普通烟草外向整流Shaker K^(+)通道NtSKOR1的组织表达分析

外向整流Shaker K^(+)通道SKOR(Stelar K^(+)outward rectifier)是一类定位于植物根部中柱细胞质膜的外向整流Shaker K^(+)通道。为探究普通烟草NtSKOR1的启动子活性和不同时期组织表达情况,克隆该基因上游2439 bp的启动子序列,创制启动子驱动β-葡萄糖苷酸酶基因(GUS)的烟草材料并进行组织化学染色,通过RT-qPCR验证该基因的表达。结果表明NtSKOR1启动子含有光响应、逆境胁迫和激素等相关的顺式作用元件;转ProNtSKOR1::GUS烟草的组织化学染色试验表明:萌发期至子叶展平期未检测到GUS活性;小十字期,在真叶叶脉和茎尖分生组织开始检测到GUS活性;生根期,除茎和叶脉的维管组织外,在根部维管组织也开始检测到明显GUS活性,且活性随烟株生长而逐渐增强;盛花期,主要在烟草的根、茎和叶脉维管组织检测到活性,且上部叶叶脉中的GUS活性高于下部叶叶脉。RT-qPCR与GUS活性检测结果基本一致。综上可知,NtSKOR1主要在烟草小十字期及后续发育阶段的根、茎、叶的维管组织中表达,烟草进入盛花期后,该基因在光合作用较强的上部叶中的表达高于下部叶,推测该基因可能参与烟草K^(+)转运和同化产物协同运输。

低盐水体Na^(+)/K^(+)对凡纳滨对虾生长、体成分与肝胰腺、鳃组织结构的影响

缺钾在内陆低盐度盐碱水中经常发生,为探究低盐水体中不同钾缺乏程度对凡纳滨对虾生长、存活、体成分以及鳃和肝胰腺组织结构的影响,实验参照海水离子组成配置盐度为2的低盐度水体,在Na^(+)/K^(+)(mg/mg)比值为27、47、67、87、107 (分别记为A、B、C、D、E组)的条件下,对体重为(1.04±0.23) g的凡纳滨对虾幼虾开展了为期60 d的养殖实验。结果显示,E组对虾的存活率为44.64%±20.95%,显著低于A、B、D三组;E组体重为(4.86±0.66) g,显著低于其他4组;A~D组之间的湿重、增重率、特定生长率差异不显著,但均大于E组且差异显著;在饲料系数上,E组最高并与A、D组差异显著。在体成分上,各组全虾钾含量、灰分含量差异不显著,E组的水分含量高于其他组,并与A、B组差异显著;E组粗蛋白含量最低,且与B组差异显著。E组对虾鳃组织角质层明显受损,红细胞数量减少,空泡增多,肝胰腺B细胞及其内部转运泡体积增大,肝小管结构受损。研究表明,低盐条件下严重缺钾引起了对虾鳃和肝胰腺损伤,降低了对虾的存活率和生长率;水体缺钾在前期即可对对虾的生长产生明显影响,而对存活的影响随养殖时间的增加而增大。实验结果有助于为内陆低盐度盐碱水养殖凡纳滨对虾提供理论依据。

K^(+)掺杂双钙钛矿Cs_(2)AgInCl_(6)的发光性质

采用固相球磨法制备了K^(+)掺杂双钙钛矿Cs_(2)AgInCl_(6)纳米材料,该方法无需配体辅助,绿色环保。通过X射线衍射和拉曼光谱对晶体结构进行研究,通过激发光谱、发射光谱和时间分辨光谱对其发光性能进行研究。结果表明,Cs_(2)AgInCl_(6)为立方晶体,属于Fm3m空间群,由于宇称禁戒跃迁,其荧光量子产率(PLQY)低,小于0.1%。低于60%的K^(+)掺杂主要取代Ag^(+)的位置,引起Cs_(2)AgInCl_(6)的晶格膨胀,消除了晶格结构的反演对称性,打破了宇称禁戒跃迁,掺杂后Cs_(2)AgInCl_(6)的光致发光强度显著增强。K^(+)的最佳掺杂比例为40%,Cs_(2)Ag_(0.6)K_(0.4)InCl_(6)材料发射中心波长为640 nm,半高宽为180 nm,平均荧光寿命达到29.2 ns,PLQY达到10.5%。当K^(+)掺杂比例超过60%,K^(+)开始取代Cs+的位置,产物发生相变,出现立方相的Cs_(2-x)K_(1+x+y)AgyInCl_(6)和单斜相的Cs_(2-x)K_(1+x)InCl_(6)产物,这些产物由于强电子-声子耦合,非辐射复合占据主导地位。

Expression levels of K_(ATP)channel subunits and morphological changes in the mouse liver after exposure to radiation

BACKGROUND ATP sensitive K+(K_(ATP))channels are ubiquitously distributed in various of cells and tissues,including the liver.They play a role in the pathogenesis of myocardial and liver ischemia.AIM To evaluate the radiation-induced changes in the expression of K_(ATP)channel subunits in the mouse liver to understand the potential role of K_(ATP)channels in radiation injury.METHODS Adult C57BL/6 mice were randomly exposed toγ-rays at 0 Gy(control,n=2),0.2 Gy(n=6),1 Gy(n=6),or 5 Gy(n=6).The livers were removed 3 and 24 h after radiation exposure.Hematoxylin and eosin staining was used for morphological observation;immunohistochemical staining was applied to determine the expression of K_(ATP)channel subunits in the liver tissue.RESULTS Compared with the control group,the livers exposed to 0.2 Gyγ-ray showed an initial increase in the expression of Kir6.1 at 3 h,followed by recovery at 24 h after exposure.Exposure to a high dose of 5.0 Gy resulted in decreased expression of Kir6.1 and increased expression of SUR2B at 24 h.However,the expression of Kir6.2,SUR1,or SUR2A had no remarkable changes at 3 and 24 h after exposure to any of these doses.CONCLUSION The expression levels of Kir6.1 and SUR2B in mouse liver changed differently in response to different radiation doses,suggesting a potential role for them in radiation-induced liver injury.

Na^(+)/K^(+)-ATPase:ion pump,signal transducer,or cytoprotective protein,and novel biological functions

Na^(+)/K^(+)-ATPase is a transmembrane protein that has important roles in the maintenance of electrochemical gradients across cell membranes by transporting three Na^(+)out of and two K^(+)into cells.Additionally,Na^(+)/K^(+)-ATPase participates in Ca^(2+)-signaling transduction and neurotransmitter release by coordinating the ion concentration gradient across the cell membrane.Na^(+)/K^(+)-ATPase works synergistically with multiple ion channels in the cell membrane to form a dynamic network of ion homeostatic regulation and affects cellular communication by regulating chemical signals and the ion balance among different types of cells.Therefo re,it is not surprising that Na^(+)/K^(+)-ATPase dysfunction has emerged as a risk factor for a variety of neurological diseases.However,published studies have so far only elucidated the important roles of Na^(+)/K^(+)-ATPase dysfunction in disease development,and we are lacking detailed mechanisms to clarify how Na^(+)/K^(+)-ATPase affects cell function.Our recent studies revealed that membrane loss of Na^(+)/K^(+)-ATPase is a key mechanism in many neurological disorders,particularly stroke and Parkinson's disease.Stabilization of plasma membrane Na^(+)/K^(+)-ATPase with an antibody is a novel strategy to treat these diseases.For this reason,Na^(+)/K^(+)-ATPase acts not only as a simple ion pump but also as a sensor/regulator or cytoprotective protein,participating in signal transduction such as neuronal autophagy and apoptosis,and glial cell migration.Thus,the present review attempts to summarize the novel biological functions of Na^(+)/K^(+)-ATPase and Na^(+)/K^(+)-ATPase-related pathogenesis.The potential for novel strategies to treat Na^(+)/K^(+)-ATPase-related brain diseases will also be discussed.

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.

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.

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.

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.

Continuous-Time Channel Prediction Based on Tensor Neural Ordinary Differential Equation

Channel prediction is critical to address the channel aging issue in mobile scenarios.Existing channel prediction techniques are mainly designed for discrete channel prediction,which can only predict the future channel in a fixed time slot per frame,while the other intra-frame channels are usually recovered by interpolation.However,these approaches suffer from a serious interpolation loss,especially for mobile millimeter-wave communications.To solve this challenging problem,we propose a tensor neural ordinary differential equation(TN-ODE)based continuous-time channel prediction scheme to realize the direct prediction of intra-frame channels.Specifically,inspired by the recently developed continuous mapping model named neural ODE in the field of machine learning,we first utilize the neural ODE model to predict future continuous-time channels.To improve the channel prediction accuracy and reduce computational complexity,we then propose the TN-ODE scheme to learn the structural characteristics of the high-dimensional channel by low-dimensional learnable transform.Simulation results show that the proposed scheme is able to achieve higher intra-frame channel prediction accuracy than existing schemes.

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.

Influence of variable viscosity and double diffusion on the convective stability of a nanofluid flow in an inclined porous channel

The influence of variable viscosity and double diffusion on the convective stability of a nanofluid flow in an inclined porous channel is investigated.The DarcyBrinkman model is used to characterize the fluid flow dynamics in porous materials.The analytical solutions are obtained for the unidirectional and completely developed flow.Based on a normal mode analysis,the generalized eigenvalue problem under a perturbed state is solved.The eigenvalue problem is then solved by the spectral method.Finally,the critical Rayleigh number with the corresponding wavenumber is evaluated at the assigned values of the other flow-governing parameters.The results show that increasing the Darcy number,the Lewis number,the Dufour parameter,or the Soret parameter increases the stability of the system,whereas increasing the inclination angle of the channel destabilizes the flow.Besides,the flow is the most unstable when the channel is vertically oriented.

Cooperative activation of sodium channels for downgrading the energy efficiency in neuronal information processing

The Hodgkin–Huxley model assumes independent ion channel activation,although mutual interactions are common in biological systems.This raises the problem why neurons would favor independent over cooperative channel activation.In this study,we evaluate how cooperative activation of sodium channels affects the neuron’s information processing and energy consumption.Simulations of the stochastic Hodgkin–Huxley model with cooperative activation of sodium channels show that,while cooperative activation enhances neuronal information processing capacity,it greatly increases the neuron’s energy consumption.As a result,cooperative activation of sodium channel degrades the energy efficiency for neuronal information processing.This discovery improves our understanding of the design principles for neural systems,and may provide insights into future designs of the neuromorphic computing devices as well as systematic understanding of pathological mechanisms for neural diseases.

Effective Capacity of URLLC over Parallel Fading Channels with Imperfect Channel State Information

This paper investigates the effective capacity of a point-to-point ultra-reliable low latency communication(URLLC)transmission over multiple parallel sub-channels at finite blocklength(FBL)with imperfect channel state information(CSI).Based on reasonable assumptions and approximations,we derive the effective capacity as a function of the pilot length,decoding error probability,transmit power and the sub-channel number.Then we reveal significant impact of the above parameters on the effective capacity.A closed-form lower bound of the effective capacity is derived and an alternating optimization based algorithm is proposed to find the optimal pilot length and decoding error probability.Simulation results validate our theoretical analysis and show that the closedform lower bound is very tight.In addition,through the simulations of the optimized effective capacity,insights for pilot length and decoding error probability optimization are provided to evaluate the optimal parameters in realistic systems.

Flight Time Minimization of UAV for Cooperative Data Collection in Probabilistic LoS Channel

This paper investigates the data collection in an unmanned aerial vehicle(UAV)-aided Internet of Things(IoT) network, where a UAV is dispatched to collect data from ground sensors in a practical and accurate probabilistic line-of-sight(LoS) channel. Especially, access points(APs) are introduced to collect data from some sensors in the unlicensed band to improve data collection efficiency. We formulate a mixed-integer non-convex optimization problem to minimize the UAV flight time by jointly designing the UAV 3D trajectory and sensors’ scheduling, while ensuring the required amount of data can be collected under the limited UAV energy. To solve this nonconvex problem, we recast the objective problem into a tractable form. Then, the problem is further divided into several sub-problems to solve iteratively, and the successive convex approximation(SCA) scheme is applied to solve each non-convex subproblem. Finally,the bisection search is adopted to speed up the searching for the minimum UAV flight time. Simulation results verify that the UAV flight time can be shortened by the proposed method effectively.

Assessment of the Recent Hydromorphological Features of Nokoue Lake and Its Channels (South-East Benin)

Based on 2022 and 2023 hydrometric data and satellite images (Sentinel 2022, SPOT 2010), this study aims to present the Nokoué Lake and its channels’ re-cent hydromorphological characteristics. Integrating flow, tributary morphology, and topography data determined specific power values along the axes studied. The values obtained range from 2.69 to 12.92 W/m2 for Ouémé River and 2.46 to 10.99 W/m2 for Sô River. The resulting water erosion on banks and bottoms is of linear, areolar, or gully and claw types. Lake bathymetry varies from -0.5 to -2.6 m (low flow period) and -1 to -4 m;in the Ouémé, Sô, and Totchè rivers, it varies from -5 m to -7 m, reaching -10 m at the Cotonou channel entrance (flood period). Bathymetric profiles reveal varied “U”, “V” and “Intermediate” bottom morphologies, influenced by erosion/sedimentation processes and human activities. The flow facies identified are lentic in the northern tributaries and lotic in the Cotonou and Totchè canals. Spatial analysis identified nine (09) thematic classes. In 2022, the surface area of the water body has increased from 274 km2 at low water to 280 km2 at high water, whereas in 2010 (a recent year of exceptional flooding), the surface area was 270 km2 at low water and 277 km2 at high water. Significant changes in land use are observed between 2010 and 2022. The floodplain area decreased slightly, from 421 km2 in 2010 (year of exceptional flooding) to 419 km2 in 2022. The evolution of land use shows a progressive expansion of the urban environment to the detriment of the natural environment. In the medium to long term, this trend could threaten the hydromorphological balance and even the existence of this important lagoon ecosystem.