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Number of results: 12
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Abstract

Senecavirus A (SVA) the only member of the Senecavirus genus within the Picornaviridae family, is an emerging pathogen causing swine idiopathic vesicular disease and epidemic transient neonatal losses. Here, SVA strain (CH-HNKZ-2017) was isolated from a swine farm exhibiting vesicular disease in Henan Province of Central China. A phylogenetic analysis based on complete genome sequence indicated that CH-HNKZ-2017 was closely related to US-15-40381IA, indica- ting that a new SVA isolate had emerged in China.
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Abstract

In order to predict the distribution of shrinkage porosity in steel ingot efficiently and accurately, a criterion R√L and a method to obtain its threshold value were proposed. The criterion R√L was derived based on the solidification characteristics of steel ingot and pressure gradient in the mushy zone, in which the physical properties, the thermal parameters, the structure of the mushy zone and the secondary dendrite arm spacing were all taken into consideration. The threshold value of the criterion R√L was obtained with combination of numerical simulation of ingot solidification and total solidification shrinkage rate. Prediction of the shrinkage porosity in a 5.5 ton ingot of 2Cr13 steel with criterion R√L>0.21 m・℃1/2・s -3/2 agreed well with the results of experimental sectioning. Based on this criterion, optimization of the ingot was carried out by decreasing the height-to-diameter ratio and increasing the taper, which successfully eliminated the centreline porosity and further proved the applicability of this criterion.
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Abstract

Compared with the robots, humans can learn to perform various contact tasks in unstructured environments by modulating arm impedance characteristics. In this article, we consider endowing this compliant ability to the industrial robots to effectively learn to perform repetitive force-sensitive tasks. Current learning impedance control methods usually suffer from inefficiency. This paper establishes an efficient variable impedance control method. To improve the learning efficiency, we employ the probabilistic Gaussian process model as the transition dynamics of the system for internal simulation, permitting long-term inference and planning in a Bayesian manner. Then, the optimal impedance regulation strategy is searched using a model-based reinforcement learning algorithm. The effectiveness and efficiency of the proposed method are verified through force control tasks using a 6-DoFs Reinovo industrial manipulator.
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Abstract

Pseudorabies (PR) outbreaks have devastated many swine farms in several parts of China since late 2011. The outbreak-associated pseudorabies virus (PRV) variant strains exhibited some typical amino acid changes in glycoprotein E (gE), a diagnostic antigen used for discriminating between PRV-infected and vaccinated animals (DIVA). To counteract the potential impact of epitope variations on current serological diagnostics of PRV, we produced monoclonal antibodies (mAbs) against gE protein of one representative PRV variant strain and developed a blocking immunoperoxidase monolayer assay (b-IPMA) for DIVA. The b-IPMA was based on the inhibition of binding between PRV-infected cells and mAb by PRV-specific antibodies present in clinical swine sera and was validated by comparison with a commercial PRV gpI Antibody Test Kit (IDEXX Laboratories, USA). The diagnostic sensitivity, diagnostic specificity and agreement were determined to be 99.25%, 98.18% and 99.02% respectively upon testing 509 serum samples. b-IPMA detected only PRV-specific antibodies and showed no cross- -reactivity with antibodies elicited by gE-deleted vaccine or other common swine pathogens. Thus, b-IPMA has the potential to be used for high-throughput screening of PRV-infected animals in veterinary clinics.
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