Genomic prediction using composite training sets is an effective method for exploiting germplasm conserved in rice gene banks

Germplasm conserved in gene banks is underutilized,owing mainly to the cost of characterization.Genomic prediction can be applied to predict the genetic merit of germplasm.Germplasm utilization could be greatly accelerated if prediction accuracy were sufficiently high with a training population of practical size.Large-scale resequencing projects in rice have generated high quality genome-wide variation information for many diverse accessions,making it possible to investigate the potential of genomic prediction in rice germplasm management and exploitation.We phenotyped six traits in nearly 2000 indica(XI)and japonica(GJ)accessions from the Rice 3K project and investigated different scenarios for forming training populations.A composite core training set was considered in two levels which targets used for prediction of subpopulations within subspecies or prediction across subspecies.Composite training sets incorporating 400 or 200 accessions from either subpopulation of XI or GJ showed satisfactory prediction accuracy.A composite training set of 600 XI and GJ accessions showed sufficiently high prediction accuracy for both XI and GJ subspecies.Comparable or even higher prediction accuracy was observed for the composite training set than for the corresponding homogeneous training sets comprising accessions only of specific subpopulations of XI or GJ(within-subspecies level)or pure XI or GJ accessions(across-subspecies level)that were included in the composite training set.Validation using an independent population of 281 rice cultivars supported the predictive ability of the composite training set.Reliability,which reflects the robustness of a training set,was markedly higher for the composite training set than for the corresponding homogeneous training sets.A core training set formed from diverse accessions could accurately predict the genetic merit of rice germplasm.

Effect of Tuina-SET Sling Exercise Therapy on Analgesic Substances in Serum of Patients with Nonspecific Low Back Pain

[Objectives]This paper aimed to investigate the effect of Tuina combined with sling exercise therapy(SET)and psoas&abdominal training on serum 5-hydroxytryptamine(5-HT)andβ-endorphin(β-EP)levels in patients with nonspecific low back pain(NLBP).[Methods]Total 46 patients with NLBP who visited the Tuina Department of the First Affiliated Hospital of Hainan Medical University from August 2019 to May 2020 were randomly and evenly divided into control group and treatment group.On the basis of Tuina therapy,the patients in the control group and treatment group were treated with psoas&abdominal training and SET,respectively.After the treatment,the serum 5-HT andβ-EP levels of the patients were detected.[Results]The serum 5-HT andβ-EP levels in the treatment group were significantly improved compared with the control group(P<0.05).[Conclusions]The nerve&muscle reconstruction techniques of Tuina combined with SET or psoas&abdominal training can improve serum 5-HT andβ-EP levels in patients with NLBP.

Experimental Analysis of Methods Used to Solve Linear Regression Models

Predicting the value of one or more variables using the values of other variables is a very important process in the various engineering experiments that include large data that are difficult to obtain using different measurement processes.Regression is one of the most important types of supervised machine learning,in which labeled data is used to build a prediction model,regression can be classified into three different categories:linear,polynomial,and logistic.In this research paper,different methods will be implemented to solve the linear regression problem,where there is a linear relationship between the target and the predicted output.Various methods for linear regression will be analyzed using the calculated Mean Square Error(MSE)between the target values and the predicted outputs.A huge set of regression samples will be used to construct the training dataset with selected sizes.A detailed comparison will be performed between three methods,including least-square fit;Feed-Forward Artificial Neural Network(FFANN),and Cascade Feed-Forward Artificial Neural Network(CFFANN),and recommendations will be raised.The proposed method has been tested in this research on random data samples,and the results were compared with the results of the most common method,which is the linear multiple regression method.It should be noted here that the procedures for building and testing the neural network will remain constant even if another sample of data is used.

Reconstructing the 3D digital core with a fully convolutional neural network

In this paper, the complete process of constructing 3D digital core by fullconvolutional neural network is described carefully. A large number of sandstone computedtomography (CT) images are used as training input for a fully convolutional neural networkmodel. This model is used to reconstruct the three-dimensional (3D) digital core of Bereasandstone based on a small number of CT images. The Hamming distance together with theMinkowski functions for porosity, average volume specifi c surface area, average curvature,and connectivity of both the real core and the digital reconstruction are used to evaluate theaccuracy of the proposed method. The results show that the reconstruction achieved relativeerrors of 6.26%, 1.40%, 6.06%, and 4.91% for the four Minkowski functions and a Hammingdistance of 0.04479. This demonstrates that the proposed method can not only reconstructthe physical properties of real sandstone but can also restore the real characteristics of poredistribution in sandstone, is the ability to which is a new way to characterize the internalmicrostructure of rocks.

A knowledge matching approach based on multiclassification radial basis function neural network for knowledge push system

We present an exploratory study to improve the performance of a knowledge push system in product design. We focus on the domain of knowledge matching, where traditional matching algorithms need repeated calculations that result in a long response time and where accuracy needs to be improved. The goal of our approach is to meet designers’ knowledge demands with a quick response and quality service in the knowledge push system. To improve the previous work, two methods are investigated to augment the limited training set in practical operations,namely, oscillating the feature weight and revising the case feature in the case feature vectors. In addition, we propose a multi-classification radial basis function neural network that can match the knowledge from the knowledge base once and ensure the accuracy of pushing results. We apply our approach using the training set in the design of guides by computer numerical control machine tools for training and testing, and the results demonstrate the benefit of the augmented training set. Moreover, experimental results reveal that our approach outperforms other matching approaches.

The Pharmacophore Hypothesis of Novel Inhibitors for Aurora A Kinase

A three-dimensional pharmacophore model was developed from a series of inhibitors of Aurora A kinase to discover new potent anti-cancer agents using the HypoGen module in the Catalyst software. The pharmacophore model was developed based on the structure of 20 currently available inhibitors, which were carefully selected from the literature. The best hypothesis （Hypo 1） was defined by four features： one hydrogen-bond donor and three hy- drophobic points, with the best correlation coefficient of 0.909, the lowest rms deviation of 1.563, and the highest cost difference of 99.075. The Hypo 1 was then validated by a test set consisting of 24 compounds and by a cross-validation of 95% confidence level through randomizing the data using the CatScramble program, which suggested that a predictive pharmacophore model had been successfully obtained.

Aero-engine fault diagnosis applying new fast support vector algorithm

A new fast learning algorithm was presented to solve the large-scale support vector machine ( SVM ) training problem of aero-engine fault diagnosis.The relative boundary vectors ( RBVs ) instead of all the original training samples were used for the training of the binary SVM fault classifiers.This pruning strategy decreased the number of final training sample significantly and can keep classification accuracy almost invariable.Accordingly , the training time was shortened to 1 / 20compared with basic SVM classifier.Meanwhile , owing to the reduction of support vector number , the classification time was also reduced.When sample aliasing existed , the aliasing sample points which were not of the same class were eliminated before the relative boundary vectors were computed.Besides , the samples near the relative boundary vectors were selected for SVM training in order to prevent the loss of some key sample points resulted from aliasing.This can improve classification accuracy effectively.A simulation example to classify 5classes of combination fault of aero-engine gas path components was finished and the total fault classification accuracy reached 96.1%.Simulation results show that this fast learning algorithm is effective , reliable and easy to be implemented for engineering application.