Second, the GHuST framework explains network properties regardless of network size. This supports the comparison among networks with different number of nodes and edges. Third, it only considers 2-node and 3-node graphlets and they follow easily from the adjacency matrix. It reduces computational ...
2. Real-Time Streaming Anomaly Detection in Dynamic Graphs. (from Christos Faloutsos) 3. Holistic Filter Pruning for Efficient Deep Neural Networks. (from Wolfram Burgard) 4. Demand Forecasting of individual Probability Density Functions with Machine Learning. (from U. Kerzel) 5. Matrix Profile ...
35. Entry of endocytosed EVs into the nucleoplasmic reticulum (NR), specifically in type II nuclear envelope invaginations (NEIs)36, and subsequent nuclear
Host phylogeny and diet significantly explain the aspects of microbiome diversity. The plots show the BH-adjustedpvalues (Adj.pvalue) and partial regression coefficients (Coef.) for multiple regression on matrix (MRM) tests used to determine how much alpha- or beta-diversity variance was explained...
(bn = 3).c,dRepresentation of alternative protocols (c) and quantification of the EGFP expression in infected PHA/IL-2-activated CD4+T cells by FC (dn = 3 per condition). In addition to negative and positive controls, i.e., without (#i) or in constant presence of PRR851 ...
This point, which is clearly in agreement with the representation of the binding poses in Figure 1B,C, reflects a greater consistency in the docked poses for our studied DPDAs. An additional analysis was performed to check whether the neck groups of the studied compounds occupied a similar 3D...
Significance levels for the PCA were identified by adding random numbers to the columns and the rows of the PCA matrix and then calculating 95% confidence intervals for the random numbers. Adding random numbers distorts the PCA algorithm to a certain degree, so the confidence estimates are only...