水禽细小病毒SYBR Green Ⅰ荧光定量PCR检测方法的建立与应用

为了建立水禽细小病毒(WPV)快速检测方法,根据序列比对结果在水禽细小病毒NS基因SF3保守区域内设计特异性引物,建立SYBR Green Ⅰ荧光定量PCR通用检测方法。该方法的扩增效率(E)为90.0%,相关系数(R~2)=0.99,标准曲线方程为y=-3.607x+38.77;除WPV出现S形扩增曲线外,新城疫病毒(NDV)、H9亚型禽流感病毒(H9 AIV)、鸭坦布苏病毒(DTMUV)、鸭肝炎病毒(DHAV)、鸭肠炎病毒(DEV)、鸭呼肠孤病毒(DRV)样品均未出现S形阳性扩增曲线;批内变异系数(CV)为0.15%~0.23%,批间变异系数为0.09%~0.28%。结果表明,SYBR Green Ⅰ荧光定量PCR检测方法重复性好、灵敏度高和特异性强。临床样品检测结果表明,SYBR Green Ⅰ荧光定量PCR与普通PCR的符合率达98.4%,灵敏度是普通PCR的1 000倍。SYBR Green Ⅰ荧光定量PCR检测方法不仅能定性检测WPV,还可以进行定量检测,可用于种鸭场、种鹅场的WPV净化检测,也可用于WPV临床大量样品的快速检测。

盆底超声检查和Green分型诊断产妇产后早期盆底结构及压力性尿失禁

目的:探究盆底超声检查和Green分型在产妇产后早期盆底结构及压力性尿失禁(SUI)诊断中的价值。方法:选取2021年3月-2023年3月本院就诊经产妇204例,根据产后早期(6~8周)SUI发生情况分为SUI组(82例)和非SUI组(122例)。检查产妇静息和Valsalva动作状态下膀胱颈距耻骨联合下缘的距离(BSD)、膀胱尿道后角(PUA)、膀胱颈移动度(BND)和尿道旋转角(UR),对比Green分型;采用受试者工作特征(ROC)曲线分析相关参数诊断SUI价值。结果:静息状态下,两组盆底超声参数无差异(P>0.05);SUI组Valsalva动作状态下的BSD、PUA、BND和UR值均大于非SUI组,Green分型Ι型占比(25.6%)低于非SUI组(59.8%)(均P<0.05)。ROC曲线分析,Green分型曲线下面积(AUC)0.579,对SUI诊断价值不佳(P=0.057),Valsalva动作状态下BSD(AUC=0.758)、PUA(AUC=0.743)、BND(AUC=0.769)和UR(AUC=0.705)对SUI诊断价值良好(均P<0.05)。结论:盆底超声检查和Green分型能有效评估经产妇早期盆底结构状况,前者可诊断SUI。

约束Green函数与非线性地基梁模态分析

在生物和医学领域,微机电系统(MEMS)中的微梁结构在植入人体的使用时,由于体内的细胞环境类似于水凝胶,在这种环境下工作,设备和仪器的精度和稳定性很大程度上受到细胞弹性的影响.为了分析此类地基梁的动力学问题,本文建立了非线性基础上的梁振动模型,研究了任意位置弹簧和非线性弹簧基础上的梁模态.通过Laplace变换和线性叠加原理,得到了一种约束Green函数,利用数值计算验证方案的有效性,并研究了各种重要物理参数的影响,发现弹簧位置向跨中移动时,模态对称性被打破,弹簧刚度增加,模态阶数改变.

猪流行性腹泻病毒SYBR Green I实时荧光定量PCR检测方法的建立

为了建立高效、灵敏的猪流行性腹泻病毒(PEDV)检测方法,本研究从GenBank数据库中获取PEDV N基因序列,扩增出PEDV N基因标准质粒,并在N基因的保守区域内设计了一对特异性荧光定量引物,成功建立了SYBR Green I实时荧光定量PCR检测方法。经过一系列试验表明,该检测方法线性关系良好,R^(2)值为0.99;特异性强,敏感性高,最低可检测至2.23 copies/μL,比普通PCR灵敏约100倍;重复性好,组内变异系数为0.25%~0.43%,组间变异系数为0.67%~0.97%;对于各地区96份临床样品检测出PEDV阳性率为25%。本研究建立的实时荧光定量PCR检测方法为PEDV的临床诊断、流行病学调查以及定量研究提供了有效的检测工具。

美洲鳗鲡腺瘤病毒(AEAdoV)普通PCR和SYBR Green I实时荧光定量PCR检测方法的建立及应用

为建立美洲鳗鲡腺瘤病毒(AEAdoV)的检测方法,根据AEAdoV福建株(AEAdoVFJ)的superfamily 3 helicases(S3H)序列,设计引物,建立了AEAdoV的普通PCR和qPCR检测方法;进一步评价检测方法的灵敏性、特异性及重复性,利用2种方法对美洲鳗鲡“出血性烂鳃”病料进行了检测,并对美洲鳗鲡体内不同组织的病毒含量进行分析。结果显示,普通PCR扩增的目的片段长度约300 bp,利用其构建的qPCR质粒标准品,其拷贝数与qPCR阈值循环数(C_(t))线性关系良好,线性范围广,标准曲线相关系数(R2)达到0.999,扩增效率为105.067%。建立的普通PCR法和qPCR的最低检测AEAdoV拷贝数分别为100个和10个。2种方法均可特异性检测AEAdoV,而对蛙虹彩病毒(RGV)、鳗鲡疱疹病毒(AngHV)、鲤疱疹病毒(KHV)、对虾白斑综合征病毒(WSSV)、日本鳗鲡内皮细胞坏死病毒(JEAdoV)和花鳗鲡腺瘤病毒(MEAdoV)均无扩增反应。qPCR法的组内和组间变异系数均小于2%,表明其重复性良好。临床应用结果显示,35份美洲鳗鲡“出血性烂鳃”病料,采用普通PCR法的AEAdoV检出率为82.8%,而qPCR法的AEAdoV检出率为97%。对美洲鳗鲡不同组织的病毒含量分析结果显示,心脏、肝脏、鳃、鳍的AEAdoV相对含量较高,而黏液、皮肤和脾脏的病毒含量相对较低。研究表明,建立的AEAdoV的灵敏度高、特异性强的普通PCR和qPCR检测方法,证实AEAdoV与美洲鳗鲡“出血性烂鳃”病密切相关,且在感染鳗鲡主要组织中都存在。实验结果对于研究AEAdoV的致病性,开展其流行情况和病原学情况分析具有重要意义。

A Correlation-Based Stochastic Model for Massive MIMO Channel

In this paper,the channel impulse response matrix(CIRM)can be expressed as a sum of couplings between the steering vectors at the base station(BS)and the eigenbases at the mobile station(MS).Nakagami distribution was used to describe the fading of the coupling between the steering vectors and the eigenbases.Extensive measurements were carried out to evaluate the performance of this proposed model.Furthermore,the physical implications of this model were illustrated and the capacities are analyzed.In addition,the azimuthal power spectrum(APS)of several models was analyzed.Finally,the channel hardening effect was simulated and discussed.Results showed that the proposed model provides a better fit to the measured results than the other CBSM,i.e.,Weichselberger model.Moreover,the proposed model can provide better tradeoff between accuracy and complexity in channel synthesis.This CIRM model can be used for massive MIMO design in the future communication system design.

Statistical Channel Modeling for Indoor VLC Communications Based on Channel Measurements

Visible light communication(VLC)has attracted much attention in the research of sixthgeneration(6G)systems.Furthermore,channel modeling is the foundation for designing efficient and robust VLC systems.In this paper,we present extensive VLC channel measurement campaigns in indoor environments,i.e.,an office and a corridor.Based on the measured data,the large-scale fading characteristics and multipath-related characteristics,including omnidirectional optical path loss(OPL),K-factor,power angular spectrum(PAS),angle spread(AS),and clustering characteristics,are analyzed and modeled through a statistical method.Based on the extracted statistics of the above-mentioned channel characteristics,we propose a statistical spatial channel model(SSCM)capable of modeling multipath in the spatial domain.Furthermore,the simulated statistics of the proposed model are compared with the measured statistics.For instance,in the office,the simulated path loss exponent(PLE)and the measured PLE are 1.96and 1.97,respectively.And,the simulated medians of AS and measured medians of AS are 25.94°and 24.84°,respectively.Generally,the fact that the simulated results fit well with measured results has demonstrated the accuracy of our SSCM.

和美乡村视角下的苏南农田“GREEN”水环境治理模式应用研究

随着中国宜居宜业和美乡村建设的不断深入,乡村水环境的治理与保护作为乡村生态文明建设的重要组成部分日益受到社会的重视和关注。在以往大规模灰色基础设施建设的背景下,乡村忽略了对于水生态绿色基础设施的构建,生态格局趋于破碎。农田空间作为农业面源污染的重要源头,结合多学科视角探索农田水环境污染治理新模式具有高度的必要性。立足于苏南地区独特的农田水网体系,紧密结合农业空间生产属性,研究现有农田水环境治理理论方法与实际建设,提炼以“源头绿色—过程净化—末端吸收—生态保育—循环回用”的苏南农田“GREEN”水环境治理模式,探索风景园林学科水治理方法在农田沟渠、河道及湿地等乡村空间的运用路径,并以常州市中天黄金大农场总体规划为例进行实践应用。推动风景园林学科于乡村生产空间的应用模式探索,为未来农业面源污染防治提供参考借鉴。

Does Green Food Certification promote agri-food export quality?Evidence from China

The construction of a food certification system plays a vital role in upgrading export quality, which previous studies have largely overlooked. We match China's industry-level data of Green Food Certification with its HS6-digit export data of agri-food products to quantify the impact of Green Food Certification on export quality. We identify the significant and positive effect of Green Food Certification on export quality. The 2SLS estimation based on instrumental variables and a range of robustness checks confirm the validity and robustness of the benchmark conclusions. Further analysis discloses that Green Food Certification improves export quality by raising agricultural production efficiency and brand premiums. Heterogeneity analysis shows that the effect of Green Food Certification varies across regions, notably improving the quality of agri-food products exported to developed regions and regions with high levels of import supervision. Furthermore, among various product types, Green Food Certification significantly improves the export quality of primary products and products vulnerable to non-tariff measures. The above findings could guide the future development of agri-food quality certification systems, potentially leading to a transformation and promotion of the agri-food trade.

Artificial Intelligence Based Multi-Scenario mmWave Channel Modeling for Intelligent High-Speed Train Communications

A large amount of mobile data from growing high-speed train(HST)users makes intelligent HST communications enter the era of big data.The corresponding artificial intelligence(AI)based HST channel modeling becomes a trend.This paper provides AI based channel characteristic prediction and scenario classification model for millimeter wave(mmWave)HST communications.Firstly,the ray tracing method verified by measurement data is applied to reconstruct four representative HST scenarios.By setting the positions of transmitter(Tx),receiver(Rx),and other parameters,the multi-scenarios wireless channel big data is acquired.Then,based on the obtained channel database,radial basis function neural network(RBF-NN)and back propagation neural network(BP-NN)are trained for channel characteristic prediction and scenario classification.Finally,the channel characteristic prediction and scenario classification capabilities of the network are evaluated by calculating the root mean square error(RMSE).The results show that RBF-NN can generally achieve better performance than BP-NN,and is more applicable to prediction of HST scenarios.

A Probing Model of Secret Key Generation Based on Channel Autocorrelation Function

Secret key generation(SKG)is a promising solution to the problem of wireless communications security.As the first step of SKG,channel probing affects it significantly.Although there have been some probing schemes,there is a lack of research on the optimization of the probing process.This study investigates how to optimize correlated parameters to maximize the SKG rate(SKGR)in the time-division duplex(TDD)mode.First,we build a probing model which includes the effects of transmitting power,the probing period,and the dimension of sample vectors.Based on the model,the analytical expression of the SKGR is given.Next,we formulate an optimization problem for maximizing the SKGR and give an algorithm to solve it.We conclude the SKGR monotonically increases as the transmitting power increases.Relevant mathematical proofs are given in this study.From the simulation results,increasing appropriately the probing period and the dimension of the sample vector could increase the SKGR dramatically compared to a yardstick,which indicates the importance of optimizing the parameters related to the channel probing phase.

Cooperative Covert Communication in Multi-Antenna Broadcast Channels

With the gradual popularization of 5G communications,the application of multi-antenna broadcasting technology has become widespread.Therefore,this study aims to investigate the wireless covert communication in the two-user cooperative multi-antenna broadcast channel.We focus on the issue that the deteriorated reliability and undetectability are mainly affected by the transmission power.To tackle this issue,we design a scheme based on beamforming to increase the reliability and undetectability of wireless covert communication in the multi-antenna broadcast channel.We first modeled and analyzed the cooperative multi-antenna broadcasting system,and put forward the target question.Then we use the SCA(successive convex approximation)algorithm to transform the target problem into a series of convex subproblems.Then the convex problems are solved and the covert channel capacity is calculated.In order to verify the effectiveness of the scheme,we conducted simulation verification.The simulation results show that the proposed beamforming scheme can effectively improve the reliability and undetectability of covert communication in multi-antenna broadcast channels.

Near-infrared cholangiography with intragallbladder indocyanine green injection in minimally invasive cholecystectomy

Laparoscopic cholecystectomy(LC)remains one of the most commonly performed procedures in adult and paediatric populations.Despite the advances made in intraoperative biliary anatomy recognition,iatrogenic bile duct injuries during LC represent a fatal complication and consist an economic burden for healthcare systems.A series of methods have been proposed to prevent bile duct injury,among them the use of indocyanine green(ICG)fluorescence.The most commonly reported method of ICG injection is the intravenous administration,while literature is lacking studies investigating the direct intragallbladder ICG injection.This narrative mini-review aims to assess the potential applications,usefulness,and limitations of intragallbladder ICG fluorescence in LC.Authors screened the available international literature to identify the reports of intragallbladder ICG fluorescence imaging in minimally invasive cholecystectomy,as well as special issues regarding its use.Literature search retrieved four prospective cohort studies,three case-control studies,and one case report.In the three case-control studies selected,intragallbladder near-infrared cholangiography(NIRC)was compared with standard LC under white light,with intravenous administration of ICG for NIRC and with standard intraoperative cholangiography(IOC).In total,133 patients reported in the literature have been administered intragallbladder ICG administration for biliary mapping during LC.Literature includes several reports of intragallbladder ICG administration,but a standardized technique has not been established yet.Published data suggest that NIRC with intragallbladder ICG injection is a promising method to achieve biliary mapping,overwhelming limitations of IOC including intervention and radiation exposure,as well as the high hepatic parenchyma signal and time interval needed in intravenous ICG fluorescence.Evidence-based guidelines on the role of intragallbladder ICG fluorescence in LC require the assessment of further studies and multicenter data collection into large registries.

Structural and Luminescent Properties of Mg_(0.25-x)Al_(2.57)O_(3.79)N_(0.21):xMn^(2+)Green-Emitting Transparent Ceramic Phosphor

A series of spinel-type Mg_(0.25-x)Al_(2.57)O_(3.79)N_(0.21):xMn^(2+)(MgAlON:xMn^(2+))phosphors were synthesized by the solid-state reaction route.The transparent ceramic phosphors were fabricated by pressureless sintering followed by hot-isostatic pressing(HIP).The crystal structure,luminescence and mechanical properties of the samples were systematically investigated.The transparent ceramic phosphors with tetrahedrally coordinated Mn^(2+)show strong green emission centered around 515 nm under blue light excitation.As the Mn^(2+)concentration increases,the crystal lattice expands slightly,resulting in a variation of crystal field and a slight red-shift of green emission peak.Six weak absorption peaks in the transmittance spectra originate from the spin-forbidden ^(4)T_(1)(^(4)G)→^(6)A_(1) transition of Mn^(2+).The decay time was found to decrease from 5.66 to 5.16 ms with the Mn^(2+)concentration.The present study contributes to the systematic understanding of crystal structure and properties of MgAlON:xMn^(2+)green-emitting transparent ceramic phosphor which has a potential application in high-power light-emitting diodes.

A Denoiser for Correlated Noise Channel Decoding: Gated-Neural Network

This letter proposes a sliced-gated-convolutional neural network with belief propagation(SGCNN-BP) architecture for decoding long codes under correlated noise. The basic idea of SGCNNBP is using Neural Networks(NN) to transform the correlated noise into white noise, setting up the optimal condition for a standard BP decoder that takes the output from the NN. A gate-controlled neuron is used to regulate information flow and an optional operation—slicing is adopted to reduce parameters and lower training complexity. Simulation results show that SGCNN-BP has much better performance(with the largest gap being 5dB improvement) than a single BP decoder and achieves a nearly 1dB improvement compared to Fully Convolutional Networks(FCN).

Dilation,discrimination and Uhlmann's theorem of link products of quantum channels

We establish the Stinespring dilation theorem of the link product of quantum channels in two different ways,discuss the discrimination of quantum channels,and show that the distinguishability can be improved by self-linking each quantum channel n times as n grows.We also find that the maximum value of Uhlmann's theorem can be achieved for diagonal channels.

A green cross-linking method for the preparation of renewable threedimensional graphene sponges for efficient adsorption of Congo red dye

Graphene-based materials possess significant potential for the treatment of dye wastewater due to their exceptional adsorption properties toward stubborn pollutants.However,their utilization is hindered by high preparation costs,low yields,environmental pollution during synthesis,and challenges in regenerating the adsorbent.This study proposes a novel approach to address these limitations by developing nitrogen-doped three-dimensional(3D)polyvinyl alcohol(PVA)crosslinked graphene sponges(N-PGA)using a cross-linking method with ammonium carbonate.This method offers a relatively mild,environmentally friendly approach.Ammonium carbonate serves as both a reducing and modifying agent,facilitating the formation of the intrinsic structure of N-PGA and acting as a nitrogen source.Meanwhile,PVA is utilized as the cross-linking agent.The results demonstrate that N-PGA exhibits a favorable internal 3D hierarchical porous structure and possesses robust mechanical properties.The measured specific surface area(BET)of N-PGA was as high as406.538 m^(2)·g^(-1),which was favorable for its efficient adsorption of Congo red(CR)dye molecules.At an initial concentration of 50 mg·L^(-1),N-PGA achieved an impressive removal rate of 89.6%and an adsorption capacity of 112 mg·g^(-1)for CR dye.Furthermore,it retained 79%of its initial adsorption capacity after 10 cycles,demonstrating excellent regeneration performance.In summary,the synthesized N-PGA displays remarkable efficacy in the adsorption of CR dye in wastewater,opening up new possibilities for utilizing 3D porous graphene nanomaterials as efficient adsorbents in wastewater treatment.

Channel Correlation Based User Grouping Algorithm for Nonlinear Precoding Satellite Communication System

Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works.

Green Synthesis of Silver Nanoparticles Using Aqueous Orange and Lemon Peel Extract and Evaluation of Their Antimicrobial Properties

Green synthesis of silver nanoparticles (AgNPs) using aqueous extracts of orange and lemon peels, as a reducing agent, and silver nitrate salts as a source of silver ions is a promising field of research due to the versatility of biomedical applications of metal nanoparticles. In this paper, AgNPs were synthetized at different reaction parameters such as the type and concentration of the extracts, metal salt concentration, temperature, speed stirring, and pH. The antibacterial properties of the obtained silver nanoparticles against E. coli, as well as the physical and chemical characteristics of the synthesized silver nanoparticles, were investigated. UV-Vis spectroscopy was used to confirm the formation of AgNPs. In addition to green biogenic synthesis, chemical synthesis of silver nanoparticles was also carried out. The optimal temperature for extraction was 65˚C, while for the synthesis of AgNPs was 35˚C. The synthesis is carried out in an acidic environment (pH = 4.7 orange and pH = 3.8 lemon), neutral (pH = 7) and alkaline (pH = 10), then for different concentrations of silver nitrate solution (0.5 mM - 1 mM), optimal time duration of the reaction was 60 min and optimal stirring speed rotation was 250 rpm on the magnetic stirrer. The physical properties of the synthesized silver nanoparticles (conductivity, density and refractive index) were also studied, and the passage of laser light through the obtained solution and distilled water was compared. Positive inhibitory effect on the growth of new Escherichia coli colonies have shown AgNPs synthesized at a basic pH value and at a 0.1 mM AgNO3 using orange or lemon peel extract, while for a 0.5 mM AgNO3 using lemon peel extract.

Two-Staged Method for Ice Channel Identification Based on Image Segmentation and Corner Point Regression

Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.