Vital contribution of residential emissions to atmospheric fine particles (PM2.5) during the severe wintertime pollution episodes in Western China.

To mitigate severe wintertime pollution events in Western China, identifying the source of atmospheric fine particles with an aerodynamic diameter of ≤2.5 µm (PM2.5) is a crucial step. In this study, we first analyzed the meteorological and emission factors that caused a considerable increase in the PM2.5 concentration in December 2016. This severe pollution episode was found to be related with unfavorable meteorological conditions and increased residential emissions. The WRF-Chem simulations were used to calculate the residential contribution to PM2.5 through a hybrid source apportionment method. From the validation that used grid data and in situ observations in terms of meteorological elements, PM2.5 and its compounds, the simulated results indicated that the residential sector was the largest single contributor to the PM2.5 concentration (60.2%), because of its predominant contributions to black carbon (BC, 62.1%) and primary organic aerosol (POA, 86.5%), with these two primary components accounting for 70.7% of the PM2.5 mass. Compared with the remote background (RB) region covering the central part of the Tibetan Plateau, the residential sector contributed 11.3% more to PM2.5 in the highly populated mega-city (HM) region, including the Sichuan and Guanzhong Basins, due to greater contribution to the concentrations of primary PM2.5 components. As the main emission source of sulfur dioxide (SO2), nitrogen oxides (NOx), and secondary organic aerosol (SOA), the industrial sector was the second largest contributor to the PM2.5 concentration in the HM region. However, in the RB region, the dominating emissions of NOx, SOA, and BC were from the transport sector; thus, it was the next largest contributor to total PM2.5. An evaluation of the emission control experiment suggested that mitigation strategies that reduce emissions from residential sources can effectively reduce the PM2.5 concentration during heavy pollution periods.

egoSlider: Visual Analysis of Egocentric Network Evolution.

Ego-network, which represents relationships between a specific individual, i.e., the ego, and people connected to it, i.e., alters, is a critical target to study in social network analysis. Evolutionary patterns of ego-networks along time provide huge insights to many domains such as sociology, anthropology, and psychology. However, the analysis of dynamic ego-networks remains challenging due to its complicated time-varying graph structures, for example: alters come and leave, ties grow stronger and fade away, and alter communities merge and split. Most of the existing dynamic graph visualization techniques mainly focus on topological changes of the entire network, which is not adequate for egocentric analytical tasks. In this paper, we present egoSlider, a visual analysis system for exploring and comparing dynamic ego-networks. egoSlider provides a holistic picture of the data through multiple interactively coordinated views, revealing ego-network evolutionary patterns at three different layers: a macroscopic level for summarizing the entire ego-network data, a mesoscopic level for overviewing specific individuals' ego-network evolutions, and a microscopic level for displaying detailed temporal information of egos and their alters. We demonstrate the effectiveness of egoSlider with a usage scenario with the DBLP publication records. Also, a controlled user study indicates that in general egoSlider outperforms a baseline visualization of dynamic networks for completing egocentric analytical tasks.

AmbiguityVis: Visualization of Ambiguity in Graph Layouts.

Node-link diagrams provide an intuitive way to explore networks and have inspired a large number of automated graph layout strategies that optimize aesthetic criteria. However, any particular drawing approach cannot fully satisfy all these criteria simultaneously, producing drawings with visual ambiguities that can impede the understanding of network structure. To bring attention to these potentially problematic areas present in the drawing, this paper presents a technique that highlights common types of visual ambiguities: ambiguous spatial relationships between nodes and edges, visual overlap between community structures, and ambiguity in edge bundling and metanodes. Metrics, including newly proposed metrics for abnormal edge lengths, visual overlap in community structures and node/edge aggregation, are proposed to quantify areas of ambiguity in the drawing. These metrics and others are then displayed using a heatmap-based visualization that provides visual feedback to developers of graph drawing and visualization approaches, allowing them to quickly identify misleading areas. The novel metrics and the heatmap-based visualization allow a user to explore ambiguities in graph layouts from multiple perspectives in order to make reasonable graph layout choices. The effectiveness of the technique is demonstrated through case studies and expert reviews.

Rigorous assessment and integration of the sequence and structure based features to predict hot spots.

BACKGROUND: Systematic mutagenesis studies have shown that only a few interface residues termed hot spots contribute significantly to the binding free energy of protein-protein interactions. Therefore, hot spots prediction becomes increasingly important for well understanding the essence of proteins interactions and helping narrow down the search space for drug design. Currently many computational methods have been developed by proposing different features. However comparative assessment of these features and furthermore effective and accurate methods are still in pressing need. RESULTS: In this study, we first comprehensively collect the features to discriminate hot spots and non-hot spots and analyze their distributions. We find that hot spots have lower relASA and larger relative change in ASA, suggesting hot spots tend to be protected from bulk solvent. In addition, hot spots have more contacts including hydrogen bonds, salt bridges, and atomic contacts, which favor complexes formation. Interestingly, we find that conservation score and sequence entropy are not significantly different between hot spots and non-hot spots in Ab+ dataset (all complexes). While in Ab- dataset (antigen-antibody complexes are excluded), there are significant differences in two features between hot pots and non-hot spots. Secondly, we explore the predictive ability for each feature and the combinations of features by support vector machines (SVMs). The results indicate that sequence-based feature outperforms other combinations of features with reasonable accuracy, with a precision of 0.69, a recall of 0.68, an F1 score of 0.68, and an AUC of 0.68 on independent test set. Compared with other machine learning methods and two energy-based approaches, our approach achieves the best performance. Moreover, we demonstrate the applicability of our method to predict hot spots of two protein complexes. CONCLUSION: Experimental results show that support vector machine classifiers are quite effective in predicting hot spots based on sequence features. Hot spots cannot be fully predicted through simple analysis based on physicochemical characteristics, but there is reason to believe that integration of features and machine learning methods can remarkably improve the predictive performance for hot spots.