Expression, purification, and enzymatic characterization of an extracellular protease from Halococcus salifodinae.

Extracellular proteases from halophilic archaea displays increased enzymatic activities in hypersaline environment. In this study, an extracellular protease-coding gene, hly34, from the haloarchaeal strain Halococcus salifodinae PRR34, was obtained through homologous search. The protease activity produced by this strain at 20% NaCl, 42 °C, and pH 7.0 was 32.5 ± 0.5 (U·mL-1). The codon-optimized hly34 which is specific for Escherichia coli can be expressed in E. coli instead of native hly34. It exhibits proteolytic activity under a wide range of low- or high-salt concentrations, slightly acidic or alkaline conditions, and slightly higher temperatures. The Hly34 presented the highest proteolytic activity at 50 °C, pH 9.0, and 0-1 M NaCl. It was found that the Hly34 showed a higher enzyme activity under low-salt conditions. Hly34 has good stability at different NaCl concentrations (1-4 M) and pH (6.0-10.0), as well as good tolerance to some metal ions. However, at 60 °C, the stability is reduced. It has a good tolerance to some metal ions. The proteolytic activity was completely inhibited by phenylmethanesulfonyl fluoride, suggesting that the Hly34 is a serine protease. This study further deepens our understanding of haloarchaeal extracellular protease, most of which found in halophilic archaea are classified as serine proteases. These proteases exhibit a certain level of alkaline resistance and moderate heat resistance, and they may emerge with higher activity under low-salt conditions than high-salt conditions. The protease Hly34 is capable of degrading a number of proteins, including substrate proteins, such as azocasein, whey protein and casein. It has promising applications in industrial production.

Hly176B, a low-salt tolerant halolysin from the haloarchaeon Haloarchaeobius sp. FL176.

Extracellular proteases of haloarchaea can adapt to high concentrations of NaCl and can find useful applications in industrial or biotechnology processes where hypersaline conditions are desired. The diversity of extracellular proteases produced by haloarchaea is largely unknown though the genomes of many species have been sequenced and are publicly available. In this study, a gene encoding the extracellular protease Hly176B from the haloarchaeon Haloarchaeobius sp. FL176 was cloned and expressed in Escherichia coli. A related gene homolog to hly176B, hly176A, from the same strain was also expressed in E.coli, but did not show any proteinase activity after the same renaturation process. Therefore, we focus on the enzymatic properties of the Hly176B. The catalytic triad Asp-His-Ser was confirmed via site-directed mutagenesis, indicating that Hly176B belongs to the class of serine proteases (halolysin). Unlike previously reported extracellular proteases from haloarchaea, the Hly176B remained active for a relatively long time in an almost salt-free solution. In addition, the Hly176B displayed prominent tolerance to some metal ions, surfactants and organic solvents, and exerts its highest enzyme activity at 40 °C, pH 8.0 and 0.5 M NaCl. Therefore, this study enriches our knowledge of extracellular proteases and expands their applications for various industrial uses.

An Efficient Retrieval System Framework for Fabrics Based on Fine-Grained Similarity.

In the context of "double carbon", as a traditional high energy consumption industry, the textile industry is facing the severe challenges of energy saving and emission reduction. To improve production efficiency in the textile industry, we propose the use of content-based image retrieval technology to shorten the fabric production cycle. However, fabric retrieval has high requirements for results, which makes it difficult for common retrieval methods to be directly applied to fabric retrieval. This paper presents a novel method for fabric image retrieval. Firstly, we define a fine-grained similarity to measure the similarity between two fabric images. Then, a convolutional neural network with a compact structure and cross-domain connections is designed to narrow the gap between fabric images and similarities. To overcome the problems of probabilistic missing and difficult training in classical hashing, we introduce a variational network module and structural module into the hashing model, which is called DVSH. We employ list-wise learning to perform similarity embedding. The experimental results demonstrate the superiority and efficiency of the proposed hashing model, DVSH.

Online Detection of Fabric Defects Based on Improved CenterNet with Deformable Convolution.

The traditional manual defect detection method has low efficiency and is time-consuming and laborious. To address this issue, this paper proposed an automatic detection framework for fabric defect detection, which consists of a hardware system and detection algorithm. For the efficient and high-quality acquisition of fabric images, an image acquisition assembly equipped with three sets of lights sources, eight cameras, and a mirror was developed. The image acquisition speed of the developed device is up to 65 m per minute of fabric. This study treats the problem of fabric defect detection as an object detection task in machine vision. Considering the real-time and precision requirements of detection, we improved some components of CenterNet to achieve efficient fabric defect detection, including the introduction of deformable convolution to adapt to different defect shapes and the introduction of i-FPN to adapt to defects of different sizes. Ablation studies demonstrate the effectiveness of our proposed improvements. The comparative experimental results show that our method achieves a satisfactory balance of accuracy and speed, which demonstrate the superiority of the proposed method. The maximum detection speed of the developed system can reach 37.3 m per minute, which can meet the real-time requirements.

Fabric Retrieval Based on Multi-Task Learning.

Due to the potential values in many areas such as e-commerce and inventory management, fabric image retrieval, which is a special case in Content Based Image Retrieval (CBIR), has recently become a research hotspot. It is also a challenging issue with serval obstacles: variety and complexity of fabric appearance, high requirements for retrieval accuracy. To address this issue, this paper proposes a novel approach for fabric image retrieval based on multi-task learning and deep hashing. According to the cognitive system of fabric, a multi-classification-task learning model with uncertainty loss and constraint is presented to learn fabric image representation. Then we adopt an unsupervised deep network to encode the extracted features into 128-bits hashing codes. Further, the hashing codes are regarded as the index of fabrics image for image retrieval. To evaluate the proposed approach, we expanded and upgraded the dataset WFID, which was built in our previous research specifically for fabric image retrieval. The experimental results show that the proposed approach outperforms the state-of-the-art.

Automatic inspection of density in yarn-dyed fabrics by utilizing fabric light transmittance and Fourier analysis.

Yarn density measurement is a significant part of yarn-dyed fabric analysis, traditionally based on reflective image analysis. In this paper, utilizing fabric light transmittance, a method for two-dimensional discrete Fourier transform (2D DFT) analysis on the transmission fabric image is developed for fabric density inspection. First, the power spectrum is generated from the fabric image by a 2D DFT. Next, the yarn skew angles are detected based on the power spectrum analysis. Then the fabric image is reconstructed by an inverse 2D DFT. Finally, projection curves are generated from the reconstructed images and the number of yarns is counted according to the peaks and valleys to obtain the fabric density. Through a comparison between analysis on the reflective and transmission images of multiple-color fabrics, it is proved that the latter method can segment the yarns with more satisfactory accuracy. Furthermore, the experimental and theoretical analyses demonstrate that the proposed method is effective for the density inspection of yarn-dyed fabrics with good robustness and great accuracy.