An F2 and an equivalent F3 population derived from a cross between a high salt-tolerance indica variety, Nona Bokra, and a susceptible elite japonica variety, Koshihikari, were produced. We performed QTL mapping for physiological traits related to rice salt-tolerance. Three QTLs for survival days of seedlings (SDSs) under salt stress were detected on chromosomes 1, 6 and 7, respectively, and explained 13.9% to 18.0% of the total phenotypic variance. Based on the correlations between SDSs and other physiological traits, it was considered that damage of leaves was attributed to accumulation of Na+ in the shoot by transport of Na+ from the root to the shoot in external high concentration. We found eight QTLs including three for three traits of the shoots, and five for four traits of the roots at five chromosomal regions, controlled complex physiological traits related to rice salt-tolerance under salt stress. Of these QTLs, the two major QTLs with the very large effect, qSNC-7 for shoot Na+ concentration and qSKC-1 for shoot K+ concentration, explained 48.5% and 40.1% of the total phenotypic variance, respectively. The QTLs detected between the shoots and the roots almost did not share the same map locations, suggesting that the genes controlling the transport of Na+ and K+ between the shoots and the roots may be different.

Aims and Objectives To determine the health‐related quality of life (HRQoL) of COVID‐19 patients after discharge and its predicting factors. Background COVID‐19 has caused a worldwide pandemic and led a huge impact on the health of human and daily life. It has been demonstrated that physical and psychological conditions of hospitalised COVID‐19 patients are impaired, but the studies focus on physical and psychological conditions of COVID‐19 patients after discharge from hospital are rare. Design A multicentre follow‐up study. Methods This was a multicentre follow‐up study of COVID‐19 patients who had discharged from six designated hospitals. Physical symptoms and HRQoL were surveyed at first follow‐up (the third month after discharge). The latest multiple laboratory findings were collected through medical examination records. This study was performed and reported in accordance with STROBE checklist. Results Three hundred eleven patients (57.6%) were reported with one or more physical symptoms. The scores of HRQoL of COVID‐19 patients at third month after discharge, except for the dimension of general health, were significantly lower than Chinese population norm ( p < .001). Results of logistic regression showed that female (odds ratio (OR): 1.79, 95% confidence interval (CI): 1.04–3.06), older age (≥60 years) (OR: 2.44, 95% CI: 1.33–4.47) and the physical symptom after discharge (OR: 40.15, 95% CI: 9.68–166.49) were risk factors for poor physical component summary; the physical symptom after discharge (OR: 6.68, 95% CI: 4.21–10.59) was a risk factor for poor mental component summary. Conclusions Health‐related quality of life of discharged COVID‐19 patients did not come back to normal at third month after discharge and affected by age, sex and the physical symptom after discharge. Relevance to clinical practice Healthcare workers should pay more attention to the physical and psychological rehabilitation of discharged COVID‐19 patients. Long‐term follow‐up on COVID‐19 patients after discharge is needed to determine the long‐term impact of COVID‐19.

Effect of genetic background on detection of quantitative trait locus (QTL) governing salinity tolerance (ST) was studied using two sets of reciprocal introgression lines (ILs) derived from a cross between a moderately salinity tolerant japonica variety, Xiushui09 from China, and a drought tolerant but salinity susceptible indica breeding line, IR2061-520-6-9 from the Philippines. Salt toxicity symptoms (SST) on leaves, days to seedling survival (DSS), and sodium and potassium uptake by shoots were measured under salinity stress of 140 mmol/L of NaCl. A total of 47 QTLs, including 26 main-effect QTLs (M-QTLs) and 21 epistatic QTLs (E-QTLs), were identified from the two sets of reciprocal ILs. Among the 26 M-QTLs, only four (15.4%) were shared in the reciprocal backgrounds while no shared E-QTLs were detected, indicating that ST QTLs, especially E-QTLs, were very specific to the genetic background. Further, 78.6% of the M-QTLs for SST and DSS identified in the reciprocal ILs were also detected in the recombinant inbred lines (RILs) from the same cross, which clearly brings out the background effect on ST QTL detection and its utilization in ST breeding. The detection of ILs with various levels of pyramiding of nonallelic M-QTL alleles for ST from Xiushui09 into IR2061-520-6-9 allowed us to further improve the ST in rice.