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Here, we report a high-quality draft genome of a Chinese radish (Raphanus sativus) cultivar. This draft contains 387.73 Mb of assembled scaffolds, 83.93% of the scaffolds were anchored onto nine pseudochromosomes and 95.09% of 43 240 protein-coding genes were functionally annotated. 184.75 Mb (47.65%) of repeat sequences was identified in the assembled genome. By comparative analyses of the radish genome against 10 other plant genomes, 2 275 genes in 780 gene families were found unique to R. sativus. This genome is a good reference for genomic study and of great value for genetic improvement of radish.

As a common fungal disease, powdery mildew (PM) is one of the main diseases that harm the growth and development of cucumbers. Understanding the types of pathogenic fungus and analysis of the genetic and molecular mechanisms of cucumber resistance to PM at the molecular level are important when breeding disease-resistant varieties. The present review summarizes the hazards, prevention, and control of PM, and it discusses resistance inheritance rules, molecular markers, quantitative trait locus (QTL) mapping, gene cloning, omics, and gene editing technology, providing research insights on cucumber breeding varieties resistant to PM. [ABSTRACT FROM AUTHOR]

In recent years, pancreatogenic diabetes mellitus has garnered significant attention due to its high incidence, complications, and mortality rates. Glycemic variability (GV) can increase the risk of pancreatogenic diabetes mellitus and its associated complications; however, the precise mechanism remains unclear. The effective control of GV is crucial for preventing the onset of pancreatic diabetes mellitus and improving prognosis. Both diet and antidiabetic medications have substantial effects on GV. However, many patients are prescribed suboptimal or even harmful drugs. Therefore, to provide a comprehensive treatment basis for clinicians to prevent and treat pancreatogenic diabetes mellitus, this study aimed to elucidate the relationship between GV and pancreatogenic diabetes mellitus; investigate the potential mechanisms (such as oxidative stress, inflammatory response, insulin resistance, and lipid metabolism disorders); provide lifestyle guidance; and recommend drug selections to reduce the GV in patients with pancreatogenic diabetes mellitus. [ABSTRACT FROM AUTHOR]

To understand the spatial temporal distribution characteristics of Illex argentinus caught by trawl fishing vessels in the Southwestern Atlantic Ocean and their relationship with key marine environmental factors, this study analyzed the temporal and spatial changes in the fishing ground center of trawl vessels at the ten-day scale from December 2019 to May 2022, combining Chinese trawl fishing log data marine environmental data with satellite remote sensing marine environmental data. Utilizing the Maxent model, ten-day intervals were used as the temporal scale, and ten marine environmental factors, including sea surface temperature, sea surface height, sea surface salinity, chlorophyll concentration, temperature at 50 m and 100 m depth, and the meridional and zonal velocities of ocean currents were quantitatively analyzed to explore the correlation between the spatial distribution of catch and environmental factors. The study reveals that the trawl fishing grounds for Illex argentinus are divided into southern and northern grounds. The southern grounds first appear near 45°20′ S in December, gradually moving southeastward in February and March. The northern grounds do not appear until April, near 42° S in the high seas. On the ten-day time scale, the central fishing grounds of Illex argentinus show significant spatial variability but minor interannual differences. The Maxent model results indicate that sea surface temperature and chlorophyll a concentration are the key environmental factors influencing the spatial and temporal variability of the high seas trawl fishing grounds for most of the time, with high environmental contribution rates during the fishing season. While the range of suitable habitats with an HSI > 0.6 identified by the Maxent model varies significantly between years, a pattern is observed where the range expands at the start and end of the fishing season and contracts during the peak fishing season. This suggests that a more concentrated range of suitable habitats is conducive to accurate predictions of trawl fishing grounds, enabling efficient fishing operations. [ABSTRACT FROM AUTHOR]