Network-based Investigation Research Articles

Artificial neural network-based investigation of factors impacting faulting in rigid pavements for dryfreeze and dry no-freeze climatic zone

Artificial neural network-based investigation of factors impacting faulting in rigid pavements for dryfreeze and dry no-freeze climatic zone

A network-based investigation on static liquefaction of sheared granular materials

A network-based investigation on static liquefaction of sheared granular materials

Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network.

Many diseases, particularly cardiometabolic disorders, exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with intermediate endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions. Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between diseases and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and heart failure. Triglycerides, another blood lipid with known genetic causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. This work demonstrates how association with clinical biomarkers can better explain the shared genetics between cardiometabolic disorders. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities. The generated ssDDN+ can be explored at https://hdpm.biomedinfolab.com/ddn/biomarkerDDN.

Neural Network-Based Investigation of Periodic Noise Reduction Methods for High-Resolution Infrared Line Scanning Images

In the realm of neural network-based noise reduction, conventional models predominantly address Gaussian and blur artifacts across entire images. However, they encounter notable challenges when directly applied to periodic noise characteristics of high-resolution infrared sequential imagery. The high resolution also complicates the construction of suitable datasets. Our study introduces an innovative strategy that transforms two-dimensional images into one-dimensional signals, eliminating the need for processing the full image. We have developed a simulated dataset that closely mirrors natural infrared line scanning images derived from the FLIR dataset. To address low-frequency periodic noise, we propose two neural-network-based denoising approaches. The first employs a neural network to deduce noise from the one-dimensional signal, while the second utilizes discrete Fourier transforms for noise prediction within the frequency domain. Our experimental results highlight the Restormer model’s exemplary performance in direct noise prediction, where denoised images attain a PSNR of around 41 and an SSIM close to 0.9 on simulated data, leaving minimal residual noise in the actual denoised images. Further, we investigate the influence of Fourier coefficients, as predicted by neural networks, on the denoising process in the second approach. Employing 12 frequency coefficients, the Restormer and NAFNet models both reach a PSNR near 34 and an SSIM of approximately 0.842.

Exploring Molecular Mechanisms of Traditional Persian Medicine-based Herbal Remedies for Depression: A Network-based Study

Background: Major Depressive Disorder (MDD) is a prevalent mental health condition that affects a significant portion of the general population. Despite the availability of pharmacological, psychological, and novel neurological approaches, optimal outcomes are only achieved in roughly 50% of patients. Traditional Persian Medicine (TPM) recommends a holistic approach to cure diseases based on the etiologic cause of malfunctions. This study aimed to explore the potential of integrating TPM and systems pharmacology approaches for the management of MDD. background: Depression is considered one of the most prevalent mental illnesses worldwide. Depression and the effectiveness of antidepressants are both influenced by genetic factors. Various anti-depressant remedies have been listed in Traditional Persian medicine (TPM) manuscripts, which may be useful as adjuvant therapy in the treatment of depression. The use of network-based studies is becoming more and more crucial for comprehending how medications apply their pharmacological actions. The growing fields of systems pharmacology (SP) and bioinformatics make use of computing to comprehend how drugs function at cellular and molecular levels. A common biological route that involves protein-protein (drug-target) interactions can be mechanistically understood using SP. Methods: A network-based investigation was conducted to explore the molecular mechanisms of TPM-based herbal remedies for depression. The study utilized a network pharmacology approach to identify active compounds, targets, and pathways involved in the treatment of depression. The study also conducted a literature review to identify the effectiveness of TPMbased remedies in treating depression. objective: The objective of the current study was to investigate the molecular mechanisms underlying TPM approach in the treatment of depression. Results: The study identified several active compounds, targets, and pathways involved in the treatment of depression by TPM-based herbal remedies. The study also identified several TPMbased remedies with anti-inflammatory properties that may be effective in treating depression. The literature review supported the potential of TPM remedies for the management of MDD. Conclusion: The integration of TPM and systems pharmacology may provide a holistic insight into the management of MDD. The study's findings suggest the potential of TPM-based remedies with anti-depressant properties, and further research is needed to understand the molecular mechanisms underlying their effectiveness. Integrating TPM-based remedies with bioinformatics may provide a complementary therapeutic avenue to MDD management and improve the quality of life for MDD patients. result: According to our study, many phytochemicals found in the plants used to make TPM-based prescriptions may impact various genes linked to depressive disorders. conclusion: The findings of these investigations suggested that many of the identified herbal remedies may have the potential to treat depression by activating a number of different pathways. The current study encourages further study of TPM as a model for creating efficient complementary therapies.

She Must Be Seeing Things! Gender disparity in camera department networks

This paper reports on a network-based investigation of the gendered nature of work in the screen sector. Using nine years of Australian film and television production data, we explore how the networks of project-based collaboration might explain the disparities in the career trajectories of men and women. Our analysis finds that projects with men as directors tend to reproduce familiar teams to the exclusion of women, while projects led by women in key creative roles tend to make more space for women. Moreover, we find that there is a significant number of men who only work with men (regardless of whether they have worked with them before), but no corresponding group of women who only work with women. Our findings bear on proposed equity interventions, especially government policies designed to encourage women to enter the industry. Ultimately, even accounting for the statistical domination of men in the network, we argue that inequities in the organisation of the screen sector appear to be driven much more by the closed network behaviours of men than they are by the positioning or behaviour of women.

Artificial neural network-based investigation on high-speed train-induced embankment vibration in frozen regions

High-speed trains (HSTs) induce vibrations that deteriorate the embankments of ballastless tracks, particularly in frozen regions, necessitating vibration assessments for existing high-speed railways (HSRs). This study developed an indirect thermal-dynamic coupled two-dimensional (2D) finite element (FE) model to investigate the dynamic responses of the embankment of ballastless tracks to various subgrades and subsoils, train speeds, and frost depths in frozen regions. The results indicate that subgrade is the key parameter to analysing the dynamic response of the train–track–ground system considering train speeds. A fast and high-accuracy prediction method for the vibration responses was developed using an artificial neural network (ANN) and used to predict the vibration level under different conditions. Prediction results determined train speeds under 400 km/h safe for HSRs. This study can improve knowledge of ballastless track substructure design and extend the applicability of the current railway code to higher train speed conditions in frozen regions.

Why is Ghana losing the war against illegal gold mining (Galamsey)? An artificial neural network-based investigations.

Ghana, like most resource-rich countries, is saddled and inundated with resource curse challenges. Key among them is the problem of illegal small-scale gold mining activities (ISSGMAs), which is mercilessly robbing the nation of its ecological integrity, despite attempts by successive governments to remedy the situation. In the midst of this challenge, Ghana, year in and year out, performs abysmally on environmental governance score (EGC) variables. Against this framework, this study intends to uniquely establish the drivers behind Ghana's failure to overcome ISSGMAs. To achieve this, a total of 350 respondents were sampled through a structured questionnaire, with a mixed method approach from selected host communities, believed to be the epicenters of ISSGMAs in Ghana. The questionnaires were administered from March to August, 2023. AMOS Graphics and IBM SPSS vs 23 were used to analyze the data. In particular, the novel hybrid artificial neural network (ANN) and linear regression techniques were adopted to establish the relational linkages among the constructs of the study and their respective contribution to ISSGMAs in Ghana. The study displays intriguing results that explain why Ghana has failed to be victorious over ISSGMAs. In particular, the findings of the study demonstrate that the three key drivers of ISSGMAs in Ghana, in a sequential and consecutive order are as follows: bureaucratic licensing regime/weak legal framework, political/traditional leadership failures, and corrupt institutional officials. Moreover, socioeconomic factors and proliferation of foreign miners/mining equipment were also observed to contribute significantly to ISSGMAs. While the study contributes to the ongoing debate on ISSGMAs, it also proffers valuable and practical solutions to the menace as well as theoretical implications.

Common factors among three types of cells aged in mice.

The greatest risk factor for the formation of numerous significant chronic disorders is aging. Understanding the core molecular underpinnings of aging can help to slow down the inevitable process. Systematic study of gene expression or DNA methylation data is possible at the transcriptomics and epigenetics levels. DNA methylation and gene expression are both affected by aging. Gene expression is an important element in the aging of Homo sapiens. In this work, we evaluated the expression of differentially expressed genes (DEGs), proteins, and transcription factors (TFs) in three different types of cells in mice: antibody-secreting cells, cardiac mesenchymal stromal cells, and skeletal muscle cells. The goal of this article is to uncover a common cause during aging among these cells in order to increase understanding about establishing complete techniques for preventing aging and improving people's quality of life. We conducted a comprehensive network-based investigation to establish which genes and proteins are shared by the three different types of aged cells. Our findings clearly indicated that aging induces gene dysregulation in immune, pharmacological, and apoptotic pathways. Furthermore, our research developed a list of hub genes with differential expression in aging responses that should be investigated further to discover viable anti-aging treatments.

A network-based investigation on the strong contact system of granular materials under isotropic and deviatoric stress states

The contact network is essential in linking the macroscale and microscale behaviors of granular materials. Particles and contacts are equivalent to nodes and links in the framework of the complex network graph, and then the network-based metrics can be applied to assess the network topology of granular materials. In this paper, several series of 3D DEM simulations are carried out, including isotropic compression and deviatoric loading tests for assemblies with different particle size distributions. The topological features, such as cluster size, connectivity, network distance and betweenness centrality, are captured for strong force subnetworks with different force thresholds in granular materials. It is found that the particle size distribution does not influence the general topological characteristics of granular materials, and the percolation is observed in the strong network with a proper force threshold for all granular samples. Chained features are consistently identified for strong networks extracted by around 1.5 times the average contact force at all stress states, and this force threshold also supports a giant cluster spanning the overall granular system. Assemblies under deviatoric states exhibit more identical features in strong network topology than that under isotropic states, giving a hint to understanding the structural uniqueness of granular mechanics.

Peripheral gene interactions define interpretable clusters of core ASD genes in a network-based investigation of the omnigenic theory

According to the recently proposed omnigenic theory, all expressed genes in a relevant tissue are contributing directly or indirectly to the manifestation of complex disorders such as autism. Thus, holistic approaches can be complementary in studying genetics of these complex disorders to focusing on a limited number of candidate genes. Gene interaction networks can be used for holistic studies of the omnigenic nature of autism. We used Louvain clustering on tissue-specific gene interaction networks and their subgraphs exclusively containing autism-related genes to study the effects of peripheral gene interactions. We observed that the autism gene clusters are significantly weaker connected to each other and the peripheral genes in non-neuronal tissues than in brain-related tissues. The biological functions of the brain clusters correlated well with previous findings on autism, such as synaptic signaling, regulation of DNA methylation, or regulation of lymphocyte activation, however, on the other tissues they did not enrich as significantly. Furthermore, ASD subjects with disruptive mutations in specific gene clusters show phenotypical differences compared to other disruptive variants carrying ASD individuals. Our results strengthen the omnigenic theory and can advance our understanding of the genetic background of autism.

Profiling Yeast Deletion Strains Using Sample Multiplexing and Network-Based Analyses.

The yeast, Saccharomyces cerevisiae, is a widely used model system for investigating conserved biological functions and pathways. Advancements in sample multiplexing have facilitated the study of the yeast proteome, yet many yeast proteins remain uncharacterized or only partially characterized. Yeast deletion strain collections are powerful resources for yeast proteome studies, uncovering the effects of gene function, genetic interactions, and cellular stresses. As complex biological systems cannot be understood by simply analyzing the individual components, a systems approach is often required in which a protein is represented as a component of large, interacting networks. Here, we evaluate the current state of yeast proteome analysis using isobaric tag-based sample multiplexing (TMTpro16) to profile the proteomes of 75 yeast deletion strains for which we measured the abundance of nearly 5000 proteins. Using statistical approaches, we highlighted covariance and regulation subnetworks and the enrichment of gene ontology classifications for covarying and coregulated proteins. This dataset presents a resource that is amenable to further data mining to study individual deletion strains, pathways, proteins, and/or interactions thereof while serving as a template for future network-based investigations using yeast deletion strain collections.

Integrative resource for network-based investigation of COVID-19 combinatorial drug repositioning and mechanism of action

SummaryAn effective monotherapy to target the complex and multifactorial pathology of SARS-CoV-2 infection poses a challenge to drug repositioning, which can be improved by combination therapy. We developed an online network pharmacology-based drug repositioning platform, COVID-CDR (http://vafaeelab.com/COVID19repositioning.html), that enables a visual and quantitative investigation of the interplay between the primary drug targets and the SARS-CoV-2-host interactome in the human protein-protein interaction network. COVID-CDR prioritizes drug combinations with potential to act synergistically through different, yet potentially complementary, pathways. It provides the options for understanding multi-evidence drug-pair similarity scores along with several other relevant information on individual drugs or drug pairs. Overall, COVID-CDR is a first-of-its-kind online platform that provides a systematic approach for pre-clinical in silico investigation of combination therapies for treating COVID-19 at the fingertips of the clinicians and researchers.

Distance disintegration delineates the brain connectivity failure of Alzheimer's disease

Alzheimer's disease (AD) is associated with brain network dysfunction. Network-based investigations of brain connectivity have mainly focused on alterations in the strength of connectivity; however, the network breakdown in AD spectrum is a complex scenario in which multiple pathways of connectivity are affected. To integrate connectivity changes that occur under AD-related conditions, here we developed a novel metric that computes the connectivity distance between cortical regions at the voxel level (or nodes). We studied 114 individuals with mild cognitive impairment, 24 with AD, and 27 healthy controls. Results showed that areas of the default mode network, salience network, and frontoparietal network display a remarkable network separation, or greater connectivity distances, from the rest of the brain. Furthermore, this greater connectivity distance was associated with lower global cognition. Overall, the investigation of AD-related changes in paths and distances of connectivity provides a novel framework for characterizing subjects with cognitive impairment; a framework that integrates the overall network topology changes of the brain and avoids biases toward unreferenced connectivity effects.

Evidence for a Resting State Network Abnormality in Adults Who Stutter.

Neural network-based investigations of stuttering have begun to provide a possible integrative account for the large number of brain-based anomalies associated with stuttering. Here we used resting-state EEG to investigate functional brain networks in adults who stutter (AWS). Participants were 19 AWS and 52 age-, and gender-matched normally fluent speakers. EEGs were recorded and connectivity matrices were generated by LORETA in the theta (4–8 Hz), alpha (8–12 Hz), beta1 (12–20 Hz), and beta2 (20–30 Hz) bands. Small-world propensity (SWP), shortest path, and clustering coefficients were computed for weighted graphs. Minimum spanning tree analysis was also performed and measures were compared by non-parametric permutation test. The results show that small-world topology was evident in the functional networks of all participants. Three graph indices (diameter, clustering coefficient, and shortest path) exhibited significant differences between groups in the theta band and one [maximum betweenness centrality (BC)] measure was significantly different between groups in the beta2 band. AWS show higher BC than control in right temporal and inferior frontal areas and lower BC in the right primary motor cortex. Abnormal functional networks during rest state suggest an anomaly of DMN activity in AWS. Furthermore, functional segregation/integration deficits in the theta network are evident in AWS. These deficits reinforce the hypothesis that there is a neural basis for abnormal executive function in AWS. Increased beta2 BC in the right speech–motor related areas confirms previous evidence that right audio–speech areas are over-activated in AWS. Decreased beta2 BC in the right primary motor cortex is discussed in relation to abnormal neural mechanisms associated with time perception in AWS.

Network pharmacology-based strategy for predicting active ingredients and potential targets of Yangxinshi tablet for treating heart failure

Ethnopharmacological relevanceYangxinshi tablet (YXST) is an effective treatment for heart failure and myocardial infarction; it consists of 13 herbal medicines formulated according to traditional Chinese Medicine (TCM) practices. It has been used for the treatment of cardiovascular disease for many years in China. Materials and methodsIn this study, a network pharmacology-based strategy was used to elucidate the mechanism of action of YXST for the treatment of heart failure. Cardiovascular disease-related protein target and compound databases were constructed for YXST. A molecular docking platform was used to predict the protein targets of YXST. The affinity between proteins and ingredients was determined using surface plasmon resonance (SPR) assays. The action modes between targets and representative ingredients were calculated using Glide docking, and the related pathways were predicted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. ResultsA protein target database containing 924 proteins was constructed; 179 compounds in YXST were identified, and 48 compounds with high relevance to the proteins were defined as representative ingredients. Thirty-four protein targets of the 48 representative ingredients were analyzed and classified into two categories: immune and cardiovascular systems. The SPR assay and molecular docking partly validated the interplay between protein targets and representative ingredients. Moreover, 28 pathways related to heart failure were identified, which provided directions for further research on YXST. ConclusionsThis study demonstrated that the cardiovascular protective effect of YXST mainly involved the immune and cardiovascular systems. Through the research strategy based on network pharmacology, we analysis the complex system of YXST and found 48 representative compounds, 34 proteins and 28 related pathways of YXST, which could help us understand the underlying mechanism of YSXT's anti-heart failure effect. The network-based investigation could help researchers simplify the complex system of YXSY. It may also offer a feasible approach to decipher the chemical and pharmacological bases of other TCM formulas.

Photosynthetic pigment-protein complexes as highly connected networks: implications for robust energy transport.

Photosynthetic pigment-protein complexes (PPCs) are a vital component of the light-harvesting machinery of all plants and photosynthesizing bacteria, enabling efficient transport of the energy of absorbed light towards the reaction centre, where chemical energy storage is initiated. PPCs comprise a set of chromophore molecules, typically bacteriochlorophyll species, held in a well-defined arrangement by a protein scaffold; this relatively rigid distribution leads to a viewpoint in which the chromophore subsystem is treated as a network, where chromophores represent vertices and inter-chromophore electronic couplings represent edges. This graph-based view can then be used as a framework within which to interrogate the role of structural and electronic organization in PPCs. Here, we use this network-based viewpoint to compare excitation energy transfer (EET) dynamics in the light-harvesting complex II (LHC-II) system commonly found in higher plants and the Fenna-Matthews-Olson (FMO) complex found in green sulfur bacteria. The results of our simple network-based investigations clearly demonstrate the role of network connectivity and multiple EET pathways on the efficient and robust EET dynamics in these PPCs, and highlight a role for such considerations in the development of new artificial light-harvesting systems.

Impairment in subcortical suppression in schizophrenia: Evidence from the fBIRN Oddball Task

Schizophrenia patients show widespread impairments in brain activity during oddball tasks, which involve responding to infrequent target stimuli while refraining from responding during continuous non-target stimuli. In a network-based investigation comparing schizophrenia or schizoaffective patients to healthy controls, we sought to clarify which networks were specifically associated with target detection using a multivariate analysis technique that identifies task-specific functional brain networks. We acquired data from the publicly available function biomedical informatics research network collaboration, including 58 patients and 50 controls. Two task-based functional brain networks were identified: (1) a response modulation network including bilateral temporal pole, supramarginal gyrus, striatum, and thalamus, on which patients showed decreased activity relative to controls; and (2) an auditory-motor response activation network, on which patients showed a slower return to baseline than controls, but no difference in peak activation. For both groups, baseline to peak activation of the response modulation network correlated negatively with peak to baseline activity in the response activation network, suggesting a role in suppressing the motor response following targets. Patients' impaired activity in the response modulation network, and subsequent longer return to baseline in the response activation network, correspond with their later and less accurate behavioral performance, suggesting that impairment in suppression of the auditory-motor response activation network could underlie oddball task deficits in schizophrenia. In addition, the magnitude of the activity in the response modulation network was correlated with intensity of delusions of reference, supporting the notion that increased referential ideation is associated with hyperactivity within the subcortical striatal-limbic network. Hum Brain Mapp 37:4640-4653, 2016. © 2016 Wiley Periodicals, Inc.

UniHI 7: an enhanced database for retrieval and interactive analysis of human molecular interaction networks

Unified Human Interactome (UniHI) (http://www.unihi.org) is a database for retrieval, analysis and visualization of human molecular interaction networks. Its primary aim is to provide a comprehensive and easy-to-use platform for network-based investigations to a wide community of researchers in biology and medicine. Here, we describe a major update (version 7) of the database previously featured in NAR Database Issue. UniHI 7 currently includes almost 350 000 molecular interactions between genes, proteins and drugs, as well as numerous other types of data such as gene expression and functional annotation. Multiple options for interactive filtering and highlighting of proteins can be employed to obtain more reliable and specific network structures. Expression and other genomic data can be uploaded by the user to examine local network structures. Additional built-in tools enable ready identification of known drug targets, as well as of biological processes, phenotypes and pathways enriched with network proteins. A distinctive feature of UniHI 7 is its user-friendly interface designed to be utilized in an intuitive manner, enabling researchers less acquainted with network analysis to perform state-of-the-art network-based investigations.

Septomics: Application of Systems Approaches to Sepsis - Potential for Personalized Management of Septic Patients

Sepsis is one of the most complex human diseases that continue to be associated with high mortality rates and substantial medical costs. It is an infection-induced, complex traits systemic syndrome that engages most of the bodys systems and organs. Whereas several etiologic agents can elicit sepsis, disease susceptibility, severity and therapeutic outcomes are modulated by a complex interaction of numerous environmental and constitutive (e.g., genomic) factors. To effectively understand and treat different types of sepsis in susceptible individuals, we need to adopt holistic systems approaches that utilize network-based investigations to integrate disease related complex traits, themselves affected by variations in the host genomic and epigenetic contexts, physiogenomic, metabolomic status as well as by variations in the pathogen. Importantly, a systems approach to sepsis can provide a roadmap to help identify both the host and the pathogen pathways underlying sepsis or variable therapeutic responses to sepsis. This paper introduces the emerging field of “septomics” – i.e., the application of high-throughput omics technologies to sepsis related complex traits - in diagnosing and/or modulating susceptibility and outcomes of sepsis. Additionally, we discuss how unbiased, network-based systems approaches and biotools can help better understand novel gene-by-environment interactions in sepsis, and identify genetic, physiologic and soluble (e.g., proteomics) biomarkers that can precisely predict disease susceptibility, progression or response to therapeutic interventions. Finally, we note that septomics also signals a new application of genome-based medicine in the context of global public health against existing and emerging infectious diseases that are greatly affecting the world populations in both developed and developing countries.