Bone regeneration materials (BRMs) bring us new sights into the clinical management bone defects. With advances in BRMs technologies, new strategies are emerging to promote bone regeneration. The aim of this study was to comprehensively assess the existing research and recent progress on BRMs, thus providing useful insights into contemporary research, as well as to explore potential future directions within the scope of bone regeneration therapy. A comprehensive literature review using formal data mining procedures was performed to explore the global trends of selected areas of research for the past 20 years. The study applied bibliometric methods and knowledge visualization techniques to identify and investigate publications based on the publication year (between 2002 and 2021), document type, language, country, institution, author, journal, keywords, and citation number. The most productive countries were China, United States, and Italy. The most prolific journal in the BRM field was Acta Biomaterialia, closely followed by Biomaterials. Moreover, recent investigations have been focused on extracellular matrices (ECMs) (370 publications), hydrogel materials (286 publications), and drug delivery systems (220 publications). Research hotspots related to BRMs and extracellular matrices from 2002 to 2011 were growth factor, bone morphogenetic protein (BMP)-2, and mesenchymal stem cell (MSC), whereas after 2012 were composite scaffolds. Between 2002 and 2011, studies related to BRMs and hydrogels were focused on BMP-2, in vivo, and in vitro investigations, whereas it turned to the exploration of MSCs, mechanical properties, and osteogenic differentiation after 2012. Research hotspots related to BRM and drug delivery were fibroblast growth factor, mesoporous materials, and controlled release during 2002–2011, and electrospinning, antibacterial activity, and in vitro bioactivity after 2012. Overall, composite scaffolds, 3D printing technology, and antibacterial activity were found to have an important intersection within BRM investigations, representing relevant research fields for the future. Taken together, this extensive analysis highlights the existing literature and findings that advance scientific insights into bone tissue engineering and its subsequent applications.
Due to the exponentially growing bioinformatics databases and rapidly popular of GPU for general purpose computing, it is promising to employ GPU techniques to accelerate the sequence search process. Hmmsearch from HMMER bioinformatics software package is a wildly used software tool for sensitive profile HMM (Hidden Markov Model) searches of biological sequence databases. In this paper, we implement a speculative hmmsearch implementation on NVIDIA Fermi GPU and apply various optimizations to it. We test the enhancements in our GPU implementation in order to demonstrate the effectiveness of optimization strategies. Result shows that our speculative hmmsearch implementation achieves up to 6.5x speedup over previous fast single-threaded SSE implementation.
BACKGROUND: Collagen receptors are characterized by binding to and being activated by collagens. We know little about the molecular mechanism by which the integrins and discoidin domains (DDRs) recognize collagen. OBJECTIVE: The aim of this study was to investigate the expression of two main collagen receptor subfamilies, integrins and DDRs, during osteogenic and chondrogenic differentiation of human mesenehymal stem cells (hMSCs). METHODS: Using qRT-PCR, Western blots and FACS, the levels of DDR1, DDR2, integrin subunits β1, α1, α2, α10 and α11 receptors on hMSCs, were assessed upon activation by collagen type I, as well as during osteogenic and chondrogenic differentiation. RESULTS: The expression of DDR2 and integrin α11β1 was altered compared with other receptors when the cells were cultured under undifferentiated conditions. During osteogenic and chondrogenetic differentiation, DDR2 and α11 were up-regulated during early stages (6 day) of osteogenesis and chondrogenesis, respectively. The expression and activation of DDR2 was concomitant with another receptor integrin subunit β1 during osteogenetic differentiation. CONCLUSIONS: The results suggested that DDR2 was more specific for osteogenesis than chondrogenesis, while integrin α11β1 was more specific in chondrogenesis. DDR2 and α11 may play a role in the regulation of osteogenesis and chondrogenesis based on the differential expression of these receptors during lineage-dependent changes.
In computational biology, technologies of protein database search become more and more indispensable for researchers. As the database size rapidly grows, the search complexity is improving, leading to an excessively long runtime. In this paper, we provide a novel parallel protein database search model based on distributed GPU clusters. The proposed model targets clusters composed of personal computers equipped with graphic processing unit (GPU) cards and can employ computing power of GPUs and CPUs in the cluster nodes. The workload distribution strategy is designed to take the performance of each node into account, which enables the model to work well with inhomogeneous cluster nodes with different hardware configurations. A hybrid alignment approach, an extension to our previous work, is used in our model to make global and local alignments done concurrently. The parallel program is realized with compute unified device architecture (CUDA) parallel framework and Microsoft message passing interface (MS-MPI). In the experiment, the model is tested on a small cluster composed of three personal computers. The results show that the proposed model can achieve a speedup up to 159.89 times over the serial counterpart when searching the Swiss-Prot database.
Purpose The purpose of this paper is to introduce and investigate social brokers who belong to and connect multiple groups in a social network. This paper also reveals the differential effects of innovative and following social brokers on content diffusion in terms of adoption timing, speed and size. Design/methodology/approach The paper collected field data related to 69,086 users on the largest social network platform in China and analysed their adoption behaviours of 2,492 pieces of content. Findings The analysis reveals that social brokers encourage content diffusion and accelerate the speed of content adoption in a social network. Specifically, following social brokers play a greater role than innovative social brokers in accelerating the speed of content adoption and expanding the size of content adoption. However, in the early stage of content diffusion within the social network, innovative social brokers could predict the success of content adoption more effectively than following social brokers. Research limitations/implications This research extends the current diffusion literature by introducing the social broker and examining the effect of social brokers on the process of content adoption. Practical implications The findings provide suggestions to marketing managers on how to improve the diffusion of marketing-related content, such as by seeding specific people – that is, social brokers – with content, so they can serve as content transmitters in marketing campaigns. In addition, the findings suggest that to optimise content adoption in a social network, managers should strategically target innovative social brokers or following social brokers at various stages of content seeding-based marketing campaigns. Originality/value To the best of the authors’ knowledge, this research is the first to test the effects of social brokers on content adoption and identify innovative and following social brokers. The findings enrich the literature on content marketing by providing new perspectives on social structures in social networks.
Many evidences have demonstrated that protein complexes are overlapping and hierarchically organized in PPI networks. Meanwhile, the large size of PPI network wants complex detection methods have low time complexity. Up to now, few methods can identify overlapping and hierarchical protein complexes in a PPI network quickly. In this paper, a novel method, called MCSE, is proposed based on λ-module and “seed-expanding.” First, it chooses seeds as essential PPIs or edges with high edge clustering values. Then, it identifies protein complexes by expanding each seed to a λ-module. MCSE is suitable for large PPI networks because of its low time complexity. MCSE can identify overlapping protein complexes naturally because a protein can be visited by different seeds. MCSE uses the parameter λ_th to control the range of seed expanding and can detect a hierarchical organization of protein complexes by tuning the value of λ_th. Experimental results of S. cerevisiae show that this hierarchical organization is similar to that of known complexes in MIPS database. The experimental results also show that MCSE outperforms other previous competing algorithms, such as CPM, CMC, Core-Attachment, Dpclus, HC-PIN, MCL, and NFC, in terms of the functional enrichment and matching with known protein complexes.
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