Network Analysis Research Articles

Characteristics and health impacts of bioaerosols in animal barns: A comprehensive study

Bioaerosols produced during animal production have potential adverse effects on the health of workers and animals. Our objective was to investigate characteristics, antibiotic-resistance genes (ARGs), and health risks of bioaerosols in various animal barns. Poultry and swine barns had high concentrations of airborne bacteria (11156 and 10917 CFU/m3, respectively). Acinetobacter, Clostridium sensu stricto, Corynebacterium, Pseudomonas, Psychrobacter, Streptococcus, and Staphylococcus were dominant pathogenic bacteria in animal barns, with Firmicutes being the most abundant bacterial phylum. Based on linear discriminant analysis effect size (LEfSe), there were more discriminative biomarkers in cattle barns than in poultry or swine barns, although the latter had the highest abundance of bacterial pathogens and high abundances of ARGs (including tetM, tetO, tetQ, tetW sul1, sul2, ermA, ermB) and intI1). Based on network analyses, there were higher co-occurrence patterns between bacteria and ARGs in bioaerosol from swine barns. Furthermore, in these barns, relative abundance of bacteria in bioaerosol samples was greatly affected by environmental factors, mainly temperature, relative humidity, and concentrations of CO2, NH3, and PM2.5. This study provided novel data regarding airborne bio-contaminants in animal enclosures and an impetus to improve management to reduce potential health impacts on humans and animals.

Scaling up One Health: A network analysis in Lao PDR

BackgroundOne Health focuses on sustainable health for humans, animals, and ecosystems. The approach has been well demonstrated, yet most efforts have not been scaled up. Understanding the organisations involved in scaling up processes is critical to translating research into practice. The Lao People's Democratic Republic has successfully implemented One Health projects for multiple decades; however, the organisational network has not been described and scaling up efforts have been limited. MethodsData from organisations involved in One Health projects over the past five years were collected by key-informant interview or workshop. The network was investigated using a mixture of quantitative network analysis and qualitative thematic analysis. ResultsThe organisational network was quantitatively described as sparse and centralised. Organisations were required to harness pre-existing relationships to maximise scarce resources and make co-ordination and alignment of priorities more efficient. A lack of international organisations in the top 10% of resource sharing metrics suggests a potential disconnect between donors. This was reflected in the challenges faced by national organisations and a feeling of being stretched thin over numerous externally funded projects with donor-driven priorities. ConclusionsIt appears that high-level political support for country ownership of development and aid priorities remains unrealised. Developing network capacity and capability may assist scaling up efforts and build resilience in the network and its core organisations. This may allow for the inclusion of more development, education, environment, and water, sanitation, and hygiene organisations that were perceived to be lacking. Future One Health programmes should focus on practical activities that do not overload staff capacity. There is much for One Health to learn about the art of scaling up and organisations are encouraged to include implementation science in their research to inform future scaling up efforts.

Enterotoxin-related genes PPFIA4 and SCN3B promote colorectal cancer development and progression.

To identify the role of enterotoxin-related genes in colorectal cancer (CRC) development and progression. Upregulated differentially expressed genes shared by three out of five Gene Expression Omnibus (GEO) data sets were included to screen the key enterotoxin-induced oncogenes (EIOGs) according to criteria oncogene definition, enrichment, and protein-protein interaction (PPI) network analysis, followed by prognosis survival, immune infiltration, and protential drugs analyses was performed via integration of RNA-sequencing data and The Cancer Genome Atlas-derived clinical profiles. We screened nine common key EIOGs from at least three GEO data sets. A Cox proportional hazards regression models verified that more alive cases, decreased overall survival, and highest 4-year survival prediction in CRC patients with high-risk score. Protein tyrosine phosphatase receptor type F polypeptide-interacting protein alpha-4 (PPFIA4), STY11, SCN3B, and SPTBN5 were shared in the same PPI network. Immune infiltration results showed that SCN3B and synaptotagmin 11 expression were obviously associated with B cell, macrophage, myeloid dendritic cell, neutrophils, and T cell CD4+ and CD8+ in both colon adenocarcinoma and rectal adenocarcinoma. CHIR-99021, MLN4924, and YK4-279 were identified as the potential drugs for treatment. Finally, upregulated EIOGs genes PPFIA4 and SCN3B were found in colon adenocarcinoma and PPFIA4 and SCN3B were proved to promote cell proliferation and migration in vitro. We demonstrated here that EIOGs promoting a malignancy phenotype was related with poor survival and prognosis in CRC, which might be served as novel therapeutic targets in CRC management.

Liner shipping network vulnerability to component disruptions: A China-Europe container flow analysis

This study conducts a vulnerability analysis of the China-Europe liner shipping network, including all relevant liner shipping services and China Railway Express services. The standard multicommodity network flow model is extended to assess network vulnerability and the relative importance of ports and services by simulating container flow redistribution in various disruption scenarios. The results show the network is highly resilient to both individual port disruptions and individual service disruptions. Additionally, the distinct shipping network structures of the three shipping alliances result in varying degrees of vulnerability, highlighting the significance of diversified market suppliers in enhancing the overall network resilience. Specifically, the Ocean Alliance is vital in facilitating China-Europe trade, especially along the Mediterranean route, and is more resilient to individual port disruptions.

A comparative analysis of convolutional neural networks for breast cancer prediction

Breast cancer continues to be a substantial worldwide health concern, affecting millions of individuals each year; this emphasizes the critical nature of early detection in order to enhance patient prognoses. The present study aims to assess the classification performance of three convolutional neural network (CNN) architectures-visual geometry group 19 (VGG19), AlexNet, and residual network 50 (ResNet50)-with respect to breast cancer detection in medical images. Thorough assessments, encompassing metrics such as accuracy, precision, recall, and F-score, were undertaken to evaluate the diagnostic performance of the models. ResNet50 consistently outperforms other models, as evidenced by its highest accuracy and F-score. The research highlights the significant importance of carefully choosing suitable architectures for medical image analysis, with a specific focus on the detection of breast cancer. In addition, it demonstrates the capacity of deep learning models, such as ResNet50, to improve the diagnosis of breast cancer with exceptional precision and sensitivity, which is critical for reducing the occurrence of false positives and negatives in clinical environments. In addition, computational efficiency is taken into account; AlexNet is recognized as the most efficient model, which is advantageous in environments with limited resources. This study advances medical image processing by demonstrating the potential of CNNs in the detection of breast cancer. The results of this study establish a fundamental basis for sub- sequent inquiries and suggest approaches to improve timely detection and treatment, which will ultimately be advantageous for both patients and healthcare professionals.

Performance Analysis of Deep Learning and Machine Learning Methods for Music Genre Classification System

Performance Analysis of Deep Learning and Machine Learning Methods for Music Genre Classification System

Security analysis and prediction of multi-relay networks over Fisher–Snedecor [formula omitted] fading channels

This paper investigates the physical layer security (PLS) of multi-relay networks over Fisher-Snedecor F fading channels. Precise expressions for outage probability (OP), intercept probability (IP), secure outage probability (SOP), and probability of strictly positive secrecy capacity (SPSC) are derived based on this considered model, which the SOP and SPSC are provided in a unified manner using Meijer’s G-function. Moreover, simulation consequences of Monte Carlo experiments validate derived mathematical expressions. Through theoretical analysis and simulation results, the interesting results are that larger msRD, smaller mRD, μRE and msRE the better secrecy performance of this system. Because deep learning has characteristics of low latency, strong learning ability, and adaptability, in order to pursue lower latency and universal applicability, we combine deep learning and wireless communication. On the basis of constructing a data set with exact closed-form expressions, the secrecy performance is predicted by utilizing a multi-layer perceptron (MLP) deep learning model. Moreover, by comparing the three algorithms of convolutional neural network (CNN), deep neural network (DNN), and two-layer long short-term memory network (LSTM), the MLP has the advantages of higher performance and lower complexity. Experimentations exhibit that the presented algorithm enhances the precision by 75.69% with LSTM for comparison and reduces the time complexity by 80.05% compared with DNN.

Innovations in science education: A bibliometric exploration of trends and future directions

This article carries out a bibliometric research review to present the evolving landscape of innovations in science education. The study acknowledges the dynamism of the contemporary educational practices and uses the bibliometric analyses for identification of trends, assessment of impacts of scholarly outputs, and mapping the future directions in the field of science education innovation. Starting with a review of the standard bibliometric indicators, e.g., citation analysis and co-authorship, the review lays down the theoretical basis for the understanding of the scientific impact of the innovations in science education. The analysis, moreover, consists of identifying thematic foci and emerging topics through keyword mapping, which together present a holistic picture of the intellectual map of the field. The review points to seminal works and trends, demonstrating the nature of interdisciplinarity in research in science education innovation. By the analysis of collaborative networks and research hotspots the research not only demonstrates the present status of the field, but also predicts its future directions, which could help researchers and educators at the same time. Also, as a retrospective tool, the article explores the general relevance of bibliometric insights for educators, administrators, and other researchers. It highlights the application of bibliometrics in evidence-based decision-making, curricula design and international collaboration, because it is common knowledge that it can trigger the great revolution in innovative science education. This bibliometric review generates useful outcomes, which are beneficial to academic discussions and provide guidelines for future research efforts as a key reference for researchers who would like to have a deep, general overview of the innovations in science education.

Classification of Circular Mass of Breast Cancer Using Artificial Neural Network vs. Discriminant Analysis in Medical Image Processing

Classification of Circular Mass of Breast Cancer Using Artificial Neural Network vs. Discriminant Analysis in Medical Image Processing

Classification of Circular Mass of Breast Cancer Using Artificial Neural Network vs. Discriminant Analysis in Medical Image Processing

Classification of Circular Mass of Breast Cancer Using Artificial Neural Network vs. Discriminant Analysis in Medical Image Processing

School violence: a literature review based on Social Network Analysis

Resumo A pesquisa, classificada como revisão de literatura, resultou em 276 artigos, publicados no Brasil entre 1998 e 2023, em 100 periódicos diferentes hospedados nas plataformas SciELO.br, Educ@ e PEPSIC e conta com 603 autores vinculados a 182 instituições distintas, reunindo 1.064 palavras-chaves. O trabalho, a semelhança de uma cartografia, utilizou Análise de Redes Sociais e identifica os autores com maior número de produções, as coautorias em rede, as instituições com maior contribuição. A palavra-chave de maior ocorrência é bullying e indisciplina e direitos surgem como relevantes somente na rede social. A revista Ensaio é aquela que apresenta maior número de artigos no tema.

Exploratory Study of Differentially Expressed Genes of Peripheral Blood Monocytes in Patients with Carotid Atherosclerosis.

The abundance of circulating monocytes is closely associated with the development of atherosclerosis in humans. This study aimed to further research into diagnostic biomarkers and targeted treatment of carotid atherosclerosis (CAS). We performed transcriptomics analysis through weighted gene co-expression network analysis (WGCNA) of monocytes from patients in public databases with and without CAS. Differentially expressed genes (DEGs) were screened by R package limma. Diagnostic molecules were derived by the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) algorithms. NetworkAnalyst, miRWalk, and StarBase databases assisted in the construction of diagnostic molecule regulatory networks. The DrugBank database predicted drugs targeting the diagnostic molecules. RT-PCR tested expression profiles. From 14,369 hub genes and 61 DEGs, six differentially expressed monocyte-related hub genes were significantly associated with immune cells, immune responses, monocytes, and lipid metabolism. LASSO and SVM-RFE yielded five genes for CAS prediction. RT-PCR of these genes showed HMGB1 was upregulated, and CCL3, CCL3L1, CCL4, and DUSP1 were down-regulated in CAS versus controls. Then, we constructed and visualized the regulatory networks of 9 transcription factors (TFs), which significantly related to 5 diagnostic molecules. About 11 miRNAs, 19 lncRNAs, and 39 edges centered on four diagnostic molecules (CCL3, CCL4, DUSP1, and HMGB1) were constructed and displayed. Eleven potential drugs were identified, including ibrutinib, CTI-01, roflumilast etc. Conclusion: A set of five biomarkers were identified for the diagnosis of CAS and for the study of potential therapeutic targets.

Artificial neural networks analysis predicts long-term fistula function in hemodialysis patients following percutaneous transluminal angioplasty

Kidney failure is particularly common in the United States, where it affects over 700,000 individuals. It is typically treated through repeated sessions of hemodialysis to filter and clean the blood. Hemodialysis requires vascular access, in about 70% of cases through an arteriovenous fistula (AVF) surgically created by connecting an artery and vein. AVF take 6 weeks or more to mature. Mature fistulae often require intervention, most often percutaneous transluminal angioplasty (PTA), also known as fistulaplasty, to maintain the patency of the fistula. PTA is also the first-line intervention to restore blood flow and prolong the use of an AVF, and many patients undergo the procedure multiple times. Although PTA is important for AVF maturation and maintenance, research into predictive models of AVF function following PTA has been limited. Therefore, in this paper we hypothesize that based on patient-specific information collected during PTA, a predictive model can be created to help improve treatment planning. We test a set of rich, multimodal data from 28 patients that includes medical history, AVF blood flow, and interventional angiographic imaging (specifically excluding any post-PTA measurements) and build deep hybrid neural networks. A hybrid model combining a 3D convolutional neural network with a multi-layer perceptron to classify AVF was established. We found using this model that we were able to identify the association between different factors and evaluate whether the PTA procedure can maintain primary patency for more than 3 months. The testing accuracy achieved was 0.75 with a weighted F1-score of 0.75, and AUROC of 0.75. These results indicate that evaluating multimodal clinical data using artificial neural networks can predict the outcome of PTA. These initial findings suggest that the hybrid model combining clinical data, imaging and hemodynamic analysis can be useful to treatment planning for hemodialysis. Further study based on a large cohort is needed to refine the accuracy and model efficiency.

Network analysis of additional clinical features of (Internet) gaming disorder.

There are dozens of screening instruments purporting to measure the (Internet) gaming disorder (IGD/GD). The two prominent diagnostic manuals, DSM-5 and ICD-11, list several additional diagnostic or clinical features and problems (e.g., neglect of sleep, neglect of daily duties, health deterioration) that should co-occur or be caused by the IGD/GD. It remains unclear how specific IGD/GD operationalizations (different screening scales) are related to these functional impairments. To explore this, data on six measures of IGD/GD (IGDS9-SF, GDSS, GDT, GAMES test, two self-assessments) and 18 additional diagnostic features were collected from a sample of 1009 players who play digital games at least 13h per week. A network approach was utilized to determine which operationalization is most strongly associated with functional impairment. In most of the networks, IGD/GD consistently emerged as the most central node. The similar centrality of IGD/GD, irrespective of its definition (DSM-5 or ICD-11) or operationalization, provides support for the valid comparison or synthesis of results from studies that used instruments coming from both DSM-5 and ICD-11 ontologies, but only if the goal is to evaluate IGD/GD relationships to other phenomena, not the relationships between the symptoms themselves.

Clinical efficacy and gene chip expression analysis of Shenzhu Guanxin recipe granules in patients with intermediate coronary lesions.

To evaluate the clinical efficacy and safety of Shenzhu Guanxin recipe granules (, SGR) in treating patients with intermediate coronary lesions (ICL), and to investigate the potential mechanism though a transcriptome sequencing approach. ICL patients with Qi deficiency and phlegm stasis were adopted and randomly assigned to a case group or a control by random number generator in a 1:1 randomization ratio to evaluate the clinical efficacy. There was no significant difference between the two groups in coronary computed tomography angiography related indexes in the two groups before and after intervention. Through the gene chip expression analysis, it is finally concluded that there are 355 differential mRNAs (190 up-regulated genes and 165 down regulated genes) when compared the SGR group and placebo group. Through protein-protein interaction network analysis of differentially expressed genes, 10 hub genes were finally obtained: CACNA2D2, CACNA2D3, DNAJC6, FGF12, SGSM2, CACNA1G, LRP6, KIF25, OXTR, UPB1. SGR combined with Western Medicine can be safely used to treat ICL patients with Qi deficiency and phlegm stasis. The possible mechanism of action and relevant gene loci and pathway were proposed.

Chronic lead poisoning–induced budgerigar liver damage, gut microbiota dysbiosis, and metabolic disorder

Birds are sensitive to heavy metal pollution, and lead (Pb) contamination can negatively affect their liver and gut. Therefore, we used budgerigars to examine liver and gut toxicosis caused by Pb exposure in bird, and the possible toxic mechanisms. The findings showed Pb exposure increased liver weight and decreased body weight. Moreover, histopathological and immunofluorescence assay results demonstrated obvious liver damage and cell apoptosis increased in Pb- treated budgerigars. Quantitative polymerase chain reaction (qPCR) results also showed Pb caused an increase in apoptosis by inhibiting the PPAR-γ/PI3K/Akt pathway. The gut microbe analyses indicated Firmicutes, Proteobacteria, and Bacteroidetes were dominant microbial phyla, and Network analysis results shown Arthrobacter, Bradyrhizobium and Alloprevotella as the hubs of Modules I, II, and III, respectively. Phenylpropanoids and polyketides, Organoheterocyclic compounds, Organic oxygen compounds, and Organic nitrogen compounds were dominant metabolite superclasses. Tauroursodeoxycholic acid, taurochenodeoxycholic acid (sodium salt), and 2-[2-(5-bromo-2-pyridyl)diaz-1-enyl]-5-(diethylamino)phenol were significantly enriched in the Pb-treated group. It showed that 41 Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologues and 183 pathways differed between the Pb-treated and control budgerigars using microbial and metabolomic data. Moreover, orthogonal partial least-squares discrimination analysis (OPLS-DA) based on microbial and metabolite indicated distinct clusters in the Pb-treated and control groups. Additionally, the correlation analysis results indicated that a positive correlation for the Pb-treated and control groups between gut microbiota and metabolomic data, respectively. Furthermore, the microenvironment of the gut and liver were found to affect each other, and this study demonstrated heavy metal especially Pb may pose serious health risks to birds through the “gut–liver axis” too.

Suicide awareness homophily in adolescent peer support networks: A Swiss cross-sectional social network analysis

ObjectiveSuicide awareness, encompassing knowledge, attitudes, and behaviors related to suicide, plays a critical role in primary suicide prevention, especially among adolescents. However, little is known about how perceived suicide awareness is apparent in peer support networks in this population. This study examined the presence of suicide awareness homophily in adolescent peer support networks. We also explored other patterns of homophily and identified factors associated with the in-degree popularity of adolescents. MethodsWe used baseline data from a non-randomized, cluster-controlled trial assessing the effectiveness of a universal suicide prevention intervention in Swiss secondary schools (n = 194). We assessed perceived suicide awareness, support networks (including in-degree popularity, i.e., receiving a high number of nominations as a supportive peer), and other covariates. Data were analyzed using social network analyses. ResultsWe found evidence of suicide awareness homophily in peer support networks, where adolescents with high suicide awareness were more likely to connect with peers having high suicide awareness (p < .001). The same applied to those with low suicide awareness (p < .001). Age also emerged as a significant homophily factor. Girls (p = .024) and adolescents with high instrumental social support (p = .008) were more likely to be popular in peer support networks. ConclusionsThis study highlighted the homophily of suicide awareness in peer support networks and the need to focus on strengthening peer support networks and promoting suicide awareness in adolescents, particularly for those with low suicide awareness. Future suicide prevention programs, including peer-led interventions, should consider these findings to better target vulnerable subgroups and reduce suicide-related disparities.

Proteomics analysis of coronary atherosclerotic heart disease with different Traditional Chinese Medicine syndrome types before and after percutaneous coronary intervention.

To investigate the underlying protein molecular mechanisms of "Qi stagnation and blood stasis syndrome" (QS) and "Qi deficiency and blood stasis syndrome" (QD), as two subtypes of coronary artery disease (CAD) in Traditional Chinese Medicine (TCM), following percutaneous coronary intervention (PCI). In this study, a total of 227 CAD patients with QS and 211 CAD patients with QD were enrolled; all participants underwent PCI. Label-free quantification proteomics were employed to analyze the changes in serum in two subtypes of CAD patients before and 6 months after PCI, aiming to elucidate the intervention mechanism of PCI in treating CAD characterized by two different TCM syndromes. Biochemical analysis revealed significant changes in tumor necrosis factor-α, high density lipoprotein cholesterol, blood stasis clinical symptoms observation, and Gensini levels in both patient groups post-PCI; Proteomic analysis identified 79 and 95 differentially expressed proteins in the QS and QD patient groups, respectively, compared to their control groups. complement C8 alpha chain, complement factor H, apolipoprotein H, apolipoprotein B, plasminogen, carbonic anhydrase 2, and complement factor I were altered in both comparison groups. Furthermore, enrichment analysis demonstrated that cell adhesion and connectivity-related processes underwent changes in QS patients post-PCI, whereas lipid metabolism-related pathways, including the peroxisome proliferator-activated receptor signaling pathway and extracellular matrix receptor interaction, underwent changes in the QD group. The protein-protein interaction network analysis further enriched 52 node proteins, including apolipoprotein B, lipoprotein (a), complement C5, apolipoprotein A4, complement C8 alpha chain, complement C8 beta chain, complement C8 gamma chain, apolipoprotein H, apolipoprotein A-Ⅱ, albumin, complement C4-B, apolipoprotein C3, among others. The functional network of these proteins is posited to contribute to the pathophysiology of CAD characterized by TCM syndromes. The current quantitative proteomic study has preliminarily identified biomarkers of CAD in different TCM subtypes treated with PCI, potentially laying the groundwork for understanding the protein profiles associated with the treatment of various TCM subtypes of CAD.

Thermal analysis for SWCNTs and MWCTs due to coaxially stretchable disks with applications of viscous dissipation and thermal radiation: Artificial neural network analysis

Owing to exclusive thermal prospective of nanoparticles, diverse applications of such materials have been reported in heat transfer systems. The carbon nanotube (CNTs) is the modified class of nanoparticles which is preserve more stable thermal properties. The decomposition of CNTs with base materials offer special applications in enhancing thermal efficiency of energy systems, nuclear processes, sensors, diagnostic imaging etc. Following to such motivations in mind, the objective of current work is to present artificial neural network analysis for heat transfer problem due to suspension of carbon nanotubes-based nanofluid. Both single-walled and multi-walled carbon nanotubes are utilized to analyse the thermal impact of water base fluid. The source of flow is coaxially stretchable disks. The applications of porous medium phenomenon are reported to inspect the inertial effects. The problem is further updated by utilizing the radiative phenomenon, external heat generation and dissipation and slip constraints. The transport system is expressed via coupled nonlinear equations which are tackled via numerical computations.

Investigation of Electromagnetic Wave Absorption Properties of Ni-Co and MWCNT Nanocomposites.

In recent years, severe electromagnetic interference among electronic devices has been caused by the unprecedented growth of communication systems. Therefore, microwave absorbing materials are required to relieve these problems by absorbing the unwanted microwave. In the design of microwave absorbers, magnetic nanomaterials have to be used as fine particles dispersed in an insulating matrix. Besides the intrinsic properties of these materials, the structure and morphology are also crucial to the microwave absorption performance of the composite. In this study, Ni-Co-MWCNT composites were synthesized, and the changes in electric permittivity, magnetic permeability, and reflectance loss of the samples were evaluated at frequencies of 2 to 18 GHz. Nickel-Cobalt-Multi Wall Carbon Nanotubes (MWCNT) composites were successfully synthesized by the co-precipitation chemical method. The structural, morphological, and magnetic properties of the samples were characterized and investigated by X-ray diffractometer (XRD), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), Vibrating Sample Magnetometer (VSM), and Vector Network Analyzer (VNA). The results revealed that the Ni-Co-MWCNT composite has the highest electromagnetic wave absorption rate with a reflectance loss of -70.22 dB at a frequency of 10.12 GHz with a thickness of 1.8 mm. The adequate absorption bandwidth (RL <-10 dB) was 6.9 GHz at the high-frequency region, exhibiting excellent microwave absorbing properties as a good microwave absorber. Based on this study, it can be argued that the Ni-Co-MWCNT composite can be a good candidate for making light absorbers of radar waves at frequencies 2- 18 GHz.