Search Results (99)

This study examined how drivers’ eye fixations change before, during, and after recognizing road markings, and how these changes relate to driving speed, visual complexity, cognitive functions, and demographics. 20 licensed drivers viewed on-board movies showing digit or character road markings while their eye movements were tracked. Fixation positions and dispersions were analyzed. Results showed that, regardless of marking type, fixations were horizontally dispersed before and after recognition but became vertically concentrated during recognition, with fixation points shifting higher (p < 0.001) and horizontal dispersion decreasing (p = 0.01). During the recognition period, fixations moved upward and narrowed horizontally toward the final third (p = 0.034), suggesting increased focus. Longer fixations were linked to slower speeds for digits (p = 0.029) and more characters for character markings (p < 0.001). No significant correlations were found with cognitive functions or demographics. These findings suggest that drivers first scan broadly, then concentrate on markings as they approach. For optimal recognition, simple or essential information should be placed centrally or lower, while detailed content should appear higher to align with natural gaze patterns. In high-speed environments, markings should prioritize clarity and brevity in central positions to ensure safe and rapid recognition. Full article

Molecular simulation is central to modern drug discovery but is often limited by high computational cost and the complexity of molecular interactions. Deep-learning drug–target interaction (DTI) prediction can accelerate screening; however, many models underuse the local functional structure features—binding motifs, reactive groups, and residue-level fragments—that drive recognition. We present LoF-DTI, a framework that explicitly represents and couples such local features. Drugs are converted from SMILES into molecular graphs and targets from sequences into feature representations. On the drug side, a Jumping Knowledge (JK) enhanced Graph Isomorphism Network (GIN) extracts atom- and neighborhood-level patterns; on the target side, residual CNN blocks with progressively enlarged receptive fields, augmented by N-mer substructural statistics, capture multi-scale local motifs. A Gated Cross-Attention (GCA) module then performs atom-to-residue interaction learning, highlighting decisive local pairs and providing token-level interpretability through attention scores. By prioritizing locality during both encoding and interaction, LoF-DTI delivers competitive results across multiple benchmarks and improves early retrieval relevant to virtual screening. Case analyses show that the model recovers known functional binding sites, suggesting strong potential to provide mechanism-aware guidance for molecular simulation and to streamline the drug design pipeline. Full article

Background: Most immunologically “cold” tumors do not respond durably to checkpoint blockade because tumor antigen (TA) release and presentation are insufficient to prime effective T-cell immunity. While prior work demonstrated synergy between cisplatin and a TLR7/8/9 agonist (CR108) in 4T1 tumors, the underlying mechanism—particularly whether chemotherapy functions as a broad antigen-releasing agent enabling TLR-driven immune amplification—remained undefined. Methods: Using murine models of breast (4T1), melanoma (B16-F10), and colorectal cancer (CT26), we tested multiple chemotherapeutic classes combined with CR108. We quantified intratumoral and systemic soluble TAs, antigen presentation and cross-priming by antigen-presenting cells, tumor-infiltrating lymphocytes, and cytokine production by flow cytometry/ICS. T-cell receptor β (TCRβ) repertoire dynamics in tumor-draining lymph nodes were profiled to assess amplitude and breadth. Tumor microenvironment remodeling was analyzed, and public datasets (e.g., TCGA basal-like breast cancer) were interrogated for expression of genes linked to TA generation/processing and peptide loading. Results: Using cisplatin + CR108 in 4T1 as a benchmark, we demonstrate that diverse chemotherapies—especially platinum agents—broadly increase the repertoire of soluble tumor antigens available for immune recognition. Across regimens, chemotherapy combined with CR108 increased T-cell recognition of candidate TAs and enhanced IFN-γ⁺ CD8⁺ responses, with platinum agents producing the largest expansions in soluble TAs. TCRβ sequencing revealed increased clonal amplitude without loss of repertoire breadth, indicating focused yet diverse antitumor T-cell expansion. Notably, therapeutic efficacy was not predicted by canonical damage-associated molecular pattern (DAMP) signatures but instead correlated with antigen availability and processing capacity. In human basal-like breast cancer, higher expression of genes involved in TA generation and antigen processing/presentation correlated with improved survival. Conclusions: Our findings establish an antigen-centric mechanism underlying chemo–TLR agonist synergy: chemotherapy liberates a broadened repertoire of tumor antigens, which CR108 then leverages via innate immune activation to drive potent, T-cell-mediated antitumor immunity. This framework for rational selection of chemotherapy partners for TLR7/8/9 agonism and support clinical evaluation to convert “cold” tumors into immunologically responsive disease. Full article

Artificial intelligence-based image processing is critical for sensor fusion and image transformation in mobility systems. Advanced driver assistance functions such as forward monitoring and digital side mirrors are essential for driving safety. Degradation due to raindrops, fog, and high-dynamic range (HDR) imbalance caused by lighting changes impairs visibility and reduces object recognition and distance estimation accuracy. This paper proposes a diffusion framework to enhance visibility under multi-degradation conditions. The denoising diffusion probabilistic model (DDPM) offers more stable training and high-resolution restoration than the generative adversarial networks. The DDPM relies on large-scale paired datasets, which are difficult to obtain in raindrop scenarios. This framework applies the Palette diffusion model, comprising data augmentation and raindrop-removal modules. The data augmentation module generates raindrop image masks and learns inpainting-based raindrop synthesis. Synthetic masks simulate raindrop patterns and HDR imbalance scenarios. The raindrop-removal module reconfigures the Palette architecture for image-to-image translation, incorporating the augmented synthetic dataset for raindrop removal learning. Loss functions and normalization strategies improve restoration stability and removal performance. During inference, the framework operates with a single conditional input, and an efficient sampling strategy is introduced to significantly accelerate the process. In post-processing, tone adjustment and chroma compensation enhance visual consistency. The proposed method preserves fine structural details and outperforms existing approaches in visual quality, improving the robustness of vision systems under adverse conditions. Full article

Despite the increasing recognition of the role of urban orchard ecosystems in sustainable urban development, the mechanistic understanding of how tree species soil biochemical heterogeneity drives microbial community assembly, the spatial patterns governing microbe-environment interactions, and their collective contributions to ecosystem multifunctionality remain poorly characterized. This study investigated how Prunus species and soil depth affect microbial biodiversity and metabolomic signatures in an urban orchard in Cluj-Napoca, Romania. Soil samples were collected from five fruit tree species (apricot, peach, plum, cherry, and sour cherry) across three depths (0–10, 10–20, and 20–30 cm), resulting in 225 samples. The microbial community structure was analyzed through phospholipid fatty acid (PLFA) profiling, whereas the soil metabolome was analyzed by mass spectrometry techniques, including gas chromatography–mass spectrometry (GC–MS/MS) and MALDI time-of-flight (TOF/TOF) MS, which identified 489 compounds across 18 chemical classes. The results revealed significant tree species-specific effects on soil microbial biodiversity, with bacterial biomarkers dominating and total microbial biomass varying among species. The soils related to apricot trees presented the highest microbial activity, particularly in the surface layers. Metabolomic analysis revealed 247 distinct KEGG-annotated metabolites, with sour cherry exhibiting unique organic acid profiles and cherry showing distinctive quinone accumulation. Depth stratification influenced both microbial communities and metabolite composition, reflecting oxygen gradients and substrate availability. These findings provide mechanistic insights into urban orchard soil biogeochemistry, suggesting that strategic species selection can harness tree species-soil microbe interactions to optimize urban soil ecosystem services and enhance urban biodiversity conservation. Full article

Harmonizing economic growth and carbon emissions is key to reaching the “dual carbon” targets. This research centers on the seven key urban agglomerations within the Yellow River Basin (YRB) and establishes an integrated research framework of decoupling effect quantification–spatial association recognition–driving factor analysis. By combining the Tapio decoupling model, a modified gravity model, social network analysis (SNA), and the Logarithmic Mean Divisia Index (LMDI) method, the study systematically evaluates the decoupling states, spatial association structure, and driving mechanisms between regional carbon emissions and economic growth from 2001 to 2020. The results show that: (1) All seven urban agglomerations exhibit a simultaneous upward trend in both carbon emissions and GDP, but significant regional disparities exist, with some agglomerations demonstrating a green growth pattern where economic growth outpaces carbon emissions. (2) Weak decoupling is the predominant type among urban agglomerations and their constituent cities in the YRB. Notably, some regions have regressed to growing connection or growing negative decoupling during 2016–2020. (3) The spatial network of carbon emission decoupling effects exhibits a core-periphery structure characterized by stronger eastern regions and weaker western regions, with the Shandong Peninsula and Guanzhong Plain urban agglomerations serving as core nodes for regional linkage. (4) Per capita GDP and technological level play a dominant role in promoting decoupling, while energy intensity and the population carrying intensity of the real economy are the primary inhibiting factors; the impact of industrial structure shows an unstable direction. Grounded in these findings, this study formulates differentiated carbon reduction pathways tailored to regional heterogeneity, providing theoretical insights and actionable guidance to facilitate the low-carbon transition and coordinated governance of urban agglomerations. Full article

Next-generation sequencing has overturned the dogma of biliary sterility, revealing low-biomass microbiota along the gut–biliary axis with metabolic and immunologic effects. This review synthesizes evidence on composition, function, and routes of colonization across benign and malignant disease. In cholelithiasis, Proteobacteria- and Firmicutes-rich consortia provide β-glucuronidase, phospholipase A₂, and bile salt hydrolase, driving bile supersaturation, nucleation, and recurrence. In primary sclerosing cholangitis, primary biliary cholangitis, and autoimmune hepatitis, intestinal dysbiosis and disturbed bile acid pools modulate pattern recognition receptors and bile acid signaling (FXR, TGR5), promote Th17 skewing, and injure cholangiocytes; bile frequently shows Enterococcus expansion linked to taurolithocholic acid. Distinct oncobiomes characterize cholangiocarcinoma subtypes; colibactin-positive Escherichia coli and intratumoral Gammaproteobacteria contribute to DNA damage and chemoresistance. In hepatocellular carcinoma, intratumoral microbial signatures correlate with tumor biology and prognosis. We critically appraise key methodological constraints—sampling route and post-sphincterotomy contamination, antibiotic prophylaxis, low biomass, and heterogeneous analytical pipelines—and outline a translational agenda: validated microbial/metabolomic biomarkers from bile, tissue, and stent biofilms; targeted modulation with selective antibiotics, engineered probiotics, fecal microbiota transplantation, and bile acid receptor modulators. Standardized protocols and spatial, multi-omic prospective studies are required to enable risk stratification and microbiota-informed therapeutics. Full article

Smart home technology enhances the quality of life, particularly with respect to in-home care and health monitoring. While video-based methods provide accurate behavior analysis, privacy concerns drive interest in non-visual alternatives. This study proposes a Bluetooth Low Energy (BLE)-enabled indoor positioning and behavior recognition system, integrating machine learning techniques to support sustainable and privacy-preserving health monitoring. Key optimizations include: (1) a vertically mounted Data Collection Unit (DCU) for improved height positioning, (2) synchronized data collection to reduce discrepancies, (3) Kalman filtering to smooth RSSI signals, and (4) AI-based RSSI analysis for enhanced behavior recognition. Experiments in a real home environment used a smart wristband to assess BLE signal variations across different activities (standing, sitting, lying down). The results show that the proposed system reliably tracks user locations and identifies behavior patterns. This research supports elderly care, remote health monitoring, and non-invasive behavior analysis, providing a privacy-preserving solution for smart healthcare applications. Full article

Ovarian cancer remains a formidable global health burden, characterized by frequent late-stage diagnosis and elevated mortality rates attributable to its elusive pathogenesis and the critical lack of reliable early-detection biomarkers. Emerging investigations into the gut–vaginal microbiome axis have unveiled novel pathogenic mechanisms and potential diagnostic targets in ovarian carcinogenesis. This comprehensive review systematically examines the compositional alterations in and functional interplay between vaginal and intestinal microbial communities in ovarian cancer patients. We elucidate three principal mechanistic pathways through which microbial dysbiosis may drive oncogenesis: (1) estrogen-mediated metabolic reprogramming via β-glucuronidase activity; (2) chronic activation of pro-inflammatory cascades (particularly NF-κB and STAT3 signaling); (3) epigenetic silencing of tumor suppressor genes through DNA methyltransferase modulation. We propose an integrative diagnostic framework synthesizing multi-omics data—incorporating microbial profiles, metabolic signatures, pathway-specific molecular alterations, established clinical biomarkers, and imaging findings—within a multifactorial etiological paradigm. This innovative approach aims to enhance early-detection accuracy through machine learning-enabled multidimensional pattern recognition. By bridging microbial ecology with tumor biology, this review provides novel perspectives for understanding ovarian cancer etiology and advancing precision oncology strategies through microbiome-targeted diagnostic innovations. Full article

Neutrophils are essential components of innate immunity, executing a range of effector functions including phagocytosis, degranulation, and the release of neutrophil extracellular traps (NETs). A key hallmark of NET formation is the presence of citrullinated histone H3 (CitH3), produced by peptidylarginine deiminases (PAD2 and PAD4) to facilitate chromatin decondensation. While NETs play critical antimicrobial roles, excessive or dysregulated NET formation, termed NETosis, can drive tissue injury, chronic inflammation, and organ dysfunction across a wide spectrum of diseases. Beyond its structural role within NETs, CitH3 acts as a damage-associated molecular pattern (DAMP), amplifying immune activation and pathological inflammation. Elevated CitH3 levels have been identified as biomarkers in sepsis, viral infections, ischemia–reperfusion injury, organ transplantation, diabetic wounds, autoimmune diseases, and cancer. Despite increasing recognition of CitH3’s pathogenic contributions, its therapeutic potential remains largely untapped. This review summarizes recent advances in understanding the role of CitH3 in NETosis and immune dysfunction, highlights emerging strategies targeting CitH3 therapeutically, and identifies critical knowledge gaps. Collectively, these insights position CitH3 as a promising druggable biomarker for the diagnosis, prognosis, and treatment of acute and chronic inflammatory diseases. Full article

Resistive gas sensors have attracted significant attention due to their simple architecture, low cost, and ease of integration, with widespread applications in environmental monitoring, industrial safety, and healthcare diagnostics. This review provides a comprehensive overview of recent advances in resistive gas sensors, focusing on their fundamental working mechanisms, sensing material design, device architecture optimization, and intelligent system integration. These sensors primarily operate based on changes in electrical resistance induced by interactions between gas molecules and sensing materials, including physical adsorption, charge transfer, and surface redox reactions. In terms of materials, metal oxide semiconductors, conductive polymers, carbon-based nanomaterials, and their composites have demonstrated enhanced sensitivity and selectivity through strategies such as doping, surface functionalization, and heterojunction engineering, while also enabling reduced operating temperatures. Device-level innovations—such as microheater integration, self-heated nanowires, and multi-sensor arrays—have further improved response speed and energy efficiency. Moreover, the incorporation of artificial intelligence (AI) and Internet of Things (IoT) technologies has significantly advanced signal processing, pattern recognition, and long-term operational stability. Machine learning (ML) algorithms have enabled intelligent design of novel sensing materials, optimized multi-gas identification, and enhanced data reliability in complex environments. These synergistic developments are driving resistive gas sensors toward low-power, highly integrated, and multifunctional platforms, particularly in emerging applications such as wearable electronics, breath diagnostics, and smart city infrastructure. This review concludes with a perspective on future research directions, emphasizing the importance of improving material stability, interference resistance, standardized fabrication, and intelligent system integration for large-scale practical deployment. Full article

Toll-like receptors (TLRs) comprise an evolutionarily conserved family of pattern recognition receptors that detect microbial-associated molecular patterns and endogenous danger signals to orchestrate innate immune responses. While traditionally positioned at the frontline of host defense, accumulating evidence suggests that TLRs are at the nexus of immuno-metabolic regulation and central nervous system (CNS) homeostasis. They regulate a wide range of immune and non-immune functions, such as cytokine and chemokine signaling, and play key roles in modulating synaptic plasticity, neurogenesis, and neuronal survival. However, alterations in TLR signaling can drive a sustained pro-inflammatory state, mitochondrial dysfunction, and oxidative stress, which are highly associated with the disruption of emotional and cognitive functions and the pathogenesis of psychiatric disorders. In this review, we integrate findings from molecular to organismal levels to illustrate the diverse roles of TLRs in regulating emotion, cognition, metabolic balance, and gut–brain interactions. We also explore emerging molecular targets with the potential to guide the development of more effective therapeutic interventions. Full article

The widespread adoption of electric vehicles (EVs) necessitates advanced energy management strategies to alleviate range anxiety and improve overall energy efficiency. This study presents a novel framework for optimizing energy consumption in EVs by integrating driver behavior patterns, road conditions, and environmental factors. Utilizing a comprehensive dataset of 3395 high-resolution charging sessions from 85 EV drivers across 25 workplace locations, we developed a multi-modal prediction model that captures the complex interactions between driving behavior and environmental conditions. The proposed methodology employs a combination of driving scenario recognition and reinforcement learning techniques to optimize energy usage. Specifically, we utilize contrastive learning to extract meaningful representations of driving states by leveraging the symmetric relationships between positive pairs and the asymmetric nature of negative pairs and implement graph attention networks to model the intricate relationships between road environments and driving behaviors. Our experimental results demonstrate that the proposed framework achieves a significant reduction in energy consumption compared to baseline methods, with an average improvement of 17.3% in energy efficiency under various driving conditions. Furthermore, we introduce an adaptive real-time optimization strategy that dynamically adjusts vehicle parameters based on instantaneous driving patterns and environmental contexts. This research contributes to the advancement of intelligent energy management systems for EVs and provides insights into the development of more efficient and environmentally sustainable transportation solutions. Full article

Temporary book-tax differences (BTDs) serve as critical proxies for understanding corporate earnings management and tax planning. However, the drivers of large negative BTDs (LNBTDs)—where book income falls below taxable income—remain underexplored. This study investigates the determinants and components of LNBTDs, focusing on their relationship with deferred tax assets (DTAs) and liabilities (DTLs). Utilizing hand-collected data from the tax disclosures of S&P 500 firms’ 10-K filings (2007–2023), I analyze 4685 firm-year observations to identify specific accounting items driving LNBTDs. Findings reveal that deferred revenue, goodwill impairments, R&D, CapEx, environmental obligations, pensions, contingency liabilities, leases, and receivables are significant contributors, often generating substantial DTAs due to timing mismatches between book and tax recognition. Notably, high-tech industries, like the pharmaceutical, medical, and computers and software industries, exhibit pronounced LNBTDs, driven by upfront revenue recognition for tax purposes and deferred recognition for financial reporting, capitalization, amortization and depreciation effects, and other deferred tax components. Regression analyses confirm strong associations between these components and LNBTDs, with asymmetry in reversal patterns suggesting that initial differences do not always offset symmetrically over time. While prior research emphasizes large positive BTDs and tax avoidance, this study highlights economic and industry-specific characteristics as key LNBTD drivers, with limited evidence of earnings manipulation via deferred taxes. These insights enhance the value relevance of deferred tax disclosures and offer implications for reporting standards, tax policy, and research into BTD dynamics. Full article

Carrots, a multi-nutrient dietary source rich in natural bioactive compounds, have gained broad recognition due to their nutritional properties and potential health-promoting effects. Studying metabolic changes during carrot maturation can provide deeper insights into the formation of their nutritional value and quality. Using Liquid Chromatograph Mass Spectrometer (LC-MS) metabolomics, we systematically profiled metabolic dynamics during orange-red carrot maturation, with large-scale compound detection, structural identification, and absolute quantification. The results showed that a total of 607 metabolites were detected. Further analysis of three distinct stages of taproot swelling and maturation revealed the following: Most sugars in primary metabolites exhibited an increasing accumulation trend across the three stages. Organic acids (including TCA cycle intermediates) displayed a pronounced decreasing accumulation pattern. Transcriptomic analysis revealed significantly upregulated expression of differentially expressed genes (DEGs) involved in the TCA cycle from the fleshy root formation stage (30 days after sowing, DAS), expansion stage (50 DAS), and maturation stage (115 DAS) in carrots. Phytochemical profiling identified 206 secondary metabolites (92 phenolic acids and 114 non-phenolic compounds). Notably, many phenolic acids maintained relatively high levels during early carrot development but exhibited a rapid decline in subsequent stages. The extensive downregulation of genes involved in phenolic acid biosynthesis pathways likely drives the rapid decline in phenolic acid content during early developmental stages. Correlation analysis further revealed significant crosstalk between primary and secondary metabolites during carrot maturation, with a pronounced negative correlation between sugars and secondary metabolites. These data provide a global perspective of carrot metabolomics and a comprehensive analysis of metabolic variations during development, establishing a molecular and metabolic basis for a deeper and more systematic understanding of carrot quality traits. Full article