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The data and experiences in mitigating harmful algal blooms (HABs) by modified clay (MC) show that a bloom does not continue after the dispersal of the MC, even though the density of the residual cells in the water is still high, at 20-30% of the initial cell density. This interesting phenomenon indicates that in addition to flocculation, MC has an additional control mechanism. Here, transcriptome sequencing technology was used to study the molecular mechanism of MC in controlling HABs. In residual cells treated with MC, the photosynthetic light reaction was the most affected physiological process. Some genes related to the light harvesting complex, photosystem (PS) I and PS II, were significantly up-regulated ( p < 0.05), and several transcripts increased by as much as 6-fold. In contrast, genes associated with the dark reaction did not significantly change. In addition to genes associated with photosynthesis, numerous genes related to energy metabolism, stress adaptation, cytoskeletal functioning, and cell division also responded to MC treatment. These results indicated that following treatment with MC, the normal physiological processes of algal cells were disrupted, which inhibited cell proliferation and growth. Thus, these findings provide scientific proof that HABs are controlled by MC.

Maturity degree and quality evaluation are important for strawberry harvest, trade, and consumption. Deep learning has been an efficient artificial intelligence tool for food and agro-products. Hyperspectral imaging coupled with deep learning was applied to determine the maturity degree and soluble solids content (SSC) of strawberries with four maturity degrees. Hyperspectral image of each strawberry was obtained and preprocessed, and the spectra were extracted from the images. One-dimension residual neural network (1D ResNet) and three-dimension (3D) ResNet were built using 1D spectra and 3D hyperspectral image as inputs for maturity degree evaluation. Good performances were obtained for maturity identification, with the classification accuracy over 84% for both 1D ResNet and 3D ResNet. The corresponding saliency maps showed that the pigments related wavelengths and image regions contributed more to the maturity identification. For SSC determination, 1D ResNet model was also built, with the determination of coefficient (R2) over 0.55 of the training, validation, and testing sets. The saliency maps of 1D ResNet for the SSC determination were also explored. The overall results showed that deep learning could be used to identify strawberry maturity degree and determine SSC. More efforts were needed to explore the use of 3D deep learning methods for the SSC determination. The close results of 1D ResNet and 3D ResNet for classification indicated that more samples might be used to improve the performances of 3D ResNet. The results in this study would help to develop 1D and 3D deep learning models for fruit quality inspection and other researches using hyperspectral imaging, providing efficient analysis approaches of fruit quality inspection using hyperspectral imaging.

A neural network model is constructed based on Van Allen Probes observations to predict the dynamic plasmapause location. The model parameterized by AE or Kp without inclusion of other parameters shows good accuracy to predict the plasmapause location. Our neural network model is capable of predicting the global plasmapause location with low RMSE.

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Increasing evidence suggests that the endophytic fungus Piriformospora indica helps plants overcome various abiotic stresses, especially heavy metals. However, the mechanism of heavy metal tolerance has not yet been elucidated. Here, the role of P. indica in alleviating cadmium (Cd) toxicities in tobacco was investigated. It was found that P. indica improved Cd tolerance to tobacco, increasing Cd accumulation in roots but decreasing Cd accumulation in leaves. The colonization of P. indica altered the subcellular repartition of Cd, increasing the Cd proportion in cell walls while reducing the Cd proportion in membrane/organelle and soluble fractions. During Cd stress, P. indica significantly enhanced the peroxidase (POD) activity and glutathione (GSH) content in tobacco. The spatial distribution of GSH was further visualized by Raman spectroscopy, showing that GSH was distributed in the cortex of P. indica-inoculated roots while in the epidermis of the control roots. A LC-MS/MS-based label-free quantitative technique evaluated the differential proteomics of P. indica treatment vs. control plants under Cd stress. The expressions of peroxidase, glutathione synthase, and photosynthesis-related proteins were significantly upregulated. This study provided extensive evidence for how P. indica enhances Cd tolerance in tobacco at physiological, cytological, and protein levels.