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Purpose: A novel implant model consisting of meso-scale cactus-inspired spikes and nano-scale bone-inspired trabeculae was recently developed to optimize meso-scale roughness on zirconia. In this model, the meso-spike dimension had a significant impact on osteoblast function. To explore how different nano-textures impact this model, here we examined the effect of different nano-trabecula sizes on osteoblast function while maintaining the same meso-spike conformation. Methods: Zirconia disks with meso-nano hybrid surfaces were created by laser etching. The meso-spikes were fixed to 40 µm high, whereas the nano-texture was etched as large and small trabeculae of average Feret diameter 237.0 and 134.1 nm, respectively. A polished surface was also prepared. Rat bone marrow-derived and human mesenchymal stromal cellinduced osteoblasts were cultured on these disks. Results: Hybrid rough surfaces, regardless of nano-trabecula dimension, robustly promoted the osteoblastic differentiation of both rat and human osteoblasts compared to those on polished surfaces. Hybrid surfaces with small nanotrabeculae further enhanced osteoblastic differentiation compared with large nano-trabeculae. However, the difference in osteoblastic differentiation between small and large nano-trabeculae was much smaller than the difference between the polished and hybrid rough surfaces. The nano-trabecula size did not influence osteoblast attachment and proliferation, or protein adsorption. Both hybrid surfaces were hydro-repellent. The atomic percentage of surface carbon was lower on the hybrid surface with small nano-trabeculae. Conclusions: Small nano-trabeculae promoted osteoblastic differentiation more than large nano-trabeculae when combined with meso-scale spikes. However, the biological impact of different nano-trabeculae was relatively small compared with that of different dimensions of meso-spikes. [ABSTRACT FROM AUTHOR]

We study the problem of processing subgraph queries on a database that consists of a set of graphs. The answer to a subgraph query is the set of graphs in the database that are supergraphs of the query. In this article, we propose an efficient index, [FG.sup.*]-index, to solve this problem. The cost of processing a subgraph query using most existing indexes mainly consists of two parts: the index probing cost and the candidate verification cost. Index probing is to find the query in the index, or to find the graphs from which we can generate a candidate answer set for the query. Candidate verification is to test whether each graph in the candidate set is indeed a supergraph of the query. We design [FG.sup.*]-index to minimize these two costs as follows. [FG.sup.*]-index consists of three components: the FG-index, the feature-index, and the FAQ-index. First, the FG-index employs the concept of Frequent subGraph (FG) to allow the set of queries that are FGs to be answered without candidate verification. We call this set of queries FG-queries. We can enlarge the set of FG-queries so that more queries can be answered without candidate verification; however, a larger set of FG-queries implies a larger FG-index and hence the index probing cost also increases. We propose the feature-index to reduce the index probing cost. The feature-index uses features to filter false results that are matched in the FG-index, so that we can quickly find the truly matching graphs for a query. For processing non-FG-queries, we propose the FAQ-index, which is dynamically constructed from the set of Frequently Asked non-FG-Queries (FAQs). Using the FAQ-index, verification is not required for processing FAQs and only a small number of candidates need to be verified for processing non-FG-queries that are not frequently asked. Finally, a comprehensive set of experiments verifies that query processing using [FG.sup.*]-index is up to orders of magnitude more efficient than state-of-the-art indexes and it is also more scalable. Categories and Subject Descriptors: H.2.4 [Database Management]: Systems--Query processing General Terms: Algorithms, Experimentation, Performance Additional Key Words and Phrases: Graph databases, graph indexing, graph query processing, frequent subgraphs

We propose a new problem of correlation mining from graph databases, called Correlated Graph Search (CGS). CGS adopts Pearson's correlation coefficient as the correlation measure to take into account the occurrence distributions of graphs. However, the CGS problem poses significant challenges, since every subgraph of a graph in the database is a candidate, but the number of subgraphs is exponential. We derive two necessary conditions that set bounds on the occurrence probability of a candidate in the database. With this result, we devise an efficient algorithm that mines the candidate set from a much smaller projected database, and thus, we are able to obtain a significantly smaller set of candidates. Three heuristic rules are further developed to refine the candidate set. We also make use of the bounds to directly answer high-support queries without mining the candidates. Our experimental results demonstrate the efficiency of our algorithm. Finally, we show that our algorithm provides a general solution when most of the commonly used correlation measures are used to generalize the CGS problem. Index Terms--Correlation, graph databases, Pearson's correlation coefficient.

We study mining correlations from quantitative databases and show that this is a more effective approach than mining associations to discover useful patterns. We propose the novel notion of quantitative correlated pattern (QCP), which is founded on two formal concepts, mutual information and all-confidence. We first devise a normalization on mutual information and apply it to the problem of QCP mining to capture the dependency between the attributes. We further adopt all-confidence as a quality measure to ensure, at a finer granularity, the dependency between the attributes with specific quantitative intervals. We also propose an effective supervised method that combines the consecutive intervals of the quantitative attributes based on mutual information, such that the interval-combining is guided by the dependency between the attributes. We develop an algorithm, QCoMine, to mine QCPs efficiently by utilizing normalized mutual information and all-confidence to perform bilevel pruning. We also identify the redundancy existing in the set of QCPs and propose effective techniques to eliminate the redundancy. Our extensive experiments on both real and synthetic datasets verify the efficiency of QCoMine and the quality of the QCPs. The experimental results also justify the effectiveness of our proposed techniques for redundancy elimination. To further demonstrate the usefulness and the quality of QCPs, we study an application of QCPs to classification. We demonstrate that the classifier built on the QCPs achieves higher classification accuracy than the state-of-the-art classifiers built on association rules. Categories and Subject Descriptors: H.2.8 [Database Management]: Database Applications-Data mining General Terms: Algorithms Additional Key Words and Phrases: Quantitative databases, correlated patterns, information-theoretic approach, mutual information