Xist is an X-linked ribonucleic acid (RNA) gene responsible for the cis induction of X chromosome inactivation (XCI). In cloned mammalian embryos, Xist is ectopically activated at the morula to blastocyst stage on the X chromosome that is supposed to be active, thus resulting in abnormal XCI. Suppression of erroneous Xist expression by injecting small interfering RNA (siRNA) remarkably increased the developmental efficiency of cloned male mouse embryos by approximately 10-fold. However, injection of anti-Xist siRNA resulted in only a slight increase in the developmental ability of injected cloned male pig embryos because the blocking effect of the injected siRNA was not maintained beyond the morula stage, which is 5 days post-activation. To develop a more effective approach for suppressing the ectopic expression of Xist in cloned pig embryos, we compared the silencing effect of short hairpin RNA (shRNA) and siRNA on Xist expression and the effects of these two Xist knockdown methods on the developmental competence of cloned male pig embryos. Results indicated that an shRNA-based RNA interference (RNAi) has a longer blocking effect on Xist expression than an siRNA-mediated RNAi. Injection of anti-Xist shRNA plasmid into two-cell-stage cloned male pig embryos effectively suppressed Xist expression, rescued XCI at the blastocyst stage, and improved the in vitro developmental ability of injected cloned embryos. These positive effects, however, were not observed in cloned male pig embryos injected with anti-Xist siRNA. This study demonstrates that vector-based rather than siRNA-mediated RNAi of Xist expression can be employed to improve pig cloning efficiency.

Porcine epidemic diarrhea (PED) is a severe contagious intestinal disease caused by the porcine epidemic diarrhea virus (PEDV), which leads to high mortality in piglets. In this study, by analyzing a total of 53 full-length spike genes and COE domain regions of PEDVs, the conserved COE fragment of the spike protein from the dominant strain SC1402 was chosen as the target protein and expressed successfully in Pichia pastoris (P. pastoris). Furthermore, an indirect enzyme-linked immunosorbent assay (iELISA) based on the recombinant COE protein was developed for the detection of anti-PEDV antibodies in pig sera. The results showed that under the optimized conditions, the cut-off value of COE-based indirect ELISA (COE-iELISA) was determined to be 0.12. Taking the serum neutralization test as standard, the relative sensitivity of the COE-iELISA was 94.4% and specificity 92.6%. Meanwhile, no cross-reactivity to other porcine pathogens was noted with this assay. The intra-assay and inter-assay coefficients of variation were less than 7%. Moreover, 164 vaccinated serum samples test showed that overall agreement between COE-iELISA and the actual diagnosis result was up to 99.4%. More importantly, the developed iELISA exhibited a 95.08% agreement rate with the commercial ELISA kit (Kappa value = 0.88), which suggested that the expressed COE protein was an effective antigen in serologic tests and the established COE-iELISA is reliable for monitoring PEDV infection in pigs or vaccine effectiveness.

Abstract-With the requirements of the power load predicting accuracy becoming higher and higher, and the actual power load is also a complex nonlinear system limited by various uncertain factors. Given only considering the influence of a single model on the load forecasting, the predicting accuracy is absolutely not high under the comprehensive effects of multiple factors. Ensuring to make the electrical model satisfy the requirements, there is access to improving the accuracy of load predicting results through analysis of the relationship between load and multiple factors in power load forecasting. This paper combines the rough set theory with information fusion, firstly a decision table of attributes is established on the basis of historical datum, for the purpose of simplifying the attribute index in data mining to obtain important information. Following step is to rank the importance of the factors influencing the load according to the degree of effect, with grouping these sorted factors into attributes combination and using the same kind of load forecasting method to predict each combination's own results, and finally all groups of the predicting results are integrated by data fusion, where then final predicting result can be obtained. The results of the algorithmic example show that groups of predicting results integrated based on information fusion theory are better than the results got through any casual single attribute combination. And fusion results are a combined action of a variety of related factors, enhancing the favorable trend, where at the same time it has reduced the uncertainty brought by multiple factor comprehensive effect. Based on information fusion the predicting results are helpful to reduce the prediction error, which is feasible and effective.