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Investigating the potential of oxidative stress-related gene as predictive markers in idiopathic pulmonary fibrosis
Uncovering subtype-specific metabolic signatures in breast cancer through multimodal integration, attention-based deep learning, and self-organizing maps
Abstract This study integrates multimodal metabolomic data from three platforms—LC–MS, GC–MS, and NMR—to systematically identify biomarkers distinguishing breast cancer subtypes. A feedforward attention-based deep learning model effectively selected 99 significant metabolites, outperforming traditional static methods in classification performance and biomarker consistency. By combining data from diverse platforms, the approach captured a comprehensive metabolic profile while maintaining biological relevance. Self-organizing map analysis revealed distinct metabolic signatures for each subtype, highlighting critical pathways. Group 1 (ER/PR-positive, HER2-negative) exhibited elevated serine, tyrosine, and 2-aminoadipic acid levels, indicating enhanced amino acid metabolism supporting nucleotide synthesis and redox balance. Group 3 (triple-negative breast cancer) displayed increased TCA cycle intermediates, such as α-ketoglutarate and malate, reflecting a metabolic shift toward energy production and biosynthesis to sustain aggressive proliferation. In Group 4 (HER2-enriched), elevated phosphatidylcholines and phosphatidylethanolamines suggested upregulated mono-unsaturated phospholipid biosynthesis. The study provides a framework for leveraging multimodal data integration, attention-based feature selection, and self-organizing map analysis to identify biologically meaningful biomarkers.
Kinetics of hematological and biochemical biomarkers are key tools for monitoring disease progression in Marburg virus-infected patients in Rwanda
Enhancing agricultural commodity price forecasting with deep learning
Abstract Accurate forecasting of agricultural commodity prices is essential for market planning and policy formulation, especially in agriculture-dependent economies like India. Price volatility, driven by factors such as weather variability and market demand fluctuations, poses significant forecasting challenges. This study evaluates the performance of traditional stochastic models, machine learning techniques, and deep learning approaches in forecasting the prices of 23 commodities using daily wholesale price data from January 2010 to June 2024. Models assessed include Autoregressive Integrated Moving Average, Support Vector Regression, Extreme Gradient Boosting, Multilayer Perceptron, Recurrent Neural Networks, Long Short-Term Memory Networks, Gated Recurrent Units, and Echo State Networks. Results show that deep learning models, particularly Long Short-Term Memory and Gated Recurrent Units, outperform others in capturing complex temporal patterns, achieving superior accuracy across error metrics. The results indicate that deep learning models, particularly Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU), demonstrate superior performance in capturing complex temporal patterns. For instance, the GRU model achieved a Root Mean Squared Error (RMSE) of 369.54 for onions and 210.35 for tomatoes, significantly outperforming the ARIMA model, which recorded RMSE values of 1564.62 and 1298.60, respectively. Furthermore, the Mean Absolute Percentage Error (MAPE) for GRU was notably lower, at 14.59% for onions and 10.58% for tomatoes. These results underscore the efficacy of deep learning approaches in addressing the inherent volatility and nonlinear dynamics of agricultural commodity prices. These findings offer valuable insights for policymakers, traders, and farmers, enabling better market interventions, crop planning, and risk management. The study recommends exploring hybrid models and incorporating external factors like weather data to further enhance forecasting reliability.
In situ NMR reveals a pH sensor motif in an outer membrane protein that drives bacterial vesicle production
The outer membrane vesicles (OMVs) produced by diderm bacteria have important roles in cell envelope homeostasis, secretion, interbacterial communication, and pathogenesis. The facultative intracellular pathogen Salmonella enterica Typhimurium (STm) activates OMV biogenesis inside the acidic vacuoles of host cells by upregulating the expression of the OM protein PagC, one of the most robustly activated genes in a host environment. Here, we used solid-state nuclear magnetic resonance (NMR) and electron microscopy (EM), with native bacterial OMVs, to demonstrate that three histidines, essential for the OMV biogenic function of PagC, constitute a key pH-sensing motif. The NMR spectra of PagC in OMVs show that they become protonated around pH 6, and His protonation is associated with specific perturbations of select regions of PagC. The use of bacterial OMVs is a key aspect of this work enabling NMR structural studies in the context of the physiological environment. PagC expression upregulates OMV production in Escherichia coli , replicating its function in STm. Moreover, the presence of PagC drives a striking aggregation of OMVs and increases bacterial cell pellicle formation at acidic pH, pointing to a potential role as an adhesin active in biofilm formation. The data provide experimental evidence for a pH-dependent mechanism of OMV biogenesis and aggregation driven by an OM protein.
Investigating hepatic fibrosis heterogeneity by three-dimensional imaging in metabolic dysfunction-associated steatotic liver disease
A fake news detection model using the integration of multimodal attention mechanism and residual convolutional network
Intestinal alkaline phosphatase is a receptor for cholesterol-lowering pentapeptide IIAEK and regulates cholesterol homeostasis in mice
Photocatalytic syntheses and evaluation of biological activities of rare disaccharides, 3-O-α-d-glucopyranosyl-d-arabinose
Abstract Recently, there has been a growing interest in rare sugars due to their potential applications in functional foods and pharmaceuticals. However, sustainable production methods for these compounds remain challenging due to their high cost, lengthy production times, and environmentally harmful reagents. Herein, we report a novel photocatalytic approach for synthesizing rare disaccharides from maltose, an abundant and renewable natural resource, under mild conditions at room temperature and atmospheric pressure using light as the energy source. The photocatalytic treatment of maltose using platinum compound-supported titanium oxide (PtCl/TiO₂) resulted in the formation of rare disaccharides, primarily 3-O-α-d-glucopyranosyl-d-arabinose and glucosyl-erythrose, which were characterized by HPLC, LC/MS, 13C NMR spectroscopy, and optical rotation measurements. Notably, biological evaluation of 3-O-α-d-glucopyranosyl-d-arabinose using HeLa and HEK293 cells demonstrated no cytotoxicity and negligible cellular uptake. Furthermore, enzymatic degradation studies using mouse intestinal α-glucosidase revealed significantly lower degradability compared to maltose, with minimal glucose production observed. These findings suggest that 3-O-α-d-glucopyranosyl-d-arabinose exhibits resistance to digestion and absorption in mammalian systems, highlighting its potential application as a low-calorie sweetener and a functional food ingredient. This study presents an environmentally benign synthetic route to rare disaccharides and demonstrates their promising biological properties.
Experimental study on mechanical performance of corrugated steel-concrete composite bridge decks
Design of a low-delay 4-bit parallel prefix adder using QCA technology
Abstract This paper presents a novel low-delay 4-bit Parallel Prefix Adder (PPA) implemented as a multilayer circuit using Quantum Dot Cellular Automata (QCA) technology. PPAs are among the most suitable architectures for high-speed digital design, offering significant advantages in scalability and performance over traditional Ripple Carry Adders (RCAs) and Carry Flow Adders (CFAs). The proposed design provides a fast, compact, ergonomic, and energy-efficient alternative to QCA adders adopting these architectures. This work enhances existing PPA modules, including XOR gates, Half Adders, Black Modules, and Gray Modules, by tailoring them to optimally fit the core PPA structure. The proposed PPA achieves a 26% reduction in cell count, a 31% reduction in area and a 57% reduction in delay compared to existing PPA designs. Utilizing a hybrid crossover methodology, the design reduces delay by 25% relative to the fastest 4-bit QCA adder reported in the literature and lowers the area-delay cost by 11% compared to the most economical design. Simulated using the QCADesigner-E Version 2.2 software, the proposed adder demonstrates energy dissipation comparable to existing designs, solidifying its practicality and efficiency for high-speed QCA-based applications.
A phototaxis assay to measure sublethal effects of pesticides on bees
A safety message dissemination method in a travel guidance system
The importance of small-island populations for the long-term survival of endangered large-bodied insular mammals
Island populations of large vertebrates have experienced higher extinction rates than mainland populations over long timescales due to demographic stochasticity, genetic drift, and inbreeding. While being more susceptible to extinction and as such potentially targeted for conservation interventions such as genetic rescue, small-island populations can experience relatively less anthropogenic habitat degradation than those on larger islands. Here, we determine the consequences and conservation implications of long-term isolation and recent human activities on genetic diversity of island populations of two forest-dependent mammals endemic to the Wallacea archipelago: the anoa ( Bubalus spp.) and babirusa ( Babyrousa spp.). Using genomic analyses and habitat suitability models, we show that, compared to closely related species, populations on mainland Sulawesi exhibit low heterozygosity, high inbreeding, a high proportion of deleterious alleles, and experience a high rate of anthropogenic disturbance. In contrast, populations on smaller islands occupy higher-quality habitats, possess fewer deleterious mutations despite exhibiting lower heterozygosity and higher inbreeding. Site frequency spectra indicate that these patterns reflect stronger, long-term purging in smaller-island populations. Our results thus suggest that conservation efforts should focus on protecting small-island high-quality habitats and avoiding translocations from mainland populations. This study highlights the crucial role of small offshore islands for the long-term survival of Wallacea’s iconic and indigenous mammals in the face of development on the mainland.
An explainable RoBERTa approach to analyzing panic and anxiety sentiment in oral health education YouTube comments
An explainable multi-task deep learning framework for crash severity prediction using multi-source data
Abstract Traffic accidents pose significant global challenges, causing substantial injuries, fatalities, and economic losses. Current research predominantly focuses on single-prediction objectives (e.g., fatality prediction) while neglecting property damage assessments and critical interactions between prediction tasks. Although neural networks demonstrate superior predictive capabilities, their application in traffic safety analysis remains constrained by inherent limitations in causal interpretability, coupled with challenges posed by data imbalance, heterogeneity, and complexity in crash datasets. This study proposes an interpretable multi-task learning framework (Adv MT-DNN) that synergistically integrates an enhanced deep neural network with post-hoc explanation methods for comprehensive crash severity prediction. Our dual-focused approach addresses multiple prediction targets (including fatalities, severe injuries, and property damage). It provides granular insights into contributing factors through SHAP-based feature importance rankings and interaction analysis. Validated using four-year (2018–2021) multi-source traffic data from China, the framework demonstrates significant improvements in prediction accuracy compared to baselines. Nonparametric estimation of the top-8 critical factors (e.g., blood alcohol content, collision type, and accident occurrence period) confirms statistically significant associations with crash severity. The explicit interpretation mechanism bridges the critical gap between predictive performance and model interpretability in traffic safety analytics, providing engineering-relevant insights. This research establishes a robust methodological foundation for developing data-driven road safety policies and intelligent transportation systems, particularly in developing countries with complex traffic ecosystems.