Motivation: It has long been hypothesized that incorporating models of network noise as well as edge directions and known pathway information into the representation of protein–protein interaction (PPI) networks might improve their utility for functional inference. However, a simple way to do this has not been obvious. We find that diffusion state distance (DSD), our recent diffusion-based metric for measuring dissimilarity in PPI networks, has natural extensions that incorporate confidence, directions and can even express coherent pathways by calculating DSD on an augmented graph.Results: We define three incremental versions of DSD which we term cDSD, caDSD and capDSD, where the capDSD matrix incorporates confidence, known directed edges, and pathways into the measure of how similar each pair of nodes is according to the structure of the PPI network. We test four popular function prediction methods (majority vote, weighted majority vote, multi-way cut and functional flow) using these different matrices on the Baker’s yeast PPI network in cross-validation. The best performing method is weighted majority vote using capDSD. We then test the performance of our augmented DSD methods on an integrated heterogeneous set of protein association edges from the STRING database. The superior performance of capDSD in this context confirms that treating the pathways as probabilistic units is more powerful than simply incorporating pathway edges independently into the network.Availability: All source code for calculating the confidences, for extracting pathway information from KEGG XML files, and for calculating the cDSD, caDSD and capDSD matrices are available from http://dsd.cs.tufts.edu/capdsdContact: lenore.cowen@tufts.edu or benjamin.hescott@tufts.eduSupplementary information: Supplementary data are available at Bioinformatics online.

The difference of leaf disease images within the class is large but the difference between the class is small, so it is very important to represent the features of the local area of the target. Moreover, the complex network occupies a large amount of computer memory and wastes a large amount of computing resources, which is difficult to meet the needs of low-cost terminals. This paper proposes a fine-grained disease categorization method based on attention network to solve the problem. In ''Classification Model'', attention mechanism is used to increase identification ability. ''Reconstruction-Generation Model'' were added during training and the ''Classification Model'' have to pay more attention to differentiate areas to find differences instead of paying more attention to global features. And adversarial loss was applied to distinguish the generated image from the original image to suppress the noise introduced by the ''Discrimination Model''. Due to the feature that ''Reconstruction-Generation Model'' and ''Discrimination Model'' are only used in training and do not participate in the operation of inference phase, which cannot increase the complexity of the model. Compared with the traditional classification network, the method of generalization ability enhancement further enhances the identification accuracy. And the method needs less memory but can achieve low performance terminal real-time identification of peach and tomato leaf diseases. And it can be applied in other crop disease identification fields with the similar application scenarios.

Identification of modules in molecular networks is at the core of many current analysis methods in biomedical research. However, how well different approaches identify disease-relevant modules in different types of gene and protein networks remains poorly understood. We launched the “Disease Module Identification DREAM Challenge”, an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology, and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies (GWAS). Our critical assessment of 75 contributed module identification methods reveals novel top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets and correctly prioritize candidate disease genes. This community challenge establishes benchmarks, tools and guidelines for molecular network analysis to study human disease biology (https://synapse.org/modulechallenge).

The microstructures of B-bearing cast steel containing 0.8-1.2 wt.%B, 0.8-1.2 wt.%Cr, 1.0-1.5 wt.%Mn, 0.6-1.0 wt.%Si and 0.10-0.25 wt.%C have been characterized by means of optical OM, SEM, EPMA and XRD. The solidification structure of B-steel consists of pearlite, ferrite, martensite and boride (Fe2B), while the hardness is 1430-1480 HV. Borides distribute along the grain boundary in the form of eutectic. Fine lath martensite and eutectic Fe2B can be obtained by water quenching at 1223 K-1273 K. The hardness and impact toughness of the B-steel exceed 55 HRC and 150 kJ/m2, respectively. The abrasion resistance determined using a pin abrasion tester is obviously higher than that of the martensitic cast steel and nears to the high chromium white cast iron.