Identification Of Networks Research Articles

Comparative transcriptome analysis of slow-twitch and fast-twitch muscles in Kazakh horses

This study conducted a thorough analysis of the myofiber type composition in the extensor digitorum longus muscle (EDL) and soleus muscle (SOL) of Kazakh horses, across different genders (male and female). The results showed significant differences in myofiber type composition between EDL and SOL, with a higher proportion of Type I fibers in SOL muscles and a greater prevalence of Type II fibers in EDL muscles. Additionally, the myofiber diameter in Kazakh horses was relatively small, potentially related to the tenderness and edible quality of their muscles. Using high-throughput sequencing technology, we constructed 32 cDNA sequencing libraries and obtained high-quality read data. Gene expression analysis revealed 278 and 372 differentially expressed genes (DEGs) in EDL and SOL muscles, respectively, including genes related to muscle contraction, metabolism, and development. Intersection analysis of DEGs between genders showed that 60 DEGs were significantly different in both male and female horses. GO annotation and KEGG analysis further elucidated the roles of these DEGs in muscle structure, function, and cellular signaling. Protein-protein interaction (PPI) network analysis and identification of hub genes provided new insights into the molecular mechanisms underlying muscle growth and development. Finally, the reliability of the DEGs data was validated through quantitative real-time PCR (qRT-PCR). This study not only enhances our understanding of the biological characteristics of horse muscles but also provides potential molecular targets for improving horse muscle performance and health.

From transcriptomics to digital twins of organ function.

Cell level functions underlie tissue and organ physiology. Gene expression patterns offer extensive views of the pathways and processes within and between cells. Single cell transcriptomics provides detailed information on gene expression within cells, cell types, subtypes and their relative proportions in organs. Functional pathways can be scalably connected to physiological functions at the cell and organ levels. Integrating experimentally obtained gene expression patterns with prior knowledge of pathway interactions enables identification of networks underlying whole cell functions such as growth, contractility, and secretion. These pathways can be computationally modeled using differential equations to simulate cell and organ physiological dynamics regulated by gene expression changes. Such computational systems can be thought of as parts of digital twins of organs. Digital twins, at the core, need computational models that represent in detail and simulate how dynamics of pathways and networks give rise to whole cell level physiological functions. Integration of transcriptomic responses and numerical simulations could simulate and predict whole cell functional outputs from transcriptomic data. We developed a computational pipeline that integrates gene expression timelines and systems of coupled differential equations to generate cell-type selective dynamical models. We tested our integrative algorithm on the eicosanoid biosynthesis network in macrophages. Converting transcriptomic changes to a dynamical model allowed us to predict dynamics of prostaglandin and thromboxane synthesis and secretion by macrophages that matched published lipidomics data obtained in the same experiments. Integration of cell-level system biology simulations with genomic and clinical data using a knowledge graph framework will allow us to create explicit predictive models that mechanistically link genomic determinants to organ function. Such integration requires a multi-domain ontological framework to connect genomic determinants to gene expression and cell pathways and functions to organ level phenotypes in healthy and diseased states. These integrated scalable models of tissues and organs as accurate digital twins predict health and disease states for precision medicine.

Classification of Cardiovascular Arrhythmia Using Deep Learning Techniques: A Review

Deep Learning (DL), an offshoot of Machine Learning (ML) has emerged as a powerful and feasible solution for medical image analysis due to advancements in robust computer software and hardware technologies. It plays a key role in Cardiovascular disease (CVD) diagnosis by detecting anomalies in Electrocardiogram (ECG) signals. Cardiac arrhythmia, which refers to irregular heartbeat, may signal an early symptom of CVD and can lead to fatal outcomes if ignored. Accurate detection of arrhythmia is very challenging even for experts to distinguish between acute and chronic conditions in ECG readings. This triggered the focus of researchers to explore the application of Artificial Intelligence for ECG classification. Traditional machine learning methods use handcrafted features that require domain knowledge. The new era in DL makes the automatic detection of Cardiovascular Disease (CVD) possible. In this paper, an exhaustive review of DL-based techniques for ECG classification has been presented. Research findings in this survey indicate the challenges and issues with arrhythmia detection, such as single lead and multiple lead ECG signals, choice of the size of the training data set, and the number of arrhythmia classes, etc. The study also signifies that there is great scope for improving the performance of arrhythmia prediction models by employing hybrid ensemble learning, time series analysis using Recurrent Neural Network architectures and identification of unexplored classes of arrhythmia.

Bioinformatics analysis of potential common pathogenic mechanisms for COVID-19 and venous thromboembolism

BackgroundA growing body of research has shown that patients with coronavirus disease 2019 (COVID-19) have significantly higher rates of venous thromboembolism (VTE) than healthy. However, the mechanism remains incompletely elucidated. This study aimed to further investigate the molecular mechanisms underlying the development of this complication. MethodsThe gene expression profiles of COVID-19 and VTE were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the common differentially expressed genes (DEGs) for COVID-19 and VTE, functional annotation, a protein–protein interactions (PPI) network, module construction, and hub gene identification were performed. Finally, we constructed a transcription factor (TF)-gene regulatory network and a TF-miRNA regulatory network for hub genes. ResultsA total of 42 common DEGs were selected for subsequent analyses. Functional analyses showed that biological function and signaling pathways collectively participated in the development and progression of VTE and COVID-19. Finally, 8 significant hub genes were identified using the cytoHubba plugin, including RSL24D1, RPS17, RPS27, HINT1, COX7C, RPL35, RPL34, and NDUFA4, which had preferable values as diagnostic markers for COVID-19 and VTE. ConclusionsOur study revealed the common pathogenesis of COVID-19 and VTE. These common pathways and pivotal genes may provide new ideas for further mechanistic studies.

Impact of land-use change on ecological vulnerability in the Yellow River Basin based on a complex network model

The Yellow River Basin in China, which is characterized by significant soil erosion and arid to semi-arid conditions, has experienced severe ecological issues due to prolonged excessive development and land-use changes. Studies on ecosystem vulnerability have become a major basis for supporting the sustainable and healthy development of watershed ecosystems. Based on the land-use data of 2000, 2005, 2010, 2015, and 2020, this study used a complex network model to identify the key types of land-use transformation in the Yellow River Basin. The cascade failure model was used to evaluate the impact of land-use transformation on ecological vulnerability in the Yellow River Basin of China. The land-use changes in the Yellow River Basin from 2000 to 2020 exhibit significant heterogeneity in both quantity and direction. Among them, the total area of agricultural land showed a downward trend as a whole, and the area rebounded slightly from 2000 to 2015. The growth trend of construction land area is obvious, with a year-on-year increase of 46.81 % in 20 years. Through network identification, we found that ecological land types such as forest, grassland, and dryland are the primary factors contributing to land area outflow. Conversely, artificial land types including urban land, rural residential land, and other built-up land predominantly serve as factors for land area inflow. The Yellow River Basin experiences frequent land-use transitions, resulting in the poor stability of the land transfer network. However, the study reveals that the ecosystem service value increases as the areas of paddy fields, dryland, urban land, rural residential land, and bare land decrease; this trend contributes to mitigating ecological vulnerability. To ensure the overall stability of the ecosystem and implement healthy and sustainable development, the maximum reduction in the areas of dryland, forest, paddy, and grass should be limited to 40 %, 30 %, 50 %, and 50 %, respectively, in future planning and design processes. In conclusion, the land-use transformation in the Yellow River Basin of China has profound implications for ecological vulnerability. The research findings can provide valuable references for future ecological research on areas facing similar challenges in protecting ecological fragility.

Identification of a polyfuran network as the initial carbonization intermediate in cellulose pyrolysis: A comparative analysis with cellulosic hydrochars

Pyrolysis of cellulose is accompanied with different complex and superimposing transformations resulting in a broad mixture of isolable products with strong variations depending on the applied reaction conditions. The operative chemistry represents a challenge for analytical chemists and process engineers alike. Especially, the reactions leading to char formation cannot be described as sufficiently understood. In ongoing efforts to shed light on the major transformations during charring of cellulose, the occurrence of a thermostable condensed phase (TSCP) previously postulated as an important carbonization intermediate formed below 300 °C was revisited. It was attempted to isolate pure TSCP intermediates without cellulose contamination by applying dehydration catalysts known from cellulose based carbon fiber production and extensive isothermal treatments. It was shown that the weight loss levels off during isothermal treatment for 6 h in the temperature range of 200–250 °C, resulting in the formation of a common intermediate with an almost identical composition – irrespective of the employed temperature or catalyst. Moreover, isothermal treatment of pure cellulose at 270–280 °C for up to 12 hours also resulted in the formation of an intermediate which had a similar composition as the material prepared with added dehydration catalysts. To test the hypothesis of a proposed polyfuranic nature of the TSCP intermediate the prepared samples were compared with hydrochars obtained from hydrothermal treatment of cellulose as reference material for a polyfuranic humin. Similarities and differences are discussed and implications for the overall carbonization mechanism are summarized.

Identification of the miRNA-mRNA regulatory network in a mouse model of early fracture.

Fracture healing is a complex process that involves multiple molecular events, and the regulation mechanism is not fully understood. We acquired miRNA and mRNA transcriptomes of mouse fractures from the Gene Expression Omnibus database (GSE76197 and GSE192542) and integrated the miRNAs and genes that were differentially expressed in the control and fracture groups to construct regulatory networks. There were 130 differentially expressed miRNAs and 4,819 differentially expressed genes, including 72 upregulated and 58 downregulated miRNAs, along with 2,855 upregulated and 1964 downregulated genes during early fracture healing. Gene ontology analysis revealed that most of the differentially expressed genes were enriched in the extracellular matrix (ECM) structure and the ECM organization. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment suggested cell cycle, DNA replication, and mismatch repair were involved in the progression of fracture healing. Furthermore, we constructed a molecular network of miRNAs and mRNAs with inverse expression patterns to elucidate the molecular basis of miRNA-mRNA regulation in fractures. The regulatory network highlighted the potential targets, which may help to provide a mechanistic basis for therapies to improve fracture patient outcomes.

Advanced methods for gene network identification and noise decomposition from single-cell data

Central to analyzing noisy gene expression systems is solving the Chemical Master Equation (CME), which characterizes the probability evolution of the reacting species’ copy numbers. Solving CMEs for high-dimensional systems suffers from the curse of dimensionality. Here, we propose a computational method for improved scalability through a divide-and-conquer strategy that optimally decomposes the whole system into a leader system and several conditionally independent follower subsystems. The CME is solved by combining Monte Carlo estimation for the leader system with stochastic filtering procedures for the follower subsystems. We demonstrate this method with high-dimensional numerical examples and apply it to identify a yeast transcription system at the single-cell resolution, leveraging mRNA time-course experimental data. The identification results enable an accurate examination of the heterogeneity in rate parameters among isogenic cells. To validate this result, we develop a noise decomposition technique exploiting time-course data but requiring no supplementary components, e.g., dual-reporters.

State estimation and structure identification of complex networks with time‐varying delays and model uncertainty based on probabilistic graph neural network

SummaryIn this paper, the state estimation problem and structure identification problem are investigated for an array of complex networks with time‐varying delays and model uncertainty. Model uncertainty is generated by uncertain connections between nodes in complex networks. Combining probabilistic graph model and graph neural network, a probabilistic graph neural network is proposed to deal with the uncertainty, and the multiple linear terms of the errors between nodes are estimated. The purpose of the addressed state estimation problem is to design a state estimator such that, in the presence of the model uncertainty, the estimation error to converge to zero can be guaranteed and the explicit expression of the estimator parameters is given. Based on a Lyapunov function, the parameters of the estimator and the updating laws of the weights of probabilistic graph neural network can be obtained by the solutions to linear matrix inequalities. The purpose of the addressed structure identification problem is to infer a definite graph structure model based on Bayes' theorem in the presence of a stable probabilistic graph neural network and state estimator. Finally, an illustrative example is provided to demonstrate the feasibility and effectiveness of the developed state estimation and structure identification scheme.

The ultrasonographic diagnosis of lymph node varicose veins in the groin - an overlooked missing link in chronic venous disease?

Chronic venous disease is a common pathology characterized by valvular incompetence and venous hypertension. The venous network of the lymph nodes at the Scarpa triangle connects the superficial and the deep venous systems. This study aimed to describe infrainguinal intranodal venous dilatations and to evaluate the connection with peripheral venous disease. The study included 183 subjects (116 women, 67 men) who underwent Doppler ultrasound examinations of the venous system of the inferior limb in the context of chronic venous disease. The diagnosis of lymph node varices was based on well-defined criteria and the severity of the lymph node varices was established using an original classification. There was a statistically significant, moderately strong association, between the presence of intranodal varices and the great saphenous vein reflux (φ=0.341, p=0.000). There was a moderate-to-high positive correlation between intranodal varices and the chronic venous disease stage (rrb=0.457, p=0.000). Patients with more advanced stages of chronic venous insufficiency have a higher probability of presenting intranodal varices. Lymph node venous network identification could considerably impact clinical decision-making and treatment choices.

Single-cell nascent RNA sequencing unveils coordinated global transcription

Transcription is the primary regulatory step in gene expression. Divergent transcription initiation from promoters and enhancers produces stable RNAs from genes and unstable RNAs from enhancers1,2. Nascent RNA capture and sequencing assays simultaneously measure gene and enhancer activity in cell populations3. However, fundamental questions about the temporal regulation of transcription and enhancer–gene coordination remain unanswered, primarily because of the absence of a single-cell perspective on active transcription. In this study, we present scGRO–seq—a new single-cell nascent RNA sequencing assay that uses click chemistry—and unveil coordinated transcription throughout the genome. We demonstrate the episodic nature of transcription and the co-transcription of functionally related genes. scGRO–seq can estimate burst size and frequency by directly quantifying transcribing RNA polymerases in individual cells and can leverage replication-dependent non-polyadenylated histone gene transcription to elucidate cell cycle dynamics. The single-nucleotide spatial and temporal resolution of scGRO–seq enables the identification of networks of enhancers and genes. Our results suggest that the bursting of transcription at super-enhancers precedes bursting from associated genes. By imparting insights into the dynamic nature of global transcription and the origin and propagation of transcription signals, we demonstrate the ability of scGRO–seq to investigate the mechanisms of transcription regulation and the role of enhancers in gene expression.

Current spectral norm and phase variation based fault region identification for active distribution network

The paper presents a fault region identification method for the active distribution network (ADN) with limited PMU. First, PMU configuration and region division strategies are proposed based on the network topology. Next, the difference in three-phase current variations between the normal and fault regions of the ADN is analyzed. A multi-dimensional state monitoring matrix is built using the measured current data. The spectral norm ratio coefficient is constructed based on the 2-norm to lower the complexity of the multi-dimensional state monitoring matrix and quantify the difference in state changes before and after the fault occurs in each region. Then, the fault region is identified by combining each region’s spectral norm ratio coefficient and the change of the current phase. Finally, an IEEE 33-node simulation model is created to simulate faults under different working conditions. According to the simulation results, the suggested approach is less impacted by fault type, neutral point grounding mode, and transition resistance. Furthermore, even if the communication does not match the rigorous synchronization requirements, the proposed method can still complete the fault identification of the distribution network correctly and has high robustness.

Multiple rumor source identification in social networks leveraging community and monitor information

Multiple rumor source identification (MRSI) in social networks has become a challenging problem for controlling rumors from spreading automatically. Even though several techniques have been introduced for MRSI, most of them were introduced based on the fact that they knew the underlying diffusion model in advance, which is mostly not possible in real-world scenarios. So, this paper proposes a new algorithm called Multi-Source Detection, which uses Community and Monitor information (MSDCM). Our algorithm has two stages: detecting multiple rumor sources using the community and monitor information. First, identify the communities in the network to find the suspicious communities with the possible sources using the monitors’ information available in those communities. Next, detect a single source in each of the most likely suspicious communities by back-tracking from each monitor using edge weights in that community. Experimented on several datasets, including small-scale, large-scale, and artificial networks. Our results demonstrate that the designed algorithm is more efficient than the state-of-the-art algorithms.

Identification of miRNA-Target Gene-Transcription Factor Regulatory Network as Functional Motifs Involved in Glomerular Diabetic Nephropathy

Identification of miRNA-Target Gene-Transcription Factor Regulatory Network as Functional Motifs Involved in Glomerular Diabetic Nephropathy

Convex optimization topology identification of distribution network under distributed conditions

Abstract The distributed phenomenon will occupy a very important proportion in the future distribution network. In order to ensure the stable operation of the power system, it is very important to study the topological adaptability identification of new distribution networks. In this paper, an adaptive topology identification regression matrix for a low-voltage distribution network is proposed, which is combined with a convex optimization algorithm to build a topology identification model for a low-voltage distribution network. This method can identify the topology of the distribution network accurately, which is helpful to improve the operation efficiency and safety of the power system.

Exploring Dynamic Changes in HIV-1 Molecular Transmission Networks and Key Influencing Factors: Cross-Sectional Study.

The HIV-1 molecular network is an innovative tool, using gene sequences to understand transmission attributes and complementing social and sexual network studies. While previous research focused on static network characteristics, recent studies' emphasis on dynamic features enhances our understanding of real-time changes, offering insights for targeted interventions and efficient allocation of public health resources. This study aims to identify the dynamic changes occurring in HIV-1 molecular transmission networks and analyze the primary influencing factors driving the dynamics of HIV-1 molecular networks. We analyzed and compared the dynamic changes in the molecular network over a specific time period between the baseline and observed end point. The primary factors influencing the dynamic changes in the HIV-1 molecular network were identified through univariate analysis and multivariate analysis. A total of 955 HIV-1 polymerase fragments were successfully amplified from 1013 specimens; CRF01_AE and CRF07_BC were the predominant subtypes, accounting for 40.8% (n=390) and 33.6% (n=321) of the specimens, respectively. Through the analysis and comparison of the basic and terminal molecular networks, it was discovered that 144 sequences constituted static molecular networks, and 487 sequences contributed to the formation of dynamic molecular networks. The findings of the multivariate analysis indicated that the factors occupation as a student, floating population, Han ethnicity, engagement in occasional or multiple sexual partnerships, participation in anal sex, and being single were independent risk factors for the dynamic changes observed in the HIV-1 molecular network, and the odds ratio (OR; 95% CIs) values were 2.63 (1.54-4.47), 1.83 (1.17-2.84), 2.91 (1.09-7.79), 1.75 (1.06-2.90), 4.12 (2.48-6.87), 5.58 (2.43-12.80), and 2.10 (1.25-3.54), respectively. Heterosexuality and homosexuality seem to exhibit protective effects when compared to bisexuality, with OR values of 0.12 (95% CI 0.05-0.32) and 0.26 (95% CI 0.11-0.64), respectively. Additionally, the National Eight-Item score and sex education experience were also identified as protective factors against dynamic changes in the HIV-1 molecular network, with OR values of 0.12 (95% CI 0.05-0.32) and 0.26 (95% CI 0.11-0.64), respectively. The HIV-1 molecular network analysis showed 144 sequences in static networks and 487 in dynamic networks. Multivariate analysis revealed that occupation as a student, floating population, Han ethnicity, and risky sexual behavior were independent risk factors for dynamic changes, while heterosexuality and homosexuality were protective compared to bisexuality. A higher National Eight-Item score and sex education experience were also protective factors. The identification of HIV dynamic molecular networks has provided valuable insights into the characteristics of individuals undergoing dynamic alterations. These findings contribute to a better understanding of HIV-1 transmission dynamics and could inform targeted prevention strategies.

An intelligent optimized users resource searching and nearest peer identification in Mobile networks

SummaryMobile networks have advanced in many fields to share information with others. But finding the required resources for each user to share the data is a complex task because of the moving environment. So, the present research article has introduced a novel Chimp‐based Random Forest Peer Identification Framework (CbRFPIF) in the mobile environment. Initially, the required nodes were designed dynamically in the moving background. Hence, the designed wireless moving environment has included sender, peer, and receiver nodes. Moreover, the Chimp fitness model predicted the users' requested resources in the primary stage, then the nearest peer and the user locations were predicted. Consequently, the requested resource by users was distributed to the particular closest peers, and the peer forwarded the data to the specific users. Finally, the planned model is simulated in the NS2 environment, and the communication parameters were calculated and compared with other associated models, which gained the highest throughput and search rate.

Effect of Cross-Well Natural Fractures and Fracture Network on Production History Match and Well Location Optimization in an Ultra-Deep Gas Reservoir

Understanding subsurface natural fracture systems is crucial to characterize well production dynamics and long-term productivity potential. In addition, the placement of future wells can benefit from in-depth fracture network connectivity investigations, vastly improving new wells’ profitability and life cycles if they are placed in dense, well-connected natural fracture zones. In this study, a novel natural fracture calibration workflow is proposed. This workflow starts with the extraction of sector geology and a natural fracture model from the pre-built full-field model. Then, a cross wellbore discrete fracture network (CW-DFN) is created using a novel CW-DFN generation tool, based on image log data. An innovative fracture network identification tool is developed to detect the interconnected regional fracture network (IcFN) with CW-DFN. The non-intrusive embedded discrete fracture model (EDFM) is utilized to numerically incorporate the complex IcFN and CW-DFN in a reservoir simulation, and it is history-matched by tuning their conductivities. This workflow is applied to a single vertical well within a natural fracture carbonate reservoir in Northwest China. The study results show that the number of CW-DFNs is 11, and the number of IcFNs is 72. The non-intersected natural fractures only account for 5.5% of the production, and thus can be removed to improve simulation efficiency. The history-matching absolute average relative deviation (AARD) is 15.16%. The calibrated effective fracture permeability is 280 millidarcy, with an aperture of 0.001 m, equating to a conductivity of 0.28 millidarcy-meter. The 30-year gas production forecast is estimated to be 1.66 billion cubic meters based on a history-matched model. Finally, if the well is drilled to the east of the sector, 30-year production declines to 1.33 billion cubic meters (a reduction of 20%). However, if the well is drilled to the west of the sector, 30-year production increases to 2 billion cubic meters (an improvement of 20.5%).

Adaptive pinning control to topology identification of output stochastic complex dynamical network

This paper concerns partial topology identification problem of stochastic delayed complex dynamical network. Compared with many network models, the novel output coupling is introduced to the network model of this paper, which does not require all state variables of the vertex to be observable. By utilizing the graph-theoretic method and adaptive pinning control technology, the identification criteria of multi-weighted stochastic complex dynamical networks are obtained. Ultimately, the Lorenz system and small-world network are chosen to do numerical simulations. The simulation results indicate that the successful identification time (SIT) of stochastic delayed complex dynamical network with output coupling is reduced by about 56% on average by comparing with the SIT of the network without output coupling, which implies the superiority of output coupling.

Rotation angle vibration suppression for variable-length flexible manipulator based on neural network identification with sliding-mode controller

Rotation angle vibration suppression for variable-length flexible manipulator based on neural network identification with sliding-mode controller