Residual network improves the prediction accuracy of genomic selection.

Genetic improvement of complex traits in animal and plant breeding depends on the efficient and accurate estimation of breeding values. Deep learning methods have been shown to be not superior over traditional genomic selection (GS) methods, partially due to the degradation problem (i.e. with the increase of the model depth, the performance of the deeper model deteriorates). Since the deep learning method residual network (ResNet) is designed to solve gradient degradation, we examined its performance and factors related to its prediction accuracy in GS. Here we compared the prediction accuracy of conventional genomic best linear unbiased prediction, Bayesian methods (BayesA, BayesB, BayesC, and Bayesian Lasso), and two deep learning methods, convolutional neural network and ResNet, on three datasets (wheat, simulated and real pig data). ResNet outperformed other methods in both Pearson's correlation coefficient (PCC) and mean squared error (MSE) on the wheat and simulated data. For the pig backfat depth trait, ResNet still had the lowest MSE, whereas Bayesian Lasso had the highest PCC. We further clustered the pig data into four groups and, on one separated group, ResNet had the highest prediction accuracy (both PCC and MSE). Transfer learning was adopted and capable of enhancing the performance of both convolutional neural network and ResNet. Taken together, our findings indicate that ResNet could improve GS prediction accuracy, affected potentially by factors such as the genetic architecture of complex traits, data volume, and heterogeneity.

Diagnostic capacity of electron cyclotron emission imaging system with continuous large observation area on EAST tokamak.

Electron cyclotron emission imaging on EAST provides direct measurements of the 2-D electron temperature dynamics in a continuous large observation area with high temporal and spatial resolution. Besides the normal MHD investigation, a system with a view field large enough to cover the core plasma region has been applied to extract more plasma information, such as the plasma center location, the deposition location of auxiliary heating, and the core toroidal rotation speed. These results solely based on electron cyclotron emission imaging (ECEI) data are consistent with the results of the equilibrium fitting (EFIT), numerical code, and other diagnostics, which indicate the powerful diagnostic capacity of this ECEI system.

In situ relative self-dependent calibration of electron cyclotron emission imaging via shape matching.

Electron Cyclotron Emission Imaging (ECEI) is a diagnostic system which measures 2-D electron temperature profiles with high spatial-temporal resolution. Usually only the normalized electron temperature fluctuations are utilized to investigate the magnetohydrodynamics modes due to the difficulties of ECEI calibration. In this paper, we developed a self-dependent calibration method for 24 × 16 channel high-resolution ECEI on the Experimental Advanced Superconducting Tokamak. The technique of shape matching is applied to solve for the matrix of the calibration coefficients. The calibrated area is further expanded to an occupation ratio of 88% observation area by utilizing the features of sawtooth crash. The result is self-consistent and consistent with calibrated 1D ECE measurement.