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Associations of healthy lifestyle and accelerated aging with incident breast cancer in pre- and postmenopausal women: a population-based cohort study
Optical and green catalytic properties of fenugreek water for diabetes and skincare treatments
Singers of the high Arctic; Seasonal acoustic presence of bowhead whales (Balaena mysticetus) around Svalbard, Norway
Abstract The bowhead whale (Balaena mysticetus) is an Arctic endemic cetacean that spends its entire life in Arctic waters, living in tight association with sea ice. In this study, passive acoustic data recorded between 2017 and 2022 at six locations around the Svalbard Archipelago, Norway, were analysed for the seasonal and spatial vocal occurrence of this endangered bowhead whale stock, using a combination of Long-Term Spectral Averages and visual/auditory verification. The results were subsequently correlated to seasonal patterns in environmental conditions. Bowhead whale vocalizations were detected at all study sites, but there was significant variation between locations and years in acoustic presence and singing behaviour. Consistent, high detection rates of singing west and east of Svalbard suggest that these sites serve as key areas for overwintering and breeding. The lower and fluctuating detection rates north of Svalbard suggest that this area serves as a movement corridor between regions. Significant positive correlations between bowhead whale acoustic activity and high sea ice concentrations emphasizes their ice-affiliated nature. This study provides novel information on the distribution and habitat use of bowhead whales in the Svalbard area and raises concern for the future of this population, in an Arctic that is experiencing rapid sea ice habitat loss.
The scaled sardine’s unique metabolic phenotype and its implications for the susceptibility of small tropical pelagic fishes to climate change
Age and weekly physical activity are correlated with ballistic VR decision making and reaction time shooting performance at rest
Comprehensive techno-economic optimization and performance analysis of molten salt concentrated solar power tower plants in Algeria
Evaluation of the quality and reliability of Turkish and English YouTube video contents on gingival recession treatment
AI-generated faces influence gender stereotypes and racial homogenization
Trade-offs between machine learning and deep learning for mental illness detection on social media
Study on the triaxial unloading failure characteristics and acoustic emission features of weakly cemented mudstone
Research on load forecasting based on CEEMDAN SE VMD and SelfAttention TCN fusion model
A novel prognostic model based on migrasome-related LncRNAs for gastric cancer
Abstract Gastric cancer (GC) represents a substantial public health challenge, characterized by elevated morbidity and mortality rates. Migrasomes, a newly discovered type of extracellular vesicle, have been highlighted as important contributors to cancer progression, though their specific role in GC remains unclear. To address this issue, we developed the first prognostic model utilizing migrasome-related long non-coding RNAs (MRLs). This model aims to deepen the understanding of GC pathogenesis and improve patient outcomes. Clinical and transcriptional data for 407 GC patients from TCGA were classified as training and testing sets. Through Pearson correlation analysis, 537 MRLs were recognized, and LASSO and Cox regression analyses further refined the list to four key lncRNAs (AC012055.1, LINC01150, AC053503.4, AC107021.2) for constructing the prognostic model. Kaplan-Meier survival analysis indicated a significantly poorer prognosis for the high-risk group. PCA confirmed the model’s robustness, and univariate and multivariate analyses validated it as an independent predictor of clinical outcomes. The ROC curve and C-index evaluations further affirmed the model’s predictive power. We developed a nomogram combining the MRLs signature with clinical parameters to enhance prognostic accuracy. GO, KEGG and GSEA were performed on migrasome-related genes associated with GC. Furthermore, high-risk patients exhibited increased immune cell infiltration and reduced tumor mutation burden, both associated with poorer outcomes. Additionally, twenty-nine potential therapeutic agents were identified. This novel MRLs-based model provides crucial insights into GC biology and represents a valuable tool for improving patient management and therapeutic strategies.
Electrostatic solitary wave modeling in lunar wake plasma
Increased risk of young-onset ovarian cancer in women with non-alcoholic fatty liver disease: A nationwide cohort study of 2.3 million women aged 20–39 years
Effects of tianeptine on mTORC1-mediated neuronal autophagy in primary rat hippocampal neurons under nutrient deprivation
Striatal functional connectivity associated with Sahaja Yoga meditation
First report of fire blight on Cotoneaster in China
Author Correction: Effect of dietary tall oil fatty acids and hydrolysed yeast in SNP2-positive and SNP2-negative piglets challenged with F4 enterotoxigenic Escherichia coli
A cubic-quadratic phenomenological model explains the spiking, chaotic and bursting behaviors of neuron
Abstract In this manuscript, we present a two-dimensional phenomenological spiking neuron model. By analyzing the bifurcation diagram and phase portraits of the two-dimensional model, Andronov-Hopf bifurcation, saddle-point bifurcation and saddle-point on invariant circle bifurcation are discussed in detail. Based on the above analysis, a periodic input current is designed to simulate the basic firing mode of a single neuron. With the change of the input current frequency, the model can reproduce rich dynamical behaviors such as spike, bursts, and chaos.