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 graphlayout strategies that optimize aesthetic criteria. However, any particular drawing approach cannot fully satisfy all these criteriasimultaneously, producing drawings with visual ambiguities that can impede the understanding of network structure. To bring attentionto these potentially problematic areas present in the drawing, this paper presents a technique that highlights common types of visualambiguities: ambiguous spatial relationships between nodes and edges, visual overlap between community structures, and ambiguityin edge bundling and metanodes. Metrics, including newly proposed metrics for abnormal edge lengths, visual overlap in communitystructures and node/edge aggregation, are proposed to quantify areas of ambiguity in the drawing. These metrics and others arethen displayed using a heatmap-based visualization that provides visual feedback to developers of graph drawing and visualizationapproaches, allowing them to quickly identify misleading areas. The novel metrics and the heatmap-based visualization allow a userto explore ambiguities in graph layouts from multiple perspectives in order to make reasonable graph layout choices. The effectivenessof 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

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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p

Assessment of multi-source observation merged 1 km-grid precipitation product during the disastrous rainstorms in Guangdong

This paper aims to assess the latest 1 km-grid Analysis Real Time (ART_1 km) precipitation product developed by the National Meteorological Information Center of China Meteorological Administration (CMA), which can provide great support for disaster weather monitoring and warning, intelligent grid forecasting and weather services. Observed precipitation data from the independent stations (including non-uploaded regional meteorological stations and hydrometric stations) that were not integrated into the ART_1 km precipitation product as well as precipitation classification inspection are used to assess the quality of this product during twenty disastrous rainstorm cases from May to August during 2019-2022 in Guangdong. The results show that the ART_1 km precipitation product successfully reproduces the precipitation location, strength, and trends in these cases, with the best performance in the Pearl River Delta, the east of eastern Guangdong, and the north of northern Guangdong. The stronger the precipitation, the greater the correlation as well as the root mean square error (RMSE) and mean error (ME) between the ART_1 km precipitation and the observed precipitation. When the hourly precipitation is not classified, about 60% of these independent stations present a correlation efficient ≥ 0.8, more than 90% of the stations present an RMSE within the range of [1.0, 5.0) mm, and more than 60% of the stations present a ME within ±0.1 mm. When the hourly precipitation is < 5 mm, most of the stations have a correlation efficient < 0.5, an RMSE within the range of [1.0, 5.0) mm, and a ME within [0.0, 0.5] mm. When the hourly precipitation is ≥ 20 mm, 42%~56% of the stations have a correlation efficient ≥ 0.5, and most of the stations have an RMSE ≥ 10 mm and a ME < 0 mm, even when the hourly precipitation is ≥ 50 mm, most of the stations have a ME < -10 mm. Overall, ART_1 km precipitation is usually underestimated at the independent stations, and integrating observations from more sites into producing ART_1 km precipitation is helpful to improve the quality of the products

Atomically dispersed antimony on carbon nitride for the artificial photosynthesis of hydrogen peroxide

Artificial photosynthesis offers a promising strategy to produce hydrogen peroxide (H2O2)—an environmentally friendly oxidant and a clean fuel. However, the low activity and selectivity of the two-electron oxygen reduction reaction (ORR) in the photocatalytic process greatly restricts the H2O2 production efficiency. Here we show a robust antimony single-atom photocatalyst (Sb-SAPC, single Sb atoms dispersed on carbon nitride) for the synthesis of H2O2 in a simple water and oxygen mixture under visible light irradiation. An apparent quantum yield of 17.6% at 420 nm together with a solar-to-chemical conversion efficiency of 0.61% for H2O2 synthesis was achieved. On the basis of time-dependent density function theory calculations, isotopic experiments and advanced spectroscopic characterizations, the photocatalytic performance is ascribed to the notably promoted two-electron ORR by forming μ-peroxide at the Sb sites and highly concentrated holes at the neighbouring N atoms. The in situ generated O2 via water oxidation is rapidly consumed by ORR, leading to boosted overall reaction kinetics

sj-docx-1-hpq-10.1177_13591053231223810 – Supplemental material for Using distance-framed narratives to foster health communication outcomes among e-cigarette users and non-users

Supplemental material, sj-docx-1-hpq-10.1177_13591053231223810 for Using distance-framed narratives to foster health communication outcomes among e-cigarette users and non-users by Sixiao Liu and Janet Z Yang in Journal of Health Psychology</p

Improving Thermal, Mechanical, and Crystalline Properties of Poly(butylene succinate) Copolyesters from a Renewable Rigid Diester

The introduction of rigid cyclic monomers into the poly(butylene succinate) (PBS) backbone is the most common way to elevate its low glass-transition temperature (Tg = −30.0 °C). However, the insertion of cyclic units always leads to very poor crystallinity and low molecular weight, which drastically hinder their industrial applications. Herein, a renewable rigid diester N,N′-trans-1,4-cyclohexane-bis(pyrrolidone-4-methyl carboxylate) (CBPC) was obtained via Michael addition. CBPC with linked rings had high spatial mobility, resulting in high reaction reactivity. A series of biobased PBCxBSy copolyesters were prepared by melt polycondensation of CBPC with succinic acid and 1,4-butanediol, achieving the high-number-average molecular weight of up to 44.5 kDa. The insertion of CBPC led to higher thermal stability and dramatically enhanced the Tg, such that the Tg of PBC80BS20 (87.5 °C) surpassed that of PBS (−30.0 °C) over 117.5 °C. Moreover, PBCxBSy showed an unexpected cocrystallization behavior, in which the rigid CBPC with a bulky tricyclic structure could be inserted into the crystal of PBS and formed a homogeneous crystalline structure. The cocrystallization was deeply analyzed by thermodynamic study and density functional theory calculation. Benefiting from the cocrystallization, PBCxBSy showed distinguished mechanical performances, which matched with or excelled those of the commercial polyesters of polyethylene terephthalate, polybutylene terephthalate, and polylactic acid. Accordingly, CBPC could be regarded as an effective biobased building block to spectacularly improve the thermal, mechanical, and crystalline performances of PBS at the same time

Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning

Graph contrastive learning has emerged as a powerful tool for unsupervised graph representation learning. The key to the success of graph contrastive learning is to acquire high-quality positive and negative samples as contrasting pairs for the purpose of learning underlying structural semantics of the input graph. Recent works usually sample negative samples from the same training batch with the positive samples, or from an external irrelevant graph. However, a significant limitation lies in such strategies, which is the unavoidable problem of sampling false negative samples. In this paper, we propose a novel method to utilize \textbf{C}ounterfactual mechanism to generate artificial hard negative samples for \textbf{G}raph \textbf{C}ontrastive learning, namely \textbf{CGC}, which has a different perspective compared to those sampling-based strategies. We utilize counterfactual mechanism to produce hard negative samples, which ensures that the generated samples are similar to, but have labels that different from the positive sample. The proposed method achieves satisfying results on several datasets compared to some traditional unsupervised graph learning methods and some SOTA graph contrastive learning methods. We also conduct some supplementary experiments to give an extensive illustration of the proposed method, including the performances of CGC with different hard negative samples and evaluations for hard negative samples generated with different similarity measurements.Comment: 10 pages, submitted to ICDE 2023 Round