Laparoscopy is an effective way to improve perioperative outcomes and reduce the complications of hemodynamically stable patients with abdominal trauma. It is worth further popularization in clinical practice.
Acid rain can directly or indirectly affect plant physiological functions, especially photosynthesis. The enzyme ATP synthase is the key in photosynthetic energy conversion, and thus, it affects plant photosynthesis. To clarify the mechanism by which acid rain affects photosynthesis, we studied the effects of acid rain on plant growth, photosynthesis, chloroplast ATP synthase activity and gene expression, chloroplast ultrastructure, intracellular H(+) level, and water content of rice seedlings. Acid rain at pH 4.5 remained the chloroplast structure unchanged but increased the expression of six chloroplast ATP synthase subunits, promoted chloroplast ATP synthase activity, and increased photosynthesis and plant growth. Acid rain at pH 4.0 or less decreased leaf water content, destroyed chloroplast structure, inhibited the expression of six chloroplast ATP synthase subunits, decreased chloroplast ATP synthase activity, and reduced photosynthesis and plant growth. In conclusion, acid rain affected the chloroplast ultrastructure, chloroplast ATPase transcription and activity, and P n by changing the acidity in the cells, and thus influencing the plant growth and development. Finally, the effects of simulated acid rain on the test indices were found to be dose-dependent.
Recent generative adversarial network based methods have shown promising results for the charming but challenging task of synthesizing images from text descriptions. These approaches can generate images with general shape and color but often produce distorted global structures with unnatural local semantic details. It is due to ineffectiveness of convolutional neural networks in capturing the high-level semantic information for pixel-level image synthesis. In this paper, we propose a Dual Attentional Generative Adversarial Network (DualAttn-GAN) in which the dual attention modules are introduced to enhance local details and global structures by attending to related features from relevant words and different visual regions. As one of the dual modules, the textual attention module is designed to explore the fine-grained interaction between vision and language. On the other hand, visual attention module models internal representations of vision from channel and spatial axes, which can better capture the global structures. Meanwhile, we apply an attention embedding module to merge multi-path features. Furthermore, we present an inverted residual structure to boost representation power of CNNs and apply spectral normalization to stabilize GAN training. With extensive experimental validation on two benchmark datasets, our method significantly improves stateof-the-art models over the evaluation metrics of inception score and Fréchet inception distance.INDEX TERMS Generative adversarial network, textual attention, visual attention, inverted residual structure, spectral normalization.
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