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African Swine Fever (ASF) is a highly contagious viral haemorrhagic disease of swine, leading to enormous economic losses in the swine industry. However, vaccines and drugs to treat ASF have yet to be developed. African swine fever virus (ASFV) encodes more than 150 proteins, but 50% of them have unknown functions. Here, we present the crystal structure of the ASFV I73R protein at a resolution of 2.0 Å. Similar search tools based solely on amino acid sequence shows that it has no relationships to any proteins of known function. Interestingly, the overall structure of the I73R protein shares a winged helix‐turn‐helix fold, structural similarity with the Z‐DNA binding domain (Zα). In accordance with this result, the I73R is capable of binding to a CpG repeats DNA duplex, which has a high propensity for forming Z‐DNA during the DNA binding assays. In addition, the I73R protein was shown to be expressed at both early and late stages of ASFV post‐infection in PAM cells as an 8.9 kDa protein. Immunofluorescence studies revealed that the I73R protein is expressed in the nucleus at early times post‐infection and gradually translocated from the nucleus to the cytoplasm. Taken together, these data indicate that the I73R could be a member of Zα family that is important in host–pathogen interaction, which paves the way for the design of inhibitors to target this severe pathogen. Further exploring the biological role of I73R during ASFV infection in vitro and in vivo will provide new clues for development of new antiviral strategies. [ABSTRACT FROM AUTHOR]

The linguistic intuitionistic fuzzy sets (LIFSs) and linguistic Pythagorean fuzzy sets (LPFSs) are two linguistic orthopair fuzzy sets whose membership grades are pairs of linguistic terms from the predefined linguistic term sets (LTSs). One linguistic term indicates the membership degree (MD), while the other one gives the nonmembership degree (NMD). In each LIFS, the sum of the subscripts of MD and NMD is less than the cardinality of LTS. In the LPFSs, the sum of the squares of the subscripts of MD and NMD is less than the square of the cardinality of LTS. In this paper, we propose a general form of these two linguistic orthopair fuzzy sets, which can be named linguistic q‐rung orthopair fuzzy sets. We devise the operational laws, based on which, the linguistic q‐rung orthopair fuzzy weighted averaging (LqROFWA) operator and linguistic q‐rung orthopair fuzzy weighted geometric (LqROFWG) operator are developed to aggregate the linguistic q‐rung orthopair fuzzy numbers (LqROFNs). Then, the novel interactional operational laws that consider the interactions between the MD and NMD from different LqROFNs are given. The partitioned geometric Heronian mean (PGHM) operator can effectively solve the decision‐making problems in which the attributes grouped into the same clusters have interrelationships and the attributes belonging to different clusters have no interrelationship. Based on these novel operational laws and PGHM operator, the linguistic q‐rung orthopair fuzzy interactional PGHM (LqROFIPGHM) operator and linguistic q‐rung orthopair fuzzy interactional weighted PGHM (LqROFIWPGHM) operator are proposed and their properties are discussed. Based on the LqROFIWPGHM operator, an efficient multiattribute group decision‐making model is given to deal with the linguistic q‐rung orthopair fuzzy information. Finally, the superiorities of the interactional operational laws and LqROFIWPGHM operator are tested using some illustrative examples. [ABSTRACT FROM AUTHOR]

Privacy is an important concern in the society, and it has been a fundamental issue when to analyze and publish data involving human individual's sensitive information. Recently, the slicing method has been popularly used for privacy preservation in data publishing, because of its potential for preserving more data utility than others such as the generalization and bucketization approaches. However, in this paper, we show that the slicing method has disclosure risks for some absolute facts, which would help the adversary to find invalid records in the sliced microdata table, resulting in breach of individual privacy. To increase the privacy of published data in the sliced tables, a new method called value swapping is proposed in this work, aimed at decreasing the attribute disclosure risk for the absolute facts and ensuring the l-diverse slicing. By value swapping, the published table contains no invalid information such that the adversary cannot breach the individual privacy. Experimental results also show that the NEW method is able to keep more data utility than the existing slicing methods in a published microdata table. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]