Search Results (198)

Accurate and non-destructive evaluation of grape quality is crucial for intelligent viticulture, yet most existing approaches address cultivar classification and soluble solid content (SSC) prediction as independent tasks based on single-modality data, limiting robustness and practical applicability. This study proposes DualStream-RTNet, a unified multimodal deep learning framework that simultaneously performs grape cultivar classification and SSC prediction by integrating RGB-HSV fused images and PCA-compressed hyperspectral spectra. The dual-stream architecture enables the complementary learning of external chromatic–textural cues and internal physicochemical information, while a Transformer-enhanced fusion module strengthens global representation and cross-modal correlation. A dataset of 864 berries from five grape cultivars was used to validate the model. DualStream-RTNet achieved 93.64% classification accuracy, outperforming ResNet18 and other CNN baselines, and produced more compact and consistent confusion-matrix patterns. For SSC prediction, it consistently yielded the highest performance across cultivars, with R2p values up to 0.9693 and RMSE as low as 0.2567, surpassing the PLSR, SVR, LSTM, and Transformer regression models. These results demonstrate the superiority of the proposed framework in capturing both visual and spectral characteristics. DualStream-RTNet provides an efficient and scalable solution for comprehensive grape quality assessment, offering strong potential for real-time sorting, precision grading, and smart agricultural applications. Full article

Background: Glioblastoma is the most lethal primary brain tumor, being characterized not only by marked intratumoral heterogeneity but also by strong systemic immunosuppression. Circulating extracellular vesicles (EVs) have gained growing recognition during the past decade as important mediators of intercellular communication, particularly through their microRNA (miRNA) cargo. However, the global EV miRNA landscape of circulating EV-associated miRNAs in glioblastoma patients and their relation with gene expression patterns in peripheral immune cells remain incompletely defined. Methods: To investigate these systemic associations, we profiled EV-associated miRNA expression in plasma samples from glioblastoma patients and matched healthy controls using the small RNA sequencing method, followed by differential expression and pathway analyses. Based on these findings and literature evidence, identified changes in selected EV miRNA levels were validated by qPCR in an extended cohort. In parallel, expression of their predicted immune-related mRNA targets was analyzed in peripheral blood mononuclear cells (PBMCs) obtained from the same individuals, allowing for the assessment of EV miRNA–PBMC mRNA correlation patterns. Results: Small RNA sequencing revealed a distinct circulating EV-associated miRNA profile in glioblastoma patients compared to controls. The validation analysis of relative expression of the identified DEmiRNAs has shown a statistically significant upregulation of hsa-miR-142-3p, hsa-miR-19b-3p, and hsa-miR-98-5p in circulating EVs of glioblastoma patients compared to controls. PBMCs from glioblastoma patients exhibited increased expression of the regulatory genes SOCS1, SOCS3, and PTEN, while CCND1 was downregulated. Correlation analyses suggested that certain EV miRNA changes parallel with alterations in PBMC gene expression in glioblastoma patients, suggesting early immune priming in the circulation. Conclusions: Our findings indicate that circulating EV miRNAs in glioblastoma patients are associated with specific gene expression patterns in peripheral immune cells, suggesting a complex regulatory balance between pro-inflammatory and anti-inflammatory cues, potentially preceding full tumor-associated macrophage polarization. These molecular interactions may offer opportunities for developing early biomarkers or new therapeutic approaches. Full article

The accelerated integration of intelligent agents in user-centered digital environments has intensified research in the field of Human–Robot Interaction, especially regarding mechanisms for adaptive, intuitive, and cognitively aligned communication. The present study develops and empirically examines a structural model of BCI-inspired adaptive agents designed to support coordinated interaction in HRI contexts. The study analyzes users’ perceptions of standardized hypothetical interaction scenarios involving BCI-inspired adaptive digital agents, where BCI inspiration is conceptual and refers to adaptive architectures interpreting behavioral cues rather than direct neural signal acquisition. The proposed model integrates four main constructs—perceived technological innovation, user involvement, agent adaptivity, and digital synergy—and examines their associations with user satisfaction in digital collaborative environments. Data were collected through an anonymous questionnaire (N = 268) and analyzed using structural equation modeling with the PLS-SEM method. The structural model demonstrates substantial explanatory power, accounting for 66.8% of the variance in user satisfaction (R² = 0.668). The study contributes by empirically supporting a scenario-based structural evaluation framework suitable for early-stage adaptive HRI system design. The results highlight the role of digital synergy in aligning innovation, engagement, and adaptive behavior in BCI-inspired adaptive HRI systems, providing directions for the design of adaptive robotic agents oriented toward coordinated interaction, user-centered integration, and responsible use in collaborative digital ecosystems. Full article

Short auditory cues in enclosed built environments (such as elevator calls, access control, navigation, and heating, ventilation, and air conditioning (HVAC) notifications) influence not only usability but also stress and perceptions of well-being in daily indoor life. However, acoustic research remains largely focused on physical properties, and the psychophysiological impact of such short auditory cues remains under-quantified. To address this gap, a neuroscience-based evaluation approach, the Acoustic User Experience and Emotion (AUEX) model, is proposed. This model integrates functional near-infrared spectroscopy (fNIRS), electrodermal activity (EDA), and the User Experience Questionnaire (UEQ). With 33 in-cabin prompt sounds as a controlled typology of short auditory cues in an enclosed setting, we set up a simulated interaction experiment with 20 participants in a driving simulator vehicle cabin to investigate the relationship between acoustic properties and cognitive load, arousal, and user experience. The results show that timbre is the key factor, which was correlated positively with overall UX (r = 0.414) and negatively with prefrontal ΔHbO (CH3: r = −0.368; l-DLPFC: r = −0.449), indicating a decrease in cognitive load and a relaxed affective state. Conversely, high-frequency signals improved pragmatic quality but increased physiological arousal, which negatively affected hedonic assessment. To facilitate the translation of evaluation results into practice, we also completed a design phase that converted the AUEX results into scenario-based parameter targets and prototype designs for functional, warning, and brand/affective cues, illustrating how evidence-based relationships can be translated into design-ready outputs for enclosed built environments. These results confirm the AUEX approach as a transferable method for designing short auditory cues for well-being and provide parameter-level implications for therapeutic and human-centered sound design in smart buildings, intelligent vehicles, and other enclosed built environments. Overall, the AUEX approach provides a transferable evaluation-to-design workflow for short auditory cues in enclosed interactive contexts; however, direct generalization from a single controlled vehicle cabin setting to real-world building environments should be validated through future field studies. Accordingly, the present findings are positioned as evidence from a controlled enclosed case rather than universal conclusions for all enclosed spaces. Full article

Colorectal cancer (CRC) is a significant cause of cancer mortality in the world, and its etiology is complicated by genetic and epigenetic changes. As one of the most important tumor progression regulators, Zinc Finger E-box Binding Homeobox 1 (ZEB1) is a transcription factor that has a key role in epithelial–mesenchymal transition (EMT), which is essential in the metastasis, drug resistance, and plasticity of cancer cells in CRC. ZEB1 silences the expression of epithelial markers, including E-cadherin, and it induces the development of mesenchymal properties, such as invasion and metastasis, i.e., tumor aggressiveness. ZEB1 drives epigenetic reprogramming in CRC by coordinating histone deacetylation, histone methylation, and DNA methylation of epithelial tumor suppressor gene promoters and by engaging in reciprocal regulatory interactions with non-coding RNAs, including the miR-200 family. Furthermore, multiple oncogenic signaling cascades, including Wnt/β-catenin, TGF-β, NF-κB, MEK-ERK, JAK/STAT3, and HIF-1α, converge on ZEB1 to amplify its transcriptional and epigenetic activity, positioning ZEB1 as a nodal integrator of extracellular cues and epigenetic reprogramming in CRC metastasis. This review integrates three interconnected regulatory layers, i.e., (1) ZEB1’s direct epigenetic control of target gene expression via histone modification and DNA methylation, (2) post-transcriptional regulation of ZEB1 itself by ncRNAs (miRNAs, circRNAs, and lncRNAs) that create feedback circuits modulating layer 1, and (3) upstream modulation of ZEB1 transcriptional activity by oncogenic signaling pathways (Wnt/β-catenin, TGF-β, NF-κB, MEK-ERK, JAK/STAT3, and HIF-1α) to provide a comprehensive picture of ZEB1 in CRC metastasis and its therapeutic implications. Full article

Effective indoor navigation remains a challenge in complex built environments such as hospitals and airports, where disorientation can lead to anxiety, inefficiency, and safety risks. While prior research has focused on outdoor wayfinding or single-metric performance assessments, few studies have examined spatial cognitive efficiency—a multidimensional metric defined as the standardized difference between spatial knowledge acquisition (P) and cognitive resource expenditure (R). In this study, P was derived from expert-rated sketch maps that captured participants’ environmental understanding, while R was indexed by navigation path length, which reflected their exploration effort. This study employed virtual reality to investigate how individual differences and environmental cues shape cognitive efficiency during indoor navigation. Thirty participants explored a high-fidelity virtual environment while behavioral, sketch-based, and questionnaire data were collected. Results revealed a non-significant linear correlation between P and R, consistent with cognitive efficiency as a distinct construct. High-efficiency participants relied more on boundary cues and exhibited “low-speed, short-distance” exploration patterns, whereas landmark-dependent strategies showed lower stability. These findings underscore the theoretical and practical value of cognitive efficiency as a multidimensional metric, offering evidence-based guidance for designing cognitively supportive indoor navigation systems. Full article

Background: The reproductive cycle of Octopus mimus is regulated by environmental and hormonal factors, with photoperiod playing a key role in spawning induction and reproductive maturation. Understanding its underlying molecular mechanisms is essential for developing strategies to enhance controlled reproduction in aquaculture. Methods: We analyzed the expression of genes involved in the photoperiod-activated spawning induction cascade in the optic lobe and its downstream effects on the oviducal gland by performing transcriptomic analyses on females exposed to continuous light (24:0), which inhibits reproductive development, and a natural photoperiod, which induces spawning. The mRNA sequencing (RNA-Seq), quality control, gene annotation, and differential expression analyses were conducted using edgeR. Results: Spawning was completely inhibited under constant light, while 80% of control females spawned. Expression profiling revealed 89 downregulated and 34 upregulated genes in the optic lobe, and 178 downregulated and 237 upregulated genes in the oviducal gland (FDR < 0.05, |log2FC| ≥ 2), including key orthologs such as FMRFamide and myomodulin. Conclusions: These results show that the optic lobe integrates photoperiodic cues that modulate reproductive activation via a neuroendocrine cascade and coordinates spawning regulation through the oviducal gland, providing insights for improving reproductive control in aquaculture systems. Full article

Oncometabolites and hypoxia-regulated exosomes orchestrate hypoxia-inducible factor (HIF)–driven macrophage reprogramming across chronic cardiometabolic and oncologic conditions. In type 2 diabetes (T2D) and obesity, regional hypoxia in expanding white adipose tissue (WAT) reconfigures macrophage immunometabolism and chemokine signaling, recruits C-C chemokine receptor 2 (CCR2⁺) monocytes, and skews adipose-tissue macrophages toward M1-like programs that sustain low-grade inflammation and blunt the physiological M1-to-M2 transition during wound repair. In atherosclerotic plaques, lipid-core hypoxia stabilizes HIF-1α, amplifies nuclear factor kappa-light-chain-enhancer of activated B cells/reactive oxygen species (NF-κB/ROS) signaling, increases matrix metalloproteinase-2/-9 (MMP-2/-9) release, and reduces ATP-binding cassette transporter A1 (ABCA1)-mediated cholesterol efflux, weakening the fibrous cap. In tumors, poorly perfused niches accumulate lactate and succinate, which act as paracrine cues. Lactate activates PKA/cAMP pathways and promotes immunosuppressive tumor-associated macrophages (TAMs), whereas succinate signals through succinate receptor 1 (SUCNR1) to reinforce HIF-1α–dependent transcription and M2-like programming. In parallel, hypoxia-regulated exosomes deliver microRNAs such as miR-301a-3p, which suppress phosphatase and tensin homolog (PTEN) and activate PI3Kγ, thereby augmenting immunosuppression and programmed death-ligand 1 (PD-L1) expression. Clinically, this hypoxia–oncometabolite–exosome triad links oxygen debt with macrophage state, plaque destabilization, impaired wound repair, and tumor immune escape. Translational entry points include selective HIF-2α inhibition, phosphoinositide 3-kinase gamma (PI3Kγ) blockade, SUCNR1 targeting, and exosome-based miRNA modulation, while a biomarker panel comprising HIF-1α, vascular endothelial growth factor A (VEGF-A), and MMP-9 offers a pragmatic readout of hypoxia burden, macrophage programming, and therapeutic response. We conducted a focused narrative review (PubMed, Scopus, Web of Science; English; 2003–2025), prioritizing mechanistic and translational studies on hypoxia–HIF, lactate/succinate, and hypoxia-regulated exosomes across T2D, atherosclerosis, and cancer. Full article

This paper presents a collaborative hazard-detection system that pairs a UAV running YOLOv9 for rapid aerial scanning with a UGV running Faster R-CNN for precise ground-level confirmation. The pipeline exploits complementary strengths, fast wide-area cueing from the air and high-precision verification on the ground, to reduce false alarms while maintaining responsiveness in complex environments. On the validation set, YOLOv9 reached mAP@0.5 = 0.969 with F1 = 0.95 at 41.7 FPS, enabling real-time scanning of large areas. Faster R-CNN attained mAP@0.5 = 0.979 with F1 = 0.95 at 1.72 FPS, providing reliable close-range confirmations where localization accuracy is critical. Together, these results show that the proposed UAV–UGV pipeline delivers a practical balance between rapid hazard identification and trustworthy validation, suitable for search and rescue, critical infrastructure monitoring, and operations in hazardous environments. Potential extensions include inference optimization on the ground platform, multi-sensor data fusion, and field trials to assess robustness under real-world conditions. Full article

As both an energy source and a signaling cue, light quality regulates graft healing by modulating endogenous phytohormone homeostasis, callus formation, and vascular reconnection. To elucidate the regulatory roles of red/blue (R/B) light ratios and green light supplementation on healing and seedling quality of grafted tomato (Solanum lycopersicum L.), a controlled-environment experiment was conducted in a plant factory using ‘Zhongza 105’ as the scion and ‘Zhezhen No. 1’ as the rootstock. LED lighting treatments were established with different R/B ratios (1.0, 2.5, 4.0, 5.5 and 7.0) with or without supplemental green light. The results show that moderate R/B ratios (4.0–5.5) significantly increased scion elongation, the stem diameter of both scion and rootstock, the mechanical strength of the graft union, and sap flow, while also enhancing leaf chlorophyll content, photosynthetic rate, and root activity. Under optimal R/B conditions, indole-3-acetic acid (IAA) and gibberellin (GA) levels were elevated, whereas abscisic acid (ABA) was reduced, favoring callus proliferation and vascular reconnection. Green light supplementation under moderate R/B further promoted stem thickening, leaf area expansion, water transport across the graft union, and total biomass accumulation. Overall, an R/B ratio of 4.0–5.5 combined with appropriate green light supplementation optimized the morphology, structure, and physiological performance of grafted tomato seedlings during the healing stage. The results aim to provide a scientific basis for optimizing light environments in a controlled environment, thus enhancing the stability and quality of grafted tomato seedlings. Full article

In forest ecosystems, rhizodeposition can lead to significant differences in the availability of soil carbon (C), nitrogen (N), and phosphorus (P) between rhizosphere and bulk soils. Soil stoichiometry affects microbial and enzyme nutrient content and determines the abundance and composition of microbes and thus regulates microbial carbon use efficiency (CUE). However, how soil stoichiometry—particularly its variation between the rhizosphere and bulk soil—regulates microbial CUE by shaping microbial biomass, extracellular enzyme stoichiometry, and community composition remains insufficiently quantified. Here, through the C:N, C:P, and N:P ratios for available soil nutrients, microbial biomass, and extracellular enzyme activities—(β-1,4-glucosidase (BG), β-1,4-N-acetylglucosaminodase (NAG), leucine aminopeptidase (LAP), and acid phosphatase (ACP))—and the composition and activity of microbial communities (based on sequencing of bacterial 16S rRNA and fungal ITS genes) in the rhizosphere and bulk soils of five temperate forest ecosystems in northeastern China, we aimed to unravel their integrated effects on microbial CUE. Results indicated that soil C, N, and P and their stoichiometry, microbial community composition, and microbial CUE were significantly different between rhizosphere and bulk soils among all tree species. The disproportionate variation in soil nutrient pools between the rhizosphere and non-rhizosphere regions has led to a stoichiometric imbalance. There was higher microbial CUE in the rhizosphere soil than that in the bulk soil among all tree species. However, the effect pathways of tree species on microbial CUE in the rhizosphere and bulk soils differed. The structural equation model (SEM) further suggested that tree species affected microbial CUE through distinct pathways in different soil compartments. In the rhizosphere, the effect was directly driven by available nutrient stoichiometry. In bulk soil, it was jointly mediated by both available nutrients and microbial biomass stoichiometry. These findings demonstrate that root rhizodeposition shapes microbial carbon cycling by altering soil stoichiometric imbalances, which can strengthen the current understanding of plant–microbe–soil interactions in temperate forests. Full article

Mitochondria govern energy transfer, redox balance, and cell fate. Tryptophan catabolism generates kynurenines (KYNs) that can tune mitochondrial function, with growing evidence that G protein-coupled receptor 35 (GPR35), aryl hydrocarbon receptor (AhR), and N-methyl-D-aspartate receptors (NMDA receptors) link extracellular cues to adenosine 5 prime triphosphate (ATP) maintenance, calcium (Ca²⁺) handling, mitophagy, and inflammasome control. In parallel, quinolinic acid (QA)-driven de novo nicotinamide adenine dinucleotide (NAD⁺) synthesis connects KYN flux to tricarboxylic acid (TCA) cycle activity and sirtuin programs across tissues. Key gaps remain: receptor pharmacology is rarely integrated with NAD⁺ economics and respiration, and clinical workflows still lack single-run assays that quantify both kynurenine and TCA nodes. We therefore integrate receptor proximal signaling, QA-driven NAD⁺ supply, and unified liquid chromatography–mass spectrometry (LC-MS) measurement into one translational framework spanning kynurenic acid (KYNA), KYN, 3-hydroxykynurenine (3-HK), and QA, using mitochondrial endpoints as the common readout. We synthesize evidence for mitochondrial GPR35 signaling that preserves ATP, AhR programs that tune oxidative defenses and mitophagy, and NMDA receptor antagonism that limits excitotoxic stress. These mechanisms are linked to QA-dependent NAD⁺ biogenesis and alpha ketoglutarate control points, then aligned with chromatography and ionization choices suited to routine LC-MS workflows. This receptor to organelle framework couples KYN flux to respiratory control and provides a practical roadmap for standardized single-run LC-MS panels. It can strengthen target validation in ischemia, neurodegeneration, psychiatry, and oncology while improving biomarker qualification through harmonized analytics and decision-grade readouts. Full article

Unmanned aerial vehicle (UAV) imagery poses persistent challenges for object detection, including dense small objects, large-scale variation, cluttered backgrounds, and stringent localization requirements, where conventional two-stage detectors often fall short in fine-grained small-object representation, efficient global context modeling, and classification–localization consistency. We specifically target low-altitude UAV-captured imagery with highly flexible viewpoints (near-nadir to oblique) and frequent platform-induced motion blur, which makes dense small-object localization substantially more challenging than in conventional remote-sensing imagery. To address these issues, we propose CASA-RCNN, a context-adaptive and scale-aware two-stage detection framework tailored to UAV scenarios. CASA-RCNN introduces a shallow-level enhancement module, ConvSwinMerge, which strengthens position-sensitive cues and suppresses background interference by combining coordinate attention with channel excitation, thereby improving discriminative high-resolution features for small objects. For deeper semantic features, we incorporate an adaptive sequence modeling module based on MambaBlock to capture long-range dependencies and support context reasoning in crowded or occluded scenes with practical computational overheadon a desktop GPU. In addition, we adopt Varifocal Loss for quality-aware classification to better align confidence scores with localization quality, and we design a ScaleAdaptiveLoss to dynamically reweight regression objectives across object scales, compensating for the reduced gradient contribution of small targets during training. Experiments on the VisDrone2021 validation benchmark show that CASA-RCNN achieves 22.9% mAP, improving Faster R-CNN by 9.0 points; it also reaches 36.6% mAP₅₀ and 25.7% mAP₇₅. Notably, performance on small objects improves to 12.5% mAP_s (from 6.9%), and ablation studies confirm the effectiveness and complementarity of the proposed components. Full article

Rotated object detection aims to achieve precise localization by strictly aligning bounding boxes with object orientations, thereby minimizing background interference. Existing methods predominantly focus on extracting intra-object features within rotated bounding boxes. However, these approaches often overlook the discriminative contextual information from the surrounding background, leading to classification ambiguity when internal features are indistinguishable. To address this limitation, we propose Background Information Fusion R-CNN (BIF-RCNN), a novel rotated object detection framework that strategically re-integrates the background context from the object’s horizontal enclosing region to validate its category, turning previously discarded “noise” into auxiliary discriminative cues. Specifically, we introduce a dual-level rotation-horizontal feature fusion module (DFM), which leverages horizontal bounding boxes enclosing the rotated objects to extract contextual background features. These features are then adaptively fused with the internal object features to enhance the overall representation capability of the model. In addition, we design a Prediction Difference and Entropy-Constrained Loss (PDE Loss), which guides the model to focus on hard-to-classify samples that are prone to confusion due to similar feature representations. This loss function improves the model’s robustness and discriminative power. Extensive experiments conducted on the DOTA benchmark dataset demonstrate the effectiveness of the proposed method. Notably, our approach achieves up to a 4.02% AP improvement in single-category detection performance compared to a strong baseline, highlighting its superiority in rotated object detection tasks. Full article

Intrinsically disordered regions enable transcription factors (TFs) to undergo structural changes upon ligand binding, facilitating the transduction of environmental signals into gene expression. In this study, we applied molecular modeling methods to explore the hypothesis that unstructured inter-domain and subdomain linkers in bacterial TFs can function as sensors for carbohydrate signaling molecules. We combined molecular dynamics simulations and carbohydrate docking to analyze six repressors with GntR-type DNA-binding domains, including UxuR, GntR and FarR from Escherichia coli, as well as AraR, NagR and YydK from Bacillus subtilis. Protein models obtained from different time points of the dynamic simulations were subjected to sequential carbohydrate docking. We found that the inter-domain linker of the UxuR monomer binds D-fructuronate, D-galacturonate, D-glucose, and D-glucuronate with an affinity comparable to nonspecific interactions. However, these ligands formed multimolecular clusters, a feature absent in the UxuR dimer, suggesting that protein dimerization may depend on linker occupancy by cellular carbohydrates. D-glucose interacted with linkers connecting subdomains of the LacI/GalR-type E-domains in GntR and AraR, forming hydrogen bonds that connected distant structural modules of the proteins, while in NagR, FarR and YydK, it bridged the inter-domain linkers and a β-sheet within the HutC-type E-domains. Hence, our results establish flexible linkers as pivotal metabolic sensors that directly integrate nutritional cues to alter gene expression in bacteria. Full article