Genetically modified pigs:Emerging animal models for hereditary hearing loss

Hereditary hearing loss(HHL),a genetic disorder that impairs auditory function,significantly affects quality of life and incurs substantial economic losses for society.To investigate the underlying causes of HHL and evaluate therapeutic outcomes,appropriate animal models are necessary.Pigs have been extensively used as valuable large animal models in biomedical research.In this review,we highlight the advantages of pig models in terms of ear anatomy,inner ear morphology,and electrophysiological characteristics,as well as recent advancements in the development of distinct genetically modified porcine models of hearing loss.Additionally,we discuss the prospects,challenges,and recommendations regarding the use pig models in HHL research.Overall,this review provides insights and perspectives for future studies on HHL using porcine models.

Genetic algorithm-optimized backpropagation neural network establishes a diagnostic prediction model for diabetic nephropathy:Combined machine learning and experimental validation in mice

Background:Diabetic nephropathy(DN)is the most common complication of type 2 diabetes mellitus and the main cause of end-stage renal disease worldwide.Diagnostic biomarkers may allow early diagnosis and treatment of DN to reduce the prevalence and delay the development of DN.Kidney biopsy is the gold standard for diagnosing DN;however,its invasive character is its primary limitation.The machine learning approach provides a non-invasive and specific criterion for diagnosing DN,although traditional machine learning algorithms need to be improved to enhance diagnostic performance.Methods:We applied high-throughput RNA sequencing to obtain the genes related to DN tubular tissues and normal tubular tissues of mice.Then machine learning algorithms,random forest,LASSO logistic regression,and principal component analysis were used to identify key genes(CES1G,CYP4A14,NDUFA4,ABCC4,ACE).Then,the genetic algorithm-optimized backpropagation neural network(GA-BPNN)was used to improve the DN diagnostic model.Results:The AUC value of the GA-BPNN model in the training dataset was 0.83,and the AUC value of the model in the validation dataset was 0.81,while the AUC values of the SVM model in the training dataset and external validation dataset were 0.756 and 0.650,respectively.Thus,this GA-BPNN gave better values than the traditional SVM model.This diagnosis model may aim for personalized diagnosis and treatment of patients with DN.Immunohistochemical staining further confirmed that the tissue and cell expression of NADH dehydrogenase(ubiquinone)1 alpha subcomplex,4-like 2(NDUFA4L2)in tubular tissue in DN mice were decreased.Conclusion:The GA-BPNN model has better accuracy than the traditional SVM model and may provide an effective tool for diagnosing DN.

Rapid Prototype Development Approach for Genetic Programming

Genetic Programming (GP) is an important approach to deal with complex problem analysis and modeling, and has been applied in a wide range of areas. The development of GP involves various aspects, including design of genetic operators, evolutionary controls and implementations of heuristic strategy, evaluations and other mechanisms. When designing genetic operators, it is necessary to consider the possible limitations of encoding methods of individuals. And when selecting evolutionary control strategies, it is also necessary to balance search efficiency and diversity based on representation characteristics as well as the problem itself. More importantly, all of these matters, among others, have to be implemented through tedious coding work. Therefore, GP development is both complex and time-consuming. To overcome some of these difficulties that hinder the enhancement of GP development efficiency, we explore the feasibility of mutual assistance among GP variants, and then propose a rapid GP prototyping development method based on πGrammatical Evolution (πGE). It is demonstrated through regression analysis experiments that not only is this method beneficial for the GP developers to get rid of some tedious implementations, but also enables them to concentrate on the essence of the referred problem, such as individual representation, decoding means and evaluation. Additionally, it provides new insights into the roles of individual delineations in phenotypes and semantic research of individuals.

The Practice and Exploration of Applying EBM to Bilingual Teaching of Medical Genetics at OSBCM

In the process of teaching medical genetics of undergraduate clinical medicine, the practice and exploration of applying EBM to the bilingual teaching of OSBCM medical genetics are carried out. Using CBL and PBL as the carrier can make up for the shortcomings of a single teaching mode, synthesize the advantages of multiple teaching modes. It starts from integrating the basic theoretical knowledge of medicine and clinical practice knowledge, improving students’ bilingual level of medical genetics, cultivating students’ literature retrieval ability, and promoting early clinical, multi-clinical and repeated clinical consciousness for medical students. Therefore, it is more conducive to cultivate students’ ability to learn independently, accurately analyze and solve problems, improve medical students’ clinical thinking ability and scientific research awareness, improve medical students’ ability of international communication, and lay a solid foundation for improving medical students’ future post competence, innovative spirit and lifelong learning ability.

Towards system genetics analysis of head and neck squamous cell carcinoma using the mouse model,cellular platform,and clinical human data

Head and neck squamous cell cancer(HNSCC)is a leading global malignancy.Every year,More than 830000 people are diagnosed with HNSCC globally,with more than 430000 fatalities.HNSCC is a deadly diverse malignancy with many tumor locations and biological characteristics.It originates from the squamous epithelium of the oral cavity,oropharynx,nasopharynx,larynx,and hypopharynx.The most frequently impacted regions are the tongue and larynx.Previous investigations have demonstrated the critical role of host genetic susceptibility in the progression of HNSCC.Despite the advances in our knowledge,the improved survival rate of HNSCC patients over the last 40 years has been limited.Failure to identify the molecular origins of development of HNSCC and the genetic basis of the disease and its biological heterogeneity impedes the development of new therapeutic methods.These results indicate a need to identify more genetic factors underlying this complex disease,which can be better used in early detection and prevention strategies.The lack of reliable animal models to investigate the underlying molecular processes is one of the most significant barriers to understanding HNSCC tumors.In this report,we explore and discuss potential research prospects utilizing the Collaborative Cross mouse model and crossing it to mice carrying single or double knockout genes(e.g.Smad 4 and P53 genes)to identify genetic factors affecting the development of this complex disease using genome-wide association studies,epigenetics,micro RNA,long noncoding RNA,lnc RNA,histone modifications,methylation,phosphorylation,and proteomics.

Evaluation of volcanic reservoirs with the "QAPM mineral model" using a genetic algorithm

Gas-bearing volcanic reservoirs have been found in the deep Songliao Basin, China. Choosing proper interpretation parameters for log evaluation is difficult due to complicated mineral compositions and variable mineral contents. Based on the QAPF classification scheme given by IUGS, we propose a method to determine the mineral contents of volcanic rocks using log data and a genetic algorithm. According to the QAPF scheme, minerals in volcanic rocks are divided into five groups： Q（quartz）, A （Alkaline feldspar）, P （plagioclase）, M （mafic） and F （feldspathoid）. We propose a model called QAPM including porosity for the volumetric analysis of reservoirs. The log response equations for density, apparent neutron porosity, transit time, gamma ray and volume photoelectrical cross section index were first established with the mineral parameters obtained from the Schlumberger handbook of log mineral parameters. Then the volumes of the four minerals in the matrix were calculated using the genetic algorithm （GA）. The calculated porosity, based on the interpretation parameters, can be compared with core porosity, and the rock names given in the paper based on QAPF classification according to the four mineral contents are compatible with those from the chemical analysis of the core samples.

Dynamic finite element model updating using meta-model and genetic algorithm

Current dynamic finite element model updating methods are not efficient or restricted to the problem of local optima. To circumvent these, a novel updating method which integrates the meta-model and the genetic algorithm is proposed. Experimental design technique is used to determine the best sampling points for the estimation of polynomial coefficients given the order and the number of independent variables. Finite element analyses are performed to generate the sampling data. Regression analysis is then used to estimate the response surface model to approximate the functional relationship between response features and design parameters on the entire design space. In the fitness evaluation of the genetic algorithm, the response surface model is used to substitute the finite element model to output features with given design parameters for the computation of fitness for the individual. Finally, the global optima that corresponds to the updated design parameter is acquired after several generations of evolution. In the application example, finite element analysis and modal testing are performed on a real chassis model. The finite element model is updated using the proposed method. After updating, root-mean-square error of modal frequencies is smaller than 2%. Furthermore, prediction ability of the updated model is validated using the testing results of the modified structure. The root-mean-square error of the prediction errors is smaller than 2%.

Genetic Regression Model for Dam Safety Monitoring

Under-fitting problems usually occur in regression models for dam safety monitoring.To overcome the local convergence of the regression, a genetic algorithm (GA) was proposed using a real parameter coding, a ranking selection operator, an arithmetical crossover operator and a uniform mutation operator, and calculated the least-square error of the observed and computed values as its fitness function. The elitist strategy was used to improve the speed of the convergence. After that, the modified genetic algorithm was applied to reassess the coefficients of the regression model and a genetic regression model was set up. As an example, a slotted gravity dam in the Northeast of China was introduced. The computational results show that the genetic regression model can solve the under-fitting problems perfectly.

Analysis of Genetic Effects of Nuclear-Cytoplasmic Interaction on Quantitative Traits:Genetic Model for Diploid Plants

A genetic model was proposed for simultaneously analyzing genetic effects of nuclear, cytoplasm, and nuclear-cytoplasmic interaction （NCI） as well as their genotype by environment （GE） interaction for quantitative traits of diploid plants. In the model, the NCI effects were further partitioned into additive and dominance nuclear-cytoplasmic interaction components. Mixed linear model approaches were used for statistical analysis. On the basis of diallel cross designs, Monte Carlo simulations showed that the genetic model was robust for estimating variance components under several situations without specific effects. Random genetic effects were predicted by an adjusted unbiased prediction （AUP） method. Data on four quantitative traits （boll number, lint percentage, fiber length, and micronaire） in Upland cotton （Gossypium hirsutum L.） were analyzed as a worked example to show the effectiveness of the model.

High-fat diet and oral infection induced type 2 diabetes and obesity development under different genetic backgrounds

Background:Type 2 diabetes(T2D)is an adult-onset and obese form of diabetes caused by an interplay between genetic,epigenetic,and environmental components.Here,we have assessed a cohort of 11 genetically different collaborative cross(CC)mouse lines comprised of both sexes for T2D and obesity developments in response to oral infection and high-fat diet(HFD)challenges.Methods:Mice were fed with either the HFD or the standard chow diet(control group)for 12 weeks starting at the age of 8 weeks.At week 5 of the experiment,half of the mice of each diet group were infected with Porphyromonas gingivalis and Fusobacterium nucleatum bacteria strains.Throughout the 12-week experimental period,body weight(BW)was recorded biweekly,and intraperitoneal glucose tolerance tests were performed at weeks 6 and 12 of the experiment to evaluate the glucose tolerance status of mice.Results:Statistical analysis has shown the significance of phenotypic variations between the CC lines,which have different genetic backgrounds and sex effects in different experimental groups.The heritability of the studied phenotypes was estimated and ranged between 0.45 and 0.85.We applied machine learning methods to make an early call for T2D and its prognosis.The results showed that classification with random forest could reach the highest accuracy classification(ACC=0.91)when all the attributes were used.Conclusion:Using sex,diet,infection status,initial BW,and area under the curve(AUC)at week 6,we could classify the final phenotypes/outcomes at the end stage of the experiment(at 12 weeks).

Application of Genetic Algorithm to Solving Nonlinear Model of Aeroengines

Solving the nonlinear model of an aeroengine is converted to an optimization problem, and thus some optimization search methods can be used. An approach to solving the nonlinear model of an aeroengine by use of the genetic algorithm (GA) is developed. By comparison with N R algorithm, the accuracy of the values of initial guesses is not required for GA. Especially, the approach developed can be used when no priori knowledges of the values of initial guesses are availabe, and the convergence is improved significantly. GA properly combined with N R algorithm can increase the convergence speed.

Geochemical characteristics and genetic model of dolomite reservoirs in the eastern margin of the Pre-Caspian Basin

The widespread Carboniferous KT-I dolomite in the eastern margin of the Pre-Caspian Basin is an important hydrocarbon reservoir. The dolomite lithology is dominated by crystalline dolomite. The δ18O values range from -6.71‰ to 2.45‰, and average 0.063‰, obviously larger than -2.5‰, indicating low-temperature dolomite of evaporation origin. Stable strontium isotope ratios （87Sr/86Sr） range from 0.70829 to 0.70875 and average 0.708365, very consistent with 87Sr/86Sr ratios in Carboniferous seawater. Chemical analysis of Ca and Mg elements shows that the dolomite has 9.1 mole% excess Ca or even higher before stabilization. The degree of order of dolomite is medium–slightly poor, varying in a range of 0.336-0.504 and averaging 0.417. It suggests that the dolomite formed under near-surface conditions. There are two models for the origin of the Carboniferous KT-I dolomite reservoir. These are 1） the evaporation concentration – weathering crust model and 2） the shoal facies – seepage reflux model. The former is mainly developed in restricted platforms – evaporate platforms of restricted marine deposition environments with a representation of dolomite associated with gypsum and mudstone. The latter mainly formed in platform edge shoals and intra-platform shoals and is controlled by dolomitization due to high salinity sea water influx from adjacent restricted sea or evaporate platform.

WOMBAT—A tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML)

WOMBAT is a software package for quantitative genetic analyses of continuous traits, fitting a linear, mixed model; estimates of covariance components and the resulting genetic parameters are obtained by restricted maximum likelihood. A wide range of models, comprising numerous traits, multiple fixed and random effects, selected genetic covariance structures, random regression models and reduced rank estimation are accommodated. WOMBAT employs up-to-date numerical and computational methods. Together with the use of efficient compilers, this generates fast executable programs, suitable for large scale analyses. Use of WOMBAT is illustrated for a bivariate analysis. The package consists of the executable program, available for LINUX and WINDOWS environments, manual and a set of worked example, and can be downloaded free of charge from http：//agbu. une.edu.au/-kmeyer/wombat.html

Genetic control of flowering time in woody plants： Roses as an emerging model

Genetic control of the timing of flowering in woody plants is complex and has yet to be adequately investigated due to their long life-cycle and difficulties in genetic modification.Studies in Populus,one of the best woody plant models,have revealed a highly conserved genetic network for flowering timing in annuals.However,traits like continuous flowering cannot be addressed with Populus.Roses and strawberries have relatively small,diploid genomes and feature enormous natural variation.With the development of new genetic populations and genomic tools,roses and strawberries have become good models for studying the molecular mechanisms underpinning the regulation of flowering in woody plants.Here,we review findings on the molecular and genetic factors controlling continuous flowering in roses and woodland strawberries.Natural variation at TFL1 orthologous genes in both roses and strawberries seems be the key plausible factor that regulates continuous flowering.However,recent efforts suggest that a two-recessive-loci model may explain the controlling of continuous flowering in roses.We propose that epigenetic factors,including non-coding RNAs or chromatin-related factors,might also play a role.Insights into the genetic control of flowering time variation in roses should benefit the development of new germplasm for woody crops and shed light on the molecular genetic bases for the production and maintenance of plant biodiversity.

A genetic algorithm based stochastic programming model for air quality management

This paper presents a model that can aid planners in defining the total allowable pollutant discharge in the planning region, accounting for the dynamic and stochastic character of meteorological conditions. This is accomplished by integrating Monte Carlo simulation and using genetic algorithm to solve the model. The model is demonstrated by using a realistic air urban scale SO 2 control problem in the Yuxi City of China. To evaluate effectiveness of the model, results of the approach are shown to compare with those of the linear deterministic procedures. This paper also provides a valuable insight into how air quality targets should be made when the air pollutant will not threat the residents' health. Finally, a discussion of the areas for further research are briefly delineated.

Genetic Types and Metallogenic Model for the Polymetallic Copper–Gold Deposits in the Tongling Ore District, Anhui Province, Eastern China

The Tongling ore district is one of the most economically important ore areas in the Middle–Lower Yangtze River Metallogenic Belt, eastern China. It contains hundreds of polymetallic copper–gold deposits and occurrences. Those deposits are mainly clustered(from west to east) within the Tongguanshan, Shizishan, Xinqiao, Fenghuangshan, and Shatanjiao orefields. Until recently, the majority of these deposits were thought to be skarn-or porphyry–skarn-type deposits; however there have been recent discoveries of numerous vein-type Au, Ag, and Pb-Zn deposits that do not fall into either of these categories. This indicates that there is some uncertainty over this classification. Here, we present the results of several systematic geological studies of representative deposits in the Tongling ore district. From investigation of the ore-controlling structures, lithology of the host rock, mineral assemblages, and the characteristics of the mineralization and alteration within these deposits, three genetic types of deposits(skarn-, porphyry-, and vein-type deposits) have been identified. The spatial and temporal relationships between the orebodies and Yanshanian intrusions combined with the sources of the ore-forming fluids and metals, as well as the geodynamic setting of this ore district, indicate that all three deposit types are genetically related each other and constitute a magmatic–hydrothermal system. This study outlines a model that relates the polymetallic copper–gold porphyry-, skarn-, and vein-type deposits within the Tongling ore district. This model provides a theoretical basis to guide exploration for deep-seated and concealed porphyry-type Cu(–Mo, –Au) deposits as well as shallow vein-type Au, Ag, and Pb–Zn deposits in this area and elsewhere.

Application of numerical modeling and genetic programming to estimate rock mass modulus of deformation

Estimation of the rock mass modulus of deformation(Em)is one of the most important design parameters in designing many structures in and on rock.This parameter can be obtained by in situ tests,empirical relations between deformation modulus and rock mass classifcation,and estimating from laboratory tests results.In this paper,a back analysis calculation is performed to present an equation for estimation of the rock mass modulus of deformation using genetic programming(GP)and numerical modeling.A database of 40,960 datasets,including vertical stress(rz),horizontal to vertical stresses ratio(k),Poisson’s ratio(m),radius of circular tunnel(r)and wall displacement of circular tunnel on the horizontal diameter(d)for input parameters and modulus of deformation for output,was established.The selected parameters are easy to determine and rock mass modulus of deformation can be obtained from instrumentation data of any size circular galleries.The resulting RMSE of 0.86 and correlation coeffcient of97%of the proposed equation demonstrated the capability of the computer program(CP)generated by GP.

Genetic Model for Fresh-keeping Character in Sweet Corn

This study was conducted to investigate the genetic regularity of indexes related to freshness keeping and its molecular basis by acquiring 6 generations （P1, P2, F1, B1, B2 and F2） of an inbred line T3 with long freshness period × an inbred line T15 with short freshness period in sweet corn. The genetic analysis of the indexes was performed by major gene＋polygene mixed genetic model combined with the genetic analysis combining six generations.The results showed that the decreasing rate of the postharvest sugar content in the T3 was controlled by two pairs of additive-dominante-epistatic major genes＋additive-dominant polygenes; each segregating generation was affected by its major genes, the heritability of major genes and polygene in the B1 generation was 74.63% and 17.67%, respectively; the heritability of major gene and potygene in the B2 was 91.98% and 0,00%, respectively; and the heritability of major gene and polygene inthe F2 was 82.67%, and 12.93%, respectively.

Linear-in-Parameter Models Based on Parsimonious Genetic Programming Algorithm and Its Application to Aero-Engine Start Modeling

A novel Parsimonious Genetic Programming （PGP） algorithm together with a novel aero-engine optimum data-driven dynamic start process model based on PGP is proposed. In application of this method, first, the traditional Genetic Programming（GP） is used to generate the nonlinear input-output models that are represented in a binary tree structure; then, the Orthogonal Least Squares algorithm （OLS） is used to estimate the contribution of the branches of the tree （refer to basic function term that cannot be decomposed anymore according to special rule） to the accuracy of the model, which contributes to eliminate complex redundant subtrees and enhance GP＇s convergence speed; and finally, a simple, reliable and exact linear-in-parameter nonlinear model via GP evolution is obtained. The real aero-engine start process test data simulation and the comparisons with Support Vector Machines （SVM） validate that the proposed method can generate more applicable, interpretable models and achieve comparable, even superior results to SVM.

Combined Novel Gate Level Model and Critical Primary Input Sharing for Genetic Algorithm Based Maximum Power Supply Noise Estimation

A gate level maximum power supply noise （PSN） model is defined that captures both IR drop and di/dt noise effects. Experimental results show that this model improves PSN estimation by 5.3% on average and reduces computation time by 10.7% compared with previous methods. Furthermore,a primary input critical factor model that captures the extent of primary inputs＇ PSN contribution is formulated. Based on these models,a novel niche genetic algorithm is proposed to estimate PSN more effectively. Compared with general genetic algorithms, this novel method can achieve up to 19.0% improvement on PSN estimation with a much higher convergence speed.