表达Strep-tag II的重组塞内卡病毒的构建与鉴定

为构建表达Strep-tag II的重组塞内卡病毒(SVA),本研究在感染性cDNA克隆p SVA-I212V/S460L的基础上,采用重叠延伸PCR扩增Strep-tag II并采用无缝克隆法将其插入SVA VP1基因C端,构建感染性cDNA克隆,命名为p SVA-Strep,并采用双酶切和测序鉴定正确后,将p SVA-Strep转染BHK-21细胞拯救重组病毒,收集病变细胞上清,并将含有病毒的细胞上清和野生型SVA(SVA-WT)分别感染BHK-21细胞,以未感染病毒的BHK-21细胞作为阴性对照,感染后24 h分别以鼠源SVA VP2蛋白单克隆抗体(MAb)5D10(1∶1000)、鼠源Strep-tag II MAb(1∶2000)作为一抗,以FITC标记的山羊抗小鼠Ig G(1∶2000)为二抗,采用间接免疫荧光试验(IFA)对重组病毒鉴定。IFA结果显示,以鼠源SVA VP2蛋白MAb 5D10为一抗,SVA-WT和重组病毒感染的细胞均出现绿色荧光,以Strep-tag II MAb作为一抗时,仅有重组病毒感染的细胞出现绿色荧光,经SVA-WT感染的BHK-21细胞及阴性对照细胞均无绿色荧光,表明拯救了Strep-tag II标记的重组病毒,并将其命名为r SVA-Strep。将r SVA-Strep在BHK-21细胞中连续传10代,每隔2代采用引物VP1-F/R经PCR鉴定并对第10代重组病毒的VP1基因测序以鉴定r SVA-Strep的遗传稳定性。结果显示,每隔两代的重组病毒经PCR扩增后均出现约780 bp的目的条带,第10代重组病毒扩增的VP1基因测序结果显示Strep-tag II基因仍在重组病毒中,且无基因突变,表明r SVA-Strep的遗传稳定性较强;将SVA-WT和r SVA-Strep分别以MOI 0.01感染BHK-21细胞,在感染后4 h、8 h、12 h、24 h、36 h、48 h和72 h收获病毒测定病毒滴度并绘制生长曲线,结果显示在病毒感染36 h内r SVA-Strep和SVA-WT生长动力学基本一致,表明Strep-tag II的引入在病毒感染36 h内对SVA的复制基本无影响。采用0.2%甲醛充分灭活r SVA-Strep,并利用Strep-TactinRXT Purification纯化试剂盒纯化该灭活病毒,将r SVA-Strep灭活病毒上清加至Strep-Tactin亲和层析柱,分别用不同体积洗脱缓冲液依次洗脱病毒并通过western blot鉴定病毒的纯化效果。对纯化过程中不同洗脱液洗脱病毒的western blot结果显示,不同体积的洗脱液中E1、E2和E3均出现VP0和VP2两条带,且E1和E2的条带最明显,表明洗脱缓冲液的最佳洗脱体积约为2.2 CV;以上结果表明r SVA-Strep可通过Strep-Tactin亲和层析柱一步法纯化。本研究在SVA中插入Strep-tag II标签,为SVA灭活疫苗的快速纯化提供技术手段,也为其他病原的快速纯化方法的建立提供参考。

Serum proteins differentially expressed in gestational diabetes mellitus assessed using isobaric tag for relative and absolute quantitation proteomics

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.

Joint Modeling of Citation Networks and User Preferences for Academic Tagging Recommender System

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.

Influence of ear tags on the results of body composition analysis in mice

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.

A Novel Anti-Collision Algorithm for Large Scale of UHF RFID Tags Access Systems

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.

A U-Shaped Network-Based Grid Tagging Model for Chinese Named Entity Recognition

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.

Differential Expression Analysis of Proteins Regulated by Rare Earth Cerium in Soybean Leaves at Seedling Stage

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.

34.TAG·西海美术馆:面朝大海的艺术长廊

设计:让·努维尔事务所地点:山东青岛西海岸新区时间:2012—2021年规模:17000 m^(2)TAG·西海美术馆位于一座半岛的海边,远离市区^([1,2])。从规划意图看,整个项目由美术馆和艺术工坊两部分组成,但由于工坊部分处于停工状态,艺术社区氛围尚未形成,作为主体建筑的美术馆便成了荒野中的孤独存在,公众可达性较差(图1)。尽管如此,从设计研究角度,这座美术馆仍有许多精彩看点值得关注。

TAG HEUER 竞速激情

继去年以卡莱拉系列Skipper计时码表的精钢版本标志着品牌重返帆船运动与航海领域之后,TAG Heuer泰格豪雅如今欣然发布一款以18K 5N玫瑰金打造的全新版本。金质的Skipper计时码表与精钢版本类似,同样基于2023年前期为纪念卡莱拉系列计时码表60周年推出且热销的“玻璃盒”(glassbox)设计,并搭载品牌自制机心Heuer 02(型号:TH20-06),这是一款为帆船赛计时而特别调整的Heuer 02机心。

一种含SNAP-tag TM底物的硫醇荧光探针

合成了一种同时含α,β-不饱和酮活性位点和SNAP-tag TM底物的香豆素母体探针香豆素-烯酮-苯甲氨基鸟嘌呤(CAKBG).通过测试探针CAKBG的紫外-可见吸收光谱和荧光发射光谱,发现探针CAKBG在极性溶剂(例如,甘油、乙二醇、水、DMSO)中有更长的吸收波长,且探针CAKBG对溶液的pH变化不敏感.此外,光谱实验发现生物硫醇Cys和Hcy均能引起探针CAKBG的最大吸收波长发生蓝移,且当激发波长为405 nm时,Cys和Hcy均能导致探针CAKBG在487 nm处的荧光发射峰明显增强.细胞成像结果表明,含有SNAP-tag TM底物的硫醇荧光探针CAKBG在活细胞中可以对Cys、Hcy进行荧光成像.综上,SNAP-Tag TM蛋白标签技术有望今后在硫醇荧光探针的细胞成像中得到推广应用.

铜麦6号苗期耐高铵与耐盐特性的鉴定及TaGS基因的表达模式分析

为鉴定小麦品种铜麦6号的耐高铵及耐盐特性,对铜麦6号、中国春及抗逆性较强的烟农19、三抗10号、矮优王、烟农21、山农25的发芽势和发芽率进行测定,发现铜麦6号的耐铵指数位列第一,耐盐指数位列第二。以中国春为对照品种,在高铵、高盐及其双重胁迫处理下,测定其苗期的叶片鲜重、根鲜重、主根和第一叶叶片长度以及根冠比,发现高铵、高盐及其双重胁迫处理7 d后,铜麦6号的上述指标均不同程度高于中国春(除铜麦6号的根冠比在高盐处理1 d后低于中国春外),说明铜麦6号具有较强的耐高铵和耐高盐特性。进一步采用qRT-PCR检测小麦苗期谷氨酰胺合成酶TaGS基因家族成员(根中TaGS1;1、TaGS1;2和TaGS1;3基因以及叶片中TaGS2基因)的表达模式,发现高铵、高盐及其双重处理均可诱导铜麦6号苗期TaGS基因上调表达,且高盐处理下上调表达更明显,其中除高铵与高盐双重处理7 d后的TaGS1;2基因以及高铵和高盐处理1 d后的TaGS2基因在铜麦6号中的表达量低于中国春外,其他三种处理不同时间点铜麦6号中这两个基因的相对表达量均不同程度高于中国春。这些结果说明,铜麦6号苗期TaGS基因家族与其耐高铵和耐高盐特性相关,其中TaGS1;2和TaGS2基因的贡献较大。

Tandem Mass Tag-based proteomics analysis reveals the vital role of inflammation in traumatic brain injury in a mouse model

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.

Efficient and robust missing key tag identification for large-scale RFID systems

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.

Mobilizing the seed storage oil in milk thistle (Silybum marianum) can supply energy for seed germination and early seedling growth and apply as TAGs breakdown marker

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.

Encrypted optical fiber tag based on encoded fiber Bragg grating array

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.

MSCNN-LSTM Model for Predicting Return Loss of the UHF Antenna in HF-UHF RFID Tag Antenna

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.

基于RFID和磁导航的AGV小车S7-1200PLC控制系统设计

该项目利用S7-1200PLC作为AGV主控制器,采取驱动轮差速进行转向控制,采用磁导航传感器和RFID标签传感器实现循迹和定位功能,实现AGV小车在立体仓库、VMC850数控加工中心、CK40S数控车床、RB20工业机器人、装配单元之间产品的搬运功能。

TAG HEUER 赛车场上的王者选择

⼤约100年前,由于技术的发展,计时的精准度得到了⼤幅提升,计时码表成为了这⼀时代最热⻔的流⾏趋势,TAG HEUER泰格豪雅作为专业计时码表的历史,也可以追溯到那个时代。伴随着泰格豪雅的计时码表品质提升,它成为了众多航空⻜⾏器和汽⻋仪表盘的专业计时配件供应商,同时也成为了众多体育赛事的专业计时。

肉芽肿性乳腺炎病灶组织差异表达蛋白质筛选及关键分子验证

目的分析肉芽肿性小叶性乳腺炎(GLM)病灶组织中的差异表达蛋白质,筛选GLM的生物标志物,再根据结果对关键分子进行验证。方法采用TMT标记定量蛋白质组学方法,对GLM患者(3例)病灶与正常乳腺组织中蛋白质组进行鉴定和定量,筛选出发生显著变化的差异表达蛋白质,导入Proteome Discoverer软件中,搜索Uniprot数据库,完成生物信息学分析。采用细胞因子抗体芯片技术对筛选出的关键分子进行定量和验证。结果GLM病灶组织与正常乳腺组织筛选出差异表达蛋白质1405个,其中1106个表达上调,299个表达下调。GO富集分析显示,这些差异表达蛋白质主要参与细胞过程、代谢过程、蛋白质代谢、生物学调节等。KEGG通路分析发现,差异表达蛋白质参与补体活化、免疫调节、炎症反应等。细胞因子定量分析验证了GLM病灶组织中IL-1β、MCP2、IL-6表达升高。结论GLM病灶和正常乳腺组织中存在差异表达的蛋白质参与炎症、免疫等多种信号通路,可能是GLM发生发展的关键分子。特定蛋白质是值得进一步研究的生物标志物。

面向买卖合同的词汇增强细粒度实体识别

对于合同文本中当事人信息、合同基本信息、合同条款等细粒度实体的正确提取,可以有效提升合同的审查效率,为智能合同管理赋能。然而现有的实体识别方法,难以解决合同文本中实体类型复杂和合同实体细化的问题。因此,该文提出一种新的基于词汇增强的细粒度实体识别模型BLBC-CFER,该方法通过对预训练语言模型提供的字级增强、字加词嵌入提供的词级增强以及词汇集合结构提供的词级增强进行融合并嵌入到模型输入中,然后采用深度神经网络获取最优标记序列。该文在自行构建的买卖合同细粒度实体语料集和两个不同领域的公开数据集上进行实验。实验结果表明,该方法不仅可以有效地完成合同文本的细粒度实体识别任务,而且具有较好的鲁棒性,效果优于基线模型。