Optimal Vector Research Articles

Trans-complementation of the viral movement protein mediates efficient expression of large target genes via a tobacco mosaic virus vector.

The development of plant virus-based expression systems has expanded rapidly owing to their potential applications in gene functional and disease resistance research, and industrial production of pharmaceutical proteins. However, the low yield of certain proteins, especially high-molecular-mass proteins, restricts the production scale. In this study, we observed that the tobacco mosaic virus (TMV)-mediated expression of a foreign protein was correlated with the amount of the movement protein (MP) and developed a TMV-derived pAT-transMP vector system incorporating trans-complementation expression of MP. The system is capable of efficient expression of exogenous proteins, in particular those with a high molecular mass, and enables simultaneous expression of two target molecules. Furthermore, viral expression of competent CRISPR-Cas9 protein and construction of CRISPR-Cas9-mediated gene-editing system in a single pAT-transMP construct was achieved. The results demonstrated a novel role for TMV-MP in enhancing the accumulation of a foreign protein produced from the viral vector or a binary expression system. Further investigation of the mechanism underlying this role will be beneficial for optimization of plant viral vectors with broad applications.

Robust Bayesian target vector optimization for multi-stage manufacturing processes

This research focuses on optimizing multi-stage manufacturing processes using Bayesian optimization (BO) with a robust Expected Improvement (EI) acquisition function. The aim is to optimize towards pre-selected target vectors, not to just minimize or maximize a function. To achieve this, we minimize the Euclidean distance between the actual and target output vectors, which requires transforming the Gaussian surrogate model posterior distribution into a non-central χ2(NCχ2) distribution. Furthermore, the distance measure additionally uses aleatoric uncertainty estimates of the actual output vectors to achieve robustness. We use a cascaded method that also considers the optimization results of intermediate stages, whereby optimization results are propagated from the last stage towards the first stage in each optimization iteration. By considering intermediate process outputs and aleatoric effects, our approach provides a robust optimization method for multi-stage manufacturing processes. To validate our method and to evaluate its properties, we use two artificial use cases. Moreover, we evaluate our approach in an industrial multi-stage forging process for the manufacturing of a nickel basis superalloy turbine disk, where involved stages are represented by corresponding 2D finite element method (FEM) DEFORM simulations. Evaluation suggests that our approach is superior in optimizing multi-stage manufacturing processes by considering all stage outcomes, robust distance measures, and the use of appropriate uncertainty distributions.

Technical note: Determining chemical composition of atmospheric single particles by a standard-free mass calibration algorithm

Abstract. The chemical composition of individual particles can be revealed by single-particle mass spectrometers (SPMSs). With higher accuracy in the ratio of mass to charge (m/z), more detailed chemical information could be obtained. In SPMSs, the conventional standard-based calibration methods (internal/external) are constrained by the inhomogeneity of ionization lasers and the finite focusing ability of the inlet system, etc.; therefore, the mass accuracy is restricted. In this study, we obtained the detailed and reliable chemical composition of single particles utilizing a standard-free mass calibration algorithm. In the algorithm, the characteristic distributions of hundreds of ions were concluded and collected in a database denoted as prototype. Each single-particle mass spectrum was initially calibrated by a function with specific coefficients. The range of coefficients was constrained by the magnitude of mass deviation to a finite vector space. To find the optimal coefficient vector, the conformity of each initially calibrated spectrum to the prototype dataset was assessed. The optimum calibrated spectrum was obtained with maximum conformity. For more than 98 % ambient particles, a 20-fold improvement in mass accuracy, from ∼ 10 000 ppm (integer) to ∼ 500 ppm (two decimal places), was achieved. The improved mass accuracy validated the determination of adjacent ions with a m/z difference ∼ 0.05 Th. Furthermore, atmospheric particulate trace elements that were poorly studied before are specified. The obtained detailed single-particle-level chemical information could help explain the source apportionment, reaction mechanism, and mixing state of atmospheric particles.

A hybrid Lagrangian–Eulerian model for vector-borne diseases

In this paper, a multi-patch and multi-group vector-borne disease model is proposed to study the effects of host commuting (Lagrangian approach) and/or vector migration (Eulerian approach) on disease spread. We first define the basic reproduction number of the model, R0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {R}_0$$\\end{document}, which completely determines the global dynamics of the model system. Namely, if R0≤1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {R}_0 \\le 1 $$\\end{document}, then the disease–free equilibrium is globally asymptotically stable, and if R0>1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {R}_0 > 1 $$\\end{document}, then there exists a unique endemic equilibrium which is globally asymptotically stable. Then, we show that the basic reproduction number has lower and upper bounds which are independent of the host residence times matrix and the vector migration matrix. In particular, nonhomogeneous mixing of hosts and vectors in a homogeneous environment generally increases disease persistence and the basic reproduction number of the model attains its minimum when the distributions of hosts and vectors are proportional. Moreover, R0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {R}_0$$\\end{document} can also be estimated by the basic reproduction numbers of disconnected patches if the environment is homogeneous. The optimal vector control strategy is obtained for a special scenario. In the two-patch and two-group case, we numerically analyze the dependence of the basic reproduction number and the total number of infected people on the host residence times matrix and illustrate the optimal vector control strategy in homogeneous and heterogeneous environments.

<i>In vivo</i> evaluation of tropism and biodistribution of synthetic and natural adeno-associated viral vectors by next-generation sequencing

INTRODUCTION. The creation of synthetic adeno-associated virus (AAV) vectors during gene therapy development is a labour-intensive and expensive process. The optimal solution to minimise the time and costs associated with gene therapy development lies in the improvement of methods aimed at assessing AAV vector biodistribution and transduction efficiency in vivo.AIM. This study aimed to develop a new bioinformatics-based assessment method for synthetic AAV vector libraries to analyse AAV vector biodistribution and transduction efficiency in vivo.MATERIALS AND METHODS. The production of synthetic AAV vectors involved assigning AAV serotype-specific barcodes (12-nucleotide tags flanked at the 5' end with a sequence encoding the green fluorescent reporter protein). Plasmids carrying unique barcodes were propagated in competent Escherichia coli XL10-Gold cells and used to create two AAV libraries: L1 with a viral genome count of 1010 and L2 with a viral genome count of 1011. AAV production involved HEK293T cell transfection. L1 and L2 library vectors were administered to C57Bl/6N mice by intravenous injection. DNA and RNA were isolated from transduced organs for analysis by next-generation sequencing. The obtained data on DNA and RNA barcode quantities in different murine organs were analysed to assess the biodistribution and transduction efficiency of synthetic AAVs. Barcodes were identified by aligning them to the expected sequences and counted. The resulting values were normalised to the quantity of barcodes in the original library.RESULTS. Seven viral constructs based on different AAV serotypes were created as part of two AAV libraries. Six of the AAV serotypes were synthetic (sAAV1, sAAV2, sAAV3, sAAV4, sAAV5, and sAAV6). Sequencing of murine organ samples revealed significant quantities of DNA barcodes from both AAV libraries in all organs except the brain. For the L1 library, RNA barcodes were detected at a sufficient level in 4 organs, including the skeletal muscles, the heart, the liver, and the adrenal glands. For the L2 library, in addition to the listed organs, sufficient RNA-barcode levels were observed in the gonads and the kidneys. According to transduction efficiency analysis based on RNA barcode levels adjusted for DNA barcodes, sAAV5 was considered the most promising variant for gene therapy of liver-related diseases, whereas sAAV2 and sAAV6 were recognised as holding the most promise for adrenal diseases.CONCLUSIONS. The developed bioinformatics-based assessment method for synthetic AAV vector libraries can analyse AAV vector biodistribution and transduction efficiency in the body. The presented approach has the potential for selecting optimal AAV vectors for specific organs and tissues in further gene therapy development.

Dynamic Covalent Boronate Chemistry Accelerates the Screening of Polymeric Gene Delivery Vectors via In Situ Complexation of Nucleic Acids.

Gene therapy provides exciting new therapeutic opportunities beyond the reach of traditional treatments. Despite the tremendous progress of viral vectors, their high cost, complex manufacturing, and side effects have encouraged the development of nonviral alternatives, including cationic polymers. However, these are less efficient in overcoming cellular barriers, resulting in lower transfection efficiencies. Although the exquisite structural tunability of polymers might be envisaged as a versatile tool for improving transfection, the need to fine-tune several structural parameters represents a bottleneck in current screening technologies. By taking advantage of the fast-forming and strong boronate ester bond, an archetypal example of dynamic covalent chemistry, a highly adaptable gene delivery platform is presented, in which the polycation synthesis and pDNA complexation occur in situ. The robustness of the strategy entitles the simultaneous evaluation of several structural parameters at will, enabling the accelerated screening and adaptive optimization of lead polymeric vectors using dynamic covalent libraries.

A novel weighting factor determined method of model predictive torque control for permanent magnet synchronous motor

AbstractIn the cost function of model predictive torque control (MPTC) of permanent magnet synchronous motor (PMSM), torque and flux linkage have different dimensions, and the weighting factor needs to be properly adjusted to optimise the control performance. Therefore, based on the discrete mathematical model of PMSM, the torque predictive error and flux predictive error expressions are mapped to the two‐phase static coordinate system, which is equivalent to the distance expressions from the endpoint of the voltage vector to the straight line and to the circle respectively. Because they have the same dimension, they can be directly superimposed to form the cost function proposed, which eliminates the selection process of the weighting factor. Then, the candidate voltage vector is substituted into the cost function in turn, and the optimal voltage vector with the minimum cost function is selected as the voltage input of the next control cycle. Finally, the simulation model and an experimental platform are established to compare the steady‐state and dynamic performance of the geometric solution‐based weighting factor determined method proposed with other MPTC methods.

Global Approximations of Vector Optimization Problems in Terms of Variational Convergence

Global Approximations of Vector Optimization Problems in Terms of Variational Convergence

Comparative analysis of reactive routing protocols for vehicular adhoc network communications

In the recent past, vehicular adhoc networks (VANETs) have gained a lot of importance. The routing protocols play a vital role to deliver payloads from one vehicle to another one while they are moving at relative speeds. It is a challenging task to select a routing protocol for VANETs because of the uneven distribution and high mobility of vehicles. In this paper, we have analysed the two standard reactive protocols, adhoc on-demand distance vector (AODV) and ant colony optimization (ACO). The performance comparison of AODV and ACO routing protocols has been presented in this paper. The results show AODV performed better in terms of energy consumption and routing overhead. While considering the throughput, energy loss ratio, and delay ACO has performed. ACO resulted as upperhand.

Comprehensive Multiple Indicators Optimizations for Two-Parallel Interleaved Three-Phase Two-Level Power Converters Using Optimal Vector Sequences

Comprehensive Multiple Indicators Optimizations for Two-Parallel Interleaved Three-Phase Two-Level Power Converters Using Optimal Vector Sequences

Deep learning the efficient frontier of convex vector optimization problems

In this paper, we design a neural network architecture to approximate the weakly efficient frontier of convex vector optimization problems (CVOP) satisfying Slater’s condition. The proposed machine learning methodology provides both an inner and outer approximation of the weakly efficient frontier, as well as an upper bound to the error at each approximated efficient point. In numerical case studies we demonstrate that the proposed algorithm is effectively able to approximate the true weakly efficient frontier of CVOPs. This remains true even for large problems (i.e., many objectives, variables, and constraints) and thus overcoming the curse of dimensionality.

Dual Kuhn-Tucker necessary conditions for strict minima of order two for nonsmooth vector optimization problems with inequality and equality constraints

In this paper, we give primal and dual Kuhn-Tucker necessary conditions for the existence of a strict local minimum of order two for vector optimization problems with equality and inequality constraints under some new regularity conditions. First, we improve the existing primal necessary conditions for such minima. Then, we apply an alternative theorem to derive dual Kuhn-Tucker necessary conditions of second and higher-order. To compare our results to the ones in the literature, we provide some examples.

Deformation Characteristics and Activation Dynamics of the Xiaomojiu Landslide in the Upper Jinsha River Basin Revealed by Multi-Track InSAR Analysis

The upper Jinsha River, located in a high-mountain gorge with complex geological features, is highly prone to large-scale landslides, which could result in the formation of dammed lakes. Analyzing the movement characteristics of the typical Xiaomojiu landslide in this area contributes to a better understanding of the dynamics of landslides in the region, which is of great significance for landslide risk prediction and analysis. True displacement data on the surface of landslides are crucial for understanding the morphological changes in landslides, providing fundamental parameters for dynamic analysis and risk assessment. This study proposes a method for calculating the actual deformation of landslide bodies based on multi-track Interferometric Synthetic Aperture Radar (InSAR) deformation data. It iteratively solves for the optimal true deformation vector of the landslide on a per-pixel basis under a least-squares constraint based on the assumption of consistent displacement direction among adjacent points on the landslide surface. Using multi-track Sentinel data from 2017 to 2023, the line of sight (LOS) accumulative de-formation of the Xiaomojiu landslide was obtained, with a maximum LOS deformation of −126 mm/year. The true surface displacement of the Xiaomojiu landslide after activation was calculated using LOS deformation. The development of two rotational sub-slipping zones on the landslide body is inferred based on the distribution of actual displacements along the central profile line. Analysis of temporal changes in water body area data revealed that the Xiaomojiu landslide was activated after a barrier lake event and continuously moved due to the influence of higher water levels’ in the river channel. In conclusion, the proposed method can be applied to calculate the true surface displacement of landslides with complex mechanisms for analyzing the movement status of landslide bodies. Furthermore, the spatiotemporal analysis of the Xiaomojiu landslide characteristics can support analyzing the mechanisms of similar landslides in the Jinsha River Basin.

Securing cloud-based medical data: an optimal dual kernal support vector approach for enhanced EHR management

Securing cloud-based medical data: an optimal dual kernal support vector approach for enhanced EHR management

Enhancing recombinant antibody yield in Chinese hamster ovary cells.

A range of recombinant monoclonal antibodies (rMAbs) have found application in treating diverse diseases, spanning various cancers and immune system disorders. Chinese hamster ovary (CHO) cells have emerged as the predominant choice for producing these rMAbs due to their robustness, ease of transfection, and capacity for posttranslational modifications akin to those in human cells. Transient transfection and/or stable expression could be conducted to express rMAbs in CHO cells. To bolster the yield of rMAbs in CHO cells, a multitude of approaches have been developed, encompassing vector optimization, medium formulation, cultivation parameters, and cell engineering. This review succinctly outlines these methodologies when also addressing challenges encountered in the production process, such as issues with aggregation and fucosylation.

On relationships between vector optimization problems and vector variational inequalities using directional convexificators

In this work, we formulate the vector variational inequalities of Stampacchia and Minty types in terms of directional convexificators, and relate them to a vector optimization problem. Our approach consists of using a suitable directional generalized convexity, given in terms of directional convexificators, to help us figure out the necessary and sufficient conditions for a point to be an efficient solution to the vector optimization problem. We also investigate the weak versions of the vector variational inequalities and provide several results for determining weak efficient solutions. An example illustrating both our findings and the limits of some earlier research is provided.

Thermodynamics-Guided Design Reveals a Cooperative Hydrogen Bond in DC-SIGN-targeted Glycomimetics.

Due to the shallow and hydrophilic binding sites of carbohydrate-binding proteins, the design of glycomimetics is often complicated by high desolvation costs as well as competition with solvent. Therefore, a careful optimization of interaction vectors and ligand properties is required in the design and optimization of glycomimetics. Here, we employ thermodynamics-guided design to optimize mannose-based glycomimetics targeting the human C-type lectin receptor dendritic cell-specific intercellular adhesion molecule 3 grabbing nonintegrin (DC-SIGN), a pathogenic host factor in viral infections. By exploring ligand rigidification and hydrogen bond engineering, a monovalent glycomimetic with an unprecedented affinity for DC-SIGN in the low μM range was discovered. A matched molecular pair analysis based on microcalorimetric data revealed a stereospecific hydrogen bond interaction with Glu358/Ser360 as the origin of this cooperative and enthalpically dominated interaction. This detailed insight into the binding mechanism paves the way for an improvement of monovalent glycomimetics targeting DC-SIGN.

Penalty weight tuning in high dose rate brachytherapy using multi-objective Bayesian optimization

Objective. Treatment plan optimization in high dose rate brachytherapy often requires manual fine-tuning of penalty weights for each objective, which can be time-consuming and dependent on the planner's experience. To automate this process, this study used a multi-criteria approach called multi-objective Bayesian optimization with q-noisy expected hypervolume improvement as its acquisition function (MOBO-qNEHVI). Approach. The treatment plans of 13 prostate cancer patients were retrospectively imported to a research treatment planning system, RapidBrachyMTPS, where fast mixed integer optimization (FMIO) performs dwell time optimization given a set of penalty weights to deliver 15 Gy to the target volume. MOBO-qNEHVI was used to find patient-specific Pareto optimal penalty weight vectors that yield clinically acceptable dose volume histogram metrics. The relationship between the number of MOBO-qNEHVI iterations and the number of clinically acceptable plans per patient (acceptance rate) was investigated. The performance time was obtained for various parameter configurations. Main results. MOBO-qNEHVI found clinically acceptable treatment plans for all patients. With increasing the number of MOBO-qNEHVI iterations, the acceptance rate grew logarithmically while the performance time grew exponentially. Fixing the penalty weight of the tumour volume to maximum value, adding the target dose as a parameter, initiating MOBO-qNEHVI with 25 parallel sampling of FMIO, and running 6 MOBO-qNEHVI iterations found solutions that delivered 15 Gy to the hottest 95% of the clinical target volume while respecting the dose constraints to the organs at risk. The average acceptance rate for each patient was 89.74% ± 8.11%, and performance time was 66.6 ± 12.6 s. The initiation took 22.47 ± 7.57 s, and each iteration took 7.35 ± 2.45 s to find one Pareto solution.Significance. MOBO-qNEHVI combined with FMIO can automatically explore the trade-offs between treatment plan objectives in a patient specific manner within a minute. This approach can reduce the dependency of plan quality on planner’s experience and reduce dose to the organs at risk.

Engineering Bacillus subtilis for highly efficient production of functional disaccharide lactulose from lactose

Bioconversion of lactose to functional lactose derivatives attracts increasing attention. Lactulose is an important high-value lactose derivative, which has been widely used in pharmaceutical, nutraceutical, and food industries. Lactulose can be enzymatically produced from lactose by cellobiose 2-epimerase (CEase). Several studies have already focused on the food-grade expression of CEase, but they are all aimed at the biosynthesis of epilactose. Herein, we reported for the first time the biosynthesis of lactulose using the recombinant food-grade Bacillus subtilis. Lactulose biosynthesis was optimized by varying lactulose-producing CEases and expression vectors. Caldicellulosiruptor saccharolyticus CEase and pP43NMK were determined to be the optimal CEase and expression vector. Fine-tuning of CEase expression was investigated by screening a beneficial N-terminal coding sequence. After fed-batch cultivation, the highest fermentation isomerization activity reached 11.6 U/mL. Lactulose was successfully produced by the broth of the engineered B. subtilis with a yield of 52.1 %.

Spectral-like conjugate gradient methods with sufficient descent property for vector optimization.

Several conjugate gradient (CG) parameters resulted in promising methods for optimization problems. However, it turns out that some of these parameters, for example, 'PRP,' 'HS,' and 'DL,' do not guarantee sufficient descent of the search direction. In this work, we introduce new spectral-like CG methods that achieve sufficient descent property independently of any line search (LSE) and for arbitrary nonnegative CG parameters. We establish the global convergence of these methods for four different parameters using Wolfe LSE. Our algorithm achieves this without regular restart and assumption of convexity regarding the objective functions. The sequences generated by our algorithm identify points that satisfy the first-order necessary condition for Pareto optimality. We conduct computational experiments to showcase the implementation and effectiveness of the proposed methods. The proposed spectral-like methods, namely nonnegative SPRP, SHZ, SDL, and SHS, exhibit superior performance based on their arrangement, outperforming HZ and SP methods in terms of the number of iterations, function evaluations, and gradient evaluations.