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Background: G protein-coupled receptor (GPCR) signaling mediates a wide spectrum of physiological functions, including bone development and remodeling. Fibrous dysplasia (FD) is a common skeletal dysplasia where normal bone and bone marrow are replaced by fibrous tissue and expansile trabecular bone lesions. The craniofacial bones are often involved, leading to pain and facial deformities. FD is a mosaic disease caused by a somatic mutation in the GNAS gene encoding the G-protein alpha subunit (Gsα) that leads to constitutive activation of the Gs signaling pathway via increased cyclic AMP (cAMP) levels. Unfortunately, FD has no effective medical treatments.Major challenges have hampered the development of pharmacologic strategies that specifically target GNAS or the Gsα protein. We previously developed the ColI(2.3)+/Rs1+ mouse model (Rs1) in which the Gs signaling pathway is activated specifically in bone by an engineered GPCR protein. These mice showed increased trabecular bone formation with loss of marrow space and cortical bone, which strongly resembles human FD. There was also a dramatic increase in the number of immature osteoblasts present in the FD lesions, suggesting that activation of Gs signaling caused an accumulation of these cells. Our prior studies showed that the FD-like lesions had increased Wnt signaling, which may be a major driver of the phenotype. Furthermore, blocking the Gs signaling could reverse the abnormal bone phenotype, providing proof-of-concept for finding drugs that could reverse the phenotype. Methods: Long bones from 2 wildtype and 2 ColI(2.3)+/Rs1+ 9-week-old male mice were used to isolate stromal cells for scRNAseq analyses. Differentially expressed genes between clusters and between experimental groups within clusters were ascertained using Seurat. The cell types in each cluster were identified using SingleR to match gene expression in each cluster to existing databases. To test the role of Wnt signaling, we performed global Wnt inhibition on the ColI(2.3)+/Rs1+mouse model using the porcupine inhibitor LGK974. The mice were evaluated by histology and micro computed tomography (micro-CT). Finally, drug repositioning analysis was performed by comparing the molecular disease signature of FD we created from our single cell gene expression data. Our disease signature was queried against pharmacological agents in Broad Institute’s Connectivity Map database (CMap). Results: The cellular compositions between WT control and ColI(2.3)+/Rs1+ bones as identified from our scRNAseq data were similar, except that there was a significant increase in the total number of cells with an expansion of osteoblastic lineage cells. The osteoblast cluster also had the highest number of differential expression (DE) genes. Expression of Gi coupled receptors were increased, potentially as a compensatory mechanism for the strong activation of Gs-GPCR pathway induced by Rs1 expression. In addition, the scRNAseq data revealed activation of the GH/IGF1 signaling pathway in osteoblastic cells in the FD-like bone lesions. The scRNAseq data also identified broad expression of many Wnt ligands within the bone cells, including within the osteoblast cluster. Treatment with the broad Wnt production inhibitor of porcupine, LGK974, showed resorption of the abnormal FD bone; however, the fibrocellular infiltrate in the ColI(2.3)+/Rs1+ mice was still present. Drugs from the Connectivity Map database that have opposite gene expression patterns to FD were identified through our computational analysis and are candidates for testing as potential treatment of FD. Conclusions: This study uses a translational approach combining a unique mouse model of FD to elucidate the cellular interactions in FD-like lesions and create a novel gene expression disease signature of FD. We found that broad Wnt inhibition can lead to decreased fibrous dysplastic bone, but the fibrocellular infiltrate does not appear to fully reverse. These results provide new insight into understanding interactions between the Wnt and Gs signaling pathways in FD pathogenesis and bone formation. Together, this work expands our understanding of FD pathogenesis and helps identify potential strategies for treatment, laying a strong foundation for future research on GPCR signaling and bone development.

This paper presents a two-stage genetic mechanism for the migration-based load balance of virtual machine hosts (VMHs) in cloud computing. Previous methods usually assume this issue as a job-assignment optimization problem and only consider the current VMHs’ loads; however, without considering loads of VMHs after balancing, these methods can only gain limited effectiveness in real environments. In this study, two genetic-based methods are integrated and presented. First, performance models of virtual machines (VMs) are extracted from their creating parameters and corresponding performance measured in a cloud computing environment. The gene expression programming (GEP) is applied for generating symbolic regression models that describe the performance of VMs and are used for predicting loads of VMHs after load-balance. Secondly, with the VMH loads estimated by GEP, the genetic algorithm considers the current and the future loads of VMHs and decides an optimal VM-VMH assignment for migrating VMs and performing load-balance. The performance of the proposed methods is evaluated in a real cloud-computing environment, Jnet, wherein these methods are implemented as a centralized load balancing mechanism. The experimental results show that our method outperforms previous methods, such as heuristics and statistics regression.

In our previous study, media milling was found to significantly increase the content of diosgenin in aqueous yam suspensions via size reduction. This study is a continuation to understand the estrogenic activity of the media-milled yam using diosgenin as a reference material. We have examined the effects of the media-milled yam on estrogen level in a yeast strain expressing hERβ and the proliferation of two cancer cell lines (MCF-7 and Ishikawa). As a common form of estrogen, 17β-estradiol exhibited high estrogenic activity, and enhanced the growth of both cancer cell lines. Diosgenin also exhibited estrogenic activity, but inhibited the growth of both cancer cell lines. The media-milled yam yielded an estrogen equivalent of 1.65 × 10−4 g/g with a moderate inhibition on the growth of both cancer cell lines. The data indicated that diosgenin was the major compound for the estrogenic activity and inhibition on cancer cells exerted by the media-milled yam.

Early diagnosis of prostate cancer (PCa) is critical for the prevention of metastasis and for early treatment; therefore, a simple and accurate device must be developed for this purpose. In this study, we reported a novel fabrication method for producing a dual-modality biosensor that can simultaneously detect vascular endothelial growth factor (VEGF) and prostate-specific antigen (PSA) in human serum for early diagnosis of PCa. This biosensor was constructed by coating graphene oxide/ssDNA (GO-ssDNA) on an Au-electrode for VEGF detection, and incorporated with poly-L-lactide nanoparticles (PLLA NPs) for signal amplification and PSA detection. The results showed that this biosensor has wide liner detection ranges (0.05-100ng/mL for VEGF and 1-100ng/mL for PSA), as well as high levels of sensitivity and selectivity (i.e., resisting interference from external factors, such as glucose, ascorbic acid human serum protein, immunoglobulin G, and immunoglobulin M), and demonstrated a high correlation with an enzyme-linked immunosorbent assay for sample detection in patients. Therefore, this biosensor could be utilized for early clinical diagnosis of PCa in the future.

Purpose: Early diagnosis of prostate cancer (PCa) is essential for the prevention of metastasis and for early treatment; therefore, we aimed to develop a simple, accurate, and multi-analyte assay system for early PCa diagnosis in this study. Experimental design: We fabricated three kinds of biochips then integrated into microfluidic device for simultaneous detection of vascularendothelial growth factor (VEGF), prostate-specific antigen (PSA), and PCa circulating tumor cells (CTC) in human serum for accurate diagnosis of PCa. Then the integrated device can be put in the ELISA reader for signal analysis after sample incubation, no necessary of further fluorescence staining or microscopy counting. Result: The integrated device has wide liner detection ranges (0.05-25 ng/mL for both PSA and VEGF, and 5-300 cells/mL for PCa CTC), as well as high levels of sensitivity and selectivity, and demonstrated a high correlation with an enzyme-linked immunosorbent assay for sample detection in patients. Also, the presented biochips could maintain their stability when stored at 37°C for 49 days without significant differences in the red-shift (

Nano/submicrometer-scaled yam particles have been prepared by using media-milling. The particle size of media-milled yam was confirmed by the laser light scattering method and scanning electron microscopy. Influences of media-milled yam on skin fibroblast cells (WS1) were evaluated. The size reduction did not significantly alter the proximate composition, and the presence of nanoparticles was not toxic to WS1 cells. The contents of bioactive compounds (diosgenin, stigmasterol, and β-sitosterol) were significantly increased by media-milling, which enhanced the secretion of hTGF-β and inhibited the formation of MMP-1. Thus, the collagen secretion from WS1 was significantly increased by size reduction. Diosgenin was employed as a positive control. Nevertheless, media-milled yam exhibited greater effects on WS1 cells than diosgenin. It appeared that both diosgenin and size reduction were helpful for enhancing the secretion of collagen by WS1 cells. In addition, the irritancy of yam was eliminated by media-milling.

This paper presents the architecture as well as the circuit implementation of a wireless personal area networks (WPAN) modulator/demodulator using 2.45GHz band compliant with the physical layer standard of IEEE 802.15.4. A noncoherent demodulation scheme is employed to resolve the complexity and power dissipation problem, where a phase-shift down sampling method is adopted to detect the maximum phase accumulation which is the location of the correct data. A prototypical system on silicon with core area of 0.39mm^2 has been realized by using 0.18@mm CMOS process. The packet error rate (PER) is measured to be

Virtualization is a key technology for cloud-computing, which creates various types of virtual computing resources on physical machines. A center of virtual machine (VM) servers manages different load situations of servers and adjusts flexibly the consumptions of physical resources to achieve better cost-performance efficiency. One of the key problems in the management of VM servers (VMSs) is load prediction with which decisions for load-balance as well as other management issues can be engaged. This study employs genetic expression programming (GEP) for deriving regression models of load of VMSs. GEP regression models are “white-boxes” that have visible structures and can be modified and integrated with other VM management mechanisms. Data representing the types of VM resources, VM loads, etc., are collected for training GEP models. With the GEP models, one can predict the work load of VMSs so that precise decisions of load-balance can be made. The experimental results show that GEP can generate precise models for load prediction of VMSs than other methods.