These findings suggest that attentional bias towards substance-related cues occurs in heroin dependents, although this bias might not be associated with the severity of drug-using behavior.
Dark septate endophytes (DSE) are typical root endophytes with the ability to enhance plant growth and tolerance to heavy metals, but the underlying mechanisms are unclear. Here, the physiological and molecular mechanisms of a DSE strain, Exophiala pisciphila, in mitigating cadmium (Cd, 20 mg/kg) toxicity in maize were investigated. Our results showed, under Cd stress, E. pisciphila inoculation enhanced the biomass of maize and reduced both inorganic and soluble forms of Cd (high toxicity) by 52.6% in maize leaves, which may be potentially contributing to Cd toxicity mitigation. Besides, E. pisciphila inoculation significantly affected the expression of genes involved in the signal transduction and polar transport of phytohormone, and then affected abscisic acid (ABA) and indole-3-acetic acid (IAA) contents in maize roots, which was the main reason for promoting maize growth. In addition, E. pisciphila also made a 27% increase in lignin content by regulating the expression of genes involved in the synthesis of it, which was beneficial to hinder the transport of Cd. In addition, E. pisciphila inoculation also activated glutathione metabolism by the up-regulation of genes related to glutathione S-transferase. This study helps to elucidate the functions of E. pisciphila under Cd stress, sheds light on the mechanism of detoxifying Cd and provides new insights into the protection of crops from heavy metals.
Pore feature is important for hardwood identification. But it's difficult to segment pores from wood cross-section images since pore, fiber and longitudinal parenchyma in the image are similar in shapes but different only in size, and the different hardwood species varies in the size of pores. In order to segment pores automatically without parameters set manually, it is necessary to design an adaptive algorithm which may be applied for all kinds of hardwood cross-section images. In the paper, an adaptive method is proposed to evaluate the optimal threshold of closed region area for pore segmentation. The method sorts all closed regions according to the area and classifies closed regions into two classes with maximum between-class variance method. We implements the method based on genetic algorithm to overcome the drawback of being time-consuming. Experiment on images of hardwood species shows that the threshold obtained by the genetic algorithm is very close to but more efficient than the ordinary enumeration algorithm. Moreover, with the obtained threshold majority of pores can be extracted except for some very small ones.
With the characteristic of high recognition rate and strong network robustness, convolutional neural network has now become the most mainstream method in the field of crop disease recognition. Aiming at the problems with insufficient numbers of labeled samples, complex backgrounds of sample images, and difficult extraction of useful feature information, a novel algorithm is proposed in this study based on attention mechanisms and convolutional neural networks for cassava leaf recognition. Specifically, a combined data augmentation strategy for datasets is used to prevent single distribution of image datasets, and then the PDRNet (plant disease recognition network) combining channel attention mechanism and spatial attention mechanism is proposed. The algorithm is designed as follows. Firstly, an attention module embedded in the network layer is deployed to establish remote dependence on each feature layer, strengthen the key feature information, and suppress the interference feature information, such as background noise. Secondly, a stochastic depth learning strategy is formulated to accelerate the training and inference of the network. And finally, a transfer learning method is adopted to load the pretrained weights into the model proposed in this study, with the recognition accuracy of the model enhanced by means of detailed parameter adjustments and dynamic changes in the learning rate. A large number of comparative experiments demonstrate that the proposed algorithm can deliver a recognition accuracy of 99.56% on the cassava disease image dataset, reaching the state-of-the-art level among CNN-based methods in terms of accuracy.
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