No abstract

Two major isoforms of theHere, Runx2-II expression was found to be specifically stimulated by BMP-2 treatment or by Dlx5 overexpression. In addition, BMP-2, Dlx5, and Runx2-II were found to be expressed in osteogenic fronts and parietal bones of the developing cranial vault and Runx2-I and Msx2 in the sutural mesenchyme. Furthermore, Runx2 P1 promoter activity was strongly stimulated by Dlx5 overexpression, whereas Runx2 P2 promoter activity was not. Runx2 P1 promoter deletion analysis indicated that the Dlx5-specific response is due to sequences between ؊756 and ؊342 bp of the P1 promoter, where three Dlx5-response elements are located. Dlx5 responsiveness to these elements was confirmed by gel mobility shift assay and site-directed mutagenesis. Moreover, Msx2 specifically suppressed the Runx2 P1 promoter, and the responsible region overlaps with that recognized by Dlx5. In summary, Dlx5 specifically transactivates the Runx2 P1 promoter, and its action on the P1 promoter is antagonized by Msx2.The Runt-related transcription factor Runx2 plays an essential role in osteoblast differentiation and bone mineralization (1, 2). Two major isoforms are expressed from the mouse Runx2 locus, and these isoforms are generated by different promoter usage. Runx2 type I (Runx2-I), 2 referred to as the Cbfa1/p56 isoform or PEBP2␣A, is a 513-amino acid protein that starts with the amino acid sequence MRIPV (3) and is derived from the proximal P2 promoter of the gene (4). More recently, upstream exons of the Runx2 gene that potentially encode the N termini of Runx2 isoforms expressed in osteoblasts have been identified (5, 6). These upstream exons contain a 5Ј-untranslated region and encode the N-terminal 19 amino acids of Runx2 type II (Runx2-II; also referred to as Cbfa1/p57 and OSF2), which starts with the sequence MASNSL (7). This isoform is expressed from the P1 or "bone-related" upstream promoter (8), and its expression is predominant in osteoblasts (9). The alternative promoter usage strongly implies that the expression pattern of each isoform differs temporally and/or spatially. Indeed, they exhibit distinct expression patterns during bone development (10, 11). Thus, it is natural to assume that these two promoters differently respond to different extracellular signals or their downstream transcription factors because these promoters have distinct transcription factor-binding sites.Runx2 plays a central role in the BMP-2-induced trans-differentiation of C2C12 cells at an early restriction point by diverting them from the myogenic pathway to the osteogenic pathway (12, 13). We found that the homeobox gene Dlx5 is an upstream target of BMP-2 signaling and that it plays a pivotal role in stimulating the downstream osteogenic master transcription factor Runx2. In turn, Runx2 acts simultaneously or sequentially to induce the expression of bone-specific genes that represent BMP-2-induced osteogenic trans-differentiation. In addition, it has also been suggested that Dlx5 is a critical target of the inhibitory action of transform...

With the continued development of artificial intelligence (AI) technology, research on interaction technology has become more popular. Facial expression recognition (FER) is an important type of visual information that can be used to understand a human's emotional situation. In particular, the importance of AI systems has recently increased due to advancements in research on AI systems applied to AI robots. In this paper, we propose a new scheme for FER system based on hierarchical deep learning. The feature extracted from the appearance feature-based network is fused with the geometric feature in a hierarchical structure. The appearance feature-based network extracts holistic features of the face using the preprocessed LBP image, whereas the geometric feature-based network learns the coordinate change of action units (AUs) landmark, which is a muscle that moves mainly when making facial expressions. The proposed method combines the result of the softmax function of two features by considering the error associated with the second highest emotion (Top-2) prediction result. In addition, we propose a technique to generate facial images with neutral emotion using the autoencoder technique. By this technique, we can extract the dynamic facial features between the neutral and emotional images without sequence data. We compare the proposed algorithm with the other recent algorithms for CK+ and JAFFE dataset, which are typically considered to be verified datasets in the facial expression recognition. The tenfold cross validation results show 96.46% of accuracy in the CK+ dataset and 91.27% of accuracy in the JAFFE dataset. When comparing with other methods, the result of the proposed hierarchical deep network structure shows up to about 3% of the accuracy improvement and 1.3% of average improvement in CK+ dataset, respectively. In JAFFE datasets, up to about 7% of the accuracy is enhanced, and the average improvement is verified by about 1.5%. INDEX TERMS Artificial intelligence (AI), facial expression recognition (FER), emotion recognition, deep learning, LBP feature, geometric feature, convolutional neural network (CNN).

A hallmark of cancer cells is the metabolic switch from oxidative phosphorylation (OXPHOS) to glycolysis, a phenomenon referred to as the ‘Warburg effect’, which is also observed in primed human pluripotent stem cells (hPSCs). Here, we report that downregulation of SIRT2 and upregulation of SIRT1 is a molecular signature of primed hPSCs and that SIRT2 critically regulates metabolic reprogramming during induced pluripotency by targeting glycolytic enzymes including aldolase, glyceraldehyde-3-phosphate dehydrogenase, phosphoglycerate kinase, and enolase. Remarkably, knockdown of SIRT2 in human fibroblasts resulted in significantly decreased OXPHOS and increased glycolysis. In addition, we found that miR-200c-5p specifically targets SIRT2, downregulating its expression. Furthermore, SIRT2 overexpression in hPSCs significantly affected energy metabolism, altering stem cell functions such as pluripotent differentiation properties. Taken together, our results identify the miR-200c–SIRT2 axis as a key regulator of metabolic reprogramming (Warburg-like effect), via regulation of glycolytic enzymes, during human induced pluripotency and pluripotent stem cell function.