Identification of Mulberry Bacterial Blight Caused by Klebsiella oxytoca in Bazhong,Sichuan,China

To provide a scientific basis for controlling mulberry bacterial blight in Bazhong,Sichuan,China(BSC),this study aimed to isolate and purify pathogenic bacteria from diseased branches of mulberry trees in the region and to clarify their taxonomic status using morphological observation,physiological and biochemical detection,molecular-level identification,and the construction of a phylogenetic tree.A total of 218 bacterial strains were isolated from samples of diseased mulberry branches.Of these,7 strains were identified as pathogenic bacteria based on pathogenicity tests conducted in accordance with Koch’s postulates.Preliminary findings from the analysis of the 16S rRNA sequence indicated that the 7 pathogenic bacteria are members of Klebsiella spp.Morphological observation revealed that the pathogenic bacteria were oval-shaped and had capsules but no spores.They could secrete pectinase,cellulase,and protease and were able to utilize D-glucose,D-mannose,D-maltose,and D-Cellobiose.The 7 strains of pathogenic bacteria exhibited the highest homology with Klebsiella oxytoca.This study identifies Klebsiella oxytoca as the causative agent of mulberry bacterial blight in BSC,laying the foundation for the prevention and control of this pathogen and further investigation into its pathogenic mechanism.

Application of Transgenic Technology in Identification for Gene Function on Grasses

Perennial grasses have developed intricate mechanisms to adapt to diverse environments,enabling their resistance to various biotic and abiotic stressors.These mechanisms arise from strong natural selection that contributes to enhancing the adaptation of forage plants to various stress conditions.Methods such as antisense RNA technology,CRISPR/Cas9 screening,virus-induced gene silencing,and transgenic technology,are commonly utilized for investigating the stress response functionalities of grass genes in both warm-season and cool-season varieties.This review focuses on the functional identification of stress-resistance genes and regulatory elements in grasses.It synthesizes recent studies on mining functional genes,regulatory genes,and protein kinase-like signaling factors involved in stress responses in grasses.Additionally,the review outlines future research directions,providing theoretical support and references for further exploration of(i)molecular mechanisms underlying grass stress responses,(ii)cultivation and domestication of herbage,(iii)development of high-yield varieties resistant to stress,and(iv)mechanisms and breeding strategies for stress resistance in grasses.

Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19

The global pandemic of novel coronavirus that started in 2019 has ser-iously affected daily lives and placed everyone in a panic condition.Widespread coronavirus led to the adoption of social distancing and people avoiding unneces-sary physical contact with each other.The present situation advocates the require-ment of a contactless biometric system that could be used in future authentication systems which makesfingerprint-based person identification ineffective.Periocu-lar biometric is the solution because it does not require physical contact and is able to identify people wearing face masks.However,the periocular biometric region is a small area,and extraction of the required feature is the point of con-cern.This paper has proposed adopted multiple features and emphasis on the periocular region.In the proposed approach,combination of local binary pattern(LBP),color histogram and features in frequency domain have been used with deep learning algorithms for classification.Hence,we extract three types of fea-tures for the classification of periocular regions for biometric.The LBP represents the textual features of the iris while the color histogram represents the frequencies of pixel values in the RGB channel.In order to extract the frequency domain fea-tures,the wavelet transformation is obtained.By learning from these features,a convolutional neural network(CNN)becomes able to discriminate the features and can provide better recognition results.The proposed approach achieved the highest accuracy rates with the lowest false person identification.

Intelligent Risk-Identification Algorithm with Vision and 3D LiDAR Patterns at Damaged Buildings

Existingfirefighting robots are focused on simple storage orfire sup-pression outside buildings rather than detection or recognition.Utilizing a large number of robots using expensive equipment is challenging.This study aims to increase the efficiency of search and rescue operations and the safety offirefigh-ters by detecting and identifying the disaster site by recognizing collapsed areas,obstacles,and rescuers on-site.A fusion algorithm combining a camera and three-dimension light detection and ranging(3D LiDAR)is proposed to detect and loca-lize the interiors of disaster sites.The algorithm detects obstacles by analyzingfloor segmentation and edge patterns using a mask regional convolutional neural network(mask R-CNN)features model based on the visual data collected from a parallelly connected camera and 3D LiDAR.People as objects are detected using you only look once version 4(YOLOv4)in the image data to localize persons requiring rescue.The point cloud data based on 3D LiDAR cluster the objects using the density-based spatial clustering of applications with noise(DBSCAN)clustering algorithm and estimate the distance to the actual object using the center point of the clustering result.The proposed artificial intelligence(AI)algorithm was verified based on individual sensors using a sensor-mounted robot in an actual building to detectfloor surfaces,atypical obstacles,and persons requiring rescue.Accordingly,the fused AI algorithm was comparatively verified.

Genome-Wide Identification of the GST Gene Family in Loquat (Eriobotrya japonica Lindl.) and Their Expression under Cold Stress with ALA Pretreatment

Loquat(Eriobotrya japonica Lindl.),a rare fruit native to China,has a long history of cultivation in China.Low temperature is the key factor restricting loquat growth and severely affects yield.Low temperature induces the regeneration and metabolism of reduced glutathione(GSH)to alleviate stress damage via the participation of glu-tathione S-transferases(GSTs)in plants.In this study,16 GSTs were identified from the loquat genome according to their protein sequence similarity with Arabidopsis GSTs.On the basis of domain characteristics and phyloge-netic analysis of AtGSTs,these EjGSTs can be divided into 4 subclasses:Phi,Theta,Tau and Zeta.The basic prop-erties,subcellular localization,structures,motifs,chromosomal distribution and collinearity of the EjGST proteins or genes were further analyzed.Tandem and segmental gene duplications play pivotal roles in EjGST expansion.Cis-elements that respond to various hormones and stresses,especially those associated with low-temperature responsiveness,were predicted to be present in the promoters of EjGSTs.Moreover,analysis of gene expression profiles revealed that 9 of 16 EjGSTs may be involved in the low-temperature responsiveness of loquat leaves.In agriculture,5-aminolevulinic acid(ALA),a potential multifunctional plant growth regulator,can improve the stress response of plants.Among the 9 low-temperature-responsive EjGSTs,the expression of EjGSTU1 and EjGSTF1 significantly differed under cold stress in response to exogenous 5-aminolevulinic acid(ALA)pretreat-ment.The remarkable increase in GST activity and GSH/GSSG ratio reflected the increase in the cold response ability of loquat plants caused by exogenous ALA,thereby alleviating H2O2 accumulation and membrane lipid preoxidation.Overall,this study provides an initial exploration of the cold tolerance function of GSTs in loquat,offering a theoretical foundation for the development of cold-resistant loquat cultivars and new antifreeze agents.

Structural Modal Parameter Recognition and Related Damage Identification Methods under Environmental Excitations: A Review

Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.

Radio frequency fingerprint identification for Internet of Things:A survey

Radio frequency fingerprint(RFF)identification is a promising technique for identifying Internet of Things(IoT)devices.This paper presents a comprehensive survey on RFF identification,which covers various aspects ranging from related definitions to details of each stage in the identification process,namely signal preprocessing,RFF feature extraction,further processing,and RFF identification.Specifically,three main steps of preprocessing are summarized,including carrier frequency offset estimation,noise elimination,and channel cancellation.Besides,three kinds of RFFs are categorized,comprising I/Q signal-based,parameter-based,and transformation-based features.Meanwhile,feature fusion and feature dimension reduction are elaborated as two main further processing methods.Furthermore,a novel framework is established from the perspective of closed set and open set problems,and the related state-of-the-art methodologies are investigated,including approaches based on traditional machine learning,deep learning,and generative models.Additionally,we highlight the challenges faced by RFF identification and point out future research trends in this field.

Genome-Wide Exploration of the Grape GLR Gene Family and Differential Responses of VvGLR3.1 and VvGLR3.2 to Low Temperature and Salt Stress

Grapes,one of the oldest tree species globally,are rich in vitamins.However,environmental conditions such as low temperature and soil salinization significantly affect grape yield and quality.The glutamate receptor(GLR)family,comprising highly conserved ligand-gated ion channels,regulates plant growth and development in response to stress.In this study,11 members of the VvGLR gene family in grapes were identified using whole-genome sequence analysis.Bioinformatic methods were employed to analyze the basic physical and chemical properties,phylogenetic trees,conserved domains,motifs,expression patterns,and evolutionary relationships.Phylogenetic and collinear analyses revealed that the VvGLRs were divided into three subgroups,showing the high conservation of the grape GLR family.These members exhibited 2 glutamate receptor binding regions(GABAb and GluR)and 3-4 transmembrane regions(M1,M2,M3,and M4).Real-time quantitative PCR analysis demonstrated the sensitivity of all VvGLRs to low temperature and salt stress.Subsequent localization studies in Nicotiana tabacum verified that VvGLR3.1 and VvGLR3.2 proteins were located on the cell membrane and cell nucleus.Additionally,yeast transformation experiments confirmed the functionality of VvGLR3.1 and VvGLR3.2 in response to low temperature and salt stress.Thesefindings highlight the significant role of the GLR family,a highly conserved group of ion channels,in enhancing grape stress resistance.This study offers new insights into the grape GLR gene family,providing fundamental knowledge for further functional analysis and breeding of stress-resistant grapevines.

E型牛肠道病毒新疆BL311株的分离鉴定及致病性分析

以采集自新疆博乐某牛场腹泻牛的粪便,接种胎牛肾细胞(Madin-Darby bovine kidney,MDBK),盲传15代分离牛肠道病毒(Bovine enterovirus,BEV),使用蚀斑纯化法连续纯化5轮得到纯化病毒,并命名为BL311。使用BL311株感染MDBK细胞后,观察细胞病变效应(cytopathic effect,CPE),采用Reed-Muench法计算病毒滴度,利用理化性质检测、透射电镜观察、基因型分析及对小鼠的致病性试验鉴定BL311分离株。结果显示,BL311分离株感染MDBK细胞24 h后细胞出现变圆、破裂和脱落等致细胞病变效应,病毒滴度显著增高(P<0.05);36 h后细胞出现大量CPE,病毒滴度极显著增高(P<0.01)。理化性质鉴定结果显示BL311分离株对有机溶剂具有一定抵抗力,紫外光照射1 h或56℃环境培养1 h可使毒株失活。电镜下观察BL311毒株为直径25~30 nm的球形颗粒,具有典型的小RNA病毒粒子形态。基因测序结果显示BL311毒株的基因全长是7514 nt,与国内报道的HY12分离株(KF748290)的同源性高达99%,属于BEV-E3型。致病性实验结果显示,BALB/c小鼠感染BL311毒株后,器官表现为肿大,且伴有出血;病理组织学观察可见组织器官炎性细胞浸润及出血。本研究分离纯化得到1株具有较强致病性的E种肠道病毒,将为新疆BEV的流行病学调查及致病机制研究提供依据。

Exploration of transferable deep learning-aided radio frequency fingerprint identification systems

Radio frequency fingerprint identification(RFFI)shows great potential as a means for authenticating wireless devices.As RFFI can be addressed as a classification problem,deep learning techniques are widely utilized in modern RFFI systems for their outstanding performance.RFFI is suitable for securing the legacy existing Internet of Things(IoT)networks since it does not require any modifications to the existing end-node hardware and communication protocols.However,most deep learning-based RFFI systems require the collection of a great number of labelled signals for training,which is time-consuming and not ideal,especially for the Io T end nodes that are already deployed and configured with long transmission intervals.Moreover,the long time required to train a neural network from scratch also limits rapid deployment on legacy Io T networks.To address the above issues,two transferable RFFI protocols are proposed in this paper leveraging the concept of transfer learning.More specifically,they rely on fine-tuning and distance metric learning,respectively,and only require only a small amount of signals from the legacy IoT network.As the dataset used for transfer is small,we propose to apply augmentation in the transfer process to generate more training signals to improve performance.A Lo Ra-RFFI testbed consisting of 40 commercial-off-the-shelf(COTS)Lo Ra IoT devices and a software-defined radio(SDR)receiver is built to experimentally evaluate the proposed approaches.The experimental results demonstrate that both the fine-tuning and distance metric learning-based RFFI approaches can be rapidly transferred to another Io T network with less than ten signals from each Lo Ra device.The classification accuracy is over 90%,and the augmentation technique can improve the accuracy by up to 20%.

The technology of radio frequency fingerprint identification based on deep learning for 5G application

User Equipment(UE)authentication holds paramount importance in upholding the security of wireless networks.A nascent technology,Radio Frequency Fingerprint Identification(RFFI),is gaining prominence as a means to bolster network security authentication.To expedite the integration of RFFI within fifth-generation(5G)networks,this research undertakes the creation of a comprehensive link-level simulation platform tailored for 5G scenarios.The devised platform emulates various device impairments,including an oscillator,IQ modulator,and power amplifier(PA)nonlinearities,alongside simulating channel distortions.Consequent to this,a plausibility analysis is executed,intertwining transmitter device impairments with 3rd Generation Partnership Project(3GPP)new radio(NR)protocols.Subsequently,an exhaustive exploration is conducted to assess the impact of transmitter impairments,deep neural networks(DNNs),and channel effects on RF fingerprinting performance.Notably,under a signal-to-noise ratio(SNR)of 15 d B,the deep learning approach demonstrates the capability to accurately classify 100 UEs with a commendable 91%accuracy rate.Through a multifaceted evaluation,it is ascertained that the Attention-based network architecture emerges as the optimal choice for the RFFI task,serving as the new benchmark model for RFFI applications.

Genome-Wide Discovery and Expression Profiling of the SWEET Sugar Transporter Gene Family in Woodland Strawberry (Fragaria vesca) under Developmental and Stress Conditions: Structural and Evolutionary Analysis

The SWEET(sugar will eventually be exported transporter)family proteins are a recently identified class of sugar transporters that are essential for various physiological processes.Although the functions of the SWEET proteins have been identified in a number of species,to date,there have been no reports of the functions of the SWEET genes in woodland strawberries(Fragaria vesca).In this study,we identified 15 genes that were highly homolo-gous to the A.thaliana AtSWEET genes and designated them as FvSWEET1–FvSWEET15.We then conducted a structural and evolutionary analysis of these 15 FvSWEET genes.The phylogenetic analysis enabled us to categor-ize the predicted 15 SWEET proteins into four distinct groups.We observed slight variations in the exon‒intron structures of these genes,while the motifs and domain structures remained highly conserved.Additionally,the developmental and biological stress expression profiles of the 15 FvSWEET genes were extracted and analyzed.Finally,WGCNA coexpression network analysis was run to search for possible interacting genes of FvSWEET genes.The results showed that the FvSWEET10 genes interacted with 20 other genes,playing roles in response to bacterial and fungal infections.The outcomes of this study provide insights into the further study of FvSWEET genes and may also aid in the functional characterization of the FvSWEET genes in woodland strawberries.

A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.

广西辣椒病毒病调查及病原种类初步鉴定

对广西9个市县辣椒病毒病的发生情况进行初步调查,并采用双抗体夹心酶联免疫吸附法(DAS-ELISA)鉴定75份辣椒疑似病毒病样本的8种常见蔬菜病毒。结果表明,桂南、桂中地区辣椒病毒病发生普遍较桂西、桂北地区的严重。共有71份辣椒样本检测呈阳性,其中辣椒脉斑驳病毒(Chilli veinal mottle virus,Chi VMV)的侵染最普遍,总检出率最高,达63.38%,为广西辣椒的优势病原种类;甜椒叶脉斑驳病毒(Pepper vein mottle virus,PVMV)、黄瓜花叶病毒(Cucumber mosaic virus,CMV)、辣椒轻斑驳病毒(Pepper mild mottle virus,PMMo V)、辣椒环斑病毒(Chilli ring spot virus,Chi RSV)的侵染也很普遍,总检出率分别为56.34%、43.66%、35.21%和33.80%。在71份阳性样本中,有87.32%的样本受2种及2种以上病毒的复合侵染,受2种或3种病毒复合侵染的分别占32.39%和29.58%;2种病毒复合侵染类型中,以ChiVMV+CMV、PVMV+PMMo V复合侵染最普遍,均占21.74%;3种病毒复合侵染类型中,以Chi VMV+CMV+PVMV复合侵染最普遍,占23.81%。

河南烟草镰刀菌的分子鉴定及致病性分析

为明确河南省不同烟叶产区危害烟草的镰刀菌种类,以河南省烟叶产区典型镰刀菌根腐病烟株及烟田土壤为供试样品,采用组织分离法和土壤分离法获得纯化菌株,根据菌株形态和r DNA-ITS序列分析对病原菌进行鉴定,并采用无创接种法测定病原菌对烟草的致病性。结果表明,获得的98个菌株在PDA培养基上均产生白色菌丝,大型分生孢子呈镰刀形或马特形,无色、多孢;小型分生孢子呈肾形、椭圆形或卵形,无色、多无分隔。序列比对结果显示,所分离的98株菌分属尖孢镰刀菌(Fusarium oxysporum)、茄病镰刀菌(Fusarium solani)、层出镰刀菌(Fusarium proliferatum)、木贼镰刀菌(Fusarium equiseti)和Fusarium nematophilum。致病性分析结果表明,尖孢镰刀菌、茄病镰刀菌、层出镰刀菌和木贼镰刀菌对烟苗具有致病性;48株致病菌中豫南烟区烟草镰刀菌根腐病致病性病原菌为尖孢镰刀菌、层出镰刀菌和木贼镰刀菌,以尖孢镰刀菌为主;豫中为尖孢镰刀菌;豫东、豫西为茄病镰刀菌和尖孢镰刀菌。因此,尖孢镰刀菌为全省烟区重点防控对象,豫南烟区还需加强层出镰刀菌和木贼镰刀菌的防控,豫东、豫西需防范茄病镰刀菌的发生。

广西火龙果采后病害主要病原菌分离与鉴定

从广西南宁市、钦州市和防城港市采收火龙果,经28℃存放,观察采后发病情况;取病部进行组织分离,根据形态学特性和rDNA-ITS序列分析对病原菌进行鉴定。结果表明,广西火龙果采后贮藏发生的病害有软腐病[Fusarium equiseti（Corda）Sacc.、Gilbertella persicaria（Eddy）Hesselt.],黑斑病[Alternaria alternata（Fr.）Keissl.、Bipolaris cactivora（Petrak）Alcorn],溃疡病[Neoscytalidium dimidiatum（Penz.）Crous et Slippers],其中木贼镰刀菌引起火龙果软腐病为首次发现。

布朗李炭疽病的病原鉴定及药效试验

采用实地调查和室内试验相结合的方法 ,对布朗李炭疽病在湖南的分布、危害、症状特点等进行了研究。根据布朗李炭疽病病原菌形态、培养特性和致病性 ,鉴定其有性世代为Glomerellacingulata ,无性世代为Colletotrichumgloeosporioides。药效试验表明 ,抑制病菌菌丝生长效果较好的化学药剂有湘研植病灵WP、70 %甲基硫菌灵WP、5 0 %多菌灵WP、扑菌特WP、4 0 %菌克星WP、5 0 %退菌特WP、6 0 %炭必灵WP、70 %红杀WP以及 1∶1∶10 0波尔多液 ;抑制孢子萌发效果较好的化学药剂有 70 %代森锰锌WP、扑菌持WP、70 %甲基硫菌灵WP、4 5 %石硫合剂WP、5 5 %疫霜锰锌WP、5 0 %退菌特WP和 1∶1∶10 0波尔多液。

一株猪细小病毒7群的鉴定和分离

猪细小病毒7群(Porcine parvovirus 7,PPV-7)是2016年首次从美国猪群发现和鉴定的新的猪细小病毒。为弄清我国猪群中是否存在PPV-7,本文用PPV-7特异性的实时荧光定量PCR和常规PCR方法对采集的10份猪流产胎儿和10份保育仔猪病料分别进行检测,结果发现其中1份保育仔猪样品的常规PCR和实时荧光定量PCR检测结果均为阳性,常规PCR扩增出的246 bp特异性条带测序和分子遗传演化时发现,该序列与PPV-7参考毒株KU5637332和KY996757的同源性分别为99.6%和98.0%,表明该样品中含有PPV-7,进一步用PK-15细胞分离病毒,连续传代5次后PK-15细胞都没有出现典型的细胞病变,但其上清液用实时荧光定量PCR方法都可以检测到该病毒。本研究结果证实我国猪群中存在PPV-7的感染,且检测到的PPV-7毒株能在PK15细胞中增殖。

张仲景肺系疾病用药规律研究

目的探索张仲景肺系疾病的用药规律,以期更好地指导中医临床。方法将张仲景《伤寒杂病论》中的条文,按照肺系统所涉及的疾病依次归类,统计肺系用药的功效、四气、五味、归经和前15味药物。结果功效用药依次为补虚药、解表药、化痰止咳平喘药、温里药、清热药、泻下药、利水渗湿药、活血化瘀药、收涩药等;四气用药依次为温、寒、平、热、凉;五味用药依次为甘、辛、苦、酸、咸;归经用药依次为:肺经、脾经、心经、肝经、胃经、肾经等;前15味药物依次为甘草、大枣、干姜、生姜、桂枝、半夏、人参、五味子、芍药、茯苓、麻黄、细辛、桔梗、石膏、杏仁、附子、蜂蜜。结论张仲景用药灵活严谨,就肺系用药规律概括为"表里同治,上下兼医,调节宣肃,匡正治节,注重肺胃,权衡攻补,辛温化饮,苦寒泄肺,治从五脏,兼顾肠腑"。

高致病性猪繁殖与呼吸综合征病毒SHxx13/2013株的分离与鉴定

2013年初上海某猪场保育猪群中出现高热、呼吸障碍等临床表现,导致大部分发病猪死亡,为了确定此次猪群中爆发疫病的病原,本研究从发病猪群中随机采集了15份血液样品,利用RT-PCR方法对本次疫病病原进行了检测。结果显示,有11份临床样品呈现猪繁殖与呼吸综合征病毒(Porcine reproductive and respiratory syndrome virus,PRRSV)强阳性,且与高致病性猪繁殖与呼吸综合征病毒(Highly pathogenic Porcine reproductive and respiratory syndrome virus,HP-PRRSV)对照大小相一致。随后从阳性样品中选取SHxx13/2013在Marc-145细胞中进行病毒的分离与鉴定,结果显示SHxx13/2013在细胞接种后第1代即出现明显的细胞效应(cytopathic effect,CPE),其病变特征与高致病性PRRSV强毒HuN4株一致。对SHxx13/2013第5代细胞分离毒进行RT-PCR和IFA等特异性鉴定,结果显示该毒株为PRRSV分离株,其蚀斑形态和生长特性与强毒HuN4株相似。分段克隆该毒株全长基因进行测序和序列分析,结果显示,新分离的流行毒SHxx13/2013株全长基因组为15 319 bp,其Nsp2基因特征与HP-PRRSV一致,在第482位和第534~562位发生两处共30个氨基酸的不连续缺失,且该毒株与高致病性PRRSV代表毒株HuN4亲缘关系较近,全长基因的核苷酸同源性为99.6%。本研究结果表明新分离的SHxx13/2013株属于高致病性PRRSV分离株,据此我们推测今年年初上海某猪场爆发的疫情主要是由HP-PRRSV感染所致。