In order to solve the citrus peel resource waste problem and minimize the drawbacks of chemical extraction of pectin, a protopectinase-overproducing
strain CD-01 for pectin production was isolated from a pit soil dumped with perished orange in Changde City, Hunan Province
of China. The strain CD-01 had the same morphology and 28S rRNA gene sequence (FJ184995) as that of Aspergillus niger (ATCC 64028). It was thus identified and named as Aspergillus niger CD-01. The fermentation condition was optimized based on L9(34) orthogonal experimental design and the variances analyses. The results show that the optimal condition for producing pectin
is as follows: time 36 h, temperature 35 °C, pH 5, and urea as the nitrogen source. Under this condition, the pectin yield
can reach up to 24.5%. This shows a great potential of Aspergillus niger CD-01 in pectin extraction from citrus.
Foundation item: Projects(50621063, 50674101) supported by the National Natural Science Foundation of China 相似文献
Multimedia Tools and Applications - Supervised hashing has achieved better accuracy than unsupervised hashing in many practical applications owing to its use of semantic label information. However,... 相似文献
Person re-identification plays important roles in many practical applications. Due to various human poses, complex backgrounds and similarity of person clothes, person re-identification is still a challenging task. In this paper, we mainly focus on the robust and discriminative appearance feature representation and proposed a novel multi-appearance method for person re-identification. First, we proposed a deep feature fusion method and get the multi-appearance feature by combining two Convolutional Neural Networks. Then, in order to further enhance the representation of the appearance feature, the multi-part model was constructed by combining the whole body and the six body parts. Additionally, we optimized the feature extraction process by adding a pooling layer. Comprehensive and comparative experiments with the state-of-the-art methods over publicly available datasets demonstrated that the proposed method can get promising results.
相似性连接技术在数据清洗、数据集成等领域中具有重要意义,近年来引起了学术界的广泛关注.随着数据量的不断增大、数据处理实时性的要求逐渐提高以及处理器性能提升瓶颈的出现,传统的串行相似性连接方法已经不能满足当前大数据处理的需求.近些年,GPU作为协处理器在机器学习等领域取得了良好的加速效果,因此基于GPU的并行算法开始成为解决各类性能问题的有效解决方案.为此,提出了基于CPU-GPU异构体系的并行相似性连接方法.首先,方法使用GPU构建倒排索引,索引采用SoA(struct of arrays)结构,从而解决了传统索引结构在并行模式下读写效率低的问题.其次,针对串行算法的性能问题,提出基于过滤验证框架的并行双重长度过滤算法,其中利用前缀过滤和构建好的倒排索引提升过滤效果.方法中相似度精确计算验证过程使用CPU计算执行,从而充分利用CPU-GPU的异构计算资源.最后,在多个数据集上进行实验验证性能.通过与串行相似性连接算法进行对比,实验结果表明所提出方法相对于已有方法具有更好的过滤效果和更低的索引生成代价,并在相似性连接上具有更好的性能和良好的加速比. 相似文献