The archaeal ATPase PINA interacts with the helicase Hjm via its carboxyl terminal KH domain remodeling and processing replication fork and Holliday junction.

PINA is a novel ATPase and DNA helicase highly conserved in Archaea, the third domain of life. The PINA from Sulfolobus islandicus (SisPINA) forms a hexameric ring in crystal and solution. The protein is able to promote Holliday junction (HJ) migration and physically and functionally interacts with Hjc, the HJ specific endonuclease. Here, we show that SisPINA has direct physical interaction with Hjm (Hel308a), a helicase presumably targeting replication forks. In vitro biochemical analysis revealed that Hjm, Hjc, and SisPINA are able to coordinate HJ migration and cleavage in a concerted way. Deletion of the carboxyl 13 amino acid residues impaired the interaction between SisPINA and Hjm. Crystal structure analysis showed that the carboxyl 70 amino acid residues fold into a type II KH domain which, in other proteins, functions in binding RNA or ssDNA. The KH domain not only mediates the interactions of PINA with Hjm and Hjc but also regulates the hexameric assembly of PINA. Our results collectively suggest that SisPINA, Hjm and Hjc work together to function in replication fork regression, HJ formation and HJ cleavage

XML Data Retrieval Model Based on Two-dimensional Table Datasets

Retrieval problems of XML-based representation of data have been researched in this paper. In order to solve the large time and space overhead problem in building content index, this paper establish a data retrieval model advantageous to xml representation using the system automatically build two-dimensional table datasets. Take crop diseases and insect pests data for an example, this paper first gives the architecture of retrieval system based on XML crop diseases and insect pests' data; it also discusses about how to construct the two-dimensional table dataset and achieve the retrieval process; then it describes the text segmentation technique and the XSL style sheet conversion technology. Finally, under the VS.NET platform, using MVC design pattern develop and implement a prototype

Trust Model in Cloud Computing Environment Based on Fuzzy Theory

Recent years have witnessed the development of cloud computing. However,there also come some security concerns in cloud computing environment, suchas emerging network attacks and intrusions, and instable cloud service provision dueto flexible cloud infrastructure and resources. To this end, we research on the trustedcomputing in cloud computing environment. Specifically, in this paper, we proposea trust model based on virtual machines, with two considerations. First, we introducetimeliness strategy to ensure the response time and also minimize the idle timeof servers. Second, we extend the linear trust chain by differentiating the trust ofthe platform domain and user domain. Besides, we develop a fuzzy theory basedmethod to calculate the trust value of cloud service providers. We also conduct someexperiments to evaluate our method

Protein functional module identification method combining topological features and gene expression data

Article conducting an intensive study on the problems of low recognition efficiency and noise in the overlapping structure of protein functional modules, based on topological characteristics of PPI network. Developing a protein function module recognition method ECTG based on Topological Features and Gene expression data for Protein Complex Identification. The experimental results show that the ECTG algorithm can detect protein functional modules better

Structural Basis of Competitive Recognition of p53 and MDM2 by HAUSP/USP7: Implications for the Regulation of the p53–MDM2 Pathway

Herpesvirus-associated ubiquitin-specific protease (HAUSP, also known as USP7), a deubiquitylating enzyme of the ubiquitin-specific processing protease family, specifically deubiquitylates both p53 and MDM2, hence playing an important yet enigmatic role in the p53–MDM2 pathway. Here we demonstrate that both p53 and MDM2 specifically recognize the N-terminal tumor necrosis factor–receptor associated factor (TRAF)–like domain of HAUSP in a mutually exclusive manner. HAUSP preferentially forms a stable HAUSP–MDM2 complex even in the presence of excess p53. The HAUSP-binding elements were mapped to a peptide fragment in the carboxy-terminus of p53 and to a short-peptide region preceding the acidic domain of MDM2. The crystal structures of the HAUSP TRAF-like domain in complex with p53 and MDM2 peptides, determined at 2.3-Å and 1.7-Å resolutions, respectively, reveal that the MDM2 peptide recognizes the same surface groove in HAUSP as that recognized by p53 but mediates more extensive interactions. Structural comparison led to the identification of a consensus peptide-recognition sequence by HAUSP. These results, together with the structure of a combined substrate-binding-and-deubiquitylation domain of HAUSP, provide important insights into regulation of the p53–MDM2 pathway by HAUSP

MSFCA-Net: A Multi-Scale Feature Convolutional Attention Network for Segmenting Crops and Weeds in the Field

Weed control has always been one of the most important issues in agriculture. The research based on deep learning methods for weed identification and segmentation in the field provides necessary conditions for intelligent point-to-point spraying and intelligent weeding. However, due to limited and difficult-to-obtain agricultural weed datasets, complex changes in field lighting intensity, mutual occlusion between crops and weeds, and uneven size and quantity of crops and weeds, the existing weed segmentation methods are unable to perform effectively. In order to address these issues in weed segmentation, this study proposes a multi-scale convolutional attention network for crop and weed segmentation. In this work, we designed a multi-scale feature convolutional attention network for segmenting crops and weeds in the field called MSFCA-Net using various sizes of strip convolutions. A hybrid loss designed based on the Dice loss and focal loss is used to enhance the model’s sensitivity towards different classes and improve the model’s ability to learn from hard samples, thereby enhancing the segmentation performance of crops and weeds. The proposed method is trained and tested on soybean, sugar beet, carrot, and rice weed datasets. Comparisons with popular semantic segmentation methods show that the proposed MSFCA-Net has higher mean intersection over union (MIoU) on these datasets, with values of 92.64%, 89.58%, 79.34%, and 78.12%, respectively. The results show that under the same experimental conditions and parameter configurations, the proposed method outperforms other methods and has strong robustness and generalization ability

Research on Practice Problems and Strategies for Professional Master's Degree Candidates for Agriculture

Taking the practice status of professional master's degree candidates for agriculture in Anhui Agricultural University as an example, this paper analyzes the key factors affecting the practical training of graduate students in this major, and puts forward some strategies for improving the practice quality of professional master's degree candidates. In terms of training professional master's degree candidates, it is considered that agricultural colleges and universities should start with collaborative education, supervision and assessment mechanism, practice content and form, make full use of a large number of agricultural enterprise resources owned by colleges and universities and a good cooperation foundation in enterprises, so as to achieve the purpose of improving the practice quality of professional master's degree candidates