Construction of Recombinant Retroviral Vector Containing HIV-1 Tat Gene and Functional Detection of Expressed Tat in Target Cells

Objective: To construct recombinant retroviral vector containing HIV-1 Tatgene and evaluate the junction of the expressed Tat in target cells. Methods: HIV-1 Tat_(101) genewas recovered from pEV plasmid by Hind Ⅲ digestion and cloned into expression plasmid LZESpBMN-Z toconstruct recombinant retroviral expression plasmid named LZRS-Tat_(101). Using the method ofcalcium phosphate, the construct of LZRS-Tat_(101) was then transfected into packaging cell linesPhoenix (ΦNX) which contained env and gal genes encoding structural proteins and pol gene codingfor 3 enzymes ( reverse transcriptase, protease and integrate) essential for retroviral integrationand replication . The stable transfected cell lines was obtained using puromycin to screen for morethan 3 days. Then, immunohistochemical (IHC ) staining was carried out to detect the expressionlevel of Tat_(101) protein in both transiently and stably trancfected ΦNX, respectively. Thesupematants containing recombinant virus collected from transient and stable transfected cells wereemployed to infect 293 cells, respectively, and the expressed Tat in 293 cells was tested by Westernblot. Meantime, the supematants of infected 293 cells was further added to HL3T1 cells which wereHela cell lines containing an HIV-1-LTR/CAT reporter construct to establish a co-culture system.After co-culture for 72 hours, the protein was extracted from HL3T1 cells and used for CAT activityassay. Results: After LZRS- Tat_(101) was transfected into ΦNX, the amount of expressed Tat intransient transfection cells was significantly higher than that in stable transfection cells; Tatcould be detected not only in 293 cells but also in the supematants from 293 cells culture, and Tatin the supematants could activate HIV-1 LTR promoter in HL3T1, resulting in high 'expression of CATlocated at the downstream of LTR. Conclusion: The construct of recombinant retrovirus LZRS-Tat_(101) could express Tat protein in target cells and the expressed Tat was functionally activeand can really exhibit the ability to activate transcription.

Construction of pINC-lacZ Retroviral Vector and its Expression in Mouse Embryonic Stem Cells

A retroviral vector pINC-lacZ containing an E coli β-galactosidase(β-gal)gene was constructed and introduced into the MESPU-13 cells by electroporation.Four G418-resistant colonies were obtained from 1×107,electroporated MESPU-13 cells. Histochemical staining for β-gal activity showed that lacZ gene was expressed in at least three of the four colonies.Southern analysis proved that one copy of foreign gene was integrated in the chromosome of one of the transformed lines(MC15).These results showed that the expression of lacZ gene driven by SiCMVIE promoter can be detected in the transformed MESPU-13 cells.

Transduction of primary rat hepatocytes with bicistronic retroviral vector

INTRODUCTIONHepatocellular transplantation (HCT) could providea therapeutic alternative to orthotopic livertransplantation(OLT) in the treatment of hepaticmetabolic defects and experimental hepaticfailure.Under appropriate conditions,theengrafted liver cells can continue to express liver-specific functions for an indefinite period of time.

Construction and expression of retroviral vector pLEGFP-N1-TERT in preparation of seed cells for skin tissue engineering

Objective:To construct the retroviral vector pLEGFP-N1-telomerase reverse transcriptase(TERT)and to investigate the expression of TERT in neonatal mouse bypodermal cells.Methods:The polymerase chain reaction(PCR)-amplified TERT gene was inserted into plasmid pLEGFPN1.The positive clone was identified by restriction enzyme digestion and sequencing,then was transfected into packaging cells to produce retrovirus particles.Neonatal mouse hypodermal cells were infected with the virus to generate a stable cell line.The TERT mRNA expression level,telomerase activity,and enhanced green fluorescent protein(EGFP)expression level were analyzed.Results:Retroviral vector pLEGFP-N1-TERT was constructed successfully,and a stable cell line of neonatal mouse hypodermal cells expressing EGFP was established.Western blot and immunohistochemical assay showed that the expression level of TERT was significantly elevated in the neonatal mouse hypodermal cells.Conclusions:A high titer of retrovirus pLEGFP-N1-TERT mediates high-level expression of the exogenous TERT gene in the neonatal mouse hypodermal cells.This protocol has potential applications for skin tissue engineering and cell transplantation therapy.

CONSTRUCTION AND IDENTIFICATION OF RETROVIRAL VECTOR EXPRESSING HUMAN INTERLEUKIN-17 GENE

Human interleukin 17(IL17) is a newly found cytokine secreted by activated T lymphocytes. Many of its characteristics remain unknown. To provide a way for understanding its role in immunology and hematology further, we constructed a recombinant retroviral vector pL17SN containing the human IL17 gene about 1.3kb with the coding region. The constructed retroviral vector packaged in CRIP cells could infect human primary fibroblasts, and could stimulate the primary fibroblasts secreting human GMCSF and IL6. The retroviral vector containing the human IL17 gene we constructed may present an efficient way to understand the biological functions of human IL17 and to investigate applications of IL17 in cancer gene therapy.

Expression of the Capsid Precursor Protein gene of Foot-and-mouth Disease Virus and Green Fluorescent Protein Gene in BHK-21 Cells Mediated by Retroviral Vector

We have constructed a retroviral vector mediated mammalian cell expression system of the capsid precursor protein of foot-and-mouth disease virus(FMDV).The recombinant retroviral vector pBABEpuro-P1-2A-EGFP was constructed by sequentially inserting capsid precursor protein gene(P1) of FMDV and enhanced green fluorescent protein gene(EGFP) into pBABEpuro.The recombinant retroviral vector and the pVSV-G plasmid were co-transfected into packaging cells(GP2-293) by liposomemediated transduction to produce the pseudovirus.The pseudovirus was used to infect BHK-21 cells and resistant cells were screened with puromycin.Green fluorescent proteins were observed by fluorescence microscopy and expression of the capsid precursor protein gene of FMDV was detected by indirect immunofluorescence.The recombinant retroviral vector pBABEpuro-P1-2A-EGFP was constructed successfully.The capsid precursor protein of FMDV and green fluorescent protein were expressed in BHK-21 cells.The mammalian cell expression system for the capsid precursor protein of FMDV has been constructed successfully,which lays the foundation of development of a FMDV subunit vaccine.

Construction of retroviral vectors to induce a strong expression of human interferon-β gene in human hepatoma cells

Establishing the hepatoma cell-specific expression of human interferon gene mediated by retroviral vectors. Methods: Human interferon-β complementary DNA (IFN-β cDNA) was inserted into polylinker site of pMNSM retroviral vector to construct recombinant retroviral vector pMNSIFNB, where the transcription of IFN-β gene was driven by SV40 early region promoter, and MNAIFNB, where the transcription of IFN-β gene was driven by SV40 early region promoter regulated by α-fetoprotein enhancer. The retroviral constructs were respectively introduced into PA317 amphotropic packaging cells by means of lipofectamine mediated gene transfer procedure. The plasmids transfection efficiency was among (4-25)ｘ103 colonies/μg DNA/106 PA317 cells. The retrovirus infection efficiency was among (4. 5-500)ｘ104 Colony Forming Units (CFU)/ml. The recombinant retroviruses were used to infect human hepatoma cells, renal cell carcinoma cells and melanoma cell lines in the presence of 4 μg/ml polybrene. Results: Dot hybridization of total RNA from the neomycin resistant colonies and interferon expression assay indicated that human α-fetoprotein enhancer induced efficient and specific transcription and expression of IFN-β gene driven by the promoter of different origin in human hepatoma cells by which α-fetoprotein was highly produced. Conclusion: Cis-active element of α-fetoprotein gene can drive IFN-β gene specifically expressed in human hepatoma cells, which presents some valuable materials for the hepatoma-specific immune gene therapy.

Construction and identification of a retroviral vector of bcl-2 mRNA-cleaving ribozyme gene

The bcl-2 mRNA-cleaving ribozyme (RZ) gene was synthesized with EcoRI and BamHI sites, each on one of the terminals. The synthesized gene was cloned to the HincII site of the plasmid pGEM-3Zf(-) and the recombinant plasmid was termed 3ZRZ (+)/(-). The gene was sequenced and found to be correct, and was used for in vitro transcription and in vitro cleaving of bcl-2 mRNA. The RZ gene was cut out from the plasmid and subcloned in a retroviral vector pDOR-neo at the BamHI and EcoRI sites, and the new recombinant retroviral vector was named pDOR-RZ.

A study on improvement of expression of human G－CSF cDNA transferred with retroviral double－copy vector

In order to overcome the interference of the internal promoter in retroviral vector with a foreign DNA expression, we used the double-copy vector(DC vector) system to improve the effect of transduced gene expression.Human G-CSF cDNA was inserted into the Bgl Ⅱ site of the polyclonal sites within the U3 region of the 3'long terminal repeat(3'-LTR) in the vector(N2A). After being identified, the gene was transduced into Ψ-2 packaging cell line by using the electroporation method. Consequently, the gene was duplicated in the infected cells, and transferred to the 5'-LRT, and then placed outside the retroviral transcriptional unit. After two weeks the neomycin resistance positive colonies were grown in the G-418 medium. The supernatant virus was titred and then transferred to NIH3T3 cells. The G-CSF value of positive clone N2AG7-4 was up to 53. 3 U/106 cells. Southern blot and Northern blot analysis showed that the chimeric gene faithfully duplicated in the cells infected with the corresponding virus and generated two copies with one in each LTR.

A HEVC Video Steganalysis Method Using the Optimality of Motion Vector Prediction

Among steganalysis techniques,detection against MV(motion vector)domain-based video steganography in the HEVC(High Efficiency Video Coding)standard remains a challenging issue.For the purpose of improving the detection performance,this paper proposes a steganalysis method that can perfectly detectMV-based steganography in HEVC.Firstly,we define the local optimality of MVP(Motion Vector Prediction)based on the technology of AMVP(Advanced Motion Vector Prediction).Secondly,we analyze that in HEVC video,message embedding either usingMVP index orMVD(Motion Vector Difference)may destroy the above optimality of MVP.And then,we define the optimal rate of MVP as a steganalysis feature.Finally,we conduct steganalysis detection experiments on two general datasets for three popular steganographymethods and compare the performance with four state-ofthe-art steganalysis methods.The experimental results demonstrate the effectiveness of the proposed feature set.Furthermore,our method stands out for its practical applicability,requiring no model training and exhibiting low computational complexity,making it a viable solution for real-world scenarios.

Whole Perfect Vectors and Fermat’s Last Theorem

A naïve discussion of Fermat’s last theorem conundrum is described. The present theorem’s proof is grounded on the well-known properties of sums of powers of the sine and cosine functions, the Minkowski norm definition, and some vector-specific structures.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

On vector variational-like inequalities and vector optimization problems with (G, α)-invexity

The aim of this paper is to study the relationship among Minty vector variational-like inequality problem, Stampacchia vector variational-like inequality problem and vector optimization problem involving (G, α)-invex functions. Furthermore, we establish equivalence among the solutions of weak formulations of Minty vector variational-like inequality problem, Stampacchia vector variational-like inequality problem and weak efficient solution of vector optimization problem under the assumption of (G, α)-invex functions. Examples are provided to elucidate our results.

A solution method for decomposing vector fields in Hamilton energy

Hamilton energy,which reflects the energy variation of systems,is one of the crucial instruments used to analyze the characteristics of dynamical systems.Here we propose a method to deduce Hamilton energy based on the existing systems.This derivation process consists of three steps:step 1,decomposing the vector field;step 2,solving the Hamilton energy function;and step 3,verifying uniqueness.In order to easily choose an appropriate decomposition method,we propose a classification criterion based on the form of system state variables,i.e.,type-I vector fields that can be directly decomposed and type-II vector fields decomposed via exterior differentiation.Moreover,exterior differentiation is used to represent the curl of low-high dimension vector fields in the process of decomposition.Finally,we exemplify the Hamilton energy function of six classical systems and analyze the relationship between Hamilton energy and dynamic behavior.This solution provides a new approach for deducing the Hamilton energy function,especially in high-dimensional systems.

An Initial Perturbation Method for the Multiscale Singular Vector in Global Ensemble Prediction

Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction(NWP) caused by errors in initial conditions(ICs). The traditional Singular Vector(SV) initial perturbation method tends only to capture synoptic scale initial uncertainty rather than mesoscale uncertainty in global ensemble prediction. To address this issue, a multiscale SV initial perturbation method based on the China Meteorological Administration Global Ensemble Prediction System(CMA-GEPS) is proposed to quantify multiscale initial uncertainty. The multiscale SV initial perturbation approach entails calculating multiscale SVs at different resolutions with multiple linearized physical processes to capture fast-growing perturbations from mesoscale to synoptic scale in target areas and combining these SVs by using a Gaussian sampling method with amplitude coefficients to generate initial perturbations. Following that, the energy norm,energy spectrum, and structure of multiscale SVs and their impact on GEPS are analyzed based on a batch experiment in different seasons. The results show that the multiscale SV initial perturbations can possess more energy and capture more mesoscale uncertainties than the traditional single-SV method. Meanwhile, multiscale SV initial perturbations can reflect the strongest dynamical instability in target areas. Their performances in global ensemble prediction when compared to single-scale SVs are shown to(i) improve the relationship between the ensemble spread and the root-mean-square error and(ii) provide a better probability forecast skill for atmospheric circulation during the late forecast period and for short-to medium-range precipitation. This study provides scientific evidence and application foundations for the design and development of a multiscale SV initial perturbation method for the GEPS.

Improved Twin Support Vector Machine Algorithm and Applications in Classification Problems

The distribution of data has a significant impact on the results of classification.When the distribution of one class is insignificant compared to the distribution of another class,data imbalance occurs.This will result in rising outlier values and noise.Therefore,the speed and performance of classification could be greatly affected.Given the above problems,this paper starts with the motivation and mathematical representing of classification,puts forward a new classification method based on the relationship between different classification formulations.Combined with the vector characteristics of the actual problem and the choice of matrix characteristics,we firstly analyze the orderly regression to introduce slack variables to solve the constraint problem of the lone point.Then we introduce the fuzzy factors to solve the problem of the gap between the isolated points on the basis of the support vector machine.We introduce the cost control to solve the problem of sample skew.Finally,based on the bi-boundary support vector machine,a twostep weight setting twin classifier is constructed.This can help to identify multitasks with feature-selected patterns without the need for additional optimizers,which solves the problem of large-scale classification that can’t deal effectively with the very low category distribution gap.

Differentially Private Support Vector Machines with Knowledge Aggregation

With the widespread data collection and processing,privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals.Support vector machine(SVM)is one of the most elementary learning models of machine learning.Privacy issues surrounding SVM classifier training have attracted increasing attention.In this paper,we investigate Differential Privacy-compliant Federated Machine Learning with Dimensionality Reduction,called FedDPDR-DPML,which greatly improves data utility while providing strong privacy guarantees.Considering in distributed learning scenarios,multiple participants usually hold unbalanced or small amounts of data.Therefore,FedDPDR-DPML enables multiple participants to collaboratively learn a global model based on weighted model averaging and knowledge aggregation and then the server distributes the global model to each participant to improve local data utility.Aiming at high-dimensional data,we adopt differential privacy in both the principal component analysis(PCA)-based dimensionality reduction phase and SVM classifiers training phase,which improves model accuracy while achieving strict differential privacy protection.Besides,we train Differential privacy(DP)-compliant SVM classifiers by adding noise to the objective function itself,thus leading to better data utility.Extensive experiments on three high-dimensional datasets demonstrate that FedDPDR-DPML can achieve high accuracy while ensuring strong privacy protection.

Towards privacy-preserving and efficient word vector learning for lightweight IoT devices

Nowadays,Internet of Things(IoT)is widely deployed and brings great opportunities to change people's daily life.To realize more effective human-computer interaction in the IoT applications,the Question Answering(QA)systems implanted in the IoT services are supposed to improve the ability to understand natural language.Therefore,the distributed representation of words,which contains more semantic or syntactic information,has been playing a more and more important role in the QA systems.However,learning high-quality distributed word vectors requires lots of storage and computing resources,hence it cannot be deployed on the resource-constrained IoT devices.It is a good choice to outsource the data and computation to the cloud servers.Nevertheless,it could cause privacy risks to directly upload private data to the untrusted cloud.Therefore,realizing the word vector learning process over untrusted cloud servers without privacy leakage is an urgent and challenging task.In this paper,we present a novel efficient word vector learning scheme over encrypted data.We first design a series of arithmetic computation protocols.Then we use two non-colluding cloud servers to implement high-quality word vectors learning over encrypted data.The proposed scheme allows us to perform training word vectors on the remote cloud servers while protecting privacy.Security analysis and experiments over real data sets demonstrate that our scheme is more secure and efficient than existing privacy-preserving word vector learning schemes.

Diffraction deep neural network-based classification for vector vortex beams

The vector vortex beam(VVB)has attracted significant attention due to its intrinsic diversity of information and has found great applications in both classical and quantum communications.However,a VVB is unavoidably affected by atmospheric turbulence(AT)when it propagates through the free-space optical communication environment,which results in detection errors at the receiver.In this paper,we propose a VVB classification scheme to detect VVBs with continuously changing polarization states under AT,where a diffractive deep neural network(DDNN)is designed and trained to classify the intensity distribution of the input distorted VVBs,and the horizontal direction of polarization of the input distorted beam is adopted as the feature for the classification through the DDNN.The numerical simulations and experimental results demonstrate that the proposed scheme has high accuracy in classification tasks.The energy distribution percentage remains above 95%from weak to medium AT,and the classification accuracy can remain above 95%for various strengths of turbulence.It has a faster convergence and better accuracy than that based on a convolutional neural network.

Performance Analysis of Support Vector Machine (SVM) on Challenging Datasets for Forest Fire Detection

This article delves into the analysis of performance and utilization of Support Vector Machines (SVMs) for the critical task of forest fire detection using image datasets. With the increasing threat of forest fires to ecosystems and human settlements, the need for rapid and accurate detection systems is of utmost importance. SVMs, renowned for their strong classification capabilities, exhibit proficiency in recognizing patterns associated with fire within images. By training on labeled data, SVMs acquire the ability to identify distinctive attributes associated with fire, such as flames, smoke, or alterations in the visual characteristics of the forest area. The document thoroughly examines the use of SVMs, covering crucial elements like data preprocessing, feature extraction, and model training. It rigorously evaluates parameters such as accuracy, efficiency, and practical applicability. The knowledge gained from this study aids in the development of efficient forest fire detection systems, enabling prompt responses and improving disaster management. Moreover, the correlation between SVM accuracy and the difficulties presented by high-dimensional datasets is carefully investigated, demonstrated through a revealing case study. The relationship between accuracy scores and the different resolutions used for resizing the training datasets has also been discussed in this article. These comprehensive studies result in a definitive overview of the difficulties faced and the potential sectors requiring further improvement and focus.