Preharvest Sprouting in Quinoa: A New Screening Method Adapted to Panicles and GWAS Components.

The introduction of quinoa into new growing regions and environments is of interest to farmers, consumers, and stakeholders around the world. Many plant breeding programs have already started to adapt quinoa to the environmental and agronomic conditions of their local fields. Formal quinoa breeding efforts in Washington State started in 2010, led by Professor Kevin Murphy out of Washington State University. Preharvest sprouting appeared as the primary obstacle to increased production in the coastal regions of the Pacific Northwest. Preharvest sprouting (PHS) is the undesirable sprouting of seeds that occurs before harvest, is triggered by rain or humid conditions, and is responsible for yield losses and lower nutrition in cereal grains. PHS has been extensively studied in wheat, barley, and rice, but there are limited reports for quinoa, partly because it has only recently emerged as a problem. This study aimed to better understand PHS in quinoa by adapting a PHS screening method commonly used in cereals. This involved carrying out panicle-wetting tests and developing a scoring scale specific for panicles to quantify sprouting. Assessment of the trait was performed in a diversity panel (N = 336), and the resulting phenotypes were used to create PHS tolerance rankings and undertake a GWAS analysis (n = 279). Our findings indicate that PHS occurred at varying degrees across a subset of the quinoa germplasm tested and that it is possible to access PHS tolerance from natural sources. Ultimately, these genotypes can be used as parental lines in future breeding programs aiming to incorporate tolerance to PHS.

A classification method for soybean leaf diseases based on an improved ConvNeXt model.

Deep learning technologies have enabled the development of a variety of deep learning models that can be used to detect plant leaf diseases. However, their use in the identification of soybean leaf diseases is currently limited and mostly based on machine learning methods. In this investigation an enhanced deep learning network model was developed to recognize soybean leaf diseases more accurately. The improved network model consists of three parts: feature extraction, attention calculation, and classification. The dataset used was first diversified through data augmentation operations such as random masking to enhance network robustness. An attention module was then used to generate feature maps at various depths. This increased the network's focus on discriminative features, reduced background noise, and enabled the use of the LeakyReLu activation function in the attention module to prevent situations in which neurons fail to learn when the input is negative. Finally, the extracted features were then integrated using a fully connected layer, and the predicted disease category inferred to improve the classification accuracy of soybean leaf diseases. The average recognition accuracy of the improved network model for soybean leaf diseases was 85.42% both higher than the six deep learning comparison models (ConvNeXt (66.41%), ResNet50 (72.22%), Swin Transformer (77.00%), MobileNetV3 (67.27%), ShuffleNetV2 (59.89%), and SqueezeNet (72.92%)), thus proving the effectiveness of the improved method.The model proposed in this paper was also tested on the grapevine leaf dataset, and the performance ability of the improved network model remained due to other common network models, and overall the proposed network model was very effective in leaf disease identification.

[Research progress in prevention and treatment of radiation-induced intestinal injury by traditional Chinese medicine active components].

Radiation-induced intestinal injury(RIII), a common complication of radiotherapy for pelvic malignancies, affects the quality of life and the radiotherapy efficacy for cancer. Currently, the main clinical approaches for the prevention and treatment of RIII include drug therapy, hyperbaric oxygen therapy, and surgical treatment. Among these methods, drug therapy is cost-effective. Traditional Chinese medicine(TCM) containing a variety of active components demonstrates mild side effects and good efficacy in preventing and treating RIII. Studies have proven that TCM active components, such as flavonoids, terpenoids, phenylpropanoids, and alkaloids, can protect the intestine against RIII by inhibiting oxidative stress, regulating the expression of inflammatory cytokines, modulating the mitochondrial apoptosis pathway, adjusting intestinal flora, and suppressing cell apoptosis. These mechanisms can help alleviate the symptoms of RIII. The paper aims to provide a theoretical reference for the discovery of new drugs for the prevention and treatment of RIII by reviewing the literature on TCM active components in the last 10 years.