H7N9 virus has caused five infection waves since it emerged in 2013. The highest number of human cases was seen in wave 5; however, the underlying reasons have not been thoroughly elucidated. In this study, the geographical distribution, phylogeny, and genetic evolution of 240 H7N9 viruses in wave 5, including 35 new isolates from patients and poultry in nine provinces, were comprehensively analyzed together with strains from first four waves. Geographical distribution analysis indicated that the newly emerging highly pathogenic (HP) and low-pathogenicity (LP) H7N9 viruses were cocirculating, causing human and poultry infections across China. Genetic analysis indicated that dynamic reassortment of the internal genes among LP-H7N9/H9N2/H6Ny and HP-H7N9, as well as of the surface genes, between the Yangtze and Pearl River Delta lineages resulted in at least 36 genotypes, with three major genotypes (G1 [A/chicken/Jiangsu/SC537/2013-like], G3 [A/Chicken/Zhongshan/ZS/2017-like], and G11 [A/Anhui/40094/2015-like]). The HP-H7N9 genotype likely evolved from G1 LP-H7N9 by the insertion of a KRTA motif at the cleavage site (CS) and then evolved into 15 genotypes with four different CS motifs, including PKGR/G, PKGR/G, PKRR/G, and PKRR/G. Approximately 46% (28/61) of HP strains belonged to G3. Importantly, neuraminidase (NA) inhibitor (NAI) resistance (R292K in NA) and mammalian adaptation (e.g., E627K and A588V in PB2) mutations were found in a few non-human-derived HP-H7N9 strains. In summary, the enhanced prevalence and diverse genetic characteristics that occurred with mammalian-adapted and NAI-resistant mutations may have contributed to increased numbers of human infections in wave 5. The highest numbers of human H7N9 infections were observed during wave 5 from October 2016 to September 2017. Our results showed that HP-H7N9 and LP-H7N9 had spread virtually throughout China and underwent dynamic reassortment with different subtypes (H7N9/H9N2 and H6Ny) and lineages (Yangtze and Pearl River Delta lineages), resulting in totals of 36 and 3 major genotypes, respectively. Notably, the NAI drug-resistant (R292K in NA) and mammalian-adapted (e.g., E627K in PB2) mutations were found in HP-H7N9 not only from human isolates but also from poultry and environmental isolates, indicating increased risks for human infections. The broad dissemination of LP- and HP-H7N9 with high levels of genetic diversity and host adaptation and drug-resistant mutations likely accounted for the sharp increases in the number of human infections during wave 5. Therefore, more strategies are needed against the further spread and damage of H7N9 in the world.

Salinity is a major abiotic stress threatening crop production. Root-derived bacteria (RDB) are hypothesized to play a role in enhancing plant adaptability to various stresses. However, it is still unclear whether and how plants build up specific RDB when challenged by salinity. In this study, we measured the composition and variation in the rhizosphere and endophyte bacteria of salt-sensitive (SSs) and salt-resistant (SRs) plants under soil conditions with/without salinity. The salt-induced RDB (both rhizobiomes and endophytes) were isolated to examine their effects on the physiological responses of SSs and SRs to salinity challenge. Moreover, we examined whether functional redundancy exists among salt-induced RDB in enhancing plant adaptability to salt stress. We observed that although SSs and SRs recruited distinct RDB and relevant functions when challenged by salinity, salt-induced recruitment of specific RDB led to a consistent growth promotion in plants regardless of their salinity tolerance capacities. Plants employed a species-specific strategy to recruit beneficial soil bacteria in the rhizosphere rather than in the endosphere. Furthermore, we demonstrated that the consortium, but not individual members of the salt-induced RDB, provided enduring resistance against salt stress. This study confirms the critical role of salt-induced RDB in enhancing plant adaptability to salt stress.

Sheep-associated malignant catarrhal fever (SA-MCF), a frequently fatal disease primarily of certain ruminants, is caused by ovine herpesvirus 2 (OvHV-2). Molecular diagnosis of SA-MCF in affected animals has relied on detection of OvHV-2 DNA using a nested PCR, which has significant potential for amplicon contamination as a routine method in diagnostic laboratories. In this report, a nonnested and a previously developed real-time PCR were validated for detection of OvHV-2 DNA in samples from clinically affected animals. Three sets of blood or tissue samples were collected: 1) 97 samples from 97 naturally affected animals with evidence of clinical SA-MCF; 2) 200 samples from 8 animals with experimentally induced SA-MCF; and 3) 100 samples from 100 animals without any evidence of clinical SA-MCF. Among 97 positive samples defined by nested PCR from clinically affected animals, 95 (98%) were positive by nonnested PCR and 93 (96%) were positive by real-time PCR, respectively. One hundred percent of the samples from the animals with experimentally induced MCF were positive by real-time PCR, while 99% were positive by nonnested PCR. Neither nonnested PCR nor real-time PCR yielded a positive result on any of the 100 nested PCR-negative samples from animals without evidence of clinical MCF. The data confirmed that both nonnested and real-time PCR maintained high specificity and sensitivity for the detection of OvHV-2 DNA in clinical samples.

LncRNAs play pivotal roles in many important biological processes, but research on the functions of lncRNAs in human disease is still in its infancy. Therefore, it is urgent to prioritize lncRNAs that are potentially associated with diseases. In this work, we developed a novel algorithm, LncPriCNet, that uses a multi-level composite network to prioritize candidate lncRNAs associated with diseases. By integrating genes, lncRNAs, phenotypes and their associations, LncPriCNet achieves an overall performance superior to that of previous methods, with high AUC values of up to 0.93. Notably, LncPriCNet still performs well when information on known disease lncRNAs is lacking. When applied to breast cancer, LncPriCNet identified known breast cancer-related lncRNAs, revealed novel lncRNA candidates and inferred their functions via pathway analysis. We further constructed the human disease-lncRNA landscape, revealed the modularity of the disease-lncRNA network and identified several lncRNA hotspots. In summary, LncPriCNet is a useful tool for prioritizing disease-related lncRNAs and may facilitate understanding of the molecular mechanisms of human disease at the lncRNA level.

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In this article, an H 1 -Galerkin mixed finite element (MFE) method for solving time fractional reaction-diffusion equation is presented. The optimal time convergence order O(Δt 2−α ) and the optimal spatial rate of convergence in H 1 and L 2 -norms for variable u and its gradient σ are derived. Moreover, some numerical results are shown to support our theoretical analysis.

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