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Drought stress impairs plant growth and other physiological functions. MeHDZ14, a homeodomain-leucine zipper I transcription factor, is strongly induced by drought stress in various cassava cultivars. However, the role of MeHDZ14 in cassava growth regulation has remained unclear. Here we report that MeHDZ14 affected plant height, such that a dwarf phenotype and altered internode elongation were observed in transgenic cassava lines. MeHDZ14 was found to negatively regulate the biosynthesis of lignin. Its overexpression resulted in abaxially rolled leaves. The morphogenesis of leaf epidermal cells was inhibited by overexpression of MeHDZ14, with decreased auxin and gibberellin and increased cytokinin contents. MeHDZ14 was found to regulate many drought-responsive genes, including genes involved in cell wall synthesis and expansion. MeHDZ14 bound to the promoter of caffeic acid 3-O-methyltransferase 1 (MeCOMT1), acting as a transcriptional repressor of genes involved in cell wall development. MeHDZ14 appears to act as a negative regulator of internode elongation and epidermal cell morphogenesis during cassava leaf development.

Morchella esculenta is a precious edible and medicinal fungus rich in protein, polysaccharides, polyphenols, amino acids, triterpenes, and other active components. In this study, MS-1 was isolated from the fruiting body of M. esculenta. Through conducting single-factor experiments and the response surface analysis of the culture conditions, the optimal culture components of an M. esculenta fermentation broth for extracellular polysaccharide production were determined, namely, 3.7% glucose, 2% yeast extract, and 0.15% sodium chloride. The polysaccharides MSF and MSL were extracted from the fruiting body of M. esculenta and the fermentation broth, respectively, and analyzed with gel permeation chromatography (GPC), monosaccharide composition, scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and in vivo and in vitro antioxidant and anti-inflammatory activity analyses. The research results show that the calculated MW of MSF is 2.34 × 105 Da, and the calculated MW of MSL is 1.40 × 105 Da. MSF is composed of three monosaccharides: D-galactose, D-glucose, and D-mannose (molar ratio of 4.34:90.22:5.45). MSL consists of five monosaccharides: D-arabinose, D-galactose, D-glucose, D-mannose, and glucuronic acid (molar ratio of 0.31:14.71:13.03:71.43:0.53). The in vitro antioxidant test results show that MSF and MSL both have significant antioxidant activities. Activity experiments on MSF and MSL in zebrafish showed that MSF and MSL have significant repair effects on the oxidative damage caused by metronidazole in zebrafish embryos, and there were significant changes in the transcriptional activity levels of the oxidative stress-related genes SOD, Keap1, and Nrf2. Therefore, the polysaccharides MSF and MSL from MS-1 can be used as important raw materials for functional foods and drugs.

Measurement(s) Clinical Data • Demographic Data • Comorbidity • drug administration • Follow-Up Information • heart failure Technology Type(s) Electronic Medical Record Factor Type(s) destination after hospital discharge Sample Characteristic - Organism Homo sapiens Sample Characteristic - Location China Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13064054

Achieving high-accuracy chicken face detection is a significant breakthrough for smart poultry agriculture in large-scale farming and precision management. However, the current dataset of chicken faces based on accurate data is scarce, detection models possess low accuracy and slow speed, and the related detection algorithm is ineffective for small object detection. To tackle these problems, an object detection network based on GAN-MAE (generative adversarial network-masked autoencoders) data augmentation is proposed in this paper for detecting chickens of different ages. First, the images were generated using GAN and MAE to augment the dataset. Afterward, CSPDarknet53 was used as the backbone network to enhance the receptive field in the object detection network to detect different sizes of objects in the same image. The 128×128 feature map output was added to three feature map outputs of this paper, thus changing the feature map output of eightfold downsampling to fourfold downsampling, which provided smaller object features for subsequent feature fusion. Secondly, the feature fusion module was improved based on the idea of dense connection. Then the module achieved feature reuse so that the YOLO head classifier could combine features from different levels of feature layers to capture greater classification and detection results. Ultimately, the comparison experiments’ outcomes showed that the mAP (mean average Precision) of the suggested method was up to 0.84, which was 29.2% higher than other networks’, and the detection speed was the same, up to 37 frames per second. Better detection accuracy can be obtained while meeting the actual scenario detection requirements. Additionally, an end-to-end web system was designed to apply the algorithm to practical applications.