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.

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.

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.

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%.

Classification of histological severity of Helicobacter pylori-associated gastritis by confocal laser endomicroscopy

AIM: To classify the histological severity of Helicobacter pylori (H. pylori) infection-associated gastritis by confocal laser endomicroscopy (CLE). METHODS: Patients with upper gastrointestinal symptoms or individuals who were screened for gastric cancer were enrolled in this study. Histological severity of H. pylori infection-associated gastritis was graded according to the established CLE criteria. Diagnostic value of CLE for histo-logical gastritis was investigated and compared with that of white light endoscopy (WLE). Targeted biopsies from the sites observed by CLE were performed. RESULTS: A total of 118 consecutive patients with H. pylori infection-associated gastritis were enrolled in this study. Receiver operating characteristic curve analysis showedthat the sensitivity and specifi city of CLE were 82.9% and 90.9% for the diagnosis of H. pylori infection, 94.6% and 97.4% for predicting gastric normal mucosa, 98.5% and 94.6% for predicting histological active inflammation, 92.9% and 95.2% for predicting glan-dular atrophy, 98.6% and 100% for diagnosing intes-tinal metaplasia, respectively. Post-CLE image analysis showed that goblet cells and absorptive cells were the two most common parameters on the CLE-diagnosed intestinal metaplasia (IM) images (P < 0.001). More his-tological lesions of the stomach could be found by CLE than by WLE (P < 0.001). CONCLUSION: CLE can accurately show the histological severity of H. pylori infection-associated gastritis. Mapping IM by CLE has a rather good diagnostic accuracy.

ICAT诱导巨噬细胞M2型极化促进宫颈癌HeLa细胞迁移及侵袭的体外研究

目的:探讨β-连环蛋白/T细胞因子抑制子(ICAT)是否影响巨噬细胞极化,分析极化后的巨噬细胞对宫颈癌细胞迁移及侵袭的作用。方法:使用生物信息学方法分析ICAT在宫颈癌中的表达情况及其与巨噬细胞的相关性;采用RNAi技术构建沉默ICAT表达的HeLa/si-ICAT及对照HeLa/si-NC重组细胞株;qRT-PCR及Western blot检测重组HeLa细胞中IL-10、TGF-β表达;收集重组HeLa细胞条件培养基(CM)分别处理巨噬细胞后,qRT-PCR及Western blot检测巨噬细胞表型变化;细胞划痕及Transwell试验评价极化后的巨噬细胞对HeLa细胞迁移及侵袭能力的影响。结果:生物信息学分析显示ICAT在宫颈癌组织中的表达显著高于正常宫颈组织,且与M2型巨噬细胞的数量呈正相关(P=0.002);与HeLa/si-NC对照组相比,HeLa/si-ICAT细胞中IL-10、TGF-β表达降低;经HeLa/si-ICAT细胞CM处理后巨噬细胞M2型巨噬标志物Arg-1(P=1.82×10^(−4))、TGF-β(P=0.0094)、MR(P=0.014)表达降低,M1型巨噬标志物iNOS表达升高(P=8.62×10^(−4)),并能显著减弱HeLa细胞的迁移及侵袭能力。结论:HeLa细胞中异常高表达的ICAT可诱导巨噬细胞向M2型极化,极化后的巨噬细胞促进HeLa细胞迁移及侵袭。

电池级碳酸锂制备工艺方法解析

电池级碳酸锂是锂离子电池的重要电极材料,其产量和质量对新型储能、电子、新能源汽车等产业有着直接的影响。文章较全面分析、梳理了盐湖卤水提纯制备电池级碳酸锂、锂矿石提纯制备电池级碳酸锂、废旧锂电池提纯制备电池级碳酸锂的主要工艺方法、工艺流程,比较了不同工艺方法的优缺点,对电池级碳酸锂的研究和生产能够起到一定的借鉴和参考作用。

1+X证书制度背景下“育训结合、能力递进、书证融通”育人模式的探索与实践

2019年《国家职业教育改革实施方案》要求启动“学历证书+若干职业技能等级证书”制度。文章基于“1+X”证书制度下的书证融通在实施过程中存在的问题,从模块化课程体系构建、信息化资源与教材开发、平台搭建、师资队伍建设、考核评价体系、体制机制保障等方面开展研究,逐步构建学生、学校、行业、企业、社会多方共赢的良好局面,为实现新时代中国职业教育事业的全面优质发展奠定基础。

过表达β联蛋白和T细胞因子抑制物(ICAT)促进人宫颈癌Caski细胞的增殖和迁移

目的探讨β联蛋白和T细胞因子抑制物(ICAT)的高表达对人宫颈癌Caski细胞增殖和迁移的影响。方法含ICAT基因腺病毒(Ad ICAT)感染Caski细胞,采用实时定量PCR检测ICAT mRNA水平,Western blot法检测ICAT蛋白水平;MTT法检测各组细胞增殖能力,流式细胞术检测细胞周期,TranswellTM小室迁移实验检测细胞迁移能力。结果 Ad ICAT感染后,Caski细胞的ICAT mRNA和蛋白水平均显著增加;过表达ICAT后,促进Caski细胞的增殖,S期细胞比例明显增加,细胞迁移能力增强。结论 Caski细胞过表达ICAT后,促进其增殖和迁移。

ICAT蛋白在HL60细胞向单核系分化前后表达差异的验证及其功能研究

目的:对HL60细胞在NSC67657作用下向单核系分化前后,双向电泳分离的表达差异蛋白β-catenin相关蛋白1(Beta-catenin-interacting protein 1,ICAT)进行验证,并对ICAT在细胞分化中的功能进行研究.方法:通过RT-PCR和Western blot方法验证药物作用细胞前后ICAT基因和蛋白的表达差异;通过免疫荧光协同分析目的蛋白的表达水平,并对其进行初步定位.构建pDsRed-ICAT真核表达载体,转染HL60细胞,筛选阳性克隆.对ICAT基因重组质粒转染细胞作细胞形态学、细胞增殖改变的观察和细胞周期检测以及超微结构观察.结果:NSC67657诱导HL60细胞向单核系分化,ICAT蛋白表达上调,其主要定位于细胞核和胞质.真核表达载体构建成功,电转后G418筛选可得90%以上阳性克隆.转染重组质粒的HL60细胞增殖受抑,电镜下胞核异染色质密集,核质比减小,表面抗原CD14表达和对照组无差异,但在药物处理后24h即可表达71.3%,明显高于对照组,瑞氏染色可见明显分化细胞.结论:ICAT蛋白在NSC67657诱导HL60细胞分化中表达上调,但仅是过表达的ICAT基因并不能诱导HL60细胞向单核系分化,却能提高HL60细胞对NSC67657诱导作用的敏感性.

ICAT干扰慢病毒表达载体构建及稳转HL60细胞株的建立

目的构建ICAT基因的干扰慢病毒表达载体,建立稳定转染的HL60细胞系。方法人工设计、合成针对ICAT基因干扰序列,退火后连接到PGLV3干扰载体上,与PG-p1-VSVG、PG-p1-REV、PG-p1-RRE共转染293T细胞,包装产生慢病毒颗粒并测定病毒滴度,感染HL60细胞,建立稳定细胞株;应用RT-PCR和Western Blot技术检测HL60稳定细胞中ICAT基因和蛋白表达情况,并与对照组进行比较。结果成功构建了针对ICAT基因的RNAi慢病毒表达载体,病毒滴度为2×10^8 TU/mL;建立稳定转染的HL60细胞株。有效干扰验证显示,shICAT能明显降低ICAT的mRNA及蛋白水平（P〈0.001）。结论成功构建ICAT基因的shRNA慢病毒表达载体,建立稳定干扰ICAT表达的HL60细胞株。

中华民族共同体视域下档案参与自我身份认同构建的维度与路径

基于自我认同的“个体—群体”两个维度划分,探究档案如何推动个人对自身身份的认同及对所属中华民族的认同。在个体维度,档案通过多元属性的身份确证、唤醒个人身份,构建个体的自我身份认同。在群体维度,档案在缔造中华民族的共同记忆脉络、蕴含“文缘”形成群体情感依附、记录中华民族伟大复兴实现历程等过程中发挥着重要作用,以此构建中华民族群体的自我身份认同。最后,从明确身份归属、传承民族集体记忆、连接共同情感等方面探讨如何强化自我认同感。

聚氯乙烯精馏生产危险因素及安全防范

近年来,聚氯乙烯在中国的应用范围逐年扩大,但其生产工艺中的安全风险不容忽视,特别是精馏工段,由于涉及氯乙烯等危险品,其安全问题备受关注。氯乙烯的泄漏、爆炸等事故后果严重,因此对聚氯乙烯精馏工艺进行综合安全分析至关重要。文章重点分析了聚氯乙烯精馏工艺流程中的危险因素,包括氯乙烯、二氯乙烷、乙炔等,并提出了相应的安全防范措施,如避免氯乙烯泄漏、建立预警和事故处理机制、加强个体防护等。这些措施对保障聚氯乙烯生产过程的安全具有重要意义。未来,相关企业应继续加强安全管理,增强员工安全意识,确保聚氯乙烯生产的安全稳定进行。

甾醇类新药NSC67657诱导的HL60细胞单核系分化中β-catenin/ICAT蛋白差异表达及相互作用研究

目的分析甾醇类新药NSC67657诱导HL60细胞单核系分化中,β-catenin/ICAT蛋白的差异表达及相互作用,探讨NSC67657诱导细胞分化的作用机制。方法应用10μmol·L^(-1)NSC67657诱导HL60细胞分化,采用细胞化学染色和流式细胞技术评估细胞的分化方向和分化程度;通过RT-PCR和Western blot方法验证药物作用细胞前后β-catenin和ICAT基因和蛋白的表达差异;采用免疫共沉淀技术分析β-catenin与ICAT蛋白的相互作用情况;采用激光共聚焦技术协同分析目的蛋白的差异表达及细胞内定位。结果NSC67657可诱导HL60细胞向单核系分化。在10μmol·L^(-1)NSC67657作用5d后,CD14的表达可达到90%以上,细胞化学染色支持细胞单核系分化结论。分化后ICAT基因和蛋白表达明显升高(P<0.01),而β-catenin基因和蛋白表达明显下降(P<0.05)。免疫共沉淀结果显示,ICAT与β-catenin蛋白在细胞分化前后都存在相互作用,药物诱导细胞分化后两者蛋白相互作用条带吸光度明显增加。激光共聚焦结果显示,ICAT蛋白和β-catenin蛋白在胞质和胞核均有荧光,药物诱导HL60细胞分化后,ICAT蛋白胞质和胞核荧光均明显增加,β-catenin蛋白在核内荧光明显减弱,但胞质荧光强度增加,有蛋白胞质转位现象。结论 NSC67657能够诱导HL60细胞向单核系分化,且引起ICAT蛋白和β-catenin蛋白的表达差异;ICAT蛋白与β-catenin蛋白的相互作用增强及β-catenin蛋白从胞核向胞质的转位现象,提示NSC67657可能通过下调并阻止β-catenin蛋白入核,从而导致HL60细胞单核系分化。

生物医用高分子材料合成与改性新进展

在生物医学领域,高分子材料因其独特的物理和化学性质,如优良的生物相容性、可定制的机械强度和可降解性,已经受到了广大研究者的关注。文章深入探讨了生物医用高分子材料的合成与改性的最新技术,并探索了如何通过这些技术来提高材料的性能和应用范围。详细描述了高分子材料的基本性质及其在医疗领域的应用,如作为药物传递载体、组织工程支架和医疗器械的材料。同时,也对当前的合成和改性技术进行了深入分析。经过对比分析,发现某些新技术能够有效提高材料的生物相容性和机械性能,以及展望了这些材料在医疗领域的未来发展趋势。

化工企业雷击风险部位精细化分级与应用

化工企业因涉及易燃易爆物质的建(构)筑物的生产、储存和运输,已成为防雷安全监管的重点对象,一旦遭受雷击,可能引发火灾、爆炸及危险品泄漏等严重后果。文章以苏州市化工企业为例,具体分析了不同功能区域建筑物的雷击风险部位。依据国家标准对风险等级进行了划分,并提出了提高化工企业雷电灾害防御能力的改进措施,为增强化工企业雷电安全防范的针对性和有效性提供了指导。

一种净水型破乳剂在孤岛油田的应用

文章探讨了孤岛油田聚合物驱原油的乳化状态,发现胶质和沥青质是影响原油破乳的主要因素,导致孤岛油田原油难以破乳并且污水含有较高的油分。为解决这一问题,实验室中通过对聚头与环氧乙烷(EO)、环氧丙烷(PO)进行聚合反应成功地制备出净水型破乳剂,并经过多次试验选定破乳剂5050作为效果最佳的配方。同时,确定了5050最佳的反应物料质量(聚头、EO、PO)比为1∶149∶149,并找到了EO和PO各自最优的聚合反应温度分别为125℃和140℃。