Virus-Encoded MicroRNAs Reveal How Ranavirus Interacts with Amphibian Immune Defense

Ranaviruses are harmful viruses that infect amphibians, fish, and reptiles, and have caused particularly devastating declines in amphibian populations. One particular type of ranavirus, called Frog Virus 3 (FV3), has been extensively studied due to its prevalence and impact on amphibians. Previous research has primarily focused on the virus’s genes, but little attention has been given to the non-coding regions of its genome. This article reviews recent studies that reveal the ability of ranaviruses, including FV3, to encode microRNA (miRNA), a type of regulatory RNA. These viral miRNAs play a crucial role in suppressing frog immune genes, modulating the virus-host interaction, and promoting viral infection. Understanding how ranaviruses use miRNAs to control disease progression is essential for addressing the health threat they pose to wildlife and ecosystems.

Upconversion Nanoparticles-Encoded Hydrogel Microbeads-Based Multiplexed Protein Detection

Fluorescently encoded microbeads are in demand for multiplexed applications in different fields.Compared to organic dye-based commercially available Luminex's x MAP technology, upconversion nanoparticles(UCNPs) are better alternatives due to their large antiStokes shift, photostability, nil background, and single wavelength excitation. Here, we developed a new multiplexed detection system using UCNPs for encoding poly(ethylene glycol) diacrylate(PEGDA) microbeads as well as for labeling reporter antibody. However, to prepare UCNPs-encoded microbeads, currently used swellingbased encapsulation leads to non-uniformity, which is undesirable for fluorescence-based multiplexing. Hence,we utilized droplet microfluidics to obtain encoded microbeads of uniform size, shape, and UCNPs distribution inside. Additionally, PEGDA microbeads lack functionality for probe antibodies conjugation on their surface.Methods to functionalize the surface of PEGDA microbeads(acrylic acid incorporation, polydopamine coating)reported thus far quench the fluorescence of UCNPs. Here,PEGDA microbeads surface was coated with silica followed by carboxyl modification without compromising the fluorescence intensity of UCNPs. In this study, droplet microfluidics-assisted UCNPs-encoded microbeads of uniform shape, size, and fluorescence were prepared.Multiple color codes were generated by mixing UCNPs emitting red and green colors at different ratios prior to encapsulation. UCNPs emitting blue color were used to label the reporter antibody. Probe antibodies were covalently immobilized on red UCNPs-encoded microbeads for specific capture of human serum albumin(HSA) as a model protein. The system was also demonstrated for multiplexed detection of both human C-reactive protein(hCRP) and HSA protein by immobilizing anti-h CRP antibodies on green UCNPs.

Flexible reduced field of view magnetic resonance imaging based on single-shot spatiotemporally encoded technique

In many ultrafast imaging applications, the reduced field-of-view（r FOV） technique is often used to enhance the spatial resolution and field inhomogeneity immunity of the images. The stationary-phase characteristic of the spatiotemporallyencoded（SPEN） method offers an inherent applicability to r FOV imaging. In this study, a flexible r FOV imaging method is presented and the superiority of the SPEN approach in r FOV imaging is demonstrated. The proposed method is validated with phantom and in vivo rat experiments, including cardiac imaging and contrast-enhanced perfusion imaging. For comparison, the echo planar imaging（EPI） experiments with orthogonal RF excitation are also performed. The results show that the signal-to-noise ratios of the images acquired by the proposed method can be higher than those obtained with the r FOV EPI. Moreover, the proposed method shows better performance in the cardiac imaging and perfusion imaging of rat kidney, and it can scan one or more regions of interest（ROIs） with high spatial resolution in a single shot. It might be a favorable solution to ultrafast imaging applications in cases with severe susceptibility heterogeneities, such as cardiac imaging and perfusion imaging. Furthermore, it might be promising in applications with separate ROIs, such as mammary and limb imaging.

A compound objective reconfiguration of distribution networks using hierarchical encoded particle swarm optimization

With the development of automation in smart grids,network reconfiguration is becoming a feasible approach for improving the operation of distribution systems.A novel reconfiguration strategy was presented to get the optimal configuration of improving economy of the system,and then identifying the important nodes.In this strategy,the objectives increase the node importance degree and decrease the active power loss subjected to operational constraints.A compound objective function with weight coefficients is formulated to balance the conflict of the objectives.Then a novel quantum particle swarm optimization based on loop switches hierarchical encoded was employed to address the compound objective reconfiguration problem.Its main contribution is the presentation of the hierarchical encoded scheme which is used to generate the population swarm particles of representing only radial connected solutions.Because the candidate solutions are feasible,the search efficiency would improve dramatically during the optimization process without tedious topology verification.To validate the proposed strategy,simulations are carried out on the test systems.The results are compared with other techniques in order to evaluate the performance of the proposed method.

Bus Encoded LUT Multiplier for Portable Biomedical Therapeutic Devices

DSP operation in a Biomedical related therapeutic hardware need to beperformed with high accuracy and with high speed. Portable DSP hardware’s likepulse/heart beat detectors must perform with reduced operational power due to lack ofconventional power sources. This work proposes a hybrid biomedical hardware chip inwhich the speed and power utilization factors are greatly improved. Multipliers are thecore operational unit of any DSP SoC. This work proposes a LUT based unsignedmultiplication which is proven to be efficient in terms of high operating speed. For n bitinput multiplication n*n memory array of 2 n bit size is required to memorize all thepossible input and output combination. Various literature works claims to be achieve highspeed multiplication with reduced LUT size by integrating a barrel shifter mechanism.This paper work address this problem, by reworking the multiplier architecture with aparallel operating pre-processing unit which used to change the multiplier and multiplicandorder with respect to the number of computational addition and subtraction stages required.Along with LUT multiplier a low power bus encoding scheme is integrated to limit the powerconstraint of the on chip DSP unit. This paper address both the speed and power optimizationtechniques and tested with various FPGA device families.

Automatic UAV Positioning with Encoded Sign as Cooperative Target

In order to achieve the goal that unmanned aerial vehicle(UAV)automatically positioning during power inspection,a visual positioning method which utilizes encoded sign as cooperative target is proposed.Firstly,we discuss how to design the encoded sign and propose a robust decoding algorithm based on contour.Secondly,the Adaboost algorithm is used to train a classifier which can detect the encoded sign from image.Lastly,the position of UAV can be calculated by using the projective relation between the object points and their corresponding image points.Experiment includes two parts.First,simulated video data is used to verify the feasibility of the proposed method,and the results show that the average absolute error in each direction is below 0.02 m.Second,a video,acquired from an actual UAV flight,is used to calculate the position of UAV.The results show that the calculated trajectory is consistent with the actual flight path.The method runs at a speed of 0.153 sper frame.

Genetically encoded fluorescent biosensor of histone 3 lysine 79 in MEFs

Introduction Post-translational modifications of core histones have emerged as a critical player in dynamical regulation of gene expression and accurate chromatin structures[1-2].In recent years it has been demonstrated that,histone lysine methylation is particularly prominent as one of the most important epigenetic modifications during cell cycles,development and differentiation,and in response to external stimuli,e.g.exogenous growth factors and mechanical stimulation.This epigenetic modification may also be an early event that regulates the gene expression dur-

Quantum-Inspired Particle Swarm Optimization Algorithm Encoded by Probability Amplitudes of Multi-Qubits

To enhance the optimization ability of particle swarm algorithm, a novel quantum-inspired particle swarm optimization algorithm is proposed. In this method, the particles are encoded by the probability amplitudes of the basic states of the multi-qubits system. The rotation angles of multi-qubits are determined based on the local optimum particle and the global optimal particle, and the multi-qubits rotation gates are employed to update the particles. At each of iteration, updating any qubit can lead to updating all probability amplitudes of the corresponding particle. The experimental results of some benchmark functions optimization show that, although its single step iteration consumes long time, the optimization ability of the proposed method is significantly higher than other similar algorithms.

Antitumor effects of cytokine gene-encoded vaccinia virus on murine melanoma

Recombinant vaccinia virus has many advantagesover more restricted vectors like retrovirus andadenovirus. The proven safety of vaccinia virus, which isrestricted to local and transitory infection, favors clinicalapplication of vaccinia virus to deliver cytokines locally.

A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

EFFECTS OF A SYNTHETIC POLYPEPTIDE ENCODED BY THE p14-6,A cDNA CLONE WITH ANTIONCOGENE ACTIVITY, ON MALIGNANT TRANSFORMED DT CELLS

A synthetic polypeptide, pt27, which is encoded by a cDNA clone with antloncogene activity, p14-6, is found to be able to reduce remarkably the soft agar colony formation ability of part of DT cells and to raise their resistance to the ouabaln toxtcity. This shows that the pt27 peptide can affect the DT cells In a manner similar to the p14- 6 done and provides evidence that the reverting action of the p14-6 to DT cells may be exerted by the expression of its cDNA.

A novel encoding mechanism for particle physics

This study proposes a novel particle encoding mechanism that seamlessly incorporates the quantum properties of particles,with a specific emphasis on constituent quarks.The primary objective of this mechanism is to facilitate the digital registration and identification of a wide range of particle information.Its design ensures easy integration with different event generators and digital simulations commonly used in high-energy experiments.Moreover,this innovative framework can be easily expanded to encode complex multi-quark states comprising up to nine valence quarks and accommodating an angular momentum of up to 99/2.This versatility and scalability make it a valuable tool.

A highly reliable encoding and decoding communication framework based on semantic information

Increasing research has focused on semantic communication,the goal of which is to convey accurately the meaning instead of transmitting symbols from the sender to the receiver.In this paper,we design a novel encoding and decoding semantic communication framework,which adopts the semantic information and the contextual correlations between items to optimize the performance of a communication system over various channels.On the sender side,the average semantic loss caused by the wrong detection is defined,and a semantic source encoding strategy is developed to minimize the average semantic loss.To further improve communication reliability,a decoding strategy that utilizes the semantic and the context information to recover messages is proposed in the receiver.Extensive simulation results validate the superior performance of our strategies over state-of-the-art semantic coding and decoding policies on different communication channels.

基于时空Transformer-encoder的跨社交网络用户匹配方法

针对目前基于签到时空数据的跨社交网络用户匹配方法未充分利用时空信息之间的耦合关系,导致时空数据特征提取困难,匹配准确率下降的问题,提出了一种基于时空Transformer-encoder的跨社交网络用户匹配方法。该方法通过网格映射将签到时空信息转换为序列数据,生成签到序列;利用序列嵌入层将离散的签到序列映射到连续高维空间;然后借助多头注意力机制和卷积神经网络提取高维签到特征,并利用卷积神经网络实现优化多头注意力模块权重变换和特征融合;最后利用前馈神经网络实现分类,输出用户匹配得分。在两组真实社交网络用户数据集上进行大量用户匹配实验,与现有方法相比,准确率提升了0.40~10.53百分点,F_(1)值提升了0.43~9.5百分点。这验证了所提方法能够有效提取用户签到耦合特征,并提高用户匹配的性能。

Variational data encoding and correlations in quantum-enhanced machine learning

Leveraging the extraordinary phenomena of quantum superposition and quantum correlation,quantum computing offers unprecedented potential for addressing challenges beyond the reach of classical computers.This paper tackles two pivotal challenges in the realm of quantum computing:firstly,the development of an effective encoding protocol for translating classical data into quantum states,a critical step for any quantum computation.Different encoding strategies can significantly influence quantum computer performance.Secondly,we address the need to counteract the inevitable noise that can hinder quantum acceleration.Our primary contribution is the introduction of a novel variational data encoding method,grounded in quantum regression algorithm models.By adapting the learning concept from machine learning,we render data encoding a learnable process.This allowed us to study the role of quantum correlation in data encoding.Through numerical simulations of various regression tasks,we demonstrate the efficacy of our variational data encoding,particularly post-learning from instructional data.Moreover,we delve into the role of quantum correlation in enhancing task performance,especially in noisy environments.Our findings underscore the critical role of quantum correlation in not only bolstering performance but also in mitigating noise interference,thus advancing the frontier of quantum computing.

A Lightweight Network with Dual Encoder and Cross Feature Fusion for Cement Pavement Crack Detection

Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this field.However,as the performance of crack detection in cement pavement improves,the depth and width of the network structure are significantly increased,which necessitates more computing power and storage space.This limitation hampers the practical implementation of crack detection models on various platforms,particularly portable devices like small mobile devices.To solve these problems,we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature fusion.Firstly,we use small channel convolution to construct shallow feature extractionmodule(SFEM)to extract low-level feature information of cracks in cement pavement images,in order to obtainmore information about cracks in the shallowfeatures of images.In addition,we construct large kernel atrous convolution(LKAC)to enhance crack information,which incorporates coordination attention mechanism for non-crack information filtering,and large kernel atrous convolution with different cores,using different receptive fields to extract more detailed edge and context information.Finally,the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module,and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction map.We evaluate our method on three public crack datasets:DeepCrack,CFD,and Crack500.Experimental results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods,which achieves Precision(P)87.2%,Recall(R)87.7%,and F-score(F1)87.4%.Thanks to our lightweight crack detectionmodel,the parameter count of the model in real-world detection scenarios has been significantly reduced to less than 2M.This advancement also facilitates technical support for portable scene detection.

The assessment of atrial function by velocity-encoded magnetic resonance imaging

Introduction: The purpose of this study was to assess velocity-encoded cardiac magnetic resonance imaging (Ve-CMR) in a population of patients referred for cardiac magnetic resonance imaging (CMR), to determine the variability of atrial function, and to identify clinical parameters associated with left atrial function. Methods: This is a prospective study evaluating patients who were referred to our CMR center for a clinical CMR. Left atrial function was obtained via Ve-CMR thru-plane images across the mitral valve after acquiring 2 perpendicular in-plane images as “scouts”. The atrial function and mitral inflow were quantified by computer analysis (Argus, Siemens). Atrial function was defined as atrial contraction (A-wave) volume divided by total inflow volume. Left atrial volumes were calculated via computer analysis. Mitral regurgitation and left ventricular ejection fractions were assessed visually. Results: Thirty-nine patients, with mean age 56 +/- 10 years, were enrolled. The mean left atrial function was 22.9% +/-14.5%;the range in left atrial function was 0% - 57%. There was a significant positive correlation between atrial function and increased left ventricular ejection fraction (r = 0.44, P < 0.01). There was a significant negative correlation between atrial function and severity of mitral regurgitation (r = -0.60, P < 0.01), as well as left atrial volume (r = -0.36, P = 0.02). Conclusion: Our results indicate a wide variability in left atrial function and a significant association between left atrial function and left ventricular ejection fraction, left atrial volume and mitral regurgitation.

Remaining Useful Life Prediction of Rail Based on Improved Pulse Separable Convolution Enhanced Transformer Encoder

In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is difficult to capture the long-term dependency relationship of the time series in the modeling of the long time series of rail damage, due to the coupling relationship of multi-channel data from multiple sensors. Here, in this paper, a novel RUL prediction model with an enhanced pulse separable convolution is used to solve this issue. Firstly, a coding module based on the improved pulse separable convolutional network is established to effectively model the relationship between the data. To enhance the network, an alternate gradient back propagation method is implemented. And an efficient channel attention (ECA) mechanism is developed for better emphasizing the useful pulse characteristics. Secondly, an optimized Transformer encoder was designed to serve as the backbone of the model. It has the ability to efficiently understand relationship between the data itself and each other at each time step of long time series with a full life cycle. More importantly, the Transformer encoder is improved by integrating pulse maximum pooling to retain more pulse timing characteristics. Finally, based on the characteristics of the front layer, the final predicted RUL value was provided and served as the end-to-end solution. The empirical findings validate the efficacy of the suggested approach in forecasting the rail RUL, surpassing various existing data-driven prognostication techniques. Meanwhile, the proposed method also shows good generalization performance on PHM2012 bearing data set.

HiSC:A Hybrid XML Index Composing Structure-Encoded with Cluster

A new way of indexing and processing twig patterns in an XML documents is proposed in this paper. Every path in XML document can be transformed into a sequence of labels by Structure-Encoded that constructs a one-to-one correspondence between XML tree and sequence. Base on identifying characteristics of nodes in XML tree, the elements are classified and clustered. During query proceeding, the twig pattern is also transformed into its Structure-Encoded. By performing subsequence matching on the set of sequences in XML documents, all the occurrences of path in the XML documents are refined. Using the index, the numbers of elements retrieved are minimized. The search results with pertinent format provide more structure information without any false dismissals or false alarms. The index also supports keyword search Experiment results indicate the index has significantly efficiency with high precision.

Self-Encoded Multiple Access Multiuser Convolutional Codes in Uplink and Downlink Cellular Systems

Self-encoded spread spectrum eliminates the need for traditional pseudo noise (PN) code generators. In a self-encoded multiple access (SEMA) system, the number of users is not limited by the number of available sequences, unlike code division multiple access (CDMA) systems that employ PN codes such as m-, Gold or Kassami sequences. SEMA provides a convenient way of supporting multi-rate, multi-level grades of service in multimedia communications and prioritized heterogeneous networking systems. In this paper, we propose multiuser convolutional channel coding in SEMA that provides fewer cross-correlations among users and thereby reducing multiple access interference (MAI). We analyze SEMA multiuser convolutional coding in additive white Gaussian noise (AWGN) channels as well as fading channels. Our analysis includes downlink synchronous system as well as asynchronous system such as uplink mobile-to-base station communication.