Identification of quantitative trait loci controlling nitrogen use efficiency-related traits in rice at the seedling stage under salt condition by genome-wide association study.

Rice cultivation is facing both salt intrusion and overuse of nitrogen fertilizers. Hence, breeding new varieties aiming to improve nitrogen use efficiency (NUE), especially under salt conditions, is indispensable. We selected 2,391 rice accessions from the 3K Rice Genomes Project to evaluate the dry weight under two N concentrations [2.86 mM - standard N (SN), and 0.36 mM - low N (LN)] crossed with two NaCl concentrations [0 (0Na) and 60 mM (60Na)] at the seedling stage. Genome-wide association studies for shoot, root, and plant dry weight (DW) were carried out. A total of 55 QTLs - 32, 16, and 7 in the whole, indica, and japonica panel - associated with one of the tested traits were identified. Among these, 27 QTLs co-localized with previously identified QTLs for DW-related traits while the other 28 were newly detected; 24, 8, 11, and 4 QTLs were detected in SN-0Na, LN-0Na, SN-60Na, and LN-60Na, respectively, and the remaining 8 QTLs were for the relative plant DW between treatments. Three of the 11 QTLs in SN-60Na were close to the regions containing three QTLs detected in SN-0Na. Eleven candidate genes for eight important QTLs were identified. Only one of them was detected in both SN-0Na and SN-60Na, while 5, 0, 3, and 2 candidate genes were identified only once under SN-0Na, LN-0Na, SN-60Na, and LN-60Na, respectively. The identified QTLs and genes provide useful materials and genetic information for future functional characterization and genetic improvement of NUE in rice, especially under salt conditions.

Age estimation based on 3D post-mortem computed tomography images of mandible and femur using convolutional neural networks.

Age assessment has attracted increasing attention in the field of forensics. However, most existing works are laborious and requires domain-specific knowledge. Modern computing power makes it is possible to leverage massive amounts of data to produce more reliable results. Therefore, it is logical to use automated age estimation approaches to handle large datasets. In this study, a fully automated age prediction approach was proposed by assessing 3D mandible and femur scans using deep learning. A total of 814 post-mortem computed tomography scans from 619 men and 195 women, within the age range of 20-70, were collected from the National Forensic Service in South Korea. Multiple preprocessing steps were applied for each scan to normalize the image and perform intensity correction to create 3D voxels that represent these parts accurately. The accuracy of the proposed method was evaluated by 10-fold cross-validation. The initial cross-validation results illustrated the potential of the proposed method as it achieved a mean absolute error of 5.15 years with a concordance correlation coefficient of 0.80. The proposed approach is likely to be faster and potentially more reliable, which could be used for age assessment in the future.