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IGF-1 regulates LARP6-mediated collagen metabolism in vaginal fibroblasts of POP patients via the PI3K/AKT pathway
Satisfaction with life in young adults with type 1 diabetes mellitus
Exploring the interconnected properties of cannabidiol suspensions and orodispersible films
Attention residual network for medical ultrasound image segmentation
Comparative physicochemical properties and anti-inflammatory activity of natural and artificial musk for quality evaluation
Transformer attention fusion for fine grained medical image classification
Influence of portal excavation of shallow-buried bias tunnel on stability of soil‒rock bedding slope: a case study of Moziping tunnel
Image region semantic enhancement and symmetric semantic completion for text-to-image person search
BGATT-GR: accurate identification of glucocorticoid receptor antagonists based on data augmentation combined with BiGRU-attention
Abstract The glucocorticoid receptor (GR) is a critical nuclear receptor that regulates a broad spectrum of physiological functions, including stress adaptation, immune response, and metabolism. Given the association between aberrant GR signaling and various pathological conditions, this pathway represents a promising therapeutic target. Several GR antagonists have been developed to block glucocorticoid binding to the receptor, showing therapeutic potential in disorders characterized by heightened or dysregulated glucocorticoid signaling. Therefore, this study proposes an innovative deep learning-based hybrid framework (termed BGATT-GR) that leverages a data augmentation method, a bidirectional gated recurrent unit (BiGRU), and a self-attention mechanism (ATT) to attain more accurate identification of GR antagonists. In BGATT-GR, we first employed AP2D, CDKExt, KR, Morgan, and RDKIT to extract molecular descriptors of GR antagonists and combined these molecular descriptors to generate multi-view features. Second, we adopted a data augmentation method that combined both random under-sampling (RUS) and the synthetic minority over-sampling technique (SMOTE) to address the issue of class imbalance. Third, the BGATT architecture was constructed to enhance the utility of the multi-view features by generating informative feature embeddings. Finally, we applied principal component analysis (PCA) to reduce the dimensionality of these feature embeddings and fed the processed feature vectors into the final classifier. Extensive experimental results showed that BGATT-GR provided more stable performance in both cross-validation and independent tests. Furthermore, the independent test results revealed that BGATT-GR attained superior predictive performance compared with several conventional ML models, with a balanced accuracy of 0.957, an MCC of 0.853, and an AUPR of 0.962. In summary, our experimental results provide strong evidence to suggest that BGATT-GR is highly accurate and effective for identifying GR antagonists.
Exploring temperature-dependent transcriptomic adaptations in Yersinia pestis using direct cDNA sequencing by Oxford Nanopore Technologies
Abstract Transcriptomics is key to understanding how bacterial pathogens adapt and cause disease, but remains constrained by cost, technical, and biosafety issues, especially for highly virulent and/or regulated pathogens. Here, we present a streamlined and cost-effective RNA-Seq workflow using Oxford Nanopore Technologies for direct cDNA sequencing, suitable for complete in-house implementation. This method avoids PCR bias, enables multiplexing, and includes built-in quality controls and alignment benchmarking. Applied to Yersinia pestis (the causative agent of plague), the workflow produced an experimentally validated operon map and revealed novel transcriptional units, including within the pathogenicity island. Transcriptomic profiling at 21 °C and 37 °C, modeling the flea and mammalian environments, highlighted temperature-driven metabolic shifts, notably the upregulation of sulfur metabolism and the dmsABCD operon. These findings provide insights into Y. pestis adaptation and illustrate how long-read RNA-Seq can support operon discovery, genome annotation, and gene regulation studies in high-risk or understudied bacterial pathogens.
Prognostic value of the six-minute walk test in patients with cardiovascular disease
Research on curing reaction kinetics and curing process of hydroxy-terminated polybutadiene (HTPB) propellants
A disinhibitory microcircuit in the temporal association cortex for fear retrieval to pure tones
Abstract Fear retrieval is fundamental for animals to rapidly enter a self-defense state, which enhances their survival rate. Although the cellular mechanisms and neural circuits underlying fear retrieval have been relatively well studied, particularly in classic fear-related nuclei such as the amygdala, the neural circuits and intrinsic microcircuits of some non-classical fear-related nuclei remain poorly understood. A disinhibitory microcircuit in the temporal association cortex (TeA) was identified in this study, demonstrating a critical role in fear retrieval. Additionally, the response of the TeA to fear retrieval is largely driven by acetylcholine (Ach), which then relay information to the tail of striatum (TS) to mediate freezing behavior. Together, these findings demonstrate the essential role of the TeA and its microcircuit in auditory fear retrieval in response to pure tones, providing new insights into the mechanisms of fear retrieval in the cortex.
Assessment of spatial autocorrelation and scalability in fine-scale wildfire random forest prediction models
Abstract Wildfire prediction models that can be applied across diverse regions at fine scales (< 100 m) are critical for wildfire management. Remote sensing offers a path forward by providing heterogeneous and dynamic measurements of fuel load, type, and flammability. Machine learning methods such as random forests provide an empirical framework that are high-accuracy, computationally efficient, interpretable and able to model complex ecological relationships. Here we use high resolution (70 m, every 3–5 days) remote sensing observations of evapotranspiration and evaporative stress index, which represent plant water stress, from Ecosystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS), as well as topography and weather data, to predict burn severity and occurrence for 8 large wildfires that burned 3715 km2 from 2021 and 2022 in New Mexico, USA. These fires ranged from low to high burn intensity, and covered a diverse range of ecoregions (deserts, grasslands, forests), plant species, and topographies. We used a single model to predict the burn severity of all wildfires one week before occurrence. The prediction accuracy was greatest when using all predictors (ECOSTRESS, weather, topography) (R2 = 0.77). We assessed the role of spatial autocorrelation in driving model performance by: (1) increasing the sample spacing of our dataset, (2) introducing new predictors that represent spatial structure in the data, and (3) training our model on half the fires and predicting the other half of the fires. We found that after increasing sample spacing, model accuracy declined. However, we found declines in model accuracy were more impacted by decreased training set size compared to the distance spacing-indicating that the models are likely accurately capturing fine-scale processes. Scalability of random forest models was also found to be more challenging for regression problems but was accurate for classification of burned pixel occurrence (total pixel accuracy of 67%). These results provide promising results for application of random forest models to predict fine-scale fire severity and occurrence with applications for fire management.