Porcine reproductive and respiratory syndrome virus (PRRSV), one of the most economically significant pathogens worldwide, has caused numerous outbreaks during the past 30 years. PRRSV infection causes reproductive failure in sows and respiratory disease in growing and finishing pigs, leading to huge economic losses for the swine industry. This impact has become even more significant with the recent emergence of highly pathogenic PRRSV strains from China, further exacerbating global food security. Since new PRRSV variants are constantly emerging from outbreaks, current strategies for controlling PRRSV have been largely inadequate, even though our understanding of PRRSV virology, evolution and host immune response has been rapidly expanding. Meanwhile, practical experience has revealed numerous safety and efficacy concerns for currently licensed vaccines, such as shedding of modified live virus (MLV), reversion to virulence, recombination between field strains and MLV and failure to elicit protective immunity against heterogeneous virus. Therefore, an effective vaccine against PRRSV infection is urgently needed. Here, we systematically review recent advances in PRRSV vaccine development. Antigenic variations resulting from PRRSV evolution, identification of neutralizing epitopes for heterogeneous isolates, broad neutralizing antibodies against PRRSV, chimeric virus generated by reverse genetics, and novel PRRSV strains with interferon-inducing phenotype will be discussed in detail. Moreover, techniques that could potentially transform current MLV vaccines into a superior vaccine will receive special emphasis, as will new insights for future PRRSV vaccine development. Ultimately, improved PRRSV vaccines may overcome the disadvantages of current vaccines and minimize the PRRS impact to the swine industry.

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The ability to decompose complex multi-object scenes into meaningful abstractions like objects is fundamental to achieve higher-level cognition. Previous approaches for unsupervised object-oriented scene representation learning are either based on spatial-attention or scene-mixture approaches and limited in scalability which is a main obstacle towards modeling real-world scenes. In this paper, we propose a generative latent variable model, called SPACE, that provides a unified probabilistic modeling framework that combines the best of spatial-attention and scene-mixture approaches. SPACE can explicitly provide factorized object representations for foreground objects while also decomposing background segments of complex morphology. Previous models are good at either of these, but not both. SPACE also resolves the scalability problems of previous methods by incorporating parallel spatial-attention and thus is applicable to scenes with a large number of objects without performance degradations. We show through experiments on Atari and 3D-Rooms that SPACE achieves the above properties consistently in comparison to SPAIR, IODINE, and GENESIS. Results of our experiments can be found on our project website: https://sites.google.com/view/space-project-page

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Nowadays, Unmanned Aerial Vehicles (UAVs) have received growing popularity in the Internet-of-Things (IoT) which often deploys many sensors in a relatively wide region. Since the battery capacity is limited, sensors cannot transmit over a long distance. It is necessary for designing efficient sensor data collection mechanisms to prolong the lifetime of the IoT and enhance data collection efficiency. In this paper, we consider a UAV-enabled data collection scenario, where multiple heterogeneous UAVs with different energy constraints are employed to collect data from sensors. The value of data depends on the importance of the monitoring area of the sensor and the freshness of collected data. Our objective is to maximize the data collection utility by jointly optimizing the communication scheduling and trajectory of each UAV. The data collection utility is determined by the amount and value of the collected data. This problem is a variant of multiple knapsack problem, which is a classical NP-hard problem. First, we transform the initial problem into a submodular function maximization problem under energy constraints, and then we design a novel trajectory planning algorithm to maximize the data collection utility, while accounting for different values of data and different energy constraints of heterogeneous UAVs. Finally, under different network settings, the performance of the proposed trajectory planning algorithm is evaluated via extensive simulations. The results show that the proposed algorithm can obtain maximum data collection utility.

Six swine coronaviruses (SCoVs), which include porcine epidemic diarrhea virus (PEDV), transmissible gastroenteritis virus (TGEV), porcine hemagglutination encephalomyelitis virus (PHEV), porcine respiratory coronavirus (PRCV), swine acute diarrhea syndrome coronavirus (SADS-CoV), and porcine delta coronavirus (PDCoV), have been reported as infecting and causing serious diseases in pigs. To investigate the genetic diversity and spatial distribution of SCoVs in clinically healthy pigs in China, we collected 6400 nasal swabs and 1245 serum samples from clinically healthy pigs at slaughterhouses in 13 provinces in 2017 and pooled them into 17 libraries by type and region for next-generation sequencing (NGS) and metavirome analyses. In total, we identified five species of SCoVs, including PEDV, PDCoV, PHEV, PRCV, and TGEV. Strikingly, PHEV was detected from all the samples in high abundance and its genome sequences accounted for 75.28% of all coronaviruses, while those belonging to TGEV (including PRCV), PEDV, and PDCoV were 20.4%, 2.66%, and 2.37%, respectively. The phylogenetic analysis showed that two lineages of PHEV have been circulating in pig populations in China. We also recognized two PRCVs which lack 672 nucleotides at the N-terminus of the S gene compared with that of TGEV. Together, we disclose preliminarily the genetic diversities of SCoVs in clinically healthy pigs in China and provide new insights into two SCoVs, PHEV and PRCV, that have been somewhat overlooked in previous studies in China.

Porcine reproductive and respiratory syndrome virus (PRRSV) is an important pathogen that causes huge losses economically to the pig industry worldwide. Previous research suggested that receptor dependence is necessary for PRRSV infection. MYH9 and CD163 are indispensable for PRRSV entry into a porcine alveolar macrophage. In the present study, human MYH9 (hMYH9) and mouse MYH9 (mMYH9), similar to swine MYH9, could also accelerate PRRSV infection in pCD163-mediated cell lines. Knockdown of MYH9 activity using the specific small interfering RNA or inhibitor (blebbistatin) concomitantly decreased PRRSV infection. C-terminal fragment of MYH9 (PRA) proteins from different mammalian species contains a conserved binding domain (aa1676-1791) for PRRSV binding, since the recombinant MYH91676−1791protein could inhibit the PRRSV infection significantly. Furthermore, the specific polyclonal antibody of MYH91676−1791 could block PRRSV infection in host cells. These data strongly supported that MYH9, a very important cofactor, participated in PRRSV entry into target cells, which may facilitate the development of a new therapeutic agent to control PRRSV infection.