NON-SYNDROMIC HEARING LOSS AND HIGH- THROUGHPUT STRATEGIES TO DECIPHER ITS GENETIC HETEROGENEITY

Hearing loss (HL) is the most common sensory disorder, affecting all age groups, ethnicities, and gen-ders. According to World Health Organization (WHO) estimates in 2005, 278 million people worldwide have moderate to profound HL in both ears. Results of the 2002 National Health Interview Survey indicate that nearly 31 million of all non-institutionalized adults (aged 18 and over) in the United States have trouble hearing. Epidemiological studies have estimated that approximately 50%of profound HL can be attributed to genetic causes. With over 60 genes implicated in nonsyndromic hearing loss, it is also an extremely het-erogeneous trait. Recent progress in identifying genes responsible for hearing loss enables otolaryngologists and other clinicians to apply molecular diagnosis by genetic testing. The advent of the $1000 genome has the potential to revolutionize the identification of genes and their mutations underlying genetic disorders. This is especially true for extremely heterogeneous Mendelian conditions such as deafness, where the muta-tion, and indeed the gene, may be private. The recent technological advances in target-enrichment methods and next generation sequencing offer a unique opportunity to break through the barriers of limitations im-posed by gene arrays. These approaches now allow for the complete analysis of all known deafness-causing genes and will result in a new wave of discoveries of the remaining genes for Mendelian disorders. This re-view focuses on describing genotype-phenotype correlations of the most frequent genes including GJB2, which is responsible for more than half of cases, followed by other common genes and on discussing the im-pact of genomic advances for comprehensive genetic testing and gene discovery in hereditary hearing loss.

Recent advances in the molecular genetics of resin biosynthesis and genetic engineering strategies to improve defenses in conifers

Since the first terpenoid synthase cDNA was obtained by the reverse genetic approach from grand fir, great progress in the molecular genetics of terpenoid formation has been made with angiosperms and genes encoding a monoterpene synthase, a sesquiterpene synthase, and a diterpene synthase. Tree killing bark beetles and their vectored fungal pathogens are the most destructive agents of conifer forests worldwide. Conifers defend against attack by the constitutive and inducible production of oleoresin that accumulates at the wound site to kill invaders and both flush and seal the injury. Although toxic to the bark beetle and fungal pathogen, oleoresin also plays a central role in the chemical ecology of these boring insects. Recent advances in the molecular genetics of terpenoid biosynthesis provide evidence for the evolutionary origins of oleoresin and permit consideration of genetic engineering strategies to improve conifer defenses as a component of modern forest biotechnology. This review described enzymes of resin biosynthesis, structural feathers of genes genomic intron and exon organization, pathway organization and evolution, resin production and accumulation, interactions between conifer and bark beetle, and engineering strategies to improve conifer defenses.

New Knowledge-based Genetic Algorithm for Excavator Boom Structural Optimization

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the conflgurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, arc taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.

Genetically modified human umbilical cord blood cells as a promising strategy for treatment of spinal cord injury

Spinal cord injury （SCI） continues to be a pressing health and social problem. The injury leads to neuronal and glial cell death accompanied by degeneration of nerve fibers. There are currently no particularly effective treatments. SCI causes profound disabil- ity of people affected and has attracted increased attention in the international field of neuroregeneration. For the past two decades, much hope has been placed in cell therapies for the restoration of both structure and function of the injured spinal cord. Embryonic and neural stem cells, olfactory ensheathing cells, microglia-like cells, Schwann cells, mesenchymal stem cells.

Multi-objective Function Optimization for Environmental Control of a Greenhouse Based on a RBF and NSGA-Ⅱ

To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solved.In this work,a radial-basis function(RBF)neural network was used to mine the potential changes of a greenhouse environment,a temperature error model was established,a multi-objective optimization function of energy consumption was constructed and the corresponding decision parameters were optimized by using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-Ⅱ).The simulation results showed that RBF could clarify the nonlinear relationship among the greenhouse environment variables and decision parameters and the greenhouse temperature.The NSGA-Ⅱ could well search for the Pareto solution for the objective functions.The experimental results showed that after 40 min of combined control of sunshades and sprays,the temperature was reduced from 31℃to 25℃,and the power consumption was 0.5 MJ.Compared with tire three days of July 24,July 25 and July 26,2017,the energy consumption of the controlled production greenhouse was reduced by 37.5%,9.1%and 28.5%,respectively.

CREATION OF OPTIMAL MOVEMENT STRATEGY OF PLURAL MOVING OB-JECTS BY GA

The topographic information of a closed world is expressed as a graph. The plural mov- ingobjects which go and back in it according to a single moving strategy are supposed.The moving strategy is expressed by numerical values as a decision table. Coding is performed with this table as chromosomes, and this is optimized by using genetic algorithm. These environments were realized on a computer, and the simulation was carried out. As the result, the learning of the method to act so that moving objects do not obstruct mutually was recognized, and it was confirmed that these methods are effective for optimizing moving strategy.

Variable-fidelity optimization with design space reduction

Advanced engineering systems, like aircraft, are defined by tens or even hundreds of design variables. Building an accurate surrogate model for use in such high-dimensional optimization problems is a difficult task owing to the curse of dimensionality. This paper presents a new algorithm to reduce the size of a design space to a smaller region of interest allowing a more accurate surrogate model to be generated. The framework requires a set of models of different physical or numerical fidelities. The low-fidelity （LF） model provides physics-based approximation of the high-fidelity （HF） model at a fraction of the computational cost. It is also instrumental in identifying the small region of interest in the design space that encloses the high-fidelity optimum. A surrogate model is then constructed to match the low-fidelity model to the high-fidelity model in the identified region of interest. The optimization process is managed by an update strategy to prevent convergence to false optima. The algorithm is applied on mathematical problems and a two-dimen-sional aerodynamic shape optimization problem in a variable-fidelity context. Results obtained are in excellent agreement with high-fidelity results, even with lower-fidelity flow solvers, while showing up to 39% time savings.

Parameter influence law analysis and optimal design of a dual mass flywheel

The influence of the dynamic parameters of a dual mass flywheel(DMF)on its vibration reduction performance is analyzed,and several optimization algorithms are used to carry out multiobjective DMF optimization design.First,the vehicle powertrain system is modeled according to the parameter configuration of the test vehicle.The accuracy of the model is verified by comparing the simulation data with the test results.Then,the model is used to analyze the influence of the moment of inertia ratio,torsional stiffness,and damping in reducing DMF vibration.The speed fluctuation amplitude at the transmission input shaft and the natural frequency of the vehicle are taken as the optimization objectives.The passive selection method,multiobjective particle swarm optimization,and the nondominated sorting genetic algorithm based on an elite strategy are used to carry out DMF multiobjective optimization design.The advantages and disadvantages of these algorithms are evaluated,and the best optimization algorithm is selected.