Flud: A Hybrid Crowd–Algorithm Approach for Visualizing Biological Networks

Modern experiments in many disciplines generate large quantities of network (graph) data. Researchers require aesthetic layouts of these networks that clearly convey the domain knowledge and meaning. However, the problem remains challenging due to multiple conflicting aesthetic criteria and complex domain-specific constraints. In this article, we present a strategy for generating visualizations that can help network biologists understand the protein interactions that underlie processes that take place in the cell. Specifically, we have developed Flud, a crowd-powered system that allows humans with no expertise to design biologically meaningful graph layouts with the help of algorithmically generated suggestions. Furthermore, we propose a novel hybrid approach for graph layout wherein crowd workers and a simulated annealing algorithm build on each other’s progress. A study of about 2,000 crowd workers on Amazon Mechanical Turk showed that the hybrid crowd–algorithm approach outperforms the crowd-only approach and state-of-the-art techniques when workers were asked to lay out complex networks that represent signaling pathways. Another study of seven participants with biological training showed that Flud layouts are more effective compared to those created by state-of-the-art techniques. We also found that the algorithmically generated suggestions guided the workers when they are stuck and helped them improve their score. Finally, we discuss broader implications for mixed-initiative interactions in layout design tasks beyond biology.

Network Pharmacology and Molecular Docking Verify the Mechanism of Qinshi Simiao San in Treating Chronic Prostatitis in the Rat Model

Background. Using network pharmacology and molecular docking, this study aimed to explore the active pharmaceutical ingredients (APIs) and molecular mechanism of Qinshi Simiao San (QSSMS) in the treatment of chronic prostatitis (CP) and verify our findings in the rat model. Methods. The APIs of QSSMS and the common targets of QSSMS and CP were screened from the TCMSP database. The STRING database and Cytoscape software were used to construct the network graph. The enriched GO and KEGG pathways were displayed by David software and R software. Molecular docking was performed to visualize key components and target genes. In addition, the rats model of CP was established to verify the molecular mechanism of QSSMS. Results. Network pharmacology showed that the APIs of QSSMS mainly included quercetin, kaempferol, formononetin, isorhamnetin, and calycosin. QSSMS alleviated CP mainly through the negative regulation of the apoptotic process, oxidation-reduction process, inflammatory response, and immune response. Molecular docking showed that the APIs could bind to the corresponding targets. QSSMS repaired the pathological damage of prostate tissue, upregulated the expression of oxidative stress scavenging enzymes CAT and SOD, and downregulated the peroxidative product MDA, inflammatory factors IL-1β, IL-6, TNF-α, COX-2, PGE2, and NGF, and immune factors IgG and SIgA. Conclusion. The APIs in QSSMS may inhibit inflammation in the rat CP model by regulating immune and oxidative stress.

Attention enhanced capsule network for text classification by encoding syntactic dependency trees with graph convolutional neural network

Text classification is a fundamental task in many applications such as topic labeling, sentiment analysis, and spam detection. The text syntactic relationship and word sequence are important and useful for text classification. How to model and incorporate them to improve performance is one key challenge. Inspired by human behavior in understanding text. In this paper, we combine the syntactic relationship, sequence structure, and semantics for text representation, and propose an attention-enhanced capsule network-based text classification model. Specifically, we use graph convolutional neural networks to encode syntactic dependency trees, build multi-head attention to encode dependencies relationship in text sequence, merge with semantic information by capsule network at last. Extensive experiments on five datasets demonstrate that our approach can effectively improve the performance of text classification compared with state-of-the-art methods. The result also shows capsule network, graph convolutional neural network, and multi-headed attention has integration effects on text classification tasks.

Sun Toughness Conditions for P 2 and P 3 Factor Uniform and Factor Critical Avoidable Graphs

The security of a network is closely related to the structure of the network graph. The denser the network graph structure is, the better it can resist attacks. Toughness and isolated toughness are used to characterize the vulnerable programs of the network which have been paid attention from mathematics and computer scholars. On this basis, considering the particularity of the sun component structures, sun toughness was introduced in mathematics and applied to computer networks. From the perspective of modern graph theory, this paper presents the sun toughness conditions of the path factor uniform graph and the path factor critical avoidable graph in P ≥ 2 -factor and P ≥ 3 -factor settings. Furthermore, examples show that the given boundaries are sharp.

A method for generating a local network graph based on the analysis of address sets

Introduction. The paper deals with the problem of automated construction of a local area network using tools and methods for traffic analysis at the link layer of OSI model. The problem is caused by two factors. These are difficulties of the manual determination of the communication between equipment and the lack of physical access to communication lines of an already functioning network. The purpose of the work is to reduce the time spent on building a local network diagram through automating the process of determining the communication between the equipment.Materials and Methods. To solve the set tasks, a method for determining the relative location of devices is proposed. The network adapters of a specialized software and hardware complex, which are connected to a communication line break at different points of the network, are used in opposite directions. The method used is based on calculations of intersections of address sets received from these adapters. The structural schemes of the construction of such a software and hardware complex and the requirements for it are given. The methods of obtaining MAC addresses from transit packets are described. Examples of libraries of software components for performing this operation are given. The structure of a relational database is proposed for storing the received data. The format and content of the fields of its table are described.Results. Using the developed methods, a typical example of an Ethernet network shows a way to determine the relative location of end devices specified by their MAC addresses, as well as at least two switches located between them. The signs by which it is possible to judge the presence of switching equipment in a particular segment are determined. A method is proposed that enables through using a set of relational operations, to sequentially refine the network topology until the required accuracy is achieved.Discussion and Conclusions. The results obtained can be used under the administration of large local networks with an extensive structure. The proposed approach allows you to reduce the time required for building a scheme. This is possible due to the automation of the process of obtaining information about devices operating on the network and their mutual location.

Assessing The Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher’s Choice Paths

There is a growing interest in the neuroscience community on the advantages of multimodal neuroimaging modalities. Functional and structural interactions between brain areas can be represented as a network (graph) allowing us to employ graph-theoretic tools in multiple research directions. Researchers usually treated brain networks acquired from different modalities or different frequencies separately. However, there is strong evidence that these networks share complementary information while their interdependencies could reveal novel findings. For this purpose, neuroscientists adopt multilayer networks, which can be described mathematically as an extension of trivial single-layer networks. Multilayer networks have become popular in neuroscience due to their advantage to integrate different sources of information. We can incorporate this information from different modalities (multi-modal case), from different frequencies (multi-frequency case), or a single modality following a dynamic functional connectivity analysis (multi-layer,dynamic case). Researchers already used multi-layer networks to model brain disorders, to detect key hubs related to a specific function, to reveal structural-functional relationships, and to define more precise connectomic biomarkers related to brain disorders. However, the construction of a multilayer network depends on the selection of multiple preprocessing steps that can affect the final network topology. Here, we analyzed the fMRI dataset from a single human performing scanning over a period of 18 months (84 scans in total). We focused on assessing the reproducibility of multi-frequency multilayer topologies exploring the effect of two filtering methods for extracting frequencies from BOLD activity, three connectivity estimators, with or without a topological filtering scheme, and two spatial scales. Finally, we untangled specific combinations of researchers’ choices that yield repeatable topologies, giving us the chance to recommend best practices over consistent topologies.

Reduced Effective Connectivity in the Motor Cortex in Parkinson’s Disease

Fast rhythms excess is a hallmark of Parkinson’s Disease (PD). To implement innovative, non-pharmacological, neurostimulation interventions to restore cortical-cortical interactions, we need to understand the neurophysiological mechanisms underlying these phenomena. Here, we investigated effective connectivity on source-level resting-state electroencephalography (EEG) signals in 15 PD participants and 10 healthy controls. First, we fitted multivariate auto-regressive models to the EEG source waveforms. Second, we estimated causal connections using Granger Causality, which provide information on connections’ strength and directionality. Lastly, we sought significant differences connectivity patterns between the two populations characterizing the network graph features—i.e., global efficiency and node strength. Causal brain networks in PD show overall poorer and weaker connections compared to controls quantified as a reduction of global efficiency. Motor areas appear almost isolated, with a strongly impoverished information flow particularly from parietal and occipital cortices. This striking isolation of motor areas may reflect an impaired sensory-motor integration in PD. The identification of defective nodes/edges in PD network may be a biomarker of disease and a potential target for future interventional trials.

Teleconsultation demand classification and service analysis

Background To enhance teleconsultation management, demands can be classified into different patterns, and the service of each pattern demand can be improved. Methods For the effective teleconsultation classification, a novel ensemble hierarchical clustering method is proposed in this study. In the proposed method, individual clustering results are first obtained by different hierarchical clustering methods, and then ensembled by one-hot encoding, the calculation and division of cosine similarity, and network graph representation. In the built network graph about the high cosine similarity, the connected demand series can be categorized into one pattern. For verification, 43 teleconsultation demand series are used as sample data, and the efficiency and quality of teleconsultation services are respectively analyzed before and after the demand classification. Results The teleconsultation demands are classified into three categories, erratic, lumpy, and slow. Under the fixed strategies, the service analysis after demand classification reveals the deficiencies of teleconsultation services, but analysis before demand classification can’t. Conclusion The proposed ensemble hierarchical clustering method can effectively category teleconsultation demands, and the effective demand categorization can enhance teleconsultation management.