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.

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.

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.

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.

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.

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.

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.

Insights into the origin of precipitation moisture for tropical cyclones during rapid intensification process

In this study,we identified the moisture sources for the precipitation associated with tropical cyclones(TCs)during the rapid intensification(RI)process from 1980 to 2018 by applying a Lagrangian moisture source diagnostic method.We detected sixteen regions on a global scale for RI events distributed as follows:four in the North Atlantic(NATL),two in the Central and East Pacific Ocean(NEPAC),the North Indian Ocean(NIO)and South Indian Ocean(SIO),and three in the South Pacific Ocean(SPO)and the Western North Pacific Ocean(WNP).The moisture uptake(MU)mostly was from the regions where TCs underwent RI.The Western NATL,tropical NATL,Caribbean Sea,the Gulf of Mexico and the Central America and Mexico landmass supported~85.4%of the precipitating moisture in the NATL,while the latter source and the eastern North Pacific Ocean provided the higher amount of moisture in NEPAC(~84.3%).The Arabian Sea,the Bay of Bengal and the Indian Peninsula were the major moisture sources in NIO,contributing approximately 81.3%.The eastern and western parts of the Indian Ocean supplied most of the atmospheric humidity in SIO(~83.8%).The combined contributions(~87.9%)from the western and central SPO and the Coral Sea were notably higher in SPO.Meanwhile,TCs in the WNP basin mostly received moisture from the western North Pacific Ocean,the Philippine Sea and the China Sea,accounting for 80.1%.The remaining moisture support in each basin came from the summed contributions of the remote sources.Overall,RI TCs gained more moisture up to 2500 km from the cyclone centre than those slow intensification(SI)and the total MU was approximately three times higher during RI than SI.Finally,the patterns of the MU differences respond to the typical pathways of moisture transport in each basin.

Comparative analysis of the rapid intensification of two super cyclonic storms in the Arabian Sea

A comparative analysis of the rapid intensification(RI)of super cyclonic storms Chapala(2015)and Kyarr(2019)in the Arabian Sea is conducted using the North Indian Ocean tropical cyclone data,microwave sounding images,the NOAA OISST data and the ERA5 reanalysis data.Results show that the subtropical westerly jet stream and the Southern Hemisphere anticyclonic circulation led to the formation of an obvious double-channel outflow from the northern and southern sides of the two storm centers,and the substantial inflow appeared at the eastern boundary layer of both storms.These promoted the vertical ascent motion and release of the latent heat of condensation.A warm sea surface is a necessary but not dominant factor for the RI of cyclonic storms in the Arabian Sea.During the RI of Chapala and Kyarr,the deep vertical wind shear was less than 10 m s-1;moreover,the mid-level humidity conditions favored the RI of the two cyclonic storms.Chapala had a single warm core,whereas Kyarr had double warm cores in the vertical direction.The impacts of the latent heat of fusion is more obvious for Chapala,and the potential vorticity in its inner core increases from 4.4 PVU to 8.8 PVU,whereas the potential vorticity and vorticity in the inner core of Kyarr do not change significantly.Microwave detection images show that both Chapala and Kyarr were accompanied by the formation of eyewalls during the RI phase,and the radius of maximum wind decreased and the maximum wind speed increased during the eyewall-thinning process.Both Chapala and Kyarr passed through a positive anomaly region of maximum potential intensity during the RI phase,which increases the possibility to develop to higher intensity after genesis.

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.

柑橘衰退病毒RT-RPA-LFD可视化检测方法的建立及应用

【目的】柑橘衰退病由柑橘衰退病毒(citrus tristeza virus,CTV)引起,是一种世界性的重要柑橘病害。为实现CTV的田间快速检测,建立一种准确、快速且可视化的检测方法。【方法】以CTV外壳蛋白(CP)的保守区域为靶标,设计3对特异性引物和探针,通过引物筛选,以及优化引物浓度、反应时间和反应温度等条件,建立CTV的反转录-重组酶聚合酶扩增-侧流层析试纸条(RT-RPA-LFD)快速检测方法,明确其灵敏度,并用于田间疑似样品的检测。【结果】建立了CTV的RT-RPA-LFD检测方法:最佳检测引物为RPA-1F/R,对应探针为RPA-P,最佳反应条件为40℃,25 min,且与其他5种柑橘病毒无交叉反应。该方法的灵敏度是RT-PCR的100倍,最低可检测到2.12×10^(1)拷贝·μL^(-1)的CTV核酸,与RT-qPCR相当。采用RT-RPA-LFD法在67份田间样品中检测出CTV阳性样品41份,与RT-PCR法检测结果一致。【结论】建立的CTV RT-RPA-LFD法具有操作简单、快速、结果可视等优点,适合基层植保工作者对田间样品开展快速检测。

Individual automatic detection and identification of big cats with the combination of different body parts

The development of facial recognition technology has become an increasingly powerful tool in wild animal indi-vidual recognition.In this paper,we develop an automatic detection and recognition method with the combinations of body features of big cats based on the deep convolutional neural network(CNN).We collected dataset including 12244 images from 47 individual Amur tigers(Panthera tigris altaica)at the Siberian Tiger Park by mobile phones and digital camera and 1940 images and videos of 12 individual wild Amur leopard(Panthera pardus orientalis)by infrared cameras.First,the single shot multibox detector algorithm is used to perform the automatic detection process of feature regions in each image.For the different feature regions of the image,like face stripe or spots,CNNs and multi-layer perceptron models were applied to automatically identify tiger and leopard individuals,in-dependently.Our results show that the identification accuracy of Amur tiger can reach up to 93.27%for face front,93.33%for right body stripe,and 93.46%for left body stripe.Furthermore,the combination of right face,left body stripe,and right body stripe achieves the highest accuracy rate,up to 95.55%.Consequently,the combination of different body parts can improve the individual identification accuracy.However,it is not the higher the number of body parts,the higher the accuracy rate.The combination model with 3 body parts has the highest accuracy.The identification accuracy of Amur leopard can reach up to 86.90%for face front,89.13%for left body spots,and 88.33%for right body spots.The accuracy of different body parts combination is lower than the independent part.For wild Amur leopard,the combination of face with body spot part is not helpful for the improvement of identification accuracy.The most effective identification part is still the independent left or right body spot part.It can be applied in long-term monitoring of big cats,including big data analysis for animal behavior,and be helpful for the individual identification of other wildlife species.

A Non-Singleton Type-3 Fuzzy Modeling: Optimized by Square-Root Cubature Kalman Filter

In many problems,to analyze the process/metabolism behavior,a mod-el of the system is identified.The main gap is the weakness of current methods vs.noisy environments.The primary objective of this study is to present a more robust method against uncertainties.This paper proposes a new deep learning scheme for modeling and identification applications.The suggested approach is based on non-singleton type-3 fuzzy logic systems(NT3-FLSs)that can support measurement errors and high-level uncertainties.Besides the rule optimization,the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalmanfilter(SCKF).In the learn-ing algorithm,the presented NT3-FLSs are deeply learned,and their nonlinear structure is preserved.The designed scheme is applied for modeling carbon cap-ture and sequestration problem using real-world data sets.Through various ana-lyses and comparisons,the better efficiency of the proposed fuzzy modeling scheme is verified.The main advantages of the suggested approach include better resistance against uncertainties,deep learning,and good convergence.

海洋来源青霉Penicillium sp.的化学成分研究

海洋微生物因生存环境特殊,常能形成结构特异、活性良好的代谢产物。本文对1株海洋沉积物中分离得到的青霉Penicillium sp.进行化学成分研究和抗菌活性评价。结果分离和鉴定了4个化合物,分别为Nuatigenin(1)、吲哚-3-乙酸(2)、胸苷(3)和3-苯基乳酸(4),其中化合物1对金黄色葡萄球菌和白色念珠菌均有一定的抑菌活性,化合物4对白色念珠菌抑菌效果良好,其余化合物对不同的菌均无抑制作用。

多位电动搅拌交换-全自动凯氏定氮法测定土壤中阳离子交换量

测定土壤阳离子交换量(CEC)的行业标准方法中,以玻璃棒手动搅拌进行离子交换和铵离子的清洗,使用传统方式进行蒸馏,手动滴定测定土壤样品中的阳离子交换量。但是这些方法搅拌操作劳动强度大、蒸馏过程较为繁琐,耗时长、效率低、不利于大批量样品的快速检测。以自制的多位电动搅拌装置代替玻璃棒搅拌,选择乙酸铵加入量、乙酸铵交换次数和交换搅拌时间3个因素,进行3因素3水平正交实验,结合实际操作需要选择加入50 mL乙酸铵溶液、交换1次、搅拌5 min、用乙醇清洗3次,运用全自动凯氏定氮仪进行蒸馏和测定土壤样品中的阳离子交换量。当搅拌时间为5 min、搅拌速度大于100 r/min时,增加搅拌速度对分析结果无影响。对不同酸碱性的国家标准物质和实际样品进行测定,测定结果的相对标准偏差为1.5%~2.4%,且国家标准物质的测定结果均在认定值的不确定范围内,表明方法有较好的精密度和准确度。与标准方法相比,以多位电动搅拌装置代替玻璃棒搅拌,用全自动凯氏定氮仪代替传统的蒸馏方式和手动滴定,自动化程度较高,蒸馏过程操作简单,解放了分析测试人员的双手,降低了劳动强度,自动滴定节约了试剂成本,整个分析效率提高一倍以上,可实现批量化快速检测土壤中的阳离子交换量。

连续棒状弱阳离子交换柱的合成及其对蛋白质保留行为

以甲基丙烯酸缩水甘油酯为单体，乙二醇二甲基丙烯酸酯为交联剂在空管柱内就地 聚合制备了一种聚合物连续棒状色谱基质，并通过“在线”化学改性将其修饰为弱阳离子交换 柱。考察了该色谱柱的孔结构特征、表面亲水性能、对标准蛋白的分离效果和ｐＨ值对保留 行为的影响以及色谱柱的重现性。结果表明，该色谱柱对蛋白的分离性能及重现性良好，柱 寿命不短于半年。对溶菌酶的活性回收率达９６．２％。流动相流速为 ８．０ｍＬ／ｍｉｎ条件下，在 ３．５

基于QuEChERS前处理技术和弱阳离子交换色谱的牛奶和奶粉中三聚氰胺的快速检测方法

应用QuEChERS前处理技术,并结合弱阳离子交换色谱,建立了牛奶和奶粉中三聚氰胺的快速检测方法。样品使用医用酒精（乙醇含量75%）和一种新型脂肪吸附（LAS）材料超声振荡处理,在沉淀（吸附）蛋白质和脂肪的同时提取三聚氰胺,然后经8 000r/min离心,上清液过膜直接分析。色谱分析在弱阳离子交换色谱柱（WCX）上进行,采用2mmol/L pH为3.8的磷酸二氢钾水溶液为流动相,在5min内实现分离分析。结果表明,该方法在0.02~20mg/L内线性相关系数大于0.999 9。在1~50mg/kg添加浓度范围内,牛奶的平均回收率为98.9%~105.2%,相对标准偏差（RSD）为0.9%~3.4%;奶粉的平均回收率为86.4%~102.9%,RSD为1.5%~6.7%。本方法的检出限为0.05mg/kg（牛奶）和0.1mg/kg（奶粉）。整个分析检测过程没有使用有毒有害有机溶剂,是一种绿色的分析方法。