Series Of Vectors Research Articles

Recombinant Yarrowia lipolytica strains for the heterologous expression of multi-component enzyme systems: Expression of mammalian steroidogenic proteins

New Yarrowia lipolytica strains for the co-expression of steroidogenic mammalian proteins were obtained in this study. For this purpose, a two-step approach for constructing recombinant strains that permits the simple introduction of several expression cassettes encoding heterologous proteins into the yeast genome was successfully applied. This study tested two series of integrative multi-copy expression vectors containing cDNAs for the mature forms of P450scc system components (cytochrome P450scc (CYP11A1), adrenodoxin reductase, adrenodoxin, or fused adrenodoxin-P450scc) or for P45017α (CYP17A1) under the control of the isocitrate lyase promoter pICL1, which were constructed using the basic plasmids p64PT or p67PT (rDNA or the long terminal repeat (LTR) zeta of Ylt1 as integration targeting sequences and ura3d4 as a multi-copy selection marker). This study demonstrated the integration of up to three expression vectors containing different heterologous cDNA via their simultaneous transformation into haploid recipient strains. Additionally, further combinations of the different expression cassettes in one strain were obtained by subsequent diploidisation using selected haploid multi-copy transformants. Thus, recombinant strains containing three to five different expression cassettes were obtained, as demonstrated by Southern blotting. Expression of P450scc system proteins was identified by western blotting. The presented method for recombinant strain construction is a useful tool for the heterologous expression of multi-component enzyme systems in Y. lipolytica.

Biorefineries as the base for accomplishing the sustainable development goals (SDGs) and the transition to bioeconomy: Technical aspects, challenges and perspectives

Sustainable development goals (SDGs) are guidelines to improve the socio-economic and environmental worldwide situation caused by excessive fossil fuel use. These goals must be accomplished before 2030 by implementing a national sustainable development framework in all UN country members. Instead, biorefineries are the seed towards a more sustainable world since biomass upgrading into a series of value-added products and energy vectors can reduce current issues related to waste generation and climate change. Besides, biorefineries are the first step on the way to implement a bioeconomy. This review paper aims to elucidate the existing relation between biorefineries, bioeconomy, and the SDGs through a comprehensive analysis of the technical requirements, challenges, and perspectives of biomass upgrading processes. In this way, this review paper includes a discussion about the biorefinery, bioeconomy, sustainable development, and sustainability concepts. Moreover, this paper elucidates how the implementation of biorefineries is linked to the SDGs accomplishment.

Effects of sgRNA length and number on gene editing efficiency and predicted mutations generated in rice

CRISPR-Cas9 is a common tool for gene editing, and appropriate sgRNAs are the key factor for successful editing. In this study, the effect of sgRNA length and number on editing efficiency was analyzed in rice using CYP81A6 as the target gene. A series of CRISPR-Cas9 plant expression vectors containing single sgRNAs with different lengths (17, 18, 19, 20, 21, 22, 23 nt) or two sgRNAs were constructed and introduced into rice cultivar Zhonghua11 by Agrobacterium-mediated transformation. Analysis of the editing status of 1283 transgenic rice plants showed that 371 were successfully edited with base preference. Single A or T insertions were the most frequent among the six edited types. The editing efficiency of transgenic rice with two sgRNAs was higher than that with a single sgRNA. Editing efficiency and sgRNA length showed a normal distribution with 20 nt sgRNA (25%) being the most efficient. The editing efficiency decreased slightly with decreases of 1–2 bases (19 nt 20%, 18 nt 21%), but decreased significantly with a decrease of 3 bases (17 nt 4.5%). Editing efficiency was significantly reduced by adding 1 to 3 bases (21 nt 16.8%, 22 nt 13%, 23 nt 13%) to the sgRNA. These results provide data for successful gene editing or rice by CRISPR-Cas9.

High-efficiency plastome base editing in rice with TAL cytosine deaminase

High-efficiency plastome base editing in rice with TAL cytosine deaminase

A Vector Series Solution for a Class of Hyperbolic System of Caputo Time-Fractional Partial Differential Equations With Variable Coefficients

In this paper, we introduce a series solution to a class of hyperbolic system of time-fractional partial differential equations with variable coefficients. The fractional derivative has been considered by the concept of Caputo. Two expansions of matrix functions are proposed and used to create series solutions for the target problem. The first one is a fractional Laurent series, and the second is a fractional power series. A new approach, via the residual power series method and the Laplace transform, is also used to find the coefficients of the series solution. In order to test our proposed method, we discuss four interesting and important applications. Numerical results are given to authenticate the efficiency and accuracy of our method and to test the validity of our obtained results. Moreover, solution surface graphs are plotted to illustrate the effect of fractional derivative arrangement on the behavior of the solution.

Self-learning regression interpolation based on Ricker kernel function for seismic data

Seismic data interpolation techniques are extremely economical for removing negative effects resulting from insufficient spatial sampling. In recent years, self-learning mechanism-based methods (relatively intelligent methods), such as machine learning (ML) and deep learning, have increasingly attracted the attention of many scholars. Support vector regression machine (SVR), a kind of ML method, can obtain good reconstructed performance for seismic data interpolation. However, the performance is influenced directly by the kernel function, which controls the map from the input space to the feature space. In other words, a good and suitable kernel function can contribute to the extraction of good features and improve the self-learning ability. In this paper, Ricker kernel function suitable for seismic data, rather than the traditional Gaussian kernel function, will be introduced and applied in the new SVR-based interpolation method. In detail, the Ricker wavelet is widely used to simulate seismic data. And the Ricker kernel function can be used specially for the seismic data process. Under the guarantee of the special kernel function, the input time-space vector series are trained as a better model for future prediction of missing seismic data. Numerical experiments on synthetic and real field data show better reconstruction ability than that of SVR based on the traditional kernel function. Furthermore, we discuss the difference between the relatively shallow ML method (SVR) and the deep learning neural networks method. Except for the computer hardware, deep neural learning methods will impose stringent requirements on the training data, not only with respect to quantity but also including data quality. From this perspective, SVR shows more flexibility in the self-learning process.

Live attenuated viral vectors based on the rubella vaccine strain RA27/3

Abstract We have developed a series of live viral vectors based on the live attenuated rubella vaccine strain RA27/3. These vectors can express a variety of heterologous vaccine antigens without interfering with vector growth or stable antigen expression. The vaccine inserts were expressed for at least 5 to 10 passages in Vero cells. Proteins as large as the ectodomain of HIV gp140 could be expressed (90 kDa), and they retained the native conformation. Recently expressed vaccine inserts include: P27 Gag protein of SIV, the NANP repeat sequence of malaria circumsporozoite protein, and the receptor binding domain of Sars CoV-2 S1 spike protein. We have studied these vectors for immunogenicity in a rhesus macaque model. The rubella vectors are based on the rubella vaccine strain, for which one or two doses can elicit lifelong immunity. The vectors are a little different: The best immune response was observed when the macaques were primed with a DNA vaccine, followed by rubella vectors, or when they were primed with rubella vector and then boosted with the native protein. In both cases, an immunization strategy with only one exposure to rubella antigens was better than giving it twice. This could occur if the secondary response to rubella antigens inhibited the response to the vaccine insert. Alternatively, the strong anamnestic response to a boost of protein antigens suggests that the level of protein expression by the vector was the limiting factor. In future studies, we will test whether vectors with better growth characteristics and stronger antigen expression levels can improve antibody titers, elicit neutralizing antibodies, and protect against a viral challenge.

Extreme Gradient Boosting for Recommendation System by Transforming Product Classification into Regression Based on Multi-Dimensional Word2Vec

Now that untact services are widespread and worldwide, the number of users visiting online shopping malls has increased. For example, the recommendation systems in Netflix, Amazon, etc., have gained a lot of attention by attracting many users and have made large profit by recommending suitable products to their users. In the paper, we conduct a study to enhance recommendation accuracy using Word2Vec, widely used in natural language processing. We collect user shopping history with personal click preference information of product items as data, representing a document for natural language processing. The sequence of product item clicks is fed into the Word2Vec technology algorithm to obtain the vectors symmetrically representing all of the product items clicked by users. Training and test data have a series of vectors representing a sequence of the clicked product items as inputs and a purchased product as a target. Machine learning models recommend a product as a symmetric vector for each input and calculate the similarity among the recommended vectors and all other registered products they sell in the system to recommend multiple products as final recommendation results. We use XGBoost regressor and classifier models to recommend some products that users would like and evaluate the recommendation accuracy. A finally recommended product by the models is a vector, and the system recommends some more products by calculating the similarity as mentioned above. We evaluated the classifier model’s recommendation accuracy without Word2Vec encoding first and then with the Word2Vec technique. Meanwhile, we can represent the products with single or multiple dimensional vectors. We noted that the recommendation accuracy increases when we use multiple dimensions of Word2Vec vectors from the experiments. We also evaluated the performances when the system recommends one or multiple products. For the recommendation of multiple products (five here), a regression model has higher accuracy than a classification model in all dimensions of vectors.

Trans-spliced mRNA products produced from circRNA expression vectors.

Circular (circ) RNA expression vectors are used as a method of identifying and characterizing RNA sequences that harbor internal ribosome entry site (IRES) activity. During the course of developing a vector series tailored for IRES discovery, we found evidence for the occurrence of trans-spliced mRNAs arising when sequences with promoter activity were embedded between the upstream CTD and downstream NTD exons of the pre-mRNA. These trans-spliced products regenerate the same open reading frame expected from a circRNA and can lead to false-positive signals in screens relying on circRNA expression vectors for IRES discovery. Our results caution against interpretations of IRES activity solely based on results obtained from circRNA expression vectors.

Acidic pH Decreases the Endonuclease Activity of Initiator RepB and Increases the Stability of the Covalent RepB-DNA Intermediate while Has Only a Limited Effect on the Replication of Plasmid pMV158 in Lactococcus lactis.

Plasmid vectors constitute a valuable tool for homologous and heterologous gene expression, for characterization of promoter and regulatory regions, and for genetic manipulation and labeling of bacteria. During the last years, a series of vectors based on promiscuous replicons of the pMV158 family have been developed for their employment in a variety of Gram-positive bacteria and proved to be useful for all above applications in lactic acid bacteria. A proper use of the plasmid vectors requires detailed knowledge of their main replicative features under the changing growth conditions of the studied bacteria, such as the acidification of the culture medium by lactic acid production. Initiation of pMV158 rolling-circle replication is catalyzed by the plasmid-encoded RepB protein, which performs a sequence-specific cleavage on one of the parental DNA strands and, as demonstrated in this work, establishes a covalent bond with the 5′-P end generated in the DNA. This covalent adduct must last until the leading-strand termination stage, where a new cleavage on the regenerated nick site and a subsequent strand-transfer reaction result in rejoining of the ends of the cleaved parental strand, whereas hydrolysis of the newly-generated adduct would release the protein from a nicked double-stranded DNA plasmid form. We have analyzed here the effect of pH on the different in vitro reactions catalyzed by RepB and on the in vivo replication ability of plasmid pMV158. We show that acidic pH greatly impairs the catalytic activity of the protein and reduces hydrolysis of the covalent RepB-DNA adduct, as expected for the nucleophilic nature of these reactions. Conversely, the ability of pMV158 to replicate in vivo, as monitored by the copy number and segregational stability of the plasmid in Lactococcus lactis, remains almost intact at extracellular pHs ranging from 7.0 to 5.0, and a significant reduction (by ∼50%) in the plasmid copy number per chromosome equivalent is only observed at pH 4.5. Moreover, the RepB to pMV158 molar ratio is increased at pH 4.5, suggesting the existence of compensatory mechanisms that operate in vivo to allow pMV158 replication at pH values that severely disturb the catalytic activity of the initiator protein.

Dimension reduction for k-power bilinear systems using orthogonal polynomials and Arnoldi algorithm

In this paper, a dimension reduction method via general orthogonal polynomials and multiorder Arnoldi algorithm is proposed, which focuses on the topic of structure-preserving for k-power bilinear systems. The main procedure is using a series of expansion coefficient vectors of each state variables in the space spanned by general orthogonal polynomials that satisfy a recurrence formula to generate a projection based on multiorder Arnoldi algorithm. The resulting reduced-order model not only matches a desired number of expansion coefficients of the original output but also retains the topology structure. Meanwhile, the stability is well preserved under some certain conditions and the error bound is also given. Finally, two numerical simulations are provided to illustrate the effectiveness of our proposed algorithm in the views of accuracy and computational cost.

Construction and evaluation of a pUC-type prokaryotic promoter reporter system based on lacZ gene

To construct a prokaryotic promoter report system with wide applicability, a series of pFGH reporter vectors based on lacZ gene and pUC replicon were constructed from plasmid pFLX107 through the replacement of multiple cloning sites and sequence modifications. The plasmid with the lowest background activity was selected as the final report system with the lacZ gene deletion strain MC4100 as the host bacterium, following by testing with inducible promoter araBAD and the constitutive promoter rpsM. The background activity of pFGH06 was significantly lower than that of other plasmids of the same series, and even lower than that of reference plasmid pRCL at 28 °C (P<0.01). Further evaluation tests show that the plasmid pFGH06 could be used to clone and determine the activity of inducible promoter or constitutive promoter, and the complete recognition of the target promoter could be achieved through blue-white selection in the simulation test of promoter screening. Compared with the reported prokaryotic promoter report systems, pFGH06 has the advantages of smaller size, more multiple clone sites, adjustable background activity, high efficiency of promoter screening and recognition, thus with a wide application prospect.

Adapted Jacobi Orthogonal Invariant Moments for Image Representation and Recognition

Images recognition and classification require an extraction technique of feature vectors of these images. These vectors must be invariant to the three geometric transformations: rotation, translation and scaling. Several authors used the theory of orthogonal moments to extract the feature vectors of images. Jacobi moments are orthogonal moments, which have been widely applied in imaging and pattern recognition. However, the invariance to rotation of Cartesian Jacobi moments is very difficult to obtain. In this paper, we obtain at first a set of transformed orthogonal Jacobi polynomials, called “Adapted Jacobi polynomials”. Based on these polynomials, a set of orthogonal moments is presented, named adapted Jacobi moments (AJMs). These moments are orthogonal on the rectangle $$\left[ {0, N\left] { \times } \right[0, M} \right],$$ where $$N \times M$$ is the size of the described image. We also provide a new series of feature vectors of images based on adapted Jacobi orthogonal invariants moments, which are a linear combination of geometric moment invariants, where the latest ones are invariant under rotation, translation and scaling of the described image. Based on k-NN algorithm, we apply a new 2D image classification system. We introduce a set of experimental tests in pattern recognition. The obtained results express the efficiency of our method. The performance of these feature vectors is compared with someones extracted from Hu, Legendre and Tchebichef invariant moments using three different 2D image databases: MPEG7-CE shape database, Columbia Object Image Library (COIL-20) database and ORL database. The results of the comparative study show the performance and superiority of our orthogonal invariant moments.

Robust tracking control for permanent magnet linear servo system using intelligent fractional-order backstepping control

In this paper, an intelligent fractional-order backstepping control (IFOBC) method combining fractional-order backstepping control (FOBC) and Takagi–Sugeno–Kang-type fuzzy neural network (TSKFNN) applicable to the permanent magnet linear synchronous motor (PMLSM) is adopted to achieve high-performance servo control fields. First, the global regulation and position tracking of the system are realized by backstepping control (BC). In order to improve the convergence speed and control accuracy of the system, a FOBC which has extra degree of freedom in the control parameters is designed. Second, since the boundary of the uncertainties in the system is difficult to estimate, which makes the switching control gain hard to select, the TSKFNN is introduced to estimate the uncertainties online. By using asymmetric Gaussian function as the membership function, the convergence speed and the accurate approximation capability of TSKFNN are improved greatly. Additionally, a robust compensator is developed to confront the uncertainties such as approximation error, optimal parameter vector and higher Taylor series. Therefore, the robustness of the system is further improved. Finally, the adaptive learning algorithms for the online training of the TSKFNN are derived using the Lyapunov theorem to guarantee the asymptotical stability of the system. The experiments implemented on a digital signal processor (DSP), TMS320F28335, demonstrate that the proposed control method provides a high-performance dynamic characteristics and that is robust with respect to parameter variations, external disturbances and nonlinear friction forces.

Binary Vector Construction for Site-Directed Mutagenesis of &amp;lt;i&amp;gt;Kafirin&amp;lt;/i&amp;gt; Genes in Sorghum

Sorghum (Sorghum bicolor (L.) Moench) is one of the world’s leading cereal crops in agricultural production, which has a special importance in the arid regions. However, unlike other cereals, sorghum grain has a lower nutritional value, which is caused, inter alia, by the resistance of its seed storage proteins (kafirins) to protease digestion. One of the effective approaches to improve the nutritional value of sorghum grain is to obtain mutants with partially or completely suppressed synthesis or altered amino acid composition of kafirins. The employment of genome editing may allow to solve this problem by introducing mutations into the nucleotide sequences of the α- and γ-kafirin genes. In this study, genomic target motifs (23 bp sequences) were selected for the introduction of mutations into the α- and γ-KAFIRIN genes of sorghum. The design of the gRNAs was conducted using the online tools CRISPROR and CHOPCHOP. Two most suitable targets were chosen for α-KAFIRIN (k1C5) and two for γ-KAFIRIN (gKAF1) genes. The insertion of respective sequences in the generic vector pSH121 was performed at the BsaI (Eco31I) sites. Validation of the cloning procedure was performed by DNA sequencing. Subcloning of the resulting constructs was performed using the SfiI restriction sites into the compatible binary vector B479p7oUZm-LH. The correct assembly of binary vectors was confirmed by restriction analysis using the MluI and SfiI cleavage sites. The four vectors created (1C - 4C) were transferred by electroporation into the Agrobacterium tumefaciens strain AGL0. Currently, this vector series is used for stable transformation of sorghum using immature embryo explants.

Measurement and Analysis of Human Body Channel Response for Biometric Recognition

This paper presents a highly secure and convenient biometric system for user recognition based on body channel characteristics for electric signal transmission. In the proposed framework, the user can provide reliable biometric features of body channel responses (BCRs) simply by touching an electrode surface on the device with a finger. To realize and verify the proposed approach, we acquired the BCR data from 15 subjects for approximately six weeks through experiments conducted in a customized measurement setup suited to the principle of signal transmission in the human body channel. The proposed BCR-based biometric feature (BBF) comprises a series of envelope vectors of the received BCR when applying up- and down-chirp signals to the human body, which is extracted by an interpolative method based on peak detection. The BBFs are effectively separable according to the subjects because the features magnify quantitative differences in the aspect of path losses and power delay profiles of individual BCRs for the frequency range between 1 and 10 MHz. The classification performance was evaluated by splitting the dataset into 80 and 20 % for the training and testing, respectively, using conventional machine learning algorithms with uncorrelated 40 session data sets for the respective subjects. The highest average classification accuracy was achieved by the kernel-based support vector machine approximately 95.8 % without observable and biased misidentification cases among the subjects. In addition, the analysis of receiver operating characteristic curves shows that the proposed classifiers are robust to decision boundaries at various threshold settings.

GeneWeld: Efficient Targeted Integration Directed by Short Homology in Zebrafish.

Efficient precision genome engineering requires high frequency and specificity of integration at the genomic target site. Multiple design strategies for zebrafish gene targeting have previously been reported with widely varying frequencies for germline recovery of integration alleles. The GeneWeld protocol and pGTag (plasmids for Gene Tagging) vector series provide a set of resources to streamline precision gene targeting in zebrafish. Our approach uses short homology of 24-48 bp to drive targeted integration of DNA reporter cassettes by homology-mediated end joining (HMEJ) at a CRISPR/Cas induced DNA double-strand break. The pGTag vectors contain reporters flanked by a universal CRISPR sgRNA sequence to liberate the targeting cassette in vivo and expose homology arms for homology-driven integration. Germline transmission rates for precision-targeted integration alleles range 22-100%. Our system provides a streamlined, straightforward, and cost-effective approach for high-efficiency gene targeting applications in zebrafish. Graphic abstract: GeneWeld method for CRISPR/Cas9 targeted integration.

Feature Reconstruction-Regression Network: A Light-Weight Deep Neural Network for Performance Monitoring in the Froth Flotation

With the rapid development of deep neural network (DNN), many DNN-based models for performance monitoring have been developed recently. However, some challenges still exist in the industrial performance monitoring: 1) different sample rates and time delays between the inputs and labeled performance; 2) a light-weight DNN architecture. Under this circumstance, we design a DNN named feature reconstruction-regression network (FR-R net) in this article. First, we extract the feature vector series as the input feature in order to capture the dynamic temporal information of the input data. Then, we design a feature reconstruction network with a weight-shared kernel network and fixed positional encoding to generate a reconstructed feature vector. Finally, we send the reconstructed feature vector into fully connected layers as a regression network to link the labeled performance. The effectiveness of the proposed FR-R net is validated on both a simulation case and an industrial froth flotation process.

Peptide Gene Delivery Vectors for Specific Transfection of Glioma Cells.

Gene therapy offers an alternative approach to malignant glioma; however, glioma cells are difficult to transfect. Peptides, as nonviral vectors, can achieve efficient gene transfection in glioma cells due to their good biocompatibility and easy functionalization. In this article, we reported a series of peptide vectors, which were composed of amphiphilic α-helical segments, cationic cell-penetrating segments, and cysteine and glycine residues. The physicochemical properties of peptide vectors or peptide/pGL3 complexes, including conformation, DNA-loading capacity, size, zeta potential, and morphology, were characterized. Their gene delivery abilities were evaluated in U373, U87, and C6 glioma cell lines and a normal cell line 293 T. Compared with Lipo 2000 and other peptide vectors, the efficiency of P-03 (CLLHHLLHHLLHHGGRKKRRQRRR) to transfect glioma cells was higher. While in 293 T cells, the transfection efficiency of P-03 was much lower than that of Lipo 2000 and another positive control P-07. Furthermore, P-03 could facilitate the pGL3 plasmids crossing a blood-brain barrier model in vitro and achieved the expression of EGFP gene in the brain sites of zebrafish.

Struktura bimodal'nosti napravleniya pribrezhnogo techeniya u Yuzhnogo berega Kryma v 2002–2008 godakh

This paper aims at improvement of knowledge about the structure of bimodal distribution of coastal current direction repeatability at Cape Kikineiz of the Southern coast of Crimea as per monitoring data obtained at the stationary oceanographic platform of the Black Sea hydrophysical sub-satellite testing area of the MHI RAS. Used for monitoring, a vertical measuring antenna with a fixed aperture of Euler testers of the current horizontal component provided verifiable and synchronic isolation of the current's structural characteristics with a vertical velocity shift and multiscale coherent oscillations of the current. New results of analysis of in situ data are presented regarding characteristics of the coastal current and its variability. The data were obtained within the period from November 2001 to 2008. Change patterns of the long-term coastal current regime are noted. The current's characteristics are matched to the results obtained at Cape Kikineiz of the Southern coast of Crimea for the period from September 2008 to 2019. Regular intense oscillations of the coastal current at the local inertial (17 hrs) and daily component as well as current oscillations with a period of 11.6 day are defined. Evolution is analyzed of probability density distribution of the velocity module and current vector direction after calculations, during which a corresponding vector distribution of intensive inertial-gravitational, mesoscale and synoptic oscillations was excluded in the original vector series of dynamics using a digital filtration method. When excluding the contribution of daily and inertial oscillations, the backward and primary mode ratio in the histograms of the coastal current direction repeatability decreases by more than three times. When excluding the contribution of mesoscale and synoptical oscillations, the backward mode of current is almost absent and there exists only a large-scale coastal current coinciding with a cyclonic direction of the coastal edge of the Black Sea Rim Current. Bimodal distribution of the coastal current direction repeatability is a result of superposition of velocity vectors of a large-scale current and variable contribution of existing orbital velocities of multiscale wave and vortex formations present in the coastal-shelf area.