Single marker analysis for leaf gas exchange traits from RILS of RD 23 (O. sativa L.) and O. longistaminata.

Rice is frequently affected by drought. However, economic water usage by the crop less impacted the stress. Its improvement should thus rely on assessing and utilizing the genetic bases of Carbon balance and water use efficient traits. These days, sequence based analysis is widely used to identify the associated hotspot loci to a given trait of interest. For two cropping seasons, 135 Oryza sativa L./Oryza longistaminata RILs were phenotyped to four leaf physiological traits and single marker analysis was integrated to identify consistently and significantly correlated SNPs. Through the RADseq technique, 20,014 SNPs were identified from the phenotypically diversified lines and in particular, 20 SNPs were defined as significantly associated hotspot loci. This study therefore, implicated marker-trait associations for leaf physiological traits. And such significantly associated loci can be used as tools for marker assisted selection of the relatively drought tolerant and highly photosynthetic lines of perennial rice.

Comparative study of radiomics, tumor morphology, and clinicopathological factors in predicting overall survival of patients with rectal cancer before surgery.

We compared the ability of a radiomics model, morphological imaging model, and clinicopathological risk model to predict 3-year overall survival (OS) in 206 patients with rectal cancer who underwent radical surgery and had magnetic resonance imaging, clinicopathological, and OS data available. The patients were randomized to a training cohort (n = 146) and a verification cohort (n = 60). Radiomics features were extracted from preoperative T2-weighted images, and a radiomics score model was constructed. Factors that were significant in the Cox multivariate analysis were used to construct the final morphological tumor model and clinicopathological model. A comprehensive model in the form of a line chart was established by combining the three models. Ten radiomics features significantly related to OS were selected to construct the radiomics feature model and calculate the radiomics score. In the morphological model, mesorectal extension depth and distance between the lower tumor margin and the anal margin were significant prognostic factors. N stage was the only significant clinicopathological factor. The comprehensive model combined with the above factors had the best prediction performance for OS. The C-index had a predictive performance of 0.872 (95% confidence interval [CI]: 0.832-0.912) in the training cohort and 0.944 (95% CI: 0.890-0.990) in the verification cohort, which was better than for any single model. The comprehensive model was divided into high-risk and low-risk groups. Kaplan-Meier curve analysis showed that all factors were significantly correlated with poor OS in the high-risk group. A comprehensive nomogram based on multi-model radiomics features can predict 3-year OS after rectal cancer surgery.