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A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein
Multidimensional pan-cancer analysis reveals the impact of PPIA on tumor epigenetic modifications and immune regulation
MEN: leveraging explainable multimodal encoding network for precision prediction of CYP450 inhibitors
Intelligent design of high-performance fluids for thermal management: integrating response surface methodology, weighted Tchebycheff method, and strength Pareto evolutionary algorithm II
Long term urban wastewater irrigation drives zinc bioaccumulation and health risks in contaminated vegetables
Ensemble boosting-based soft-computing models for predicting the bond strength between steel and CFRP plate
Mannose-modified hemocyanin enhances pathogen endocytosis by crustacean hemocytes
Analysis of disease resistance of ZmERS4 -overexpressing rice
The discovery of the novel genes with disease resistance and the cultivation of new varieties of maize was considered as the most economical and efficient strategy for the disease stress. In previous studies, our research team had screened and obtained an ethylene receptor protein gene Zea mays Ethylene Response Sensor4 (ZmERS4) from the maize leaf, then ZmERS4 was overexpressed in rice, followed by the obtaining of the T3 homozygous transgenic rice. Besides, the disease resistance of the ZmERS4 -overexpressing rice was analyzed during the research, which revealed that the overexpression of ZmERS4 in rice could enhance the resistance to Xanthomonas oryzae pv. oryzicola(Xoc) by reducing the expression of the sugar transporter genes and activating the expression of salicylic acid (SA) signaling-related genes at 24h post-inoculation with Xanthomonas oryzae pv. oryzicola. Besides, an increasing trend could be observed in the hydrogen peroxide content, which could be attributed to the overexpression of ZmERS4. Furthermore, it was indicated by high-performance liquid chromatography-tandem mass (HPLC-MS/MS) spectrometry that the SA contents of ZmERS4-overexpressing rice exhibited a significant increasing trend after the pathogen infection. Nevertheless, the improved resistance of ZmERS4-overexpressing rice was relatively inhibited after the pretreatment of the SA biosynthetic inhibitor, 1-amino-benzotriazole (ABT). According to the above results, it was assumed that ZmERS4 exhibited significant functions during the regulation of rice resistance to Xanthomonas oryzae pv. oryzicola, and the regulatory functions were mainly based on the inducement of plant oxidative burst activity and activation of the SA signaling pathway. Overall, these findings could provide genetic resources and a data basis for the exploration and evaluation of valuable disease-resistant genes from maize.
Factors influencing the intention of textile and garment SMEs to adopt digital technologies and its impact on performance
Effect of geopolymer treatment on the ultimate bearing capacity of sabkha soil under axial loading
Bearing fault diagnosis for variable operating conditions based on KAN convolution and dual branch fusion attention
Evaluation of soil carbon characteristics under different planting patterns on sloping farmland in the hilly and gully region of the loess plateau
TinyML-enabled fuzzy logic for enhanced road anomaly detection in remote sensing
A 60-year analysis of past hydroclimatic variability and trends in the Mouhoun river catchment in West Africa
Impaired intestinal calcium absorption and osteopathy in ICR/Mlac-hydro mice with hypoparathyroidism and severe hydronephrosis
Associations between positive and negative social experiences and epigenetic aging
Abstract Associations between positive and negative social experiences and epigenetic aging are not well understood. To determine associations between positive and negative social experiences and epigenetic aging. Data from Midlife in the United States (MIDUS), a US population-based longitudinal cohort study, were used to examine relationships between social experiences and epigenetic aging. Participant reports of social experiences were assessed at survey waves closest to the subsequent collection of blood samples for DNA methylation testing and epigenetic clock calculation from 2004 to 2009 (MIDUS Core Sample) or 2012–2016 (MIDUS Refresher Sample). Analyses were conducted May 2024–June 2025. Self-reported positive (e.g., marriage, attendance at social meetings) and negative (e.g., parent’s drug problems, incarceration) social experiences were examined. Epigenetic was assessed from blood DNA methylation using the GrimAge and DunedinPACE epigenetic clocks. The sample (N = 1309) was 55.5% female, 22.5% Black and averaged 51.3 (SD = 12.5) years of age. In models adjusted for sociodemographic and health confounders, participants who reported positive social experiences such as being married (GrimAge: β = − 0.807, SE = 0.269, p < 0.01; Dunedin: β = − 0.022, SE = 0.007, p < 0.01) and engaging in social meetings (GrimAge β = − 1.027, SE = 0.250, p < 0.01; Dunedin: β = − 0.020, SE = 0.007, p < 0.01) exhibited significantly decelerated GrimAge scores; participants who reported negative social experiences such as a parent experiencing drug problems (GrimAge β = 2.430, SE = 0.761, p < 0.001), dropping out of school (GrimAge β = 2.869, SE = 0.405, p < 0.001; Dunedin β = 0.046, SE = 0.011, p < 0.001), and imprisonment (GrimAge β = 1.922, SE = 0.520, p < 0.001) exhibited significantly accelerated epigenetic aging scores. Adjusted models found the sum of positive social experiences was associated with decelerated epigenetic aging scores (GrimAge β = − 0.431, SE = 0.109, p < 0.001; Dunedin β = − 0.009, SE = 0.003, p < 0.01) and that the sum of negative social experiences was associated with accelerated epigenetic aging scores (GrimAge β = 0.934, SE = 0.162, p < 0.001; Dunedin β = 0.015, SE = 0.004, p < 0.01). Respondents with a “net positive” ratio of positive to negative social experiences had GrimAge scores that were 4.63 years younger on average than those with “net negative” social experiences (β = − 4.729; SE = 0.507; p < 0.001; adjusted β including covariates = − 2.847; SE = 0.469; p < 0.001). Associations of positive and negative social experiences with epigenetic aging were found to be independent of respondents’ perceived social support and their self-rated physical and mental health. Results suggest that negative social experiences accelerate while positive social experiences decelerate epigenetic aging. Negative and positive social experiences are each independently associated with epigenetic aging and net positive social experiences are associated with slower epigenetic aging.
PLK1-mediated PDHA1 phosphorylation drives mitochondrial dysfunction, mitophagy, and cancer progression in Cr(VI)-associated lung cancer
GCN-based unsupervised community detection with refined structure centers and expanded pseudo-labeled set
Community detection is a classical problem for analyzing the structures of various graph-structured data. An efficient approach is to expand the community structure from a few structure centers based on the graph topology. Considering them as pseudo-labeled nodes, graph convolutional network (GCN) is recently exploited to realize unsupervised community detection. However, the results are highly dependent on initial structure centers. Moreover, a shallow GCN cannot effectively propagate a limited amount of label information to the entire graph, since the graph convolution is a localized filter. In this paper, we develop a GCN-based unsupervised community detection method with structure center Refinement and pseudo-labeled set Expansion (RE-GCN), considering both the network topology and node attributes. To reduce the adverse effect of inappropriate structure centers, we iteratively refine them by alternating between two steps: obtaining a temporary graph partition by a GCN trained with the current structure centers; updating each structure center to the node with the highest structure importance in the corresponding induced subgraph. To improve the label propagation ability of shallow GCN, we expand the pseudo-labeled set by selecting a few nodes whose affiliation strengths to a community are similar to that of its structure center. The final GCN is trained with the expanded pseudo-labeled set to realize community detection. Extensive experiments demonstrate the effectiveness of the proposed approach on both attributed and non-attributed networks. The refinement process yields a set of more representative structure centers, and the community detection performance of GCN improves as the number of pseudo-labeled nodes increase.