"Zhenshan 97" is the female parent of a number of widely used hybrids for rice production in China. However, this line is of poor quality because of a high amylose content (AC), a hard gel consistency (GC) and a low gelatinization temperature (GT), together with a chalky endosperm. It had been determined that the three traits for cooking and eating quality, AC, GC and GT, are controlled by the Waxy locus and/or the tightly linked genomic region. In this study we improved the eating and cooking quality of Zhenshan 97 by introgressing the Waxy gene region from Minghui 63 (wx-MH), a restorer line, that has medium AC, soft GC and high GT. The wx-MH fragment was transferred to Zhenshan 97B by three backcrosses and one selfing, then from Zhenshan 97B to Zhenshan 97A by a cross and a backcross. Molecular marker-assisted selection was applied in the series to select for individuals carrying wx-MH, to identify recombination between the Waxy and flanking markers, and also to recover the genetic background of the recurrent parent. According to the marker genotypes, the improved versions of Zhenshan 97B and Zhenshan 97A, or Zhenshan 97B(wx-MH) and Zhenshan 97A(wx-MH), were the same as the originals except for the Waxy region of less than 6.1 cM in length. The selected lines and their hybrids with Minghui 63, or Shanyou 63(wx-MH), showed a reduced AC and an increased GC and GT, coupled with a reduced grain opacity. Field examinations of agronomic performance revealed that Zhenshan 97B(wx-MH) and Shanyou 63(wx-MH) were essentially the same as the originals except for a significant decrease in grain weight. The simultaneous improvement of AC, GA, GT and opacity, indicated that the Waxy region had major effects on the four quality traits. The improved versions of Zhenshan 97 A and B should be immediately useful in hybrid rice production.

Aflatoxin B1 (AFB1) is amongst the mycotoxins commonly affecting human and animal health, raising global food safety and control concerns. The mechanisms underlying AFB1 toxicity are poorly understood. Moreover, antidotes against AFB1 are lacking. Genome-wide CRISPR/Cas9 knockout screening in porcine kidney cells identified the transcription factor BTB and CNC homolog 1 (BACH1) as a gene required for AFB1 toxicity. The inhibition of BACH1 expression in porcine kidney cells and human hepatoma cells resulted in increased resistance to AFB1. BACH1 depletion attenuates AFB1-induced oxidative damage via the upregulation of antioxidant genes. Subsequently, virtual structural screening identified the small molecule 1-Piperazineethanol, α-[(1,3-benzodioxol-5-yloxy)methyl] -4-(2-methoxyphenyl) (M2) as an inhibitor of BACH1. M2 and its analogues inhibited AFB1-induced porcine and human cell death in vitro, while M2 administration significantly improved AFB1-induced symptoms of weight loss and liver injury in vivo. These findings demonstrate that BACH1 plays a central role in AFB1-induced oxidative damage by regulating antioxidant gene expression. We also present a potent candidate small-molecule inhibitor in developing novel treatments for AFB1 toxicity.

The research expects to evaluate the capital market risk and resource allocation ability of green credit business exploration based on neural network algorithm by deep learning in the context of the Internet of things, increase the funds flowing to green environmental protection industry, accelerate the development of real economy and stabilize China’s market economy. On the basis of previous studies, the research takes the credit business in the capital market as the research object, and improves the ability of resource allocation by optimizing the financial transaction structure. On this basis, through comparative analysis, the grey system model is implemented. back propagation neural network model under deep learning is used to evaluate the capital market risk of green credit business exploration, and the data of different provinces in China from 2009 to 2019 are taken as an example to verify. The model is used to measure the relationship between green credit business and industrial structure. Additionally, it also analyzes the main factors affecting the efficiency of green credit. The results show that green credit mainly affects the industrial structure through enterprise capital and financing channels. China’s overall green credit adjustment has had a significant upgrading effect on the industrial structure. The impact of green credit on industrial structure adjustment is different in the east, middle, and west regions. Optimizing the project capital structure, promoting seasonal financial transformation, setting up the function of innovation platform, and improving the internal governance structure of enterprises can improve financing efficiency and realize green and sustainable economic development in the future. The research results can provide a theoretical basis for the green development of China’s financial market and the application of deep learning neural network algorithm under the background of Internet of things.

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