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Porcine teschovirus (PTV) is a causative agent of polioencephalomyelitis, encephalomyelitis, reproductive disorders and gastrointestinal and respiratory diseases in swine. In the present study, the PTV2 GX/2020 strain was isolated from pig intestinal tissue through the use of ST cells. Phylogenetic analysis of VP1 nucleotide sequences indicated that the GX/2020 isolate is closely related to PTV2. Furthermore, the full-length cDNA of an infectious GX/2020 clone was constructed using seamless ligation technology. The genome sequence of the rescued virus is largely consistent with the sequence of the parental virus, and it exhibits viral growth properties. The PTV2 virus was successfully isolated in the present study, and the reverse-genetic platform provides a foundation for studies of the pathogenic mechanisms of porcine teschovirus.

The natural language processing community has had a major interest in auto-regressive [4, 13] and span-prediction based language models [7] recently, while knowledge graphs are often referenced for common-sense based reasoning and fact-checking models. In this paper, we present an equivalence representation of span-prediction based language models and knowledge-graphs to better leverage recent developments of language modelling for multi-modal problem statements. Our method performed well, especially with sentiment understanding for multi-modal inputs, and discovered potential bias in naturally occurring videos when compared with movie-data interaction-understanding. We also release a dataset of an auto-generated questionnaire with ground-truths consisting of labels spanning across 120 relationships, 99 sentiments, and 116 interactions, among other labels for finer-grained analysis of model comparisons in the community.

The biomedical sciences have experienced an explosion of data which promises to overwhelm many current practitioners. Without easy access to data science training resources, biomedical researchers may find themselves unable to wrangle their own datasets. In 2014, to address the challenges posed such a data onslaught, the National Institutes of Health (NIH) launched the Big Data to Knowledge (BD2K) initiative. To this end, the BD2K Training Coordinating Center (TCC; bigdatau.org) was funded to facilitate both in-person and online learning, and open up the concepts of data science to the widest possible audience. Here, we describe the activities of the BD2K TCC and its focus on the construction of the Educational Resource Discovery Index (ERuDIte), which identifies, collects, describes, and organizes online data science materials from BD2K awardees, open online courses, and videos from scientific lectures and tutorials. ERuDIte now indexes over 9,500 resources. Given the richness of online training materials and the constant evolution of biomedical data science, computational methods applying information retrieval, natural language processing, and machine learning techniques are required - in effect, using data science to inform training in data science. In so doing, the TCC seeks to democratize novel insights and discoveries brought forth via large-scale data science training.

Cell is the basic structural and fundamental unit of all organisms; the smallest structure capable of performing all the activities vital to life. One goal of current research interest is to learn how the muscle varies the strength of its contraction in response to electric stimuli. A wide variety of techniques have been developed to monitor the mechanical response of isolated cardiac myocytes. Some success has been reported either with the use of intact rat myocytes supported by suction micropipettes or in guinea pig myocytes adhering to glass beams. However, the usual measuring techniques exhibit destructive contact performance on live cells. They could not solve the problem, since the cell may die during or after the time-consuming attachment process at the beginning of each experiment. In contrast, a novel optical system, which consists of a microglass tube with an inner diameter the same size of a real cardiac cell, is proposed to simulate real cell's twitch process. the physical parameters of synthetic cell are well known. By comparing the dynamics of the real cell with that of the simulated cell, the twitch characteristics of the real cell can be measured.

Leukocyte rolling along endothelial cells is a critical step of leukocyte-endothelium interaction, which plays important roles in tissue inflammation and wound healing [1]. The occurrence of rolling results from the dynamic balance of hemodynamic shearing force acting on the cell and adhesive bond force between cell and endothelium, while the balance strongly depends on the leukocyte deformability [2]. The objective of this study is to elucidate the effects of (1) hydrodynamic shear stress, (2) cell deformation, and (3) surface adhesion strength on the rolling adhesion event through in vitro experiment and theoretical simulation.