Volatile nitriles are present in cigarette smoke. We tested the hypothesis that the presence of any of four nitriles in the blood can serve as a marker of recent cigarette smoking. We determined the sensitivity and specificity of these nitriles as indicators of daily cigarette smoking in 24 smokers (Group A) and 18 non-smokers (Group B), as well as the correlation between intensity of daily smoking and the blood concentration of acetonitrile. A new head space GLC assay method was used. Of the four nitriles, only acetonitrile was present in the blood of any study subject. Acetonitrile was moderately sensitive (67%) and entirely specific (100%) for self-reported daily smoking. There was fair correlation between blood acetonitrile concentration and the average daily number of cigarettes smoked (r2=0.39; P=0.001), and the mean blood acetonitrile concentration was significantly higher (P=0.03) among subjects with higher ( > 10 cigarettes per day) current cigarette exposure (148.3 ± 18.0 ?g/l) than among smokers with low or minimal (1-10 cigarettes per day) exposure (43.3 ± 6.0 ?g/l). Thus, acetonitrile in blood appears to be highly specific and a moderately sensitive marker of cigarette smoking with a dose-effect relationship. As such, acetonitrile shows promise as a marker of current cigarette exposure.

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The indexing and retrieval of multimedia content is generally implemented by employing feature graphs. These graphs typically contain a significant number of nodes and edges to reflect the level of detail in feature detection. A higher level of detail increases the effectiveness of the results, but also leads to more complex graph structures. However, graph traversal-based algorithms for similarity are quite inefficient and computationally expensive, especially for large data structures. To deliver fast and effective retrieval especially for large multimedia collections and multimedia big data, an efficient similarity algorithm for large graphs in particular is desirable. Hence, in this paper, we define a graph projection into a 2D space (Graph Code) and the corresponding algorithms for indexing and retrieval. We show that calculations in this space can be performed more efficiently than graph traversals due to the simpler processing model and the high level of parallelization. As a consequence, we demonstrate experimentally that the effectiveness of retrieval also increases substantially, as the Graph Code facilitates more levels of detail in feature fusion. These levels of detail also support an increased trust prediction, particularly for fused social media content. In our mathematical model, we define a metric triple for the Graph Code, which also enhances the ranked result representations. Thus, Graph Codes provide a significant increase in efficiency and effectiveness, especially for multimedia indexing and retrieval, and can be applied to images, videos, text and social media information.

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