Network pharmacology and molecular docking reveal the mechanism of Qinghua Xiaoyong Formula in Crohn's disease.

Crohn's disease (CD) is a chronic inflammatory illness of the digestive system with unknown etiology, and its incidence is increasing worldwide. However, there are currently no effective treatments or medications available for individuals with CD. Therefore, novel therapeutic strategies are urgently needed. The bioactive compounds and targets associated with compounds of Qinghua Xiaoyong Formula (QHXYF) were examined using The Traditional Chinese Medicine Systems Pharmacology database, and 5 disease target databases were also used to identify CD-related disease targets. A total of 166 overlapping targets were identified from QHXYF-related and CD-related disease targets and they were found to be enriched in oxidative stress-related pathways and the PI3K/AKT signaling pathway. Molecular docking was then used to predict how the bioactive compounds would bind to the hub targets. It was found that quercetin could be the core bioactive compound and had good binding affinity to the top 5 hub targets. Finally, animal experiments were performed to further validate the findings, and the results revealed that QHXYF or quercetin inhibited 2,4,6-trinitrobenzenesulfonic acid-induced inflammation and oxidative stress processes by inhibiting the PI3K/AKT pathway, thereby improving CD symptoms. These findings suggest that QHXYF and quercetin may be potential novel treatments for CD.

RNA Sequencing Reveals the Wound Repair Mechanism of Cuyuxunxi Prescription in Surgical Patients with Anal Fistulas.

BACKGROUND: Anal fistula is one of the most common colorectal and perirectal diseases in the world. Cuyuxunxi (CYXX) prescription is an efficient herbal fumigant used to promote the surgical wound healing of anal fistulas. OBJECTIVES: This study aimed to explore the underlying molecular mechanism of CYXX prescription on surgical wound healing of anal fistulas. METHODS: Ten patients with anal fistula were randomized into a control group or treatment group. The wound surface of patients in the control group was rinsed by normal saline, while that in the treatment group was rinsed by CYXX prescription. The wound tissues of patients with anal fistulas seven days after the surgery were collected for hematoxylin-eosin (HE) staining and RNA sequencing. The expressions of differentially expressed genes (DEGs) were validated by real-time quantitative PCR (RT-qPCR). RESULTS: HE staining showed that CYXX treatment reduced the infiltration of inflammatory cells. A total of 472 DEGs, including 141 up-regulated genes and 331 down-regulated genes, were identified. These genes were significantly related to skin development, xenobiotic stimulus, and inflammation. In addition, the consistency rate of RT-qPCR and sequencing results was 83.33%, which showed a high relative reliability of the sequencing results. CONCLUSION: CYXX prescription could improve epidermis repair and reduce inflammatory responses.

Same Stats, Different Graphs: Exploring the Space of Graphs in Terms of Graph Properties.

Data analysts commonly utilize statistics to summarize large datasets. While it is often sufficient to explore only the summary statistics of a dataset (e.g., min/mean/max), Anscombe's Quartet demonstrates how such statistics can be misleading. We consider a similar problem in the context of graph mining. To study the relationships between different graph properties, we examine low-order non-isomorphic graphs and provide a simple visual analytics system to explore correlations across multiple graph properties. However, for larger graphs, studying the entire space quickly becomes intractable. We use different random graph generation methods to further look into the distribution of graph properties for higher order graphs and investigate the impact of various sampling methodologies. We also describe a method for generating many graphs that are identical over a number of graph properties and statistics yet are clearly different and identifiably distinct.

OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples.

One major cause of performance degradation in predictive models is that the test samples are not well covered by the training data. Such not well-represented samples are called OoD samples. In this article, we propose OoDAnalyzer, a visual analysis approach for interactively identifying OoD samples and explaining them in context. Our approach integrates an ensemble OoD detection method and a grid-based visualization. The detection method is improved from deep ensembles by combining more features with algorithms in the same family. To better analyze and understand the OoD samples in context, we have developed a novel kNN-based grid layout algorithm motivated by Hall's theorem. The algorithm approximates the optimal layout and has O(kN2) time complexity, faster than the grid layout algorithm with overall best performance but O(N3) time complexity. Quantitative evaluation and case studies were performed on several datasets to demonstrate the effectiveness and usefulness of OoDAnalyzer.

Planning priority conservation areas for biodiversity under climate change in topographically complex areas: A case study in Sichuan province, China.

Identifying priority conservation areas plays a significant role in conserving biodiversity under climate change, but uncertainties create challenges for conservation planning. To reduce uncertainties in the conservation planning framework, we developed an adaptation index to assess the effect of topographic complexity on species adaptation to climate change, which was incorporated into the conservation framework as conservation costs. Meanwhile, the species distributions were predicted by the Maxent model, and the priority conservation areas were optimized during different periods in Sichuan province by the Marxan model. Our results showed that the effect of topographic complexity was critical for species adaptation, but the adaptation index decreased with the temperature increase. Based on the conservation targets and costs, the distributions of priority conservation areas were mainly concentrated in mountainous areas around the Sichuan Basin where may be robust to the adaptation to climate change. In the future, the distributions of priority conservation areas had no evident changes, accounting for about 26% and 28% of the study areas. Moreover, most species habitats could be conserved in terms of conservation targets in these priority conservation areas. Therefore, our approach could achieve biodiversity conservation goals and be highly practical. More importantly, quantifying the effect of topography also is critical for options for planning conservation areas in response to climate change.

Exploring the Sensitivity of Choropleths under Attribute Uncertainty.

The choropleth map is an essential tool for spatial data analysis. However, the underlying attribute values of a spatial unit greatly influence the statistical analyses and map classification procedures when generating a choropleth map. If the attribute values incorporate a range of uncertainty, a critical task is determining how much the uncertainty impacts both the map visualization and the statistical analysis. In this paper, we present a visual analytics system that enhances our understanding of the impact of attribute uncertainty on data visualization and statistical analyses of these data. Our system consists of a parallel coordinates-based uncertainty specification view, an impact river and impact matrix visualization for region-based and simulation-based analysis, and a dual-choropleth map and t-SNE plot for visualizing the changes in classification and spatial autocorrelation over the range of uncertainty in the attribute values. We demonstrate our system through three use cases illustrating the impact of attribute uncertainty in geographic analysis.

Network Pharmacology Reveals the Molecular Mechanism of Cuyuxunxi Prescription in Promoting Wound Healing in Patients with Anal Fistula.

BACKGROUND: The healing process of the surgical wound of anal fistulotomy is much slower because of the presence of stool within the wound. Cuyuxunxi (CYXX) prescription is a Chinese herbal fumigant that is being used to wash surgical wound after anal fistulotomy. This study aimed at investigating the molecular mechanism of CYXX prescription using a network pharmacology-based strategy. MATERIALS AND METHODS: The active compounds in each herbal medicine were retrieved from the traditional Chinese medicine systems pharmacology (TCMSP) database and in Traditional Chinese Medicine Integrated Database (TCMID) analysis platform based on the criteria of oral bioavailability ≥40% and drug-likeness ≥0.2. The disease-related target genes were extracted from the Comparative Toxicogenomics Database. Protein-protein interaction network was built for the overlapped genes as well as functional enrichment analysis. Finally, an ingredient-target genes-pathway network was built by integrating all information. RESULTS: A total of 375 chemical ingredients of the 5 main herbal medicines in CYXX prescription were retrieved from TCMSP database and TCMID. Among the 375 chemical ingredients, 59 were active compounds. Besides, 325 target genes for 16 active compounds in 3 herbal medicines were obtained. Functional enrichment analysis revealed that these overlapped genes were significantly related with immune response, biosynthesis of antibiotics, and complement and coagulation cascades. A comprehensive network which contains 133 nodes (8 disease nodes, 3 drug nodes, 8 ingredients, 103 target gene nodes, 7 GO nodes, and 4 pathway nodes) was built. CONCLUSION: The network built in this study might aid in understanding the action mechanism of CYXX prescription at molecular level to pathway level.

An Interactive Method to Improve Crowdsourced Annotations.

In order to effectively infer correct labels from noisy crowdsourced annotations, learning-from-crowds models have introduced expert validation. However, little research has been done on facilitating the validation procedure. In this paper, we propose an interactive method to assist experts in verifying uncertain instance labels and unreliable workers. Given the instance labels and worker reliability inferred from a learning-from-crowds model, candidate instances and workers are selected for expert validation. The influence of verified results is propagated to relevant instances and workers through the learning-from-crowds model. To facilitate the validation of annotations, we have developed a confusion visualization to indicate the confusing classes for further exploration, a constrained projection method to show the uncertain labels in context, and a scatter-plot-based visualization to illustrate worker reliability. The three visualizations are tightly integrated with the learning-from-crowds model to provide an iterative and progressive environment for data validation. Two case studies were conducted that demonstrate our approach offers an efficient method for validating and improving crowdsourced annotations.

A Visual Analytics Framework for Spatiotemporal Trade Network Analysis.

Economic globalization is increasing connectedness among regions of the world, creating complex interdependencies within various supply chains. Recent studies have indicated that changes and disruptions within such networks can serve as indicators for increased risks of violence and armed conflicts. This is especially true of countries that may not be able to compete for scarce commodities during supply shocks. Thus, network-induced vulnerability to supply disruption is typically exported from wealthier populations to disadvantaged populations. As such, researchers and stakeholders concerned with supply chains, political science, environmental studies, etc. need tools to explore the complex dynamics within global trade networks and how the structure of these networks relates to regional instability. However, the multivariate, spatiotemporal nature of the network structure creates a bottleneck in the extraction and analysis of correlations and anomalies for exploratory data analysis and hypothesis generation. Working closely with experts in political science and sustainability, we have developed a highly coordinated, multi-view framework that utilizes anomaly detection, network analytics, and spatiotemporal visualization methods for exploring the relationship between global trade networks and regional instability. Requirements for analysis and initial research questions to be investigated are elicited from domain experts, and a variety of visual encoding techniques for rapid assessment of analysis and correlations between trade goods, network patterns, and time series signatures are explored. We demonstrate the application of our framework through case studies focusing on armed conflicts in Africa, regional instability measures, and their relationship to international global trade.

A Visual Analytics Framework for Identifying Topic Drivers in Media Events.

Media data has been the subject of large scale analysis with applications of text mining being used to provide overviews of media themes and information flows. Such information extracted from media articles has also shown its contextual value of being integrated with other data, such as criminal records and stock market pricing. In this work, we explore linking textual media data with curated secondary textual data sources through user-guided semantic lexical matching for identifying relationships and data links. In this manner, critical information can be identified and used to annotate media timelines in order to provide a more detailed overview of events that may be driving media topics and frames. These linked events are further analyzed through an application of causality modeling to model temporal drivers between the data series. Such causal links are then annotated through automatic entity extraction which enables the analyst to explore persons, locations, and organizations that may be pertinent to the media topic of interest. To demonstrate the proposed framework, two media datasets and an armed conflict event dataset are explored.

The Role of Teamwork in the Analysis of Big Data: A Study of Visual Analytics and Box Office Prediction.

Historically, domains such as business intelligence would require a single analyst to engage with data, develop a model, answer operational questions, and predict future behaviors. However, as the problems and domains become more complex, organizations are employing teams of analysts to explore and model data to generate knowledge. Furthermore, given the rapid increase in data collection, organizations are struggling to develop practices for intelligence analysis in the era of big data. Currently, a variety of machine learning and data mining techniques are available to model data and to generate insights and predictions, and developments in the field of visual analytics have focused on how to effectively link data mining algorithms with interactive visuals to enable analysts to explore, understand, and interact with data and data models. Although studies have explored the role of single analysts in the visual analytics pipeline, little work has explored the role of teamwork and visual analytics in the analysis of big data. In this article, we present an experiment integrating statistical models, visual analytics techniques, and user experiments to study the role of teamwork in predictive analytics. We frame our experiment around the analysis of social media data for box office prediction problems and compare the prediction performance of teams, groups, and individuals. Our results indicate that a team's performance is mediated by the team's characteristics such as openness of individual members to others' positions and the type of planning that goes into the team's analysis. These findings have important implications for how organizations should create teams in order to make effective use of information from their analytic models.

Exploring Evolving Media Discourse Through Event Cueing.

Online news, microblogs and other media documents all contain valuable insight regarding events and responses to events. Underlying these documents is the concept of framing, a process in which communicators act (consciously or unconsciously) to construct a point of view that encourages facts to be interpreted by others in a particular manner. As media discourse evolves, how topics and documents are framed can undergo change, shifting the discussion to different viewpoints or rhetoric. What causes these shifts can be difficult to determine directly; however, by linking secondary datasets and enabling visual exploration, we can enhance the hypothesis generation process. In this paper, we present a visual analytics framework for event cueing using media data. As discourse develops over time, our framework applies a time series intervention model which tests to see if the level of framing is different before or after a given date. If the model indicates that the times before and after are statistically significantly different, this cues an analyst to explore related datasets to help enhance their understanding of what (if any) events may have triggered these changes in discourse. Our framework consists of entity extraction and sentiment analysis as lenses for data exploration and uses two different models for intervention analysis. To demonstrate the usage of our framework, we present a case study on exploring potential relationships between climate change framing and conflicts in Africa.

Business intelligence from social media: a study from the VAST Box Office Challenge.

With over 16 million tweets per hour, 600 new blog posts per minute, and 400 million active users on Facebook, businesses have begun searching for ways to turn real-time consumer-based posts into actionable intelligence. The goal is to extract information from this noisy, unstructured data and use it for trend analysis and prediction. Current practices support the idea that visual analytics (VA) can help enable the effective analysis of such data. However, empirical evidence demonstrating the effectiveness of a VA solution is still lacking. A proposed VA toolkit extracts data from Bitly and Twitter to predict movie revenue and ratings. Results from the 2013 VAST Box Office Challenge demonstrate the benefit of an interactive environment for predictive analysis, compared to a purely statistical modeling approach. The VA approach used by the toolkit is generalizable to other domains involving social media data, such as sales forecasting and advertisement analysis.

Relationship between serum vasoactive factors and plaque morphology in patients with non-ST-segment elevated acute coronary syndrome.

BACKGROUND: Vasoactive factors have been reported to correlate with vulnerable plaque and acute coronary syndrome (ACS). This study aimed to investigate the relationship between vasoactive factors and plaque morphology in patients suffering from non-ST-segment elevated ACS. METHODS: From April 2007 to April 2009, 124 consecutive patients suffering from non-ST-segment elevated ACS who had received coronary angiography (CAG) and intravascular ultrasound (IVUS) in the People's Liberation Army General Hospital and Beijing Anzhen Hospital were enrolled in this study. Three serum vasoactive factors, plasma soluble vascular endothelial growth factor receptor-1 (sFlt-1), placental growth factor (PLGF) and interleukin-18 (IL-18), were measured by enzyme-linked-immunosorbent serologic assay of the patients. The levels of vasoactive factors were compared between vulnerable plaque group and stable plaque group, and between unstable angina pectoris (UAP) group and non-ST-segment elevation acute myocardial infarction (NSTE-AMI) group. The relationship between the plaque morphology and levels of vasoactive factors was analyzed. RESULTS: The levels of vasoactive factors were similar between the UAP group (69 patients) and NSTE-AMI group (55 patients). The levels of sFlt-1 and PLGF in the vulnerable plaque group were significantly higher than those in the stable plaque group. The level of IL-18 was correlated positively with plaque morphology. Multivariate Logistic regression analysis showed that the level of PLGF was an independent risk factor for vulnerable plaque (OR=2.115, 95% CI 1.415-5.758, P=0.018). Using the ROC curve, PLGF was a significant factor for the diagnosis of vulnerable plaque (the diagnostic point was 26.3 ng/L, the proportion of square area under the ROC curve was 0.799, 95%CI 0.758-0.839, P<0.001; the sensitivity of PLGF under the ROC curve was 86%, and the specificity 63%). CONCLUSION: Both IL-18 and PLGF are biomarkers for vulnerable plaques and helpful to predict vulnerable plaque.

[Influence of pravastatin on blood lipids and serum high sensitive C-reactive protein in patients undergoing conventional coronary artery bypass grafting under on-pump: a clinical study of 81 cases].

OBJECTIVE: To investigate the effect of pravastatin on blood lipids and serum high sensitive C-reactive protein (HsCRP) in patients undergoing conventional coronary artery bypass grafting under on-pump bypass (CCABG). METHODS: Eighty-one patients underwent CCABG. Among which 40 took orally pravastatin 20 mg once daily to at least 28 days after operation, and 41 were used as control group. The serum levels of total cholesterol (TC), triglyceride (TG), HDL-C cholesterol (HDL-C), LDL-C cholesterol (LDL-C), and HsCRP were monitored before and 24 h, 72 h, 7 days, 10 days, 14 days, and 28 days postoperatively. RESULTS: In the control group the levels of different blood lipids after operation remarkably decreased after operation compared with those before operation (all P < 0.05), reached the lowest levels 24 h after operation, then gradually increased, however, still lower than those before operation (all P < 0.05), and recovered to the baseline level 28 hours after operation; and the HsCRP level increased 24 h after operation and peaked 72 h after, then gradually decreased, and recovered to the baseline level 28 days after operation. In the pravastatin group the TC level reached its lowest level 24 h after operation, then gradually increased, however, still lower than that before operation, and recovered to the baseline level 28 days after operation; and the TG level reached the lowest level 24 h after operation (P < 0.05), and then gradually increased 3 d after operation (P > 0.05). The TC, TG, and LDL-C levels 7, 10, 14, and 28 d after operation of the pravastatin group were all significantly lower than those of the control group (all P < 0.05). The HsCRP levels at different time points of the pravastatin group were all significantly lower than those of the control group (all P < 0.05). CONCLUSION: The use of pravastatin in the early stage of CCABG is safe and can decrease systemic inflammatory reaction.

[Change of N-terminal pro-brain natriuretic peptide and big endothelin in patients undergoing coronary artery bypass grafting].

OBJECTIVE: To examine the change in N-terminal pro-brain natriuretic peptide (Nt-proBNP) and big endothelin (big ET) in patients undergoing coronary artery bypass grafting (CABG), and to evaluate their value in predicting postoperative mortality and complication. METHODS: Forty-seven patients undergoing coronary artery bypass grafting under on-pump (CCABG) and 43 patients undergoing off-pump bypass (OPCAB) were included for study. The levels of Nt-proBNP and big ET were determined before and 24 hours after operation in all patients. RESULTS: (1)There were no differences between two groups. The serum levels of Nt-proBNP and big ET increased significantly 24 hours after operation. Compared with those before operation, Nt-proBNP [(1 083.5 +/- 717.9) pmol/L] in CCABG group was increased [(1 579.2 +/- 719.7)pmol/L, t = -4.30, P<0.01], big ET was increased from (1.10 +/- 1.82 ) pmol/L to (1.68 +/- 1.73)pmol/L(t = -5.35, P<0.01) 24 hours after operation; Nt-proBNP [(999.6 +/- 843.6) pmol/L] in OPCAB group was increased [(1 460.8+/-830.0) pmol/L, t = -4.20, P<0.01], big ET was increased from (1.35 +/- 1.65) pmol/L to (1.73 +/- 1.50) pmol/L (t = -2.46, P=0.018) 24 hours after operation. (2)The level of Nt-proBNP before operation was showed to be negatively correlated with left ventricular ejection fraction (LVEF) (r = -0.43, P<0.001). (3)By univariate and multivariate Logistic regression analysis, the association of clinical variable with postoperative complication was assessed. Multivariable predictors, including the level of LVEF (OR = 1.045, 95%CI:0.999-1.092, P = 0.050) and Nt-proBNP 24 hours after operation (OR = 0.990, 95%CI:0.999-1.000, P = 0.014), were significantly associated with a higher postoperative mortality, lower cardiac output, and higher incidence of myocardial infarction and congestive heart failure. Receiver operating characteristic curves (ROC) for Nt-proBNP 24 hours after operation was valid for the prediction of postoperative complication, and the area under the curve was 0.698 (95% CI:0.585-0.811, P<0.003), sensitivity and specificity were 88.9% and 57.1%, respectively. CONCLUSION: Significant increase in Nt-proBNP and ET is found after CABG. BNP and LVEF are showed to be risk factors for postoperative complications in patients undergoing CABG.

[Comparative studies of the blood lipids and apolipoproteins A1 and B in patients underwent coronary artery bypass grafting under on-pump and off-pump bypass].

OBJECTIVE: To compare the influences of coronary artery bypass grafting under on-pump (CCABG) and off-pump bypass (OPCAB) on blood lipids and Apolipoproteins A(1) and B. METHODS: 41 patients underwent CCABG and 38 patients OPCAB. The serum levels of total cholesterol (TC), triglycerides (TG), HDL-C cholesterol (HDL-C), LDL-C cholesterol (LDL-C) and Apolipoproteins A(1) and B were monitored before and 24 h, 72 h, 7 d, 10 d, 14 d, 28 d after operation in all patients. None of them received lipid-lowering drug during the test. RESULTS: (1) There were no difference between two groups. The serum levels of TC, LDL-C decreased significantly in 24 h, 72 h, 7d, 10 d, 14 d after operation (P < 0.001). The level in 24 h was the lowest, then began increasing; until 28 days after operation the serum levels of TC, LDL-C exceeded preoperative baseline level (P < 0.05). (2) the level of TG and HDL-C in 24 h was the lowest (P < 0.001), then began increasing (P > 0.05). Until 28 days after operation the serum levels of TG exceeded preoperative baseline level (P < 0.05). (3) the serum levels of ApoA(1) decreased significantly in 24 h, 72 h, 7 day, 10 day, 14 day after operation (P < 0.001). The level in 24 h was the lowest, and lasted until 28 days after operation (P > 0.05); the serum levels of ApoB in 24 h was the lowest, then began increasing (P > 0.05). CONCLUSIONS: There presents significant influence on blood lipids and Apolipoproteins A(1) and B after CABG, and initiate lipid (s)-lowering therapy as soon as possible.

Strain relief in Cu-Pd heteroepitaxy.

We present experimental and theoretical studies of Pd/Cu(100) and Cu/Pd(100) heterostructures in order to explore their structure and misfit strain relaxation. Ultrathin Pd and Cu films are grown by pulsed laser deposition at room temperature. For Pd/Cu, compressive strain is released by networks of misfit dislocations running in the [100] and [010] directions, which appear after a few monolayers (ML) already. In striking contrast, for Cu/Pd the tensile overlayer remains coherent up to about 9 ML, after which multilayer growth occurs. The strong asymmetry between tensile and compressive cases is in contradiction with continuum elasticity theory and is also evident in the structural parameters of the strained films. Molecular dynamics calculations based on classical many-body potentials confirm the pronounced tensile-compressive asymmetry and are in good agreement with the experimental data.