Network Analysis Research Articles

Bibliometric analysis of microphthalmos and anophthalmos over 20 years: from 2004 to 2023

AIM: To conduct a bibliometric analysis of studies on microphthalmos and anophthalmos (M/A), explore research hotspots, and provide information on future research interests in this field to benefit clinicians and researchers. METHODS: Totally 751 publications related to M/A from the year 2004 to 2023 were collected from the Web of Science Core Collection database. These publications consist of both original and review articles, that are composed in English. The contributions of different countries, institutions, journals, and authors were analyzed, and network analysis was conducted by using Microsoft Excel 2021, VOSviewer, and R Studio to visualize research hotspots. RESULTS: Among all publications included, the highest number of publications came from USA (218, 29.03%). China followed with 99 publications (13.18%), and England with 86 publications (11.45%). The publications from the USA had the highest frequency of citations, with 16 699 citations, and the highest H-index of 49. The American Journal of Medical Genetics Part A (43, 5.73%) published the largest number of papers, and the University of London had the most publications (41, 5.46%). The genetic and molecular mechanisms of M/A were still unclear and the clinical intervention for M/A had gained a lot of attention as an emerging area of interest. CONCLUSION: Data have been gathered on the yearly count of published materials and citations, as well as the rise in publication trends, the efficiency of regions or countries, authors, journals, and organizations, along with the high-cited publications in M/A. The recent trend of research has shifted from genetic mechanisms to different clinical phenotypes and corresponding clinical interventions, which can give direction to future research.

Integrative pan-cancer analysis reveals a common architecture of dysregulated transcriptional networks characterized by loss of enhancer methylation

Aberrant DNA methylation contributes to gene expression deregulation in cancer. However, these alterations’ precise regulatory role and clinical implications are still not fully understood. In this study, we performed expression-methylation Quantitative Trait Loci (emQTL) analysis to identify deregulated cancer-driving transcriptional networks linked to CpG demethylation pan-cancer. By analyzing 33 cancer types from The Cancer Genome Atlas, we identified and confirmed significant correlations between CpG methylation and gene expression (emQTL) in cis and trans, both across and within cancer types. Bipartite network analysis of the emQTL revealed groups of CpGs and genes related to important biological processes involved in carcinogenesis including proliferation, metabolism and hormone-signaling. These bipartite communities were characterized by loss of enhancer methylation in specific transcription factor binding regions (TFBRs) and the CpGs were topologically linked to upregulated genes through chromatin loops. Penalized Cox regression analysis showed a significant prognostic impact of the pan-cancer emQTL in many cancer types. Taken together, our integrative pan-cancer analysis reveals a common architecture where hallmark cancer-driving functions are affected by the loss of enhancer methylation and may be epigenetically regulated.

Genome-Wide Identification and Characterization of bHLH Gene Family in Hevea brasiliensis

The basic helix-loop-helix (bHLH) transcription factors play crucial roles in plant growth, development, and stress responses. However, their identification and insights into the understanding of their role in rubber trees remain largely uncovered. In this study, the bHLH gene family was explored and characterized in rubber trees using systematic bioinformatics approaches. In total, 180 bHLH genes were identified in the rubber tree genome, distributed unevenly across 18 chromosomes, and phylogenetic analysis classified these genes into 23 distinct subfamilies. Promoter regions revealed a high density of cis-elements responsive to light and hormones. Enrichment analysis indicated involvement in numerous biological processes, including growth, development, hormone responses, abiotic stress resistance, and secondary metabolite biosynthesis. Protein interaction network analysis identified extensive interactions between HbbHLH genes and other functional genes, forming key clusters related to iron homeostasis, plant growth, and stomatal development. Expression profiling of HbbHLH genes have demonstrated varied responses to endogenous and environmental changes. RT-qPCR of eleven HbbHLH genes in different tissues and under ethylene, jasmonic acid, and cold treatments revealed tissue-specific expression patterns and significant responses to these stimuli, highlighting the roles of these genes in hormone and cold stress responses. These findings establish a framework for exploring the molecular functions of bHLH transcription factors in rubber trees.

Social tolerance and success-biased social learning underlie the cultural transmission of an induced extractive foraging tradition in a wild tool-using primate

The last two decades have seen great advances in the study of social learning (learning from others), in part due to efforts to identify it in the wild as the basis of behavioral traditions. Theoretical frameworks suggest that both the dynamics of social tolerance and transmission biases (or social learning strategies) influence the pathways of information diffusion in social groups. Bearded capuchins ( Sapajus libidinosus ) inhabiting the semiarid seasonal caatinga biome of the Serra da Capivara National Park (SCNP) form highly tolerant societies that possess the largest “tool-kit” described for monkeys, a feat likely facilitated by social learning. Here, we used social network analysis and an open diffusion experiment using an extractive foraging task to identify the occurrence of social learning and describe the pathways of social transmission of information in two wild primate populations. The dynamics of social tolerance outside of task introductions predicted opportunities for social learning, but it was tolerance during task introductions that predicted the actual pathways of social information diffusion. Our results also indicated that the capuchins mainly learned from others via direct observation and naïve individuals exhibited an observation bias toward successful males. This study supports the claims of cultural transmission in robust capuchins and empirically supports the role of social tolerance and social learning strategies in human and nonhuman primate cultural evolution.

Transcriptomic and Metabolomics Joint Analyses Reveal the Influence of Gene and Metabolite Expression in Blood on the Lactation Performance of Dual-Purpose Cattle (Bos taurus)

Blood is an important component for maintaining animal lives and synthesizing sugars, lipids, and proteins in organs. Revealing the relationship between genes and metabolite expression and milk somatic cell count (SCC), milk fat percentage, milk protein percentage, and lactose percentage in blood is helpful for understanding the molecular regulation mechanism of milk formation. Therefore, we separated the buffy coat and plasma from the blood of Xinjiang Brown cattle (XJBC) and Chinese Simmental cattle (CSC), which exhibit high and low SCC/milk fat percentage/milk protein percentage/lactose percentages, respectively. The expression of genes in blood and the metabolites in plasma was detected via RNA-Seq and LC-MS/MS, respectively. Based on the weighted gene coexpression network analysis (WGCNA) and functional enrichment analysis of differentially expressed genes (DEGs), we further found that the expression of genes in the blood mainly affected the SCC and milk fat percentage. Immune or inflammatory-response-related pathways were involved in the regulation of SCC, milk fat percentage, milk protein percentage, and lactose percentage. The joint analysis of the metabolome and transcriptome further indicated that, in blood, the metabolism pathways of purine, glutathione, glycerophospholipid, glycine, arginine, and proline are also associated with SCC, while lipid metabolism and amino-acid-related metabolism pathways are associated with milk fat percentage and milk protein percentage, respectively. Finally, related SCC, milk fat percentage, and milk protein percentage DEGs and DEMs were mainly identified in the blood.

Network analysis of lawyer referral markets: Evidence from Indiana

AbstractUsing network analysis, we study referrals among plaintiff‐side lawyers handling medical malpractice cases in Indiana. The referral network is stratified, with a few highly connected “hub” firms. Firm connectivity follows a power law distribution—suggesting that new entrants tend to associate with already well‐connected firms, rather than starting a new network. Regression analysis shows that, for a given firm, connectivity (i.e., node degree) in the referral network and being loyal to a smaller number of firms both lead to better outcomes in non‐referred cases. The referral network also became more concentrated over time. The stratification of the market for plaintiff‐side representation is reinforced through these processes.

Analysis of Existing Area, Clean Water Treatment, and Distribution Network Evaluation in Nagari Magek, Agam Regency, West Sumatra

Nagari Magek Village in Agam Regency, West Sumatra, faces significant challenges in managing clean water resources. To address these challenges comprehensively, a holistic approach is essential, involving through area analysis, water treatment, and evaluation of distribution networks to ensure efficient and equitable water distribution. It is crucial to adhere to the principles of water resource conservation, utilization, and management as stipulated in Law Number 17 of 2019 on water resource management, which must be observed to ensure legal clarity in water management practices. The community service team handles clean water problems in Nagari Magek by designing a clean water treatment system that includes aeration, sedimentation, and filtration units. This process improves water quality by reducing yellowish color and eliminating unpleasant odors. However, the capacity of the treatment plant is not able to meet the increasing water demand, while the galvanized pipe distribution network has not been functioning optimally. Based on simulations using EPANET 2.2 software, it was found that the water flow speed in the pipes was still below the optimal range due to the diameter of the pipes being too large. The solution applied was to increase the processing discharge to 10 L/s (0,01 m3/s), which succeeded in increasing the flow rate according to the regulatory criteria. This recommendation improves the efficiency of the distribution system and ensures better water quality for the people in Nagari Magek.

Identification of Key Genes Involved in Seed Germination of Astragalus mongholicus

Seed germination is a fundamental process in plant reproduction, and it involves a series of complex physiological mechanisms. The germination rate of Astragalus mongholicus (AM) seeds is significantly lower under natural conditions. To investigate the key genes associated with AM seed germination, seeds from AM plants were collected at 0, 12, 24, and 48 h for a transcriptomic analysis, weighted gene co-expression network analysis (WGCNA), and machine learning (ML) analysis. The primary pathways involved in AM seed germination include plant-pathogen interactions and plant hormone signaling. Four key genes were identified through the WGCNA and ML: Cluster-28,554.0, FAS4, T10O24.10, and EPSIN2. These findings were validated using real-time quantitative reverse transcription PCR (qRT-PCR), and results from RNA sequencing demonstrated a high degree of concordance. This study reveals, for the first time, the key genes related to AM seed germination, providing potential gene targets for further research. The discovery of N4-acetylcysteine (ac4C) modification during seed germination not only enhances our understanding of plant ac4C but also offers valuable insights for future functional research and application exploration.

Machine learning model based on SERPING1, C1QB, and C1QC: A novel diagnostic approach for latent tuberculosis infection

AbstractBackgroundLatent tuberculosis infection (LTBI) is a significant source of active tuberculosis (ATB), yet distinguishing between them is challenging because specific biomarkers are lacking.MethodsWe analyzed four microarray datasets (GSE19491, GSE37250, GSE54992, GSE28623) from the gene expression omnibus to identify differentially expressed genes (DEGs). Using protein–protein interaction (PPI) networks and LASSO‐SVM algorithms, we selected three candidate biomarkers and evaluated their diagnostic efficacy. The expression levels of core genes were validated by RNA sequencing of healthy, ATB, and LTBI groups in a real‐world cohort. We conducted Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, predicted shared upstream miRNAs, constructed miRNA–hub and transcription factor (TF)–hub gene networks, and performed immune infiltration analysis.ResultsThree hub genes (SERPING1, C1QC, C1QB) were identified from 45 DEGs by PPI networks and machine learning screening. The diagnostic model based on the three hub genes had an area under the curve (AUC) value of 0.843 in the training set GSE19491 and 0.865 in the validation set GSE28623. Real‐world transcriptome sequencing confirmed the expression trends of the hub genes across healthy, LTBI, and ATB groups. GO analysis showed that the 45 hub genes were primarily associated with immune inflammatory responses and pattern recognition receptors, whereas KEGG analysis indicated enrichment in complement and coagulation cascades. The miRNA–hub and TF–hub gene network analysis identified nine miRNAs and the zinc finger TF GATA2 as potential co‐regulators of SERPING1, C1QC, and C1QB. Immune cell infiltration analysis identified significant differences in the immune microenvironment between LTBI and ATB, with macrophages and natural killer cells showing significant correlations with tuberculosis infection.ConclusionThe diagnostic model with SERPING1, C1QC, and C1QB shows promise in distinguishing LTBI from ATB, indicating its potential as a diagnostic tool.

Online Therapy for Children: Yay or Nay? Clinicians’ Insights from the COVID-19 Era

Abstract Background The COVID-19 pandemic necessitated an almost overnight shift in mental healthcare from in-person to remote delivery. However, the feasibility of online therapy for children and its potential as a long-term fixture remain underexplored. Objective This study aimed to explore the advantages, disadvantages, and suitability of online therapy for children, as perceived by pediatric clinicians delivering remote psychological interventions during the COVID-19 pandemic. Method Six qualified child psychologists with limited pre-pandemic online therapy experience were surveyed about their perspectives on online therapy for children using a qualitative descriptive approach. Data was analyzed using thematic network analysis. Results Analysis revealed themes relating to: issues encountered in the areas of technology, resource availability, and therapeutic alliance; perceived advantages in children being digital natives, and online therapy’s wide accessibility; and (un)suitability of specific client profiles and therapeutic techniques. Conclusions Psychological interventions delivered via videoconferencing are not suitable for all children, but can be greatly beneficial for some. Client- and intervention-specific findings from this study can be used to guide future research aimed at clinical practice, developing tailored approaches, and informing policy for improving access to pediatric mental healthcare.

Meme-Ing Waves: unpacking political narratives in the Romanian context

ABSTRACT Political memes are no longer (only) a humorous feature of internet culture, but also act as a vehicle for information diffusion and reimaging of politics. Using a mixed-methodology approach, combining content analysis, descriptive statistics, and social network analysis, this study unravels the complex dynamics of political memes within the Romanian context, uncovering how memes reflect and shape political narratives. By focusing on the political discourse surrounding the 2020 Romanian parliamentary elections, this research highlights the underlying power dynamics and themes prevalent within the Romanian political landscape. The study finds that memes frequently amplify the visibility of incumbent political actors from governing parties, unveiling their symbolic importance. Memes also tend to reflect salient themes and issues, such as corruption and incompetence, serving as a means of criticizing the personal attributes of politicians and overshadowing policy matters. Finally, the analysis reveals the intricate web of political affiliations formed through cross-cutting communities within the emerging subthemes of the meme corpus. Overall, this paper contributes to a deeper understanding of memes as a driving force for political commentary, framing, and engagement.

Identification of potential mechanisms of Schisandrin B in the treatment of idiopathic pulmonary fibrosis by integrating network pharmacology and experimental validation.

Idiopathic pulmonary fibrosis (IPF) is a worsening fibrotic condition characterized by a short survival rate and limited treatment options. This study evaluates the potential anti-fibrotic properties of Schisandrin B (Sch B) through network pharmacology and experimental validation. A mouse model of bleomycin-induced pulmonary fibrosis was established, and the modeled mice were treated with Sch B at three doses (20 mg/kg/day, 40 mg/kg/day, and 80 mg/kg/day). A fibrotic model was developed in NIH/3T3 cells by treating them with TGF-β (10 ng/mL) and administering Sch B at various concentrations (10, 20, and 40 µM). The results revealed that Sch B treatment delayed the development of bleomycin-induced pulmonary fibrosis and substantially decreased the transcription levels of collagen I and α-SMA in TGF-β-induced fibroblasts. Core targets were screened with protein-protein interaction network analysis, molecular complex detection (MCODE), and CytoHubba plugin. The application of Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and molecular docking highlighted the significance of the HIF-1α signaling pathway in the potential mechanism of Sch B in IPF therapy. Western blot, PCR, and immunofluorescence were performed to validate the effects of Sch B on HIF-1α. In vivo and in vitro, Sch B administration reduced HIF-1α expression. These outcomes provide valuable insights into the potential mechanism by which Sch B delays IPF development, with HIF-1α potentially serving as a key target. However, further investigation is warranted to assess the safety and efficacy of Sch B in clinical settings.

Comprehensive analysis and validation of TP73 as a biomarker for calcium oxalate nephrolithiasis using machine learning and in vivo and in vitro experiments.

Calcium oxalate (CaOx) nephrolithiasis constitutes approximately 75% of nephrolithiasis cases, resulting from the supersaturation and deposition of CaOx crystals in renal tissues. Despite their prevalence, precise biomarkers for CaOx nephrolithiasis are lacking. With advances in high-throughput sequencing, we aimed to identify biomarkers of CaOx nephrolithiasis by combining twoCaOx nephrolithiasis datasets (GSE73680 and GSE117518). Utilizing weighted gene co-expression network analysis (WGCNA) and four machine learning, we identified six hub genes (DLK2, BHLHA15, C12orf5, ICMT, LOXHD1, and TP73) as potential biomarkers. Additionally, CIBERSORT immune infiltration analysis suggested that these core genes may influence immune cell recruitment and infiltration in CaOx nephrolithiasis. Then, TP73 emerged as a significant hub gene in CaOx nephrolithiasis via receiver operating characteristic (ROC) analysis (AUC = 0.885). Furthermore, the role of TP73 was validated in CaOx nephrolithiasis rat models induced by 1% ethylene glycol, as well as clinical samples and renal tubular epithelial cell models treated with 1mM oxalate. Immunohistochemistry, RNA-Sequencing, and RT-qPCR experiments demonstrated an increased expression of TP73 in CaOx nephrolithiasis rat models and clinical samples. After transfection with TP73 lentivirus, CCK-8 assays suggested that TP73 could inhibit the proliferation of HK-2 and NRK-52E cells. In oxalate-induced cell models, dihydroethidium staining and flow cytometry apoptosis assays indicated that TP73 could enhance ROS levels and cell apoptosis. In summary, our study preliminarily identified TP73 as a diagnostic biomarker and elucidated the promoting role of TP73 in CaOx nephrolithiasis, providing a deeper understanding of the clinical diagnosis and pathogenesis.

Single-cell expression and immune infiltration analysis of polyamine metabolism in breast cancer.

Breast cancer is one of the most threatening women health diseases worldwide and its molecular heterogeneity offers a range of response to therapy. The role of polyamine metabolism is receiving increasing attention. Polyamine metabolism not only plays an important role in the occurrence and development of breast cancer, but also interacts with tumor immune microenvironment. In this work, we applied single-cell RNA-sequencing (scRNA-seq) and systems immunological approaches to interrogate immune cell infiltration gene-to-gene co-expressions in the bulk tumor transcriptomes of breast cancer. We acquired breast cancer sample data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), evaluated the infiltration status of 22 immune cell types using CIBERSORTx tool, respectively. By leveraging the Retrospective Breast sample of various technologies including gene expression and methylation, we identified 46 breast cancer proliferation-associated co-expression modules using weighted gene coexpression network analysis (WGCNA) approach along with machine learning models which in turn delineated single cell level expressions features that these selected module possessed. We observed substantial cellular heterogeneity in the breast cancer microenvironment, where lineage-specific gene expression patterns were highly associated with tumor progression. Moreover, we also identified the gene modules correlated with immune cell infiltration level that could function as regulators in response to tumors for immune therapy. Moreover, risk scores were correlated with immune cell function in different patient groups defined by high- and low-risk. The findings of this study shed a new light upon molecular classification prognostic assessment and personalized treatment in breast cancer.

Identification of central symptoms in problematic WeChat use and depression among Chinese college students: a network analysis.

Previous research has identified a positive relationship between problematic social media use and depression. However, with the increasing diversification and personalization of social media platforms, the impact of different platforms on college students varies significantly. It is necessary to examine the specific symptom network of problematic use in certain platforms and its relationship with depression. Therefore, this study aims to address these gaps by investigating the symptom network of problematic WeChat use among college students and identifying bridge symptoms linking problematic WeChat use to depression. A total of 6,060 Chinese college students (Mean age = 18.32; range 16-21; 60.66% male, 39.34% female) were recruited and provided self-reports on their problematic WeChat use and depression levels. Latent profile analysis was employed to identify the profiles of problematic WeChat users. Subsequently, four network structures relating to problematic WeChat use and its association with depression were constructed among problematic users. The analysis revealed three heterogeneous groups of college students regarding their WeChat use, with 11.4% classified as problematic users. Network analysis indicated that the central symptoms of problematic WeChat use were "I feel the need to use WeChat with increasing amounts of time to achieve satisfaction" and "I have used WeChat to relieve of loneliness and stress". No significant gender differences were found in the network structure of problematic WeChat use; the network strength was significantly higher among male students compared to female students. Within the combined network, the central symptoms included "self-dislike," "pessimism," "I feel happy and satisfied when I am on WeChat," and "guilt." A strong association was observed between problematic WeChat use and depressive symptoms. The findings of this study further elucidate the impact of problematic WeChat use on depression among college students, providing both theoretical and practical insights for developing interventions targeting WeChat use and depression within this demographic.

Genetic variation perspective reveals potential drug targets for subtypes of endometrial cancer.

The study aims to identify potential drug targets for endometrial cancer (EC) subtypes through a Mendelian randomization (MR) approach, assessing their clinical relevance. We utilized genetic instruments for 4,907 plasma proteins from the deCODE Genetics study dataset, and data with EC (n = 12,906) from a genome-wide study (GWAS) meta-analysis in European populations for MR analyses. Complementary analyses included protein-protein interactions (PPI) network analysis, therapeutic efficacy evaluation, differential gene expression assessment, and prognosis evaluation. The expression levels of key drug targets were quantitatively measured at both the transcriptional and translational stages utilizing reverse transcription quantitative PCR (RT-qPCR) and immunohistochemistry (IHC). Additionally, we analyzed various clinicopathological features. Five drug targets for EC (CBR3, GSTO1, HHIP, IGF2R, and MMP10), seven for endometrioid subtypes (ACAP2, CBR3, GSTO1, HHIP, IGF2R, MMP10, and TLR2), and seven for non-endometrioid subtypes (CST3, DNAJB14, FSTL5, GMPR2, IFI16, MAPK9, and NEO1) were identified. Among these, IGF2R (OR = 1.165; 95% CI 1.067-1.272; p = 1.046 × 10- 2) and CST3 (OR = 0.523; 95% CI 0.339-0.804; p = 7.010 × 10- 3) were highlighted as key drug targets with causal evidence both at transcriptional and translational levels. This study preliminarily confirms that IGF2R and CST3 may serve as novel targets for the treatment of EC, providing a foundational reference for innovative clinical approaches to this disease.

Evaluating the roles of microRNAs associated with nonalcoholic fatty liver disease in hepatocellular carcinoma tumorigenesis: a systematic review and network analysis

IntroductionNon-alcoholic fatty liver disease (NAFLD) has become the leading cause of chronic liver disease worldwide. Hepatocellular carcinoma (HCC) is the fifth most common malignancy worldwide, with high morbidity and mortality. The rapidly increasing incidence of NAFLD is becoming an essential precursor of HCC globally. MicroRNAs (miRNAs) are involved in the progression of NAFLD and HCC.MethodPotential miRNAs associated with NAFLD in HCC tumorigenesis were identified through a systematic review, and their roles were evaluated by data mining analysis. The biological function of the potential miRNA and its target genes in NAFLD and HCC were evaluated by bioinformatic analysis.ResultMIR122 was identified as the potential miRNA associated with NAFLD and HCC. Then, MIR122 expression was significantly lower in HCC patients, and higher MIR122 levels were associated with significantly better overall survival. Next, the biological functions of MIR122 and target genes were predicted to be involved in inflammation, fibrosis, cell proliferation, invasion, metastasis, and apoptosis. In particular, the FOXO signaling pathway may regulate the above biological functions.ConclusionMIR122 was suggested to be involved in progressing from NAFLD to HCC through the PI3K/AKT/FOXO pathway.Systematic review registrationPROSPERO, identifier: CRD 42024517940.

Comparative analyses of RNA-seq and phytohormone data of sweetpotatoes inoculated with Dickeya dadantii causing bacterial stem and root rot of sweetpotato.

Bacterial stem and root rot (BSRR) in sweetpotato caused by Dickeya dadantii is one of the ten major diseases of sweetpotatoes in China. However, the molecular mechanism underlying the resistance of sweetpotato to D. dadantii remains unclear. This study adopted a resistance identification assay that conformed Guangshu87 (GS87) as BSRR-resistant and Xinxiang (XX) as susceptible. Compared to XX, GS87 effectively prevented the invasion and dissemination of D. dadantii in planta. An RNA sequencing (RNA-seq) analysis identified 54,844 expressed unigenes between GS87 and XX at four different stages. Further, it revealed that GS87 was more able to regulate the expressions of more unigenes after the inoculation with D. dadantii, including resistance (R) and transcription factors (TF) genes. Moreover, content measurements of disease resistance-related phytohormones showed that both jasmonic acids (JAs) and salicylic acids (SAs) accumulated in D. dadantii-inoculated sweetpotatoes, and JAs may negatively regulate sweetpotato resistance against D. dadantii and accumulated faster than SAs. Meanwhile, determinations of ROS production rate and relevant enzymatic/non-enzymatic activity highlighted the vital roles of reactive oxygen species (ROS) and superoxide dismutase (SOD) in confering GS87 resistance against D. dadantii. Additionally, several hub genes with high connectivity were highlighted through Protein-Protein interaction (PPI) network analysis. In summary, the findings in this study contribute to the understanding of the different responses of resistant and susceptible sweetpotato cultivars to D. dadantii infection, and it also provide the first insight into the relevant candidate genes and phytohormones involved in the resistance of sweetpotato to D. dadantii.

Analysis of single-cell RNA sequencing in human oocytes with diminished ovarian reserve uncovers mitochondrial dysregulation and translation deficiency.

Diminished ovarian reserve (DOR) is clinically characterized by a decrease in the number of available ovarian follicles and a decline in the quality of oocytes, accompanied by hormonal changes. Low quality of DOR oocyte leads to impaired embryo development, an increased risk of aneuploid pregnancies and miscarriages. However, the specific pathogenic mechanism remains unclear, posing a significant challenge for assisted reproductive technology. For the first time, our study employed single-cell RNA sequencing to reveal the altered transcriptomic landscape of DOR oocytes at GV stage after ovarian stimulation. Differentially expressed genes analysis (DEGs), functional enrichment analysis, weighted gene co-expression network analysis (WGCNA) and protein-protein interactions network analysis were performed. We found 132 up-regulated genes and 466 down-regulated genes in DOR oocytes, with the down-regulated genes primarily enriched in mitochondrial function and translation. Hub genes, identified through integrated analysis of WGCNA and DEGs, were further validated in DOR and control oocytes using RT-qPCR. By utilizing hub genes and employing transcription factor enrichment tools, it had been predicted that pleomorphic adenoma gene 1 (PLAG1) played a crucial role as a transcriptional regulatory factor in DOR oocytes. Additionally, we conformed the PLAG1-IGF2 axis was dysregulated in DOR oocytes. Transcriptome analysis revealed that DOR oocytes exhibited mitochondrial dysfunction and translational defects, and the PLAG1-IGF2 axis might be a potential contributor for the low quality of DOR oocytes.

An active queue management method for multiple bottleneck networks based on distributed average tracking

The active queue management (AQM) is a key technology for the information infrastructure and the Internet. The AQM for multiple bottleneck networks is especially challenging than that for single-bottleneck networks because the stability analysis of multiple bottleneck networks is more complex. In this paper, the active queue management problem for multiple bottleneck networks is considered. A novel random early detection algorithm is designed based on distributed average tracking technology, which is used to estimate the global queue length and improve the network's performance. Sufficient conditions for global asymptotic stability of the closed-loop active queue management system are presented, and some simulation results with NS-2 are given. Compared with the RED algorithm, simulation results show that the presented algorithm has smaller oscillations at routers, which highlights its effectiveness.