BackgroundCo-expression measures are often used to define networks among genes. Mutual information (MI) is often used as a generalized correlation measure. It is not clear how much MI adds beyond standard (robust) correlation measures or regression model based association measures. Further, it is important to assess what transformations of these and other co-expression measures lead to biologically meaningful modules (clusters of genes).ResultsWe provide a comprehensive comparison between mutual information and several correlation measures in 8 empirical data sets and in simulations. We also study different approaches for transforming an adjacency matrix, e.g. using the topological overlap measure. Overall, we confirm close relationships between MI and correlation in all data sets which reflects the fact that most gene pairs satisfy linear or monotonic relationships. We discuss rare situations when the two measures disagree. We also compare correlation and MI based approaches when it comes to defining co-expression network modules. We show that a robust measure of correlation (the biweight midcorrelation transformed via the topological overlap transformation) leads to modules that are superior to MI based modules and maximal information coefficient (MIC) based modules in terms of gene ontology enrichment. We present a function that relates correlation to mutual information which can be used to approximate the mutual information from the corresponding correlation coefficient. We propose the use of polynomial or spline regression models as an alternative to MI for capturing non-linear relationships between quantitative variables.ConclusionThe biweight midcorrelation outperforms MI in terms of elucidating gene pairwise relationships. Coupled with the topological overlap matrix transformation, it often leads to more significantly enriched co-expression modules. Spline and polynomial networks form attractive alternatives to MI in case of non-linear relationships. Our results indicate that MI networks can safely be replaced by correlation networks when it comes to measuring co-expression relationships in stationary data.

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The effects of dopamine reduced graphene oxide (pDop-rGO) on the curing activity and mechanical properties of epoxy-based composites were evaluated. Taking advantage of self-polymerization of mussel-inspired dopamine, pDop-rGO was prepared through simultaneous functionalization and reduction of graphene oxide (GO) via polydopamine coating. Benefiting from the universal binding ability of polydopamine, good dispersion of pDop-rGO in epoxy matrix was able to be achieved as the content of pDop-rGO being below 0.2 wt %. Curing kinetics of epoxy composites with pDop-rGO were systematically studied by nonisothermal differential scanning calorimetry (DSC). Compared to the systems of neat epoxy or epoxy composites containing GO, epoxy composites loaded with pDop-rGO showed lower activation energy (Eα) over the range of cure (α). It revealed that the amino-bearing pDop-rGO was able to react with epoxy matrix and enhance the curing reactions as an amine-type curing agent. The nature of the interactions at GO-epoxy interface was further evaluated by Raman spectroscopy, confirming the occurrence of chemical bonding. The strengthened interfacial adhesion between pDop-rGO and epoxy matrix thus enhanced the effective stress transfer in the composites. Accordingly, the tensile and flexural properties of EP/pDop-rGO composites were enhanced due to both the well dispersion and strong interfacial bonding of pDop-rGO in epoxy matrix.

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BackgroundEnsemble predictors such as the random forest are known to have superior accuracy but their black-box predictions are difficult to interpret. In contrast, a generalized linear model (GLM) is very interpretable especially when forward feature selection is used to construct the model. However, forward feature selection tends to overfit the data and leads to low predictive accuracy. Therefore, it remains an important research goal to combine the advantages of ensemble predictors (high accuracy) with the advantages of forward regression modeling (interpretability). To address this goal several articles have explored GLM based ensemble predictors. Since limited evaluations suggested that these ensemble predictors were less accurate than alternative predictors, they have found little attention in the literature.ResultsComprehensive evaluations involving hundreds of genomic data sets, the UCI machine learning benchmark data, and simulations are used to give GLM based ensemble predictors a new and careful look. A novel bootstrap aggregated (bagged) GLM predictor that incorporates several elements of randomness and instability (random subspace method, optional interaction terms, forward variable selection) often outperforms a host of alternative prediction methods including random forests and penalized regression models (ridge regression, elastic net, lasso). This random generalized linear model (RGLM) predictor provides variable importance measures that can be used to define a “thinned” ensemble predictor (involving few features) that retains excellent predictive accuracy.ConclusionRGLM is a state of the art predictor that shares the advantages of a random forest (excellent predictive accuracy, feature importance measures, out-of-bag estimates of accuracy) with those of a forward selected generalized linear model (interpretability). These methods are implemented in the freely available R software package randomGLM.

Figure 1: Diagram of transitional state. There are some ambiguous states around but not belong to the target actions, and it is hard to distinguish them. We define the states as "transitional state" (red boxes). If we can effectively distinguish these states, we can improve the ability of temporal extent detection. AbstractCurrent state-of-the-art approaches for spatio-temporal action detection have achieved impressive results but remain unsatisfactory for temporal extent detection. The main reason comes from that, there are some ambiguous states similar to the real actions which may be treated as target actions even by a well-trained network. In this paper, we define these ambiguous samples as "transitional states", and propose a Transition-Aware Context Network (TACNet) to distinguish transitional states. The proposed TACNet includes two main components, i.e., temporal context detector and transition-aware classifier. The temporal context detector can extract long-term context information with constant time complexity by constructing a recurrent network. The transition-aware classifier can further distinguish transitional states by classifying action and transitional states simultaneously. Therefore, the proposed TACNet can substantially improve the performance of spatio-temporal action detection. We extensively evaluate the proposed TAC-Net on UCF101-24 and J-HMDB datasets. The experimental results demonstrate that TACNet obtains competitive performance on JHMDB and significantly outperforms * indicates equal contribution.† indicates corresponding author.the state-of-the-art methods on the untrimmed UCF101-24 in terms of both frame-mAP and video-mAP.

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