function of Ssp411.Our research revealed that Ssp411 is expressed in the round spermatids,elongating spermatids,elongated spermatids,and epididymal spermatozoa.The comprehensive examination of the distribution of Ssp411 in these germ cells offers new perspectives on its involvement in spermiogenesis.Nevertheless,rigorous further inquiry is imperative to elucidate the precise mechanistic underpinnings of these functions.Ssp411 is not detectable in metaphase Ⅱ(MⅡ)oocytes,zygotes,or 2-cell stage embryos,highlighting its intricate role in early embryonic development.These findings not only advance our understanding of the role of Ssp411 in reproductive physiology but also significantly contribute to the overarching goals of the GTP,fostering groundbreaking advancements in the f ields of spermiogenesis and reproductive biology.展开更多
Utilizing small molecules as markers for specific cells or organs within biosystems is a crucial approach for studying and regulating physiological processes. However, current tagging strategies, due to the presence o...Utilizing small molecules as markers for specific cells or organs within biosystems is a crucial approach for studying and regulating physiological processes. However, current tagging strategies, due to the presence of exposed highly reactive groups, suffer from drawbacks such as low tagging efficiency or insufficient spatial specificity, thereby diminishing their expected effectiveness. Consequently, there is a pressing need to develop a strategy capable of in situ labeling of active groups in response to cellular or in vivo stimuli, ensuring both high tagging efficiency and spatial specificity. In this work, we devised a strategy for releasing aldehyde groups activated by hypochlorous acid(HOCl). Compounds synthesized through this strategy can release the fiuorophore methylene blue(MB) and aldehyde-based compounds upon HOCl activation. Given high reactivity of the released aldehyde group, it can effectively interact with macromolecules in biological systems, facilitating tagging and enabling prolonged imaging. To validate this concept, we further incorporated a naphthalimide structure with stable light emission to create SW-110. SW-110 can specifically respond to in vitro and endogenous HOCl, when release MB, it also releases naphthalimide fiuorophore with highly reactive aldehyde group for tagging within cells. This strategy provides a simple but efficient strategy for proximity tagging in situ.展开更多
Tag recommendation systems can significantly improve the accuracy of information retrieval by recommending relevant tag sets that align with user preferences and resource characteristics.However,metric learning method...Tag recommendation systems can significantly improve the accuracy of information retrieval by recommending relevant tag sets that align with user preferences and resource characteristics.However,metric learning methods often suffer from high sensitivity,leading to unstable recommendation results when facing adversarial samples generated through malicious user behavior.Adversarial training is considered to be an effective method for improving the robustness of tag recommendation systems and addressing adversarial samples.However,it still faces the challenge of overfitting.Although curriculum learning-based adversarial training somewhat mitigates this issue,challenges still exist,such as the lack of a quantitative standard for attack intensity and catastrophic forgetting.To address these challenges,we propose a Self-Paced Adversarial Metric Learning(SPAML)method.First,we employ a metric learning model to capture the deep distance relationships between normal samples.Then,we incorporate a self-paced adversarial training model,which dynamically adjusts the weights of adversarial samples,allowing the model to progressively learn from simpler to more complex adversarial samples.Finally,we jointly optimize the metric learning loss and self-paced adversarial training loss in an adversarial manner,enhancing the robustness and performance of tag recommendation tasks.Extensive experiments on the MovieLens and LastFm datasets demonstrate that SPAML achieves F1@3 and NDCG@3 scores of 22%and 32.7%on the MovieLens dataset,and 19.4%and 29%on the LastFm dataset,respectively,outperforming the most competitive baselines.Specifically,F1@3 improves by 4.7%and 6.8%,and NDCG@3 improves by 5.0%and 6.9%,respectively.展开更多
Influenza A viruses(IAVs)possess variable pathogenic potency causing great economic losses in the poultry industry worldwide and threatening public health.The control of IAV epidemics desperately necessitates an effic...Influenza A viruses(IAVs)possess variable pathogenic potency causing great economic losses in the poultry industry worldwide and threatening public health.The control of IAV epidemics desperately necessitates an efficient platform for screening antiviral compounds and evaluating vaccine efficacy.In this study,we utilized the H9N2 subtype IAV as the working model.An 11-amino-acid HiBiT tag,derived from NanoLuc luciferase,was incorporated into the flexible linker region of the NS1 protein.Subsequently,the recombinant HiBiT-tagged virus was rescued.The recombinant virus exhibited high genetic stability and similar virological characteristics to the parental virus,both in vitro and in vivo.Particularly importantly,the replication profile of the HiBiT-tagged virus can be easily measured using the Nano-Glo assay system,achieving an efficient screening platform.Based on this platform,we have developed assays with both convenience and efficiency for screening antiviral reagents,evaluating immunization efficacy,and measuring neutralizing antibodies.展开更多
In this paper,we present a Deep Neural Network(DNN)based framework that employs Radio Frequency(RF)hologram tensors to locate multiple Ultra-High Frequency(UHF)passive Radio-Frequency Identification(RFID)tags.The RF h...In this paper,we present a Deep Neural Network(DNN)based framework that employs Radio Frequency(RF)hologram tensors to locate multiple Ultra-High Frequency(UHF)passive Radio-Frequency Identification(RFID)tags.The RF hologram tensor exhibits a strong relationship between observation and spatial location,helping to improve the robustness to dynamic environments and equipment.Since RFID data is often marred by noise,we implement two types of deep neural network architectures to clean up the RF hologram tensor.Leveraging the spatial relationship between tags,the deep networks effectively mitigate fake peaks in the hologram tensors resulting from multipath propagation and phase wrapping.In contrast to fingerprinting-based localization systems that use deep networks as classifiers,our deep networks in the proposed framework treat the localization task as a regression problem preserving the ambiguity between fingerprints.We also present an intuitive peak finding algorithm to obtain estimated locations using the sanitized hologram tensors.The proposed framework is implemented using commodity RFID devices,and its superior performance is validated through extensive experiments.展开更多
Small molecule inhibitors have dominated the pharmaceutical landscape for a long time as the primary therapeutic paradigm targeting pathogenic proteins.However,their efficacy heavily relies on the amino acid compositi...Small molecule inhibitors have dominated the pharmaceutical landscape for a long time as the primary therapeutic paradigm targeting pathogenic proteins.However,their efficacy heavily relies on the amino acid composition and spatial constitution of proteins,rendering them susceptible to drug resistance and failing to target undruggable proteins.In recent years,the advent of targeted protein degradation(TPD)technology has captured substantial attention from both industry and academia.Employing an event-driven mode,TPD offers a novel approach to eliminate pathogenic proteins by promoting their degrada-tion,thus circumventing the limitations associated with traditional small molecule inhibitors.Hydropho-bic tag tethering degrader(HyTTD)technology represents one such TPD approach that is currently in the burgeoning stage.HyTTDs employ endogenous protein degradation systems to induce the degrada-tion of target proteins through the proteasome pathway,which displays significant potential for medical value.In this review,we provide a comprehensive overview of the development history and the reported mechanism of action of HyTTDs.Additionally,we delve into the physiological roles,structure-activity re-lationships,and medical implications of HyTTDs targeting various disease-associated proteins.Moreover,we propose insights into the challenges that necessitate resolution for the successful development of HyTTDs,with the ultimate goal of initiating a new age of clinical treatment leveraging the immense po-tential of HyTTDs.展开更多
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
Nicotinamide phosphoribosyl transferase(NAMPT)is considered as a promising target for cancer therapy to its crucial role in cancer metabolism.Despite the therapeutic potential of NAMPT enzymatic inhibitors,their effec...Nicotinamide phosphoribosyl transferase(NAMPT)is considered as a promising target for cancer therapy to its crucial role in cancer metabolism.Despite the therapeutic potential of NAMPT enzymatic inhibitors,their effectiveness is limited by dose-related toxicity and the inability to suppress nonenzymatic functions of extracellular NAMPT(e NAMPT).Herein,we designed and synthesized the first hydrophobic tagging NAMPT degraders.Among them,compound NH-11 selectively degraded NAMPT in leukemia cells through the ubiquitin-proteasome system.Compound NH-11 effectively induced apoptosis and showed low toxicity to normal cells,representing a promising anti-leukemia lead compound.?2024 Published by Elsevier B.V.on behalf of Chinese Chemical Society and Institute of Materia Medica,Chinese Academy of Medical Sciences.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
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
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
基金the support from the National Natural Science Foundation of China(No.32070849)The Foundation of Science and Technology Commission of Shanghai Municipality(No.22DX1900400)+1 种基金Science and Technology Commission of Shanghai Municipality(No.23JC1403803)Shanghai Municipal Science and Technology Commission Targeted Funding Project(No.22DX1900400).
文摘The genome tagging project(GTP)plays a pivotal role in addressing a critical gap in the understanding of protein functions.Within this framework,we successfully generated a human influenza hemagglutinin-tagged sperm-specific protein 411(HA-tagged Ssp411)mouse model.This model is instrumental in probing the expression and function of Ssp411.Our research revealed that Ssp411 is expressed in the round spermatids,elongating spermatids,elongated spermatids,and epididymal spermatozoa.The comprehensive examination of the distribution of Ssp411 in these germ cells offers new perspectives on its involvement in spermiogenesis.Nevertheless,rigorous further inquiry is imperative to elucidate the precise mechanistic underpinnings of these functions.Ssp411 is not detectable in metaphase Ⅱ(MⅡ)oocytes,zygotes,or 2-cell stage embryos,highlighting its intricate role in early embryonic development.These findings not only advance our understanding of the role of Ssp411 in reproductive physiology but also significantly contribute to the overarching goals of the GTP,fostering groundbreaking advancements in the f ields of spermiogenesis and reproductive biology.
基金financially supported by the National Natural Science Foundation of China (Nos. 22177019, 22377010, 22371038)State Key Laboratory for Modification of Chemical Fibers and Polymer Materials (No. KF2206)。
文摘Utilizing small molecules as markers for specific cells or organs within biosystems is a crucial approach for studying and regulating physiological processes. However, current tagging strategies, due to the presence of exposed highly reactive groups, suffer from drawbacks such as low tagging efficiency or insufficient spatial specificity, thereby diminishing their expected effectiveness. Consequently, there is a pressing need to develop a strategy capable of in situ labeling of active groups in response to cellular or in vivo stimuli, ensuring both high tagging efficiency and spatial specificity. In this work, we devised a strategy for releasing aldehyde groups activated by hypochlorous acid(HOCl). Compounds synthesized through this strategy can release the fiuorophore methylene blue(MB) and aldehyde-based compounds upon HOCl activation. Given high reactivity of the released aldehyde group, it can effectively interact with macromolecules in biological systems, facilitating tagging and enabling prolonged imaging. To validate this concept, we further incorporated a naphthalimide structure with stable light emission to create SW-110. SW-110 can specifically respond to in vitro and endogenous HOCl, when release MB, it also releases naphthalimide fiuorophore with highly reactive aldehyde group for tagging within cells. This strategy provides a simple but efficient strategy for proximity tagging in situ.
基金supported by the Key Research and Development Program of Zhejiang Province(No.2024C01071)the Natural Science Foundation of Zhejiang Province(No.LQ15F030006).
文摘Tag recommendation systems can significantly improve the accuracy of information retrieval by recommending relevant tag sets that align with user preferences and resource characteristics.However,metric learning methods often suffer from high sensitivity,leading to unstable recommendation results when facing adversarial samples generated through malicious user behavior.Adversarial training is considered to be an effective method for improving the robustness of tag recommendation systems and addressing adversarial samples.However,it still faces the challenge of overfitting.Although curriculum learning-based adversarial training somewhat mitigates this issue,challenges still exist,such as the lack of a quantitative standard for attack intensity and catastrophic forgetting.To address these challenges,we propose a Self-Paced Adversarial Metric Learning(SPAML)method.First,we employ a metric learning model to capture the deep distance relationships between normal samples.Then,we incorporate a self-paced adversarial training model,which dynamically adjusts the weights of adversarial samples,allowing the model to progressively learn from simpler to more complex adversarial samples.Finally,we jointly optimize the metric learning loss and self-paced adversarial training loss in an adversarial manner,enhancing the robustness and performance of tag recommendation tasks.Extensive experiments on the MovieLens and LastFm datasets demonstrate that SPAML achieves F1@3 and NDCG@3 scores of 22%and 32.7%on the MovieLens dataset,and 19.4%and 29%on the LastFm dataset,respectively,outperforming the most competitive baselines.Specifically,F1@3 improves by 4.7%and 6.8%,and NDCG@3 improves by 5.0%and 6.9%,respectively.
基金the Animal Ethics Committee of the Lanzhou Veterinary Research Institute,Chinese Academy of Agricultural Sciences(SYXK-2020-0010).
文摘Influenza A viruses(IAVs)possess variable pathogenic potency causing great economic losses in the poultry industry worldwide and threatening public health.The control of IAV epidemics desperately necessitates an efficient platform for screening antiviral compounds and evaluating vaccine efficacy.In this study,we utilized the H9N2 subtype IAV as the working model.An 11-amino-acid HiBiT tag,derived from NanoLuc luciferase,was incorporated into the flexible linker region of the NS1 protein.Subsequently,the recombinant HiBiT-tagged virus was rescued.The recombinant virus exhibited high genetic stability and similar virological characteristics to the parental virus,both in vitro and in vivo.Particularly importantly,the replication profile of the HiBiT-tagged virus can be easily measured using the Nano-Glo assay system,achieving an efficient screening platform.Based on this platform,we have developed assays with both convenience and efficiency for screening antiviral reagents,evaluating immunization efficacy,and measuring neutralizing antibodies.
基金supported in part by the U.S.National Science Foundation(NSF)under Grants ECCS-2245608 and ECCS-2245607。
文摘In this paper,we present a Deep Neural Network(DNN)based framework that employs Radio Frequency(RF)hologram tensors to locate multiple Ultra-High Frequency(UHF)passive Radio-Frequency Identification(RFID)tags.The RF hologram tensor exhibits a strong relationship between observation and spatial location,helping to improve the robustness to dynamic environments and equipment.Since RFID data is often marred by noise,we implement two types of deep neural network architectures to clean up the RF hologram tensor.Leveraging the spatial relationship between tags,the deep networks effectively mitigate fake peaks in the hologram tensors resulting from multipath propagation and phase wrapping.In contrast to fingerprinting-based localization systems that use deep networks as classifiers,our deep networks in the proposed framework treat the localization task as a regression problem preserving the ambiguity between fingerprints.We also present an intuitive peak finding algorithm to obtain estimated locations using the sanitized hologram tensors.The proposed framework is implemented using commodity RFID devices,and its superior performance is validated through extensive experiments.
基金supported by grants from the National Natural Science Foundation of China(Nos.82103978,81874286)the Natural Science Foundation of Jiangsu Province(No.BK20210423)“Double-First-Class”University Project(Nos.CPU 2018PZQ02,CPU 2018GY07).
文摘Small molecule inhibitors have dominated the pharmaceutical landscape for a long time as the primary therapeutic paradigm targeting pathogenic proteins.However,their efficacy heavily relies on the amino acid composition and spatial constitution of proteins,rendering them susceptible to drug resistance and failing to target undruggable proteins.In recent years,the advent of targeted protein degradation(TPD)technology has captured substantial attention from both industry and academia.Employing an event-driven mode,TPD offers a novel approach to eliminate pathogenic proteins by promoting their degrada-tion,thus circumventing the limitations associated with traditional small molecule inhibitors.Hydropho-bic tag tethering degrader(HyTTD)technology represents one such TPD approach that is currently in the burgeoning stage.HyTTDs employ endogenous protein degradation systems to induce the degrada-tion of target proteins through the proteasome pathway,which displays significant potential for medical value.In this review,we provide a comprehensive overview of the development history and the reported mechanism of action of HyTTDs.Additionally,we delve into the physiological roles,structure-activity re-lationships,and medical implications of HyTTDs targeting various disease-associated proteins.Moreover,we propose insights into the challenges that necessitate resolution for the successful development of HyTTDs,with the ultimate goal of initiating a new age of clinical treatment leveraging the immense po-tential of HyTTDs.
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 Key Research and Development Program of China(No.2022YFC3401500 to C.S.)the National Natural Science Foundation of China(No.82030105 to C.S.)and Shanghai Rising-Star Program(No.20QA1411700 to G.D.)。
文摘Nicotinamide phosphoribosyl transferase(NAMPT)is considered as a promising target for cancer therapy to its crucial role in cancer metabolism.Despite the therapeutic potential of NAMPT enzymatic inhibitors,their effectiveness is limited by dose-related toxicity and the inability to suppress nonenzymatic functions of extracellular NAMPT(e NAMPT).Herein,we designed and synthesized the first hydrophobic tagging NAMPT degraders.Among them,compound NH-11 selectively degraded NAMPT in leukemia cells through the ubiquitin-proteasome system.Compound NH-11 effectively induced apoptosis and showed low toxicity to normal cells,representing a promising anti-leukemia lead compound.?2024 Published by Elsevier B.V.on behalf of Chinese Chemical Society and Institute of Materia Medica,Chinese Academy of Medical Sciences.
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.
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 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.
