Hub Nodes Research Articles

Functional Connectivity and White Matter integrity of intercommunity hub nodes in Subjective Cognitive Decline

AbstractBackgroundSubjective Cognitive Decline (SCD) is considered an early preclinical stage of Alzheimer’s Disease and related dementias (ADRD) which may show promise for targeted preventative treatments. Subtle changes in functional connectivity and white matter integrity (WMI) may occur in SCD. Few studies have applied both functional graph theory (GT) and diffusional kurtosis imaging (DKI) techniques in tandem to identify differences in vulnerable network hubs. We addressed these issues by applying GT and DKI to study differences in intercommunity brain hubs. The hypothesis was that in hubs identified in healthy controls, SCD subjects’ hubs would show decreased GT diversity coefficient (DC) and DKI mean kurtosis (MK).MethodImages were acquired on a Siemens PRISMA scanner. Matlab was used to calculate DC and identify 5 hubs in 107 healthy controls (HC) and DC in 30 SCD (93 females, mean age = 67.51). SCD classification was determined by a comprehensive clinician evaluation or Everyday Cognition average item score>1.6, with objectively healthy cognitive performance (Montreal Cognitive Assessment score>22). Hub DC was the target in GLMM analysis, with diagnosis as predictor. Mean Kurtosis (MK) was analyzed in DSI Studio’s cross‐sectional connectometry using the hubs as seeds. Only the covariate of gray matter volume approached significance (p = .059), and was included in analysis. Mean age, education, head motion, and sex balance did not significantly differ between groups.ResultAll hubs had lower DC in SCD compared to HC (p ≤ .006). Connectometry identified positive correlation between MK and diagnosis in Left hemisphere tracts associated with two hubs: Inferior Fronto‐Occipital, Extreme capsule, and Uncinate tracts from Insular cortex, and Superior Longitudinal, Superior Corticalstriatal, Arcuate, Frontal Aslant, and Corticalspinal tracts from Middle Frontal gyrus.Conclusion Conclusion Rs‐fMRI findings show that intercommunity functional hubs are weaker in SCD compared to healthy individuals. Furthermore, the study identified a relationship of higher MK values in SCD for hubs located in insular cortex and middle frontal gyrus, all left hemisphere biased. Higher MK in SCD also runs counter to our hypothesis but is supported by recent paradoxical increase theories of decline. WMI differences between groups may be subtler than functional changes, warranting examination of the 2 modalities’ relationship.

Genome-Wide Characterization of Fennel (Anethum foeniculum) MiRNome and Identification of its Potential Targets in Homo sapiens and Arabidopsis thaliana: An Inter and Intra-species Computational Scrutiny.

MicroRNAs could be promising biomarkers for various diseases, and small RNA drugs have already been FDA approved for clinical use. This area of research is rapidly expanding and has significant potential for the future. Fennel (Anethum foeniculum) is a highly esteemed spice plant with economic and medicinal benefits, making it an invaluable asset in the pharmaceutical industry. To characterize the fennel miRNAs and their Arabidopsis thaliana and Homo sapience targets with functional enrichment analysis and human disease association. A homology-based computational approach characterized the MiRnome of the Anethum foeniculumgenome and assessed its impact on Arabidopsis thaliana andHomo sapience transcriptomes. In addition, functional enrichment analysis was evaluated for both species' targets. Moreover, PPI network analysis, hub gene identification, and MD simulation analysis of the top hub node with fennel miRNA were incorporated. We have identified 100 miRNAs of fennel and their target genes, which include 2536 genes in Homo sapiens and 1314 genes in Arabidopsis thaliana. Functional enrichment analysis reveals 56 Arabidopsis thaliana targets of fennel miRNAs showed involvement in metabolic pathways. Highly enriched human KEGG pathways were associated with several diseases, especially cancer. The protein-protein interaction network of human targets determined the top ten nodes; from them, seven hub nodes, namely MAPK1, PIK3R1, STAT3, EGFR, KRAS, CDC42, and SMAD4, have shown their involvement in the pancreatic cancer pathway. Based on the Blast algorithm, 21 fennel miRNAs are homologs to 16 human miRNAs were predicted; from them, the CSPP1 target was a common target for afo-miR11117a-3p and has-miR-6880-5p homologs miRNAs. Our results are the first to report the 100 fennel miRNAs, and predictions for their endogenous and human target genes provide a basis for further understanding of Anethum foeniculum miRNAs and the biological processes and diseases with which they are associated.

On clustering coherent optics point-to-multipoint trees for cost-effective bandwidth assignment in MANs

Point-to-multipoint (P2MP) coherent pluggable transceivers leveraging digital subcarrier multiplexing (DSCM) techniques are gaining momentum among telecommunication operators as a technology that can potentially provide broadband connectivity in metropolitan area networks (MANs) with flexible bandwidth allocation at reduced costs. This technology allows trees to be built where central metro (hub) nodes are interconnected with remote access/aggregation (spoke) nodes with dedicated and flexible bandwidth slices, enabling better resource management, simplified architectures, and reduced costs. This paper proposes a modified version of AI/ML clustering techniques that concentrates on the same P2MP tree as those low-rate leaf nodes with uncorrelated daily traffic patterns in a cost-effective manner, thus allowing 10%–18% tree reductions. This, when compared with fixed P2P transceivers, translates into potential CAPEX savings between 40% and 50% in the different scenarios here considered.

Bioinformatics Analysis Identifies Potential Hub Genes, Therapeutic Agents and Crucial Pathways in the Pathogenesis of Refsum Disease

Refsum Disease is a infrequent, inherited, autosomal recessive condition. The purpose of this study was to investigate prospective therapeutic drugs to cure Refsum disease and to determine the critical genes/pathways through bioinformatics analysis. Using the text mining method, Refsum disease-related genes were discovered and MCODE plugin, GO & KEGG analyses was used to examine modules. A PPI network was created by STRING and visualized in Cytoscape. Drug-gene interactions were investigated using the drug-gene interaction database. Using the text mining of gene (TMG) method, 64 distinct genes in Homo sapiens linked to Refsum illness. There were 71 edges and 48 nodes in a PPI network. 13 hub node genes were discovered using a cluster analysis of filtering nodes. The genes were then significantly elevated in protein trans-membrane transport, protein import to the peroxisome matrix and fatty acid beta-oxidation, according to the enrichment analysis. We discovered that two already-approved medications could target two of the 13 chosen genes. The findings revealed the hub genes for Refsum illness to be SLC27A2, AGXT, GNPAT, CRAT, HSD17B4, AMACR, CAT, SCP2, PEX13, PEX10, PEX1, PEX7, and PEX2. Additionally, Ibuprofen and alcohol were named as prospective therapeutic agents for the management and therapy of Refsum’s illness. Only bioinformatics tools were used in this study further in-vitro and in-vivo studies are required because phenotypic differences will vary from person to person depending on the expressivity of the gene.

Evolution of structural characteristics and its determinants of global human hair waste trade network based on temporal exponential random graph model

Human hair waste (HHW), used primarily for wigs, saw its global trade value rise from $6.36 million in 1991 to $149.4 million in 2020. This study maps the global HHW trade network from 1991 to 2020, analyzing its structural evolution using the temporal exponential random graph model (TERGM). The network reveals hub nodes acting as bridges, with Southeast Asia, particularly India and China, as key players in exports and imports respectively. Factors like fashion trends, religious culture, and linguistic and geographic elements influence trade network formation. Countries with pronounced fashion inclinations and religious disparities exhibit stronger trade cooperation. This study offers insights for businesses to navigate HHW markets and emphasizes the importance of this resource for a stable global supply chain, promoting a shift towards a circular development model.

ITRAQ-based proteomics reveals potential markers and treatment pathways for acute Achilles tendon rupture

BackgroundDue to its limited blood supply and irregular mechanical loading, the Achilles tendon is the most frequently ruptured tendon. Despite the rising incidence of acute Achilles tendon rupture (AATR), the optimal treatment remains controversial. Missed diagnoses and delayed treatments lead to poor outcomes and limited treatment options. This study aimed to identify potential biomarkers for diagnosing and developing therapies for AATR.MethodsWe employed the coupled isobaric tag for relative and absolute quantitation-liquid chromatography–electrospray ionization-tandem mass spectrometry approach to investigate protein expression in tissues from AATR patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to identify differentially expressed proteins (DEPs) between AATR patients and healthy individuals. A protein–protein interaction (PPI) network of DEPs was constructed using the Search Tool for the Retrieval of Interacting Genes. The screened hub genes were selectively verified by immunohistochemical staining.ResultsWe identified 410 DEPs between AATR patients and controls. The DEPs were significantly enriched in GO terms such as the extracellular region, extracellular region part, and defense response, as well as KEGG pathways, including complement and coagulation cascades, focal adhesion, and regulation of actin cytoskeleton. The main hub nodes in the PPI network comprised fibronectin 1 (FN1), major histocompatibility complex, class I, B (HLA-B), filamin A (FLNA), heat shock 27-kDa protein 1 (HSPB1), heat shock protein family A member 5 (HSPA5), apolipoprotein A4 (APOA4), and myosin IC (MYO1C). Although APOA4 and collagens I, II, and III were detectable in healthy tendons, immunohistochemical staining confirmed higher expression of these proteins in the acutely ruptured Achilles tendon.ConclusionsOur findings lay a foundation for further molecular studies of AATR. Inflammation and age-related degeneration may contribute to the pathogenesis of AATR. Moreover, the identified DEPs could be potential biomarkers for AATR diagnosis and treatment.

Consensus analysis of chain star networks coupled by leaf nodes

Abstract Coupled star networks coupled by hub nodes have attracted significant interest due to the interplay between their coupling forms and dynamics. This paper introduces a novel family of chain star networks that are connected via a small number of leaf nodes. The coherence of these star networks is quantified using the Laplacian spectrum. By leveraging the network structures, we calculate the characteristic polynomial of the Laplacian matrix and obtain an exact solution for network coherence in relation to network parameters. In addition, we explore the robustness of network coherence against variations in network parameters. Furthermore, we compare the coherence performance of coupled star networks across different coupling forms Our findings demonstrate that the networks coupled by leaf nodes exhibit better coherence.

Towards Further Understanding the Role of Curcumin in Wound Healing: A Systems Biology Approach

Background: Curcumin, a polyphenolic compound present in the turmeric plant (Curcuma longa) is well known for its anti-aging, anti-tumor, anti-inflammatory, anti-mutagenic and antioxidative properties due to which turmeric has been used as a medicinal plant from ages. Objective: Our current study aims at finding the most potent targets of curcumin displaying efficient binding by using various systems biology tools. Around 560 genes related to wound healing are extracted from PubMed using the combination of words like wound healing, curcumin, Homo sapiens, etc. Methods: For the investigation of the mechanism of curcumin interference at the system level, proteinprotein interaction network (PPIN) of the proteins involved in the wound healing process was generated using the STRING database. The noise of the data generated in PPIN was removed by modulation of the network with the help of Molecular Complex Detection (MCODE) and finding the seed proteins. GO enrichment analysis along with network topology analysis and molecular docking will help in pinpointing the most important and efficient curcumin binding proteins. Results: The findings of this study shows that besides SCR, PPARG and MAPK3, AKT3 is one of the novel targets for wound healing as the binding affinity of AKT3 is -4.53 Kcal/mol, which is close to SRC with the highest binding affinity with binding energy of -6.6 Kcal/mol. The binding energy of PPARG was -6.2 Kcal/mol and for MAPK3 the binding energy was -5.95 Kcal/mol. Besides AKT3, FLT4 and RPS6KB1 were also the novel targets of curcumin with binding affinities of -4.13 Kcal/mol and -4.04 Kcal/mol. In network analysis, we obtained PIK3R1 as a connector node which acted as a hub node with highest betweenness score. Conclusion: From the results obtained, we can say that curcumin finds its role in all four stages of wound healing and it also prevents the healing cells from turning into tumors.

Hippocampal tandem mass tag (TMT) proteomics analysis during kindling epileptogenesis in rat

Epilepsy is a neurological disorder that remains difficult to treat due to the lack of a clear molecular mechanism and incomplete understanding of involved proteins. To identify potential therapeutic targets, it is important to gain insight into changes in protein expression patterns related to epileptogenesis. One promising approach is to analyze proteomic data, which can provide valuable information about these changes.In this study, to evaluate the changes in gene expression during epileptogenesis, LC-MC2 analysis was carried out on hippocampus during stages of electrical kindling in rat models. Subsequently, progressive changes in the expression of proteins were detected as a result of epileptogenesis development. In line with behavioral kindled seizure stages and according to the proteomics data, we described epileptogenesis phases by comparing Stage3 versus Control (S3/C0), Stage5 versus Stage3 (S5/S3), and Stage5 versus Control group (S5/C0). Gene ontology analysis on differentially expressed proteins (DEPs) showed significant changes of proteins involved in immune responses like Csf1R, Aif1 and Stat1 during S3/C0, regulation of synaptic plasticity like Bdnf, Rac1, CaMK, Cdc42 and P38 during S5/S3, and nervous system development throughout S5/C0 like Bdnd, Kcc2 and Slc1a3.There were also proteins like Cox2, which were altered commonly among all three phases.The pathway enrichment analysis of DEPs was also done to discover molecular connections between phases and we have found that the targets like Csf1R, Bdnf and Cox2 were analyzed throughout all three phases were highly involved in the PPI network analysis as hub nodes. Additionally, these same targets underwent changes which were confirmed through Western blotting. Our results have identified proteomic patterns that could shed light on the molecular mechanisms underlying epileptogenesis which may allow for novel targeted therapeutic strategies.

MiR-223–3p overexpressed adipose mesenchymal stem cell-derived exosomes promote wound healing via targeting MAPK10

BackgroundAdipose mesenchymal stem cell (AMSC)-derived exosomes are promising novel factors for wound repair and regeneration. This study aimed to explore the potential roles and underlying mechanisms of specific miRNA in wound healing using AMSC-derived exosomes as carriers. MethodsThe expression profiles of GSE197840 were downloaded to screen for differentially expressed miRNAs (DEmiRNAs), and the corresponding genes of the identified miRNAs were predicted. Next, miRNA-mRNA co-expression networks were constructed and the genes in these networks were subjected to functional analysis. miR-223–3p overexpressed AMSCs were then established to isolate exosomes, and the effects of AMSC-derived exosomes carrying miR-223–3p on wound healing and the related potential mechanisms were further investigated in vivo. Results35 DEmiRNAs were identified and a co-expression network containing 22 miRNAs and 91 target genes was constructed. Based on the network, miR-223–3p was the hub node and the genes were significantly enriched in 15 GO terms of biological processes and 14 KEGG pathways, including cAMP, PI3K-Akt, cGMP-PKG, neurotrophin signaling pathway, and dopaminergic synapse. Then, miR-223–3p overexpressed AMSCs-derived exosomes were successfully extracted, and miR-223–3p was found to directly bind with MAPK10. In vivo experiments validated that AMSCs-derived exosomal miR-223–3p could promote wound healing, and up-regulated α-SMA, CD31, COL1A1, COL2A1, COL3A1, and down-regulated MAPK10, TNF-α, IL-β, and IL-6. ConclusionsAMSC-derived exosomal miR-223–3p may accelerate wound healing by targeting MAPK10.

Uncovering the System Vulnerability and Criticality of Human Brain Under Dynamical Neuropathological Events in Alzheimer's Disease.

Despite the striking efforts in investigating neurobiological factors behind the acquisition of amyloid-β (A), protein tau (T), and neurodegeneration ([N]) biomarkers, the mechanistic pathways of how AT[N] biomarkers spreading throughout the brain remain elusive. To disentangle the massive heterogeneities in Alzheimer's disease (AD) progressions and identify vulnerable/critical brain regions to AD pathology. In this work, we characterized the interaction of AT[N] biomarkers and their propagation across brain networks using a novel bistable reaction-diffusion model, which allows us to establish a new systems biology underpinning of AD progression. We applied our model to large-scale longitudinal neuroimages from the ADNI database and studied the systematic vulnerability and criticality of brains. Our model yields long term prediction that is statistically significant linear correlated with temporal imaging data, produces clinically consistent risk prediction, and captures the Braak-like spreading pattern of AT[N] biomarkers in AD development. Our major findings include (i) tau is a stronger indicator of regional risk compared to amyloid, (ii) temporal lobe exhibits higher vulnerability to AD-related pathologies, (iii) proposed critical brain regions outperform hub nodes in transmitting disease factors across the brain, and (iv) comparing the spread of neuropathological burdens caused by amyloid-β and tau diffusions, disruption of metabolic balance is the most determinant factor contributing to the initiation and progression of AD.

Literature-Based Discovery to Elucidate the Biological Links between Resistant Hypertension and COVID-19.

Multiple studies have reported new or exacerbated persistent or resistant hypertension in patients previously infected with COVID-19. We used literature-based discovery to identify and prioritize multi-scalar explanatory biology that relates resistant hypertension to COVID-19. Cross-domain text mining of 33+ million PubMed articles within a comprehensive knowledge graph was performed using SemNet 2.0. Unsupervised rank aggregation determined which concepts were most relevant utilizing the normalized HeteSim score. A series of simulations identified concepts directly related to COVID-19 and resistant hypertension or connected via one of three renin-angiotensin-aldosterone system hub nodes (mineralocorticoid receptor, epithelial sodium channel, angiotensin I receptor). The top-ranking concepts relating COVID-19 to resistant hypertension included: cGMP-dependent protein kinase II, MAP3K1, haspin, ral guanine nucleotide exchange factor, N-(3-Oxododecanoyl)-L-homoserine lactone, aspartic endopeptidases, metabotropic glutamate receptors, choline-phosphate cytidylyltransferase, protein tyrosine phosphatase, tat genes, MAP3K10, uridine kinase, dicer enzyme, CMD1B, USP17L2, FLNA, exportin 5, somatotropin releasing hormone, beta-melanocyte stimulating hormone, pegylated leptin, beta-lipoprotein, corticotropin, growth hormone-releasing peptide 2, pro-opiomelanocortin, alpha-melanocyte stimulating hormone, prolactin, thyroid hormone, poly-beta-hydroxybutyrate depolymerase, CR 1392, BCR-ABL fusion gene, high density lipoprotein sphingomyelin, pregnancy-associated murine protein 1, recQ4 helicase, immunoglobulin heavy chain variable domain, aglycotransferrin, host cell factor C1, ATP6V0D1, imipramine demethylase, TRIM40, H3C2 gene, COL1A1+COL1A2 gene, QARS gene, VPS54, TPM2, MPST, EXOSC2, ribosomal protein S10, TAP-144, gonadotropins, human gonadotropin releasing hormone 1, beta-lipotropin, octreotide, salmon calcitonin, des-n-octanoyl ghrelin, liraglutide, gastrins. Concepts were mapped to six physiological themes: altered endocrine function, 23.1%; inflammation or cytokine storm, 21.3%; lipid metabolism and atherosclerosis, 17.6%; sympathetic input to blood pressure regulation, 16.7%; altered entry of COVID-19 virus, 14.8%; and unknown, 6.5%.

Effects of plant-derived protein and rapeseed oil on growth performance and gut microbiomes in rainbow trout

BackgroundRainbow trout (Oncorhynchus mykiss) is becoming popular with the increased demand for fish protein. However, the limited resources and expense of fish meal and oil have become restrictive factors for the development of the rainbow trout related industry. To solve this problem, plant-derived proteins and vegetable oils have been developed as alternative resources. The present study focuses on evaluating the effects of two experimental diets, FMR (fish meal replaced with plant-derived protein) and FOR (fish oil replaced with rapeseed oil), through the alteration of the gut microbiota in triploid rainbow trout. The commercial diet was used in the control group (FOM).ResultsAmplicon sequencing of the 16S and 18S rRNA genes was used to assess the changes in gut bacteria and fungi. Our analysis suggested that the α-diversity of both bacteria and fungi decreased significantly in the FMR and FOR groups, and β-diversity was distinct between FOM/FMR and FOM/FOR based on principal coordinate analysis (PCoA). The abundance of the Planctomycetota phylum increased significantly in the FMR group, while that of Firmicutes and Bacteroidetes decreased. We also found that the fungal phylum Ascomycota was significantly increased in the FMR and FOR groups. At the genus level, we found that the abundance of Citrobacter was the lowest and that of pathogenic Schlesneria, Brevundimonas, and Mycoplasma was highest in the FMR and FOR groups. Meanwhile, the pathogenic fungal genera Verticillium and Aspergillus were highest in the FMR and FOR groups. Furthermore, canonical correspondence analysis (CCA) and network analysis suggested that the relatively low-abundance genera, including the beneficial bacteria Methylobacterium, Enterococcus, Clostridium, Exiguobacterium, Sphingomonas and Bacteroides and the fungi Papiliotrema, Preussia, and Stachybotrys, were positively correlated with plant protein or rapeseed oil. There were more modules that had the above beneficial genera as the hub nodes in the FMR and FOR groups.ConclusionsOur study suggested that the FMR and FOR diets could affect the gut microbiome in rainbow trout, which might offset the effects of the dominant and pathogenic microbial genera. This could be the underlying mechanism of explaining why no significant difference was observed in body weight between the different groups.

Decoding the mechanism of andrographolide to combat hepatocellular carcinoma: a network pharmacology integrated molecular docking and dynamics approach

HepatoCellular Carcinoma, being one of the most mortally convoluted malignancy with mounting number of occurrences across the world and being classified as the third most prevalent cause of cancer-associated mortalities and sixth most prevalent neoplasia. The active phytoconstituent andrographolide, derived from Andrographis paniculata is conveyed to reconcile a number of human ailments including various oncologies. However, the molecular mechanism underlying the anti-oncogenic effects of Andrographolide on HCC remains skeptical and unclear, emerging as a budding challenge for researchers and oncologists. The present study intends to analyze the underlying pharmacological mechanism of Andrographolide over HCC, established via assimilated approach of network pharmacology. Herein, the Network pharmacology stratagem was instigated to investigate potential HCC targets. The Andrographolide targets along with HCC targets were extracted from multiple databases. A total of 162 potential overlapping targets among HCC and Andrographolide were obtained and further subjected to gene ontology and Pathway enrichment analysis by employing OmicsBox and DAVID database, respectively. Subsequently, Protein–protein interaction network construction by Cytoscape software identified the top 10 hub nodes which were validated by survival and expression analysis. Further, the results derived from molecular docking and dynamic simulations by CB-Dock2 server and Desmond module (Schrodinger software) indicate ALB, CCND1, HIF1A, TNF, and VEGFA as potential Andrographolide related targets with high binding affinity and promising complex stability. Our findings not only reveal the antioncogenic role of andrographolide but also provide novel insights illuminating the identified targets as scientific foundation for anti-oncogenic clinical application of andrographolide in HCC therapeutics. Communicated by Ramaswamy H. Sarma

Identification and validation of key biomarkers and potential therapeutic targets for primary open-angle glaucoma.

Primary open-angle glaucoma (POAG) is a prevalent cause of blindness worldwide, resulting in degeneration of retinal ganglion cells and permanent damage to the optic nerve. However, the underlying pathogenetic mechanisms of POAG are currently indistinct, and there has been no effective nonsurgical treatment regimen. The objective of this study is to identify novel biomarkers and potential therapeutic targets for POAG. The mRNA expression microarray datasets GSE27276 and GSE138125, as well as the single-cell high-throughput RNA sequencing (scRNA-seq) dataset GSE148371 were utilized to screen POAG-related differentially expressed genes (DEGs). Functional enrichment analyses, protein-protein interaction (PPI) analysis, and weighted gene co-expression network analysis (WGCNA) of the DEGs were performed. Subsequently, the hub genes were validated at a single-cell level, where trabecular cells were annotated, and the mRNA expression levels of target genes in different cell clusters were analyzed. Immunofluorescence and quantitative real-time PCR (qPCR) were performed for further validation. DEGs analysis identified 43 downregulated and 32 upregulated genes in POAG, which were mainly enriched in immune-related pathways, oxidative stress, and endoplasmic reticulum (ER) stress. PPI networks showed that FN1 and DUSP1 were the central hub nodes, while GPX3 and VAV3 were screened out as hub genes through WGCNA and subsequently validated by qPCR. Finally, FN1, GPX3, and VAV3 were determined to be pivotal core genes via single-cell validation. The relevant biomarkers involved in the pathogenesis of POAG, may serve as potential therapeutic targets. Further studies are necessary to unveil the mechanisms underlying the expression variations of these genes in POAG.

Network Toxicology and Molecular Docking to Investigate the Non-AChE Mechanisms of Organophosphate-Induced Neurodevelopmental Toxicity.

Organophosphate pesticides (OPs) are toxic substances that contaminate aquatic environments, interfere with the development of the nervous system, and induce Neurodevelopmental Toxicity (NDT) in animals and humans. The canonical mechanism of OP neurotoxicity involves the inhibition of acetylcholinesterase (AChE), but other mechanisms non-AChE are also involved and not fully understood. We used network toxicology and molecular docking to identify molecular targets and toxicity mechanisms common to OPs. Targets related to diazinon-oxon, chlorpyrifos oxon, and paraoxon OPs were predicted using the Swiss Target Prediction and PharmMapper databases. Targets related to NDT were compiled from GeneCards and OMIM databases. In order to construct the protein-protein interaction (PPI) network, the common targets between OPs and NDT were imported into the STRING. Network topological analyses identified EGFR, MET, HSP90AA1, and SRC as hub nodes common to the three OPs. Using the Reactome pathway and gene ontology, we found that signal transduction, axon guidance, cellular responses to stress, and glutamatergic signaling activation play key roles in OP-induced NDT.

Identification of LncPVT1 and CircPVT1 as prognostic biomarkers in human colorectal polyps

LncPVT1 and CircPVT1 are isoforms for the PVT1 gene and are associated with cancer progression and carcinogenesis. Our study investigated the expression of LncPVT1 and CircPVT1 in colon adenoma polyps. 40 tissues of colorectal polyps and 40 normal-adjacent tissues (NATs) were taken. The expression of LncPVT1 and CircPVT1 was evaluated through qRael-Time PCR. The relation between expression and features of clinicopathological was explored. The ceRNA network was constructed by LncPVT1 and CircPVT1 and predicted miRNAs and miRNAs targets. Further, hub nodes in this network were determined using the cytoHubba package. Over-expressed LncPVT1 and CircPVT1 were differentiated in polyp and NATs. The expression level of LncPVT1 and CircPVT1 were significantly higher in adenoma polyps than in hyperplastic polyps. The area under the curve of the ROC estimate for the LncPVT1 and CircPVT1 was 0.74 and 0.77, respectively. A positive correlation was observed between the LncPVT1 expression and CircPVT1. Three miRNAs, including hsa-miR-484, hsa-miR-24-3p, hsa-miR-423-5p, and CircPVT1, were detected as ceRNA hub nodes. In this study, expression profiles of LncPVT1 and CircPVT1 were significantly higher in precancerous polyps. In addition, based on our in silico analysis, LncPVT1, CircPVT1/miR-484, miR-24-3p, miR-423-5p/PLAGL2 axis might be involved in colon cancer development. LncPVT1 and CircPVT1 can be prescribed as warning problems as potential prognostic biomarkers in patients with pre-CRC colon polyps.

Functional and spatial rewiring principles jointly regulate context-sensitive computation.

Adaptive rewiring provides a basic principle of self-organizing connectivity in evolving neural network topology. By selectively adding connections to regions with intense signal flow and deleting underutilized connections, adaptive rewiring generates optimized brain-like, i.e. modular, small-world, and rich club connectivity structures. Besides topology, neural self-organization also follows spatial optimization principles, such as minimizing the neural wiring distance and topographic alignment of neural pathways. We simulated the interplay of these spatial principles and adaptive rewiring in evolving neural networks with weighted and directed connections. The neural traffic flow within the network is represented by the equivalent of diffusion dynamics for directed edges: consensus and advection. We observe a constructive synergy between adaptive and spatial rewiring, which contributes to network connectedness. In particular, wiring distance minimization facilitates adaptive rewiring in creating convergent-divergent units. These units support the flow of neural information and enable context-sensitive information processing in the sensory cortex and elsewhere. Convergent-divergent units consist of convergent hub nodes, which collect inputs from pools of nodes and project these signals via a densely interconnected set of intermediate nodes onto divergent hub nodes, which broadcast their output back to the network. Convergent-divergent units vary in the degree to which their intermediate nodes are isolated from the rest of the network. This degree, and hence the context-sensitivity of the network's processing style, is parametrically determined in the evolving network model by the relative prominence of spatial versus adaptive rewiring.

Bifractality of fractal scale-free networks.

The presence of large-scale real-world networks with various architectures has motivated active research towards a unified understanding of diverse topologies of networks. Such studies have revealed that many networks with scale-free and fractal properties exhibit the structural multifractality, some of which are actually bifractal. Bifractality is a particular case of the multifractal property, where only two local fractal dimensions d_{f}^{min} and d_{f}^{max}(>d_{f}^{min}) suffice to explain the structural inhomogeneity of a network. In this work we investigate analytically and numerically the multifractal property of a wide range of fractal scale-free networks (FSFNs) including deterministic hierarchical, stochastic hierarchical, nonhierarchical, and real-world FSFNs. Then we demonstrate how commonly FSFNs exhibit the bifractal property. The results show that all these networks possess the bifractal nature. We conjecture from our findings that any FSFN is bifractal. Furthermore, we find that in the thermodynamic limit the lower local fractal dimension d_{f}^{min} describes substructures around infinitely high-degree hub nodes and finite-degree nodes at finite distances from these hub nodes, whereas d_{f}^{max} characterizes local fractality around finite-degree nodes infinitely far from the infinite-degree hub nodes. Since the bifractal nature of FSFNs may strongly influence time-dependent phenomena on FSFNs, our results will be useful for understanding dynamics such as information diffusion and synchronization on FSFNs from a unified perspective.

A Multi-Objective Tri-Level Algorithm for Hub-and-Spoke Network in Short Sea Shipping Transportation

Hub-and-Spoke (H&S) network modeling is a form of transport topology optimization in which network joins are connected through intermediate hub nodes. The Short Sea Shipping (SSS) problem aims to efficiently disperse passenger flows involving multiple vessel routes and intermediary hubs through which passengers are transferred to their final destination. The problem contains elements of the Hub-and-Spoke and Travelling Salesman, with different levels of passenger flows among islands, making it more demanding than the typical H&S one, as the hub selection within nodes and the shortest routes among islands are internal optimization goals. This work introduces a multi-objective tri-level optimization algorithm for the General Network of Short Sea Shipping (GNSSS) problem to reduce travel distances and transportation costs while improving travel quality and user satisfaction, mainly by minimizing passenger hours spent on board. The analysis is performed at three levels of decisions: (a) the hub node assignment, (b) the island-to-line assignment, and (c) the island service sequence within each line. Due to the magnitude and complexity of the problem, a genetic algorithm is employed for the implementation. The algorithm performance has been tested and evaluated through several real and simulated case studies of different sizes and operational scenarios. The results indicate that the algorithm provides rational solutions in accordance with the desired sub-objectives. The multi-objective consideration leads to solutions that are quite scattered in the solution space, indicating the necessity of employing formal optimization methods. Typical Pareto diagrams present non-dominated solutions varying at a range of 30 percent in terms of the total distance traveled and more than 50 percent in relation to the cumulative passenger hours. Evaluation results further indicate satisfactory algorithm performance in terms of result stability (repeatability) and computational time requirements. In conclusion, the work provides a tool for assisting network operation and transport planning decisions by shipping companies in the directions of cost reduction and traveler service upgrade. In addition, the model can be adapted to other applications in transportation and in the supply chain.