Ectopically, expression of defined factors could reprogram mammalian somatic cells into induced pluripotent stem cells (iPSCs), which initiates a new strategy to obtain pluripotent stem cell lines. Attempts have been made to generate buffalo pluripotent stem cells by culturing primary germ cells or inner cell mass, but the efficiency is extremely low. Here, we report a successful method to reprogram buffalo fetal fibroblasts (BFFs) into pluripotent stem cells [buffalo induced pluripotent stem cell (biPSCs)] by transduction of buffalo defined factors (Oct4, Sox2, Klf4, and c-Myc) using retroviral vectors. The established biPSCs displayed typical morphological characteristics of pluripotent stem cells, normal karyotype, positive staining of alkaline phosphatase, and expressed pluripotent markers including Oct4, Sox2, Nanog, Lin28, E-Cadherin, SSEA-1, SSEA-4, TRA-1-81, STAT3, and FOXD3. They could form embryoid bodies (EBs) in vitro and teratomas after injecting into the nude BALB/C mice, and 3 germ layers were identified in the EBs and teratomas. Methylation assay revealed that the promoters of Oct4 and Nanog were hypomethylated in biPSCs compared with BFFs and pre-biPSCs, while the promoters of Sox2 and E-Cadherin were hypomethylated in both BFFs and biPSCs. Further, inhibiting p53 expression by coexpression of SV40 large T antigen and buffalo defined factors in BFFs or treating BFFs with p53 inhibitor pifithrin-a (PFT) could increase the efficiency of biPSCs generation up to 3-fold, and nuclear transfer embryos reconstructed with biPSCs could develop to blastocysts. These results indicate that BFFs can be reprogrammed into biPSCs by buffalo defined factors, and the generation efficiency of biPSCs can be increased by inhibition of p53 expression. These efforts will provide a feasible approach for investigating buffalo stem cell signal pathways, establishing buffalo stem cell lines, and producing genetic modification buffaloes in the future.

Incomplete epigenetic reprogramming of donor cell nuclei is one of the main contributors to the low efficiency of somatic cell nuclear transfer (SCNT). To improve the success of SCNT, somatic cell DNA methylation levels must be reduced to those levels found in totipotent embryonic cells. Recent studies have demonstrated that miR-148a can affect DNA methylation via DNMT1 modulation in various cancers. Therefore, the focus of this study was to examine the influence of miR-148a on DNA methylation in donor cells and in SCNT embryo development. Thus, a stable cell line overexpressing miR-148a was established and used to produce SCNT embryos. Upon examination, DNMT1 was found to be a miR-148a target in porcine fetal fibroblasts (PFF). Furthermore, miR-148a overexpression in PFFs significantly decreased DNMT1 expression and global DNA methylation levels (P < 0.05). Moreover, miRNA-148a expression levels in SCNT embryos were significantly lower at the 2-cell and 4-cell stages when compared to IVF and parthenogenetic embryos. The group overexpressing miRNA-148a also showed a significant increase in blastocyst formation and total cell numbers (P < 0.05). Additionally, miR-148a overexpression altered the immunofluorescence signal of 5-mC and H3K9ac, and enhanced pluripotent gene (Oct4 and Nanog) expression levels during embryo development. These results indicate that miR-148a overexpression enhances the developmental potential of SCNT embryos and modifies epigenetic status.

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With the upsurge in use of Unmanned Aerial Vehicles (UAVs), drone detection and pose estimation by using optical sensors becomes an important research subject in cooperative flight and low-altitude security. The existing technology only obtains the position of the target UAV based on object detection methods. To achieve better adaptability and enhanced cooperative performance, the attitude information of the target drone becomes a key message to understand its state and intention, e.g., the acceleration of quadrotors. At present, most of the object 6D pose estimation algorithms depend on accurate pose annotation or a 3D target model, which costs a lot of human resource and is difficult to apply to non-cooperative targets. To overcome these problems, a quadrotor 6D pose estimation algorithm was proposed in this paper. It was based on keypoints detection (only need keypoints annotation), relational graph network and perspective-n-point (PnP) algorithm, which achieves state-of-the-art performance both in simulation and real scenario. In addition, the inference ability of our relational graph network to the keypoints of four motors was also evaluated. The accuracy and speed were improved significantly compared with the state-of-the-art keypoints detection algorithm.

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