Aim To evaluate the association between different degrees of hyperglycaemia and the risk of all‐cause mortality among hospitalized patients with COVID‐19. Materials and Methods In a retrospective study conducted from 22 January to 17 March 2020, 453 patients were admitted to Union Hospital in Wuhan, China, with laboratory‐confirmed severe acute respiratory syndrome coronavirus 2 infection. Patients were classified into four categories: normal glucose, hyperglycaemia (fasting glucose 5.6‐6.9 mmol/L and/or HbA1c 5.7%‐6.4%), newly diagnosed diabetes (fasting glucose ≥7 mmol/L and/or HbA1c ≥6.5%) and known diabetes. The major outcomes included in‐hospital mortality, intensive care unit (ICU) admission and invasive mechanical ventilation (IMV). Results Patients with newly diagnosed diabetes constituted the highest percentage to be admitted to the ICU (11.7%) and require IMV (11.7%), followed by patients with known diabetes (4.1%; 9.2%) and patients with hyperglycaemia (6.2%; 4.7%), compared with patients with normal glucose (1.5%; 2.3%), respectively. The multivariable‐adjusted hazard ratios of mortality among COVID‐19 patients with normal glucose, hyperglycaemia, newly diagnosed diabetes and known diabetes were 1.00, 3.29 (95% confidence interval [CI] 0.65‐16.6), 9.42 (95% CI 2.18‐40.7) and 4.63 (95% CI 1.02‐21.0), respectively. Conclusion We showed that COVID‐19 patients with newly diagnosed diabetes had the highest risk of all‐cause mortality compared with COVID‐19 patients with known diabetes, hyperglycaemia and normal glucose. Patients with COVID‐19 need to be kept under surveillance for blood glucose screening.

Genomic selection is being used increasingly in plant breeding to accelerate genetic gain per unit time. One of the most important applications of genomic selection in maize breeding is to predict and select the best un-phenotyped lines in bi-parental populations based on genomic estimated breeding values. In the present study, 22 bi-parental tropical maize populations genotyped with low density SNPs were used to evaluate the genomic prediction accuracy (rMG) of the six trait-environment combinations under various levels of training population size (TPS) and marker density (MD), and assess the effect of trait heritability (h2), TPS and MD on rMG estimation. Our results showed that: (1) moderate rMG values were obtained for different trait-environment combinations, when 50% of the total genotypes was used as training population and ~200 SNPs were used for prediction; (2) rMG increased with an increase in h2, TPS and MD, both correlation and variance analyses showed that h2 is the most important factor and MD is the least important factor on rMG estimation for most of the trait-environment combinations; (3) predictions between pairwise half-sib populations showed that the rMG values for all the six trait-environment combinations were centered around zero, 49% predictions had rMG values above zero; (4) the trend observed in rMG differed with the trend observed in rMG/h, and h is the square root of heritability of the predicted trait, it indicated that both rMG and rMG/h values should be presented in GS study to show the accuracy of genomic selection and the relative accuracy of genomic selection compared with phenotypic selection, respectively. This study provides useful information to maize breeders to design genomic selection workflow in their breeding programs.

Methylation of Lys residues in the tail of the H3 histone is a key regulator of chromatin state and gene expression, conferred by a large family of enzymes containing an evolutionarily conserved SET domain. One of the main types of SET domain proteins are those controlling H3K4 di-and trimethylation. The genome of Arabidopsis (Arabidopsis thaliana) encodes 12 such proteins, including five ARABIDOPSIS TRITHORAX (ATX) proteins and seven ATX-Related proteins. Here, we examined three untilnow-unexplored ATX proteins, ATX3, ATX4, and ATX5. We found that they exhibit similar domain structures and expression patterns and are redundantly required for vegetative and reproductive development. Concurrent disruption of the ATX3, ATX4, and ATX5 genes caused marked reduction in H3K4me2 and H3K4me3 levels genome-wide and resulted in thousands of genes expressed ectopically. Furthermore, atx3/atx4/atx5 triple mutants resulted in exaggerated phenotypes when combined with the atx2 mutant but not with atx1. Together, we conclude that ATX3, ATX4, and ATX5 are redundantly required for H3K4 di-and trimethylation at thousands of sites located across the genome, and genomic features associated with targeted regions are different from the ATXR3/SDG2-controlled sites in Arabidopsis.