Dual-branch hybrid network for lesion segmentation in gastric cancer images.

The effective segmentation of the lesion region in gastric cancer images can assist physicians in diagnosing and reducing the probability of misdiagnosis. The U-Net has been proven to provide segmentation results comparable to specialists in medical image segmentation because of its ability to extract high-level semantic information. However, it has limitations in obtaining global contextual information. On the other hand, the Transformer excels at modeling explicit long-range relations but cannot capture low-level detail information. Hence, this paper proposes a Dual-Branch Hybrid Network based on the fusion Transformer and U-Net to overcome both limitations. We propose the Deep Feature Aggregation Decoder (DFA) by aggregating only the in-depth features to obtain salient lesion features for both branches and reduce the complexity of the model. Besides, we design a Feature Fusion (FF) module utilizing the multi-modal fusion mechanisms to interact with independent features of various modalities and the linear Hadamard product to fuse the feature information extracted from both branches. Finally, the Transformer loss, the U-Net loss, and the fused loss are compared to the ground truth label for joint training. Experimental results show that our proposed method has an IOU of 81.3%, a Dice coefficient of 89.5%, and an Accuracy of 94.0%. These metrics demonstrate that our model outperforms the existing models in obtaining high-quality segmentation results, which has excellent potential for clinical analysis and diagnosis. The code and implementation details are available at Github, https://github.com/ZYY01/DBH-Net/ .

Lesion Segmentation in Gastroscopic Images Using Generative Adversarial Networks.

The segmentation of the lesion region in gastroscopic images is highly important for the detection and treatment of early gastric cancer. This paper proposes a novel approach for gastric lesion segmentation by using generative adversarial training. First, a segmentation network is designed to generate accurate segmentation masks for gastric lesions. The proposed segmentation network adds residual blocks to the encoding and decoding path of U-Net. The cascaded dilated convolution is also added at the bottleneck of U-Net. The residual connection promotes information propagation, while dilated convolution integrates multi-scale context information. Meanwhile, a discriminator is used to distinguish the generated and real segmentation masks. The proposed discriminator is a Markov discriminator (Patch-GAN), which discriminates each [Formula: see text] matrix in the image. In the process of network training, the adversary training mechanism is used to iteratively optimize the generator and the discriminator until they converge at the same time. The experimental results show that the dice, accuracy, and recall are 86.6%, 91.9%, and 87.3%, respectively. These metrics are significantly better than the existing models, which proves the effectiveness of this method and can meet the needs of clinical diagnosis and treatment.

Problems with the microbial production of butanol.

With the incessant fluctuations in oil prices and increasing stress from environmental pollution, renewed attention is being paid to the microbial production of biofuels from renewable sources. As a gasoline substitute, butanol has advantages over traditional fuel ethanol in terms of energy density and hygroscopicity. A variety of cheap substrates have been successfully applied in the production of biobutanol, highlighting the commercial potential of biobutanol development. In this review, in order to better understand the process of acetone-butanol-ethanol production, traditional clostridia fermentation is discussed. Sporulation is probably induced by solvent formation, and the molecular mechanism leading to the initiation of sporulation and solventogenesis is also investigated. Different strategies are employed in the metabolic engineering of clostridia that aim to enhancing solvent production, improve selectivity for butanol production, and increase the tolerance of clostridia to solvents. However, it will be hard to make breakthroughs in the metabolic engineering of clostridia for butanol production without gaining a deeper understanding of the genetic background of clostridia and developing more efficient genetic tools for clostridia. Therefore, increasing attention has been paid to the metabolic engineering of E. coli for butanol production. The importation and expression of a non-clostridial butanol-producing pathway in E. coli is probably the most promising strategy for butanol biosynthesis. Due to the lower butanol titers in the fermentation broth, simultaneous fermentation and product removal techniques have been developed to reduce the cost of butanol recovery. Gas stripping is the best technique for butanol recovery found so far.