Preferred Vector Research Articles

Transduction of Adeno-Associated Virus Vectors Targeting Hair Cells and Supporting Cells in the Neonatal Mouse Cochlea.

Adeno-associated virus (AAV) is the preferred vector for gene therapy of hereditary deafness, and different viral serotypes, promoters and transduction pathways can influence the targeting of AAV to different types of cells and the expression levels of numerous exogenous genes. To determine the transduction and expression patterns of AAV with different serotypes or promoters in hair cells and supporting cells in the neonatal mouse cochlea, we examined the expression of enhanced green fluorescent protein (eGFP) for five different types of AAV vectors [serotypes 2, 9, and Anc80L65 with promoter cytomegalovirus (CMV)-beta-Globin and serotypes 2 and 9 with promoter chicken beta-actin (CBA)] in in vitro cochlear explant cultures and we tested the transduction of AAV2/2-CBA, AAV2/9-CBA, and AAV2/Anc80L65-CMV by in vivo microinjection into the scala media of the cochlea. We found that each AAV vector had its own transduction and expression characteristics in hair cells and supporting cells in different regions of the cochlea. There was a tonotopic gradient for the in vitro transduction of AAV2/2-CBA, AAV2/9-CBA, AAV2/2-CMV, and AAV2/9-CMV in outer hair cells (OHCs), with more OHCs expressing eGFP at the base of the cochlea than at the apex. AAV2/2-CBA in vitro and AAV2/Anc80L65-CMV in vivo induced more supporting cells expressing eGFP at the apex than in the base. We found that AAV vectors with different promoters had different expression efficacies in hair cells and supporting cells of the auditory epithelium. The CMV-beta-Globin promoter could drive the expression of the delivered construct more efficiently in hair cells, while the CBA promoter was more efficient in supporting cells. The in vitro and in vivo experiments both demonstrated that AAV2/Anc80L65-CMV was a very promising vector for gene therapy of deafness because of its high transduction rates in hair cells. These results might be useful for selecting the appropriate vectors for gene delivery into different types of inner ear cells and thus improving the effectiveness of gene therapy.

Group Decision Making with Heterogeneous Preference Structures: An Automatic Mechanism to Support Consensus Reaching

In real-world decision problems, decision makers usually express their opinions with different preference structures. In order to deal with the heterogeneous preference information in group decision making, this paper presents an optimization-based consensus model for group decision making with heterogeneous preference structures (utility values, preference orderings, multiplicative preference relations and additive preference relations). This proposal seeks to minimize the information loss between decision makers’ heterogeneous preference information and individual preference vectors and also seeks the collective solution with a consensus. Meanwhile, in order to justify the consensus model, we discuss its internal aggregation operator between the obtained individual and group preference vectors, demonstrate that the proposed model satisfies the Pareto principle of social choice theory, and prove the uniqueness of the solution to the optimization model. Furthermore, based on the proposed optimization-based consensus model, we present an automatic mechanism to support consensus reaching in the group decision making with heterogeneous preference structures. In the consensus reaching process, the obtained individual and group preference vectors are considered as a decision aid which decision makers can use as a reference to adjust their preference opinions. Finally, detailed simulation experiments and comparison analysis are conducted to demonstrate the feasibility and effectiveness of our proposed model.

Orientational instability and spontaneous rotation of active nematic droplets.

In experiments, an individual chemically-active liquid crystal (LC) droplet submerged in the bulk of a surfactant solution may self-propel along a straight, helical, or random trajectory. In this paper, we develop a minimal model capturing all three types of self-propulsion trajectories of a drop in the case of a nematic LC with homeotropic anchoring at the LC-fluid interface. We emulate the director field within the drop by a single preferred polarization vector that is subject of two reorientation mechanisms, namely, the internal flow-induced displacement of the hedgehog defect and the droplet's rotation. Within this reduced-order model, the coupling between the nematic ordering of the drop and the surfactant transport is represented by variations of the droplet's interfacial properties with nematic polarization. Our analysis reveals that a novel mode of orientational instability emerges from the competition of the two reorientation mechanisms and is characterized by a spontaneous rotation of the self-propelling drop responsible for helical self-propulsion trajectories. In turn, we also show that random trajectories in isotropic and nematic drops alike stem from the advection-driven transition to chaos. The succession of the different propulsion modes is consistent with experimentally-reported transitions in the shape of droplet trajectories as the drop size is varied.

A characterization of the Logarithmic Least Squares Method

We provide an axiomatic characterization of the Logarithmic Least Squares Method (sometimes called row geometric mean), used for deriving a preference vector from a pairwise comparison matrix. This procedure is shown to be the only one satisfying two properties, correctness in the consistent case, which requires the reproduction of the inducing vector for any consistent matrix, and invariance to a specific transformation on a triad, that is, the weight vector is not influenced by an arbitrary multiplication of matrix elements along a 3-cycle by a positive scalar.

Trends and Factors of the Real Economy Development and Current Tools of Support: Road Mapping for Domestic Investment

The subject of the research is the development of the real sector of the economy as a core element in the complex of sustainable spatial development of Russia. It is through the real sector of the economy that the effectiveness of processes of development and saturation of the Russian national space is ensured. Domestic investments as a factor attribute of economic growth and development are conditioned by objective assessment of economic realities and trends in public administration. However, the scientific foresight is essential for the investment process. The former, in fact, ensures the sustainable development of the economy. The purpose of the paper was to substantiate, with account for world trends and existing domestic macroeconomic features and factors (external and internal), the use of the road mapping method for creating an effective investment mechanism aimed at the development and growth of the real sector of the national economy. The relevance of the research stems from the fact that it reveals the principles and attributes of the foresight technology the concept of which is essential for understanding the structure and specifics of using the roadmap approach as one of the most effective management methods for the implementation of forecasting and planning functions according to foreign practices. The advantages and preferred vectors of the roadmap as a method of the foresight research technology are shown. The types of roadmaps for the development of individual investment objects including industries/businesses, enterprises/companies and regions are considered. Along with that, the features common for all mapping objects, attributes and elements of regional road mapping are highlighted. It is concluded that it is necessary to combine the methods of foresight and GIS technologies.

A novel consensus reaching framework for heterogeneous group decision making based on cumulative prospect theory

This paper examines decision makers’ bounded rationality in consensus building, and proposes a direct consensus framework based on cumulative prospect theory to solve group decision making problem with eight kinds of preference representation structures. This framework consists of two processes: selection process and consensus process. In selection process, we first transform different preference structures into uniform individual preference vectors using different transformation functions. Then, according to the individual preference vectors and gain and loss matrices, we determine reference points using preference-approval structures and the prospect value matrix. Third, we design an optimization model to obtain decision weights, and further get the prospect values of alternatives. In consensus process, we define a consensus measure by applying cumulative prospect theory, design a feedback adjustment to make consensus better, and output satisfying comprehensive prospect value after several iterations if necessary; then, the final optimal solution is obtained. At last, an example is provided to illustrate the effectiveness of framework.

The 2-Rank Consensus Reaching Model in the Multigranular Linguistic Multiple-Attribute Group Decision-Making

In the multiple-attribute group decision-making (MAGDM), the decision objective is to obtain a complete ranking of the alternatives from best to worst. In the real world, however, obtaining such a complete ranking of alternatives is very time-consuming, and it is sometimes not necessary. There are many MAGDM problems that we need to assign two rank levels only, so as to create a ranking of one subset of alternatives above another subset. This type of MAGDM problem is called a 2-rank MAGDM problem. This paper investigates the 2-rank MAGDM problem under the multigranular linguistic context, and proposes a 2-rank consensus reaching framework with the minimum adjustments. In the consensus reaching framework, a 2-rank selection process is proposed to find the individual and collective 2-rank preference vectors, and a 2-rank consensus reaching process with the minimum adjustments is put forwarded to help decision makers achieve a consensus. To demonstrate the consensus efficiency of the 2-rank consensus reaching model, a comparison analysis between our proposal and the classical MAGDM models is presented. The main improvement of this paper is to provide a flexible framework to constitute a better approximate decision model to real-world MAGDM problems.

Modified bidirectional extreme learning machine with Gram–Schmidt orthogonalization method

Abstract Incremental extreme learning machine has been proved to be an efficient and simple universal approximator. However, the network architecture may be very large due to the inefficient nodes which have a tiny effect on reducing the residual error. More to the point, the output weights are not the least square solution. To reduce such inefficient nodes, a method called bidirectional ELM (B-ELM), which analytically calculates the input weights of even nodes, was proposed. By analyzing, B-ELM can be further improved to achieve better performance on compacting structure. This paper proposes the modified B-ELM (MB-ELM), in which the orthogonalization method is involved in B-ELM to orthogonalize the output vectors of hidden nodes and the resulting vectors are taken as the output vectors. MB-ELM can greatly diminish inefficient nodes and obtain a preferable output weight vector which is the least square solution, so that it has better convergence rate and a more compact network architecture. Specifically, it has been proved that in theory, MB-ELM can reduce residual error to zero by adding only two nodes into network. Simulation results verify these conclusions and show that MB-ELM can reach smaller low limit of residual error than other I-ELM methods.

Cultural distance for service composition in cyber–physical–social systems

Cyber–physical–social systems can be organized as workflows that interconnect the resources in physical, cyber, and social worlds in real time. This integration of these worlds with various resource as services requires service composition approaches that can integrate essential components and share relevant information in these worlds. Although numerous service composition approaches have been proposed, there still exist many challenges in the representation of user preference of service composition in cyber–physical–social systems. In this paper, a service composition approach based on the cultural distance is proposed to improve the reliability and satisfaction. This approach employs the cultural distance to measure the user preference quantitatively in a simple mathematical form. The user preference degree and the user preference vector are defined to select the service from a global view and an accurate point separately. Then, a 0-1 mixed-integer programming algorithm is used to identify the most suitable services for composition. The experimental results based on two real-world datasets show that the proposed approach for representing the user preference quantitatively is more simple and effective than other approaches. In a word, the proposed approach yields satisfactory performance of service composition in CPSS with regard to the user preference and efficiency.

Faster Algorithms for Computing Plurality Points

Let V be a set of n points in R d , which we call voters. A point p ∈ R d is preferred over another point p ′ ∈ R d by a voter υ ∈ V if dist(υ, p ) < dist(υ, p ′). A point p is called a plurality point if it is preferred by at least as many voters as any other point p ′. We present an algorithm that decides in O ( n log n ) time whether V admits a plurality point in the L 2 norm and, if so, finds the (unique) plurality point. We also give efficient algorithms to compute a minimum-cost subset W ⊂ V such that V \ W admits a plurality point, and to compute a so-called minimum-radius plurality ball. Finally, we consider the problem in the personalized L 1 norm, where each point υ ∈ V has a preference vector 〈 w 1 (υ),…, w d (υ)〉 and the distance from υ to any point p ∈ R d is given by ∑ i =1 d w i (υ)· | x i (υ)− x i ( p )|. For this case we can compute in O ( n d −1 ) time the set of all plurality points of V . When all preference vectors are equal, the running time improves to O ( n ).

Origin of the Local Group satellite planes

We attempt to understand the planes of satellite galaxies orbiting the Milky Way (MW) and M31 in the context of Modified Newtonian Dynamics (MOND), which implies a close MW-M31 flyby occurred ${\approx 8}$ Gyr ago. Using the timing argument, we obtain MW-M31 trajectories consistent with cosmological initial conditions and present observations. We adjust the present M31 proper motion within its uncertainty in order to simulate a range of orbital geometries and closest approach distances. Treating the MW and M31 as point masses, we follow the trajectories of surrounding test particle disks, thereby mapping out the tidal debris distribution. Around each galaxy, the resulting tidal debris tends to cluster around a particular orbital pole. We find some models in which these preferred spin vectors align fairly well with those of the corresponding observed satellite planes. The radial distributions of material in the simulated satellite planes are similar to what we observe. Around the MW, our best-fitting model yields a significant fraction (0.22) of counter-rotating material, perhaps explaining why Sculptor counter-rotates within the MW satellite plane. In contrast, our model yields no counter-rotating material around M31. This is testable with proper motions of M31 satellites. In our best model, the MW disk is thickened by the flyby 7.65 Gyr ago to a root mean square height of 0.75 kpc. This is similar to the observed age and thickness of the Galactic thick disk. Thus, the MW thick disk may have formed together with the MW and M31 satellite planes during a past MW-M31 flyby.

Consensus Building for the Heterogeneous Large-Scale GDM With the Individual Concerns and Satisfactions

Nowadays, societal and technological trends demand the management of large scale of decision makers in group decision-making (GDM) contexts. In a large-scale GDM, decision makers often have individual concerns and satisfactions, and also they will use heterogeneous preference representation structures to express their preferences. Meanwhile, it is difficult to set the numerical consensus threshold to judge whether a consensus degree can be acceptable or not in the consensus reaching process in a large-scale GDM. This study proposes a novel consensus reaching model for the heterogeneous large-scale GDM with the individual concerns and satisfactions. In this consensus reaching model, a selection process is proposed to obtain the individual preference vectors, to divide decision makers into different clusters, and to yield the preference vector of the large group. Following this, a consensus measure method that considers the individual concerns on alternatives is defined for measuring the consensus degree, and a linguistic approach is developed to measure the individual and collective satisfactions regarding the consensus degree. Finally, a feedback adjustment process is proposed and utilized to help decision makers adjust their preferences. A practical example and a simulation analysis are presented to demonstrate the validity of the proposed consensus reaching model.

Recombinant Chimpanzee Adenovirus Vaccine AdC7-M/E Protects against Zika Virus Infection and Testis Damage.

The recent outbreak of Zika virus (ZIKV) has emerged as a global health concern. ZIKV can persist in human semen and be transmitted by sexual contact, as well as by mosquitoes, as seen for classical arboviruses. We along with others have previously demonstrated that ZIKV infection leads to testis damage and infertility in mouse models. So far, no prophylactics or therapeutics are available; therefore, vaccine development is urgently demanded. Recombinant chimpanzee adenovirus has been explored as the preferred vaccine vector for many pathogens due to the low preexisting immunity against the vector among the human population. Here, we developed a ZIKV vaccine based on recombinant chimpanzee adenovirus type 7 (AdC7) expressing ZIKV M/E glycoproteins. A single vaccination of AdC7-M/E was sufficient to elicit potent neutralizing antibodies and protective immunity against ZIKV in both immunocompetent and immunodeficient mice. Moreover, vaccinated mice rapidly developed neutralizing antibody with high titers within 1 week postvaccination, and the elicited antiserum could cross-neutralize heterologous ZIKV strains. Additionally, ZIKV M- and E-specific T cell responses were robustly induced by AdC7-M/E. Moreover, one-dose inoculation of AdC7-M/E conferred mouse sterilizing immunity to eliminate viremia and viral burden in tissues against ZIKV challenge. Further investigations showed that vaccination with AdC7-M/E completely protected against ZIKV-induced testicular damage. These data demonstrate that AdC7-M/E is highly effective and represents a promising vaccine candidate for ZIKV control.IMPORTANCE Zika virus (ZIKV) is a pathogenic flavivirus that causes severe clinical consequences, including congenital malformations in fetuses and Guillain-Barré syndrome in adults. Vaccine development is a high priority for ZIKV control. In this study, to avoid preexisting anti-vector immunity in humans, a rare serotype chimpanzee adenovirus (AdC7) expressing the ZIKV M/E glycoproteins was used for ZIKV vaccine development. Impressively, AdC7-M/E exhibited exceptional performance as a ZIKV vaccine, as follows: (i) protective efficacy by a single vaccination, (ii) rapid development of a robust humoral response, (iii) durable immune responses, (iv) robust T cell responses, and (v) sterilizing immunity achieved by a single vaccination. These advantages of AdC7-M/E strongly support its potential application as a promising ZIKV vaccine in the clinic.

Research for Optimizing Band-Edge Characteristics of the LC Elliptic Filter About Low Voltage–Power Line’s High-Speed Communication in the Coal Mine: LC Elliptic Filter About Low Voltage PLC in the Coal Mine

Because the environment is bad in the coal mine, it is difficult to establish an effective information sharing network. Low voltage–power line has many excellent features, so it is preferred vector to establish covering the whole coal mine information network system. This paper proposes an approach which achieves the coupling connection between high-speed data signal receiving or sending device and low voltage–power line through short-circuit protector, elliptic filter and coupling transformer in underground coal mine. The filter has optimizing band-edge characteristic and it gives a sort of simple elliptic filter design methods which has lesser calculated amount, simple and clear design steps. This paper also carried out simulation experiment for elliptic low-pass filter, elliptic high-pass filter and elliptic function BPF. The experimental results show that this design method is effective and it can give an important reference to the PLC communication which also could be generalized to other environment.

Complete sequence of the tumor-inducing plasmid pTiChry5 from the hypervirulent Agrobacterium tumefaciens strain Chry5

Agrobacterium tumefaciens strain Chry5 is hypervirulent on many plants including soybean that are poorly transformed by other A. tumefaciens strains. Therefore, it is considered as a preferred vector for genetic transformation of plants. Here we report the complete nucleotide sequence of its chrysopine-type Ti-plasmid pTiChry5. It is comprised of 197,268 bp with an overall GC content of 54.5%. Two T-DNA regions are present and 219 putative protein-coding sequences could be identified in pTiChry5. Roughly one half of the plasmid is highly similar to the agropine-type Ti plasmid pTiBo542, including the virulence genes with an identical virG gene, which is responsible for the supervirulence caused by pTiBo542. The remaining part of pTiChry5 is less related to that of pTiBo542 and embraces the trb operon of conjugation genes, genes involved in the catabolism of Amadori opines and the gene for chrysopine synthase, which replaces the gene for agropine synthase in pTiBo542. With the exception of an insertion of IS869, these Ti plasmids differ completely in the set of transposable elements present, reflecting a different evolutionary history from a common ancestor.

Projection Clustering Unfolding: A New Algorithm for Clustering Individuals or Items In A Preference Matrix

In the framework of preference rankings, the interest can lie in clustering individuals or items in order to reduce the complexity of the preference space for an easier interpretation of collected data. The last years have seen a remarkable owering of works about the use of decision tree for clustering preference vectors. As a matter of fact, decision trees are useful and intuitive, but they are very unstable: small perturbations bring big changes. This is the reason why it could be necessary to use more stable procedures in order to clustering ranking data. In this work, a Projection Clustering Unfolding (PCU) algorithm for preference data will be proposed in order to extract useful information in a low-dimensional subspace by starting from an high but mostly empty dimensional space. Comparison between unfolding configurations and PCU solutions will be carried out through Procrustes analysis.

Design of an Optimized Architecture for Manned and Unmanned Combat System-of-Systems: Formulation and Coevolutionary Optimization

With the rapid advancement of unmanned combat systems and the maturation of unmanned swarm technology, integrating unmanned combat systems into existing combat system of systems has become a hot topic. However, the current research at the organizational level is basically nonexistent. Therefore, we propose a methodology for designing an optimized architecture that is a significant product at this level to fill this technical gap. The design process consists of two steps: architecture modeling and coevolutionary optimization. The architecture model is composed of three basic elements, which are mission, equipment, and the command and control structure. The selection of the best scheme from possible solutions is a multiobjective optimization problem, and its computational complexity increases rapidly with the increase in the scale of combat system of systems. To overcome this difficulty, we introduce a decomposition-based evolutionary algorithm NSGA-III and improve it by using preference vectors to enhance the algorithm’s local search capability. Finally, we conduct the comparative experiments on benchmark test suites and design an operation case to demonstrate the advance and effectiveness of the enhanced algorithm. The proposed architecture modeling and optimization method can achieve a set of nondominated solutions and provide auxiliary decision-making information to help commanders make decisions and arrangements.

High-Titer Production of HIV-Based Lentiviral Vectors in Roller Bottles for Gene and Cell Therapy.

Lentiviral vectors are becoming preferred vectors of choice for clinical gene therapy trials due to their safety, efficacy, and the long-term gene expression they provide. Although the efficacy of lentiviral vectors is mainly predetermined by the therapeutic genes they carry, they must be produced at high titers to exert therapeutic benefit for in vivo applications. Thus, there is need for practical, robust, and scalable viral vector production methods applicable to any laboratory setting. Here, we describe a practical lentiviral production technique in roller bottles yielding high-titer third-generation lentiviral vectors useful for in vivo gene transfer applications. CaPO4-mediated transient transfection protocol involving the use of a transfer vector and three different packaging plasmids is employed to generate lentivectors in roller bottles. Following clearance of cellular debris via low-speed centrifugation and filtration, virus is concentrated by high-speed ultracentrifugation over sucrose cushion.

A multi-objective MILP model for the design and operation of future integrated multi-vector energy networks capturing detailed spatio-temporal dependencies

A multi-objective optimisation model, based on mixed integer linear programming, is presented that can simultaneously determine the design and operation of any integrated multi-vector energy networks. It can answer variants of the following questions:What is the most effective way, in terms of cost, value/profit and/or emissions, of designing and operating the integrated multi-vector energy networks that utilise a variety of primary energy sources to deliver different energy services, such as heat, electricity and mobility, given the availability of primary resources and the levels of demands and their distribution across space and time? When to invest in technologies, where to locate them; what resources should be used, where, when and how to convert them to the energy services required; how to transport the resources and manage inventory?Scenarios for Great Britain were examined involving different primary energy sources, such as natural gas, biomass and wind power, in order to satisfy demands for heat, electricity and mobility via various energy vectors such as electricity, natural gas, hydrogen and syngas. Different objectives were considered, such as minimising cost, maximising profit, minimising emissions and maximising renewable energy production, subject to the availability of suitable land for biomass and wind turbines as well as the maximum local production and import rates for natural gas.Results suggest that if significant mobility demands are met by hydrogen-powered fuel cell vehicles, then hydrogen is the preferred energy vector, over natural gas, for satisfying heat demands. If natural gas is not used and energy can only be generated from wind power and biomass, electricity and syngas are the preferred energy carriers for satisfying electricity and heat demands.

User-centered recommendation using US-ELM based on dynamic graph model in E-commerce

The recommender systems can gain the needs and interests of users by analyzing the user history data and then help the users making decisions on appropriate choices in E-commerce. However, with the increasing of data volume and the popularization of information network, the participation of users in E-commerce activities is growing deeply. How to analyze the user preferences and make a user-centered efficient recommendation is an urgent problem to be further researched. In this paper, we first propose the user-centered recommendation based on dynamic graph model to express the user preferences and gain the user preference vectors for recommendation. Then, after gaining the user preferences vectors, we propose the user clustering algorithm using US-ELM to cluster the users into different clusters. Last, we provide two recommendation algorithms, which can present top-k recommendation, respectively the group recommendation and personal recommendation. With the extensive experiments, our recommendation algorithms can effectively express the user preferences and reach a good performance.