Estimation of indica-tropical japonica genome proportion in wide compatible restorer lines derived through inter sub specific hybridization and molecular diversity analysis among rice genotypes

The present investigation was carried out to estimate indica-tropical japonica genome proportion in two newly developed wide compatible restorer lines (RP6367 and RP6368) derived through indica-tropical japonica crosses and to study the molecular diversity among 12 rice genotypes namely, five promising indica restorers (RPHR1005, RPHR1096, IBL57, DR714-1-2 and Akshayadhan), two maintainers (IR58025B and APMS6B), two newly identified wide compatible restorer lines (RP6367and RP6368), two typical tropical japonica lines (IRGC66651 and IRGC66577) and one typical indica genotype (Nagina22) using 50 SSR markers and 45 InDELs. Out of the 95 markers, 54 were found to be polymorphic. The genotypic data of 54 polymorphic markers was used to estimate indica-tropical japonica genome proportion in derived lines and molecular diversity among 12 genotypes. The percentage of tropical japonica genome proportion in derived lines RP6367 and RP6368 was found to be 50% and 55.55% respectively. Our results suggest that the proportion of tropical japonica in hybrid rice parental lines is the key to select the best parents for efficient utilization of the heterosis present between indica and tropical japonica subspecies. Out of the 54 markers, 19 markers recorded PIC value above 0.5 and can be considered as highly informative and useful to study molecular genetic diversity. In this study, the SSRs primers showed higher PIC values compared to InDels. It is concluded that the use of highly polymorphic molecular markers detected in this study gives a better understanding of genetic relationship among closely related rice genotypes

A Novel Zernike Moment-Based Real-Time Head Pose and Gaze Estimation Framework for Accuracy-Sensitive Applications

A real-time head pose and gaze estimation (HPGE) algorithm has excellent potential for technological advancements either in human–machine or human–robot interactions. For example, in high-accuracy advent applications such as Driver’s Assistance System (DAS), HPGE plays a crucial role in omitting accidents and road hazards. In this paper, the authors propose a new hybrid framework for improved estimation by combining both the appearance and geometric-based conventional methods to extract local and global features. Therefore, the Zernike moments algorithm has been prominent in extracting rotation, scale, and illumination invariant features. Later, conventional discriminant algorithms were used to classify the head poses and gaze direction. Furthermore, the experiments were performed on standard datasets and real-time images to analyze the accuracy of the proposed algorithm. As a result, the proposed framework has immediately estimated the range of direction changes under different illumination conditions. We obtained an accuracy of ~85%; the average response time was 21.52 and 7.483 ms for estimating head poses and gaze, respectively, independent of illumination, background, and occlusion. The proposed method is promising for future developments of a robust system that is invariant even to blurring conditions and thus reaching much more significant performance enhancement