Search Results (1,387)

Traffic sign detection is a core function of autonomous driving systems, requiring real-time and accurate target recognition in complex road environments. Existing lightweight detection models struggle to balance accuracy, efficiency, and robustness under computational constraints of vehicle-mounted edge devices. To address this, we propose a lightweight model integrating FasterNet, Efficient Multi-scale Attention (EMA), Bidirectional Feature Pyramid Network (BiFPN), and Group Separable Convolution (GSConv) based on YOLOv8s (FEBG-YOLOv8s). Key innovations include reconstructing the Cross Stage Partial Network 2 with Focus (C2f) module using FasterNet blocks to minimize redundant computation; integrating an EMA mechanism to enhance robustness against small and occluded targets; refining the neck network based on BiFPN via channel compression, downsampling layers, and skip connections to optimize shallow–deep semantic fusion; and designing a GSConv-based hybrid serial–parallel detection head (GSP-Detect) to preserve cross-channel information while reducing computational load. Experiments on Tsinghua–Tencent 100K (TT100K) show FEBG-YOLOv8s improves mean Average Precision at Intersection over Union 0.5 (mAP50) by 3.1% compared to YOLOv8s, with 4 million fewer parameters and 22.5% lower Giga Floating-Point Operations (GFLOPs). Generalizability experiments on the CSUST Chinese Traffic Sign Detection Benchmark (CCTSDB) validate robustness, with 3.3% higher mAP50, demonstrating its potential for real-time traffic sign detection on edge platforms. Full article

Neurodegeneration is increasingly recognized not as a linear trajectory of protein accumulation, but as a multidimensional collapse of biological organization—spanning intracellular signaling, transcriptional identity, proteostatic integrity, organelle communication, and network-level computation. This review intends to synthesize emerging frameworks that reposition neurodegenerative diseases (ND) as progressive breakdowns of interpretive cellular logic, rather than mere terminal consequences of protein aggregation or synaptic attrition. The discussion aims to provide a detailed mapping of how critical signaling pathways—including PI3K–AKT–mTOR, MAPK, Wnt/β-catenin, and integrated stress response cascades—undergo spatial and temporal disintegration. Special attention is directed toward the roles of RNA-binding proteins (e.g., TDP-43, FUS, ELAVL2), m6A epitranscriptomic modifiers (METTL3, YTHDF1, IGF2BP1), and non-canonical post-translational modifications (SUMOylation, crotonylation) in disrupting translation fidelity, proteostasis, and subcellular targeting. At the organelle level, the review seeks to highlight how the failure of ribosome-associated quality control (RQC), autophagosome–lysosome fusion machinery (STX17, SNAP29), and mitochondrial import/export systems (TIM/TOM complexes) generates cumulative stress and impairs neuronal triage. These dysfunctions are compounded by mitochondrial protease overload (LONP1, CLPP), UPR maladaptation, and phase-transitioned stress granules that sequester nucleocytoplasmic transport proteins and ribosomal subunits, especially in ALS and FTD contexts. Synaptic disassembly is treated not only as a downstream event, but as an early tipping point, driven by impaired PSD scaffolding, aberrant endosomal recycling (Rab5, Rab11), complement-mediated pruning (C1q/C3–CR3 axis), and excitatory–inhibitory imbalance linked to parvalbumin interneuron decay. Using insights from single-cell and spatial transcriptomics, the review illustrates how regional vulnerability to proteostatic and metabolic stress converges with signaling noise to produce entropic attractor collapse within core networks such as the DMN, SN, and FPCN. By framing neurodegeneration as an active loss of cellular and network “meaning-making”—a collapse of coordinated signal interpretation, triage prioritization, and adaptive response—the review aims to support a more integrative conceptual model. In this context, therapeutic direction may shift from damage containment toward restoring high-dimensional neuronal agency, via strategies that include the following elements: reprogrammable proteome-targeting agents (e.g., PROTACs), engineered autophagy adaptors, CRISPR-based BDNF enhancers, mitochondrial gatekeeping stabilizers, and glial-exosome neuroengineering. This synthesis intends to offer a translational scaffold for viewing neurodegeneration as not only a disorder of accumulation but as a systems-level failure of cellular reasoning—a perspective that may inform future efforts in resilience-based intervention and precision neurorestoration. Full article

Diabetes mellitus, as a metabolic disorder, has received growing attention for its detrimental effects on the male reproductive system (particularly the testes) manifesting as increased oxidative stress, reduced blood perfusion, heightened inflammation, and germ cell apoptosis under hyperglycemic conditions. Hypoxia-inducible factor (HIF)-1α, a pivotal transcription factor in cellular hypoxia responses, plays a crucial role in regulating metabolism, angiogenesis, and apoptosis. Emerging evidence underscores its significant physiological and pathological roles in diabetic testicular injury. This review outlines the structural domains, activation mechanisms, and key target genes of HIF-1α, and further examines its involvement in diabetes-induced oxidative stress, impaired perfusion, endocrine dysregulation, and the imbalance of apoptosis and autophagy in testicular tissue. Notably, HIF-1α exerts protective effects by activating canonical signaling pathways such as phosphoinositide-3 kinase (PI-3K)/protein kinase B (Akt), mitogen-activated protein kinase (MAPK)/extracellular signal-regulated kinase (ERK), and nuclear factor (NF)-κB, thereby enhancing antioxidant gene expression, promoting angiogenesis, and upregulating anti-apoptotic proteins. Furthermore, HIF-1α may help stabilize androgen levels by preserving Leydig cell function, potentially alleviating diabetes-associated gonadal dysfunction. This review also discusses the feasibility of targeting HIF-1α as a novel therapeutic strategy. In conclusion, a comprehensive understanding of HIF-1α’s mechanistic role in diabetic testicular damage provides valuable insights into the pathogenesis of diabetes-related reproductive disorders and offers new avenues for therapeutic intervention. Full article

The Bi-Sn, Bi-Sn-Ag, and Bi-Sn-Sb solder alloy systems represent lead-free, environmentally friendly alternatives for reliable electronic assembly. These alloys comply with increasingly strict environmental and health regulations, while offering low melting points suitable for soldering temperature-sensitive components. Microstructural analysis revealed distinct phase segregation in all alloys, with Sb promoting coarse Sn₂Sb₃ intermetallic compounds and Ag inducing fine needle-like Ag₃Sn precipitates. Eutectic refinement and compositional contrast were confirmed by SEM-BSE and EDS mapping. Vickers microhardness measurements revealed increased hardness in Sb- and Ag-modified Bi–Sn alloys, with Ag₃Sn dispersion yielding the highest strengthening effect, indicating enhanced mechanical potential. This study also reports the thermal and electrical conductivities of Bi60Sn40, Bi60Sn35Ag5, and Bi60Sn35Sb5 alloys over the 25–140 °C range. Bi60Sn40 showed an increase in thermal conductivity across the full temperature range from 16.93 to 26.93 W/m·K, while Bi60Sn35Ag5 reached 18.28 W/m·K at 25 °C, and Bi60Sn35Sb5 exhibited 13.90 W/m·K. These findings underline the critical influence of alloying elements on microstructure, phase stability, and thermophysical behavior, supporting their application in low-temperature soldering technologies. Full article

Reactive oxygen species (ROS) act as double-edged swords in cancer biology—facilitating tumor growth, survival, and metastasis at moderate levels while inducing oxidative damage and cell death when exceeding cellular buffering capacity. To survive under chronic oxidative stress, cancer cells rely on robust antioxidant systems such as the glutathione (GSH) and thioredoxin (Trx), and superoxide dismutases (SODs). These systems maintain redox homeostasis and sustain ROS-sensitive signaling pathways including MAPK/ERK, PI3K/Akt/mTOR, NF-κB, STAT3, and HIF-1α. Targeting the antioxidant defense mechanisms of cancer cells has emerged as a promising therapeutic strategy. Inhibiting the glutathione system induces ferroptosis, a non-apoptotic form of cell death driven by lipid peroxidation, with compounds like withaferin A and altretamine showing strong preclinical activity. Disruption of the Trx system by agents such as PX-12 and dimethyl fumarate (DMF) impairs redox-sensitive survival signaling. Trx reductase inhibition by auranofin or mitomycin C further destabilizes redox balance, promoting mitochondrial dysfunction and apoptosis. SOD1 inhibitors, including ATN-224 and disulfiram, selectively enhance oxidative stress in tumor cells and are currently being tested in clinical trials. Mounting preclinical and clinical evidence supports redox modulation as a cancer-selective vulnerability. Pharmacologically tipping the redox balance beyond the threshold of cellular tolerance offers a rational and potentially powerful approach to eliminate malignant cells while sparing healthy tissue, highlighting novel strategies for targeted cancer therapy at the interface of redox biology and oncology. Full article

The peach landrace ‘Liuyefeitao’ exhibits the unique reproductive trait of self-compatibility combined with cross-incompatibility, contrasting with typical Prunus species in this way. In preliminary studies involving controlled pollination assays, we showed complete pollen tube arrest in cross-pollinated styles, whereas self-pollination enabled full tube elongation. S-genotyping identified a homozygous S₂S₂ genotype with intact S₂-RNase but a truncated PpSFB2 due to a frameshift mutation. Transcriptome profiling of the styles revealed 7937 differentially expressed genes (DEGs) between self- and cross-pollination treatments, with significant enrichment in plant MAPK signaling, plant–pathogen interactions, and plant hormone signaling transduction pathways (|Fold Change| ≥ 2, FDR < 0.01). Notably, PpSLFL3 (a pollen F-box gene) showed down-regulation in cross-pollinated styles, as validated by means of qRT-PCR. Protein interaction assays revealed direct binding between PpSLFL3 and S₂-RNase via Y2H and BiFC analysis, suggesting its role in mediating SCF complex-dependent degradation. We propose that insufficient PpSLFL3 expression during cross-pollination disrupts SCF ubiquitin ligase complex-mediated degradation of non-self S₂-RNase, leading to the toxic degradation of RNA in pollen tubes by S₂-RNase. This mechanism is mechanistically similar to unilateral reproductive barriers in Solanaceae but represents a novel regulatory module in Rosaceae. Our findings provide critical insights into the evolution of cross-incompatibility systems and molecular breeding strategies for Prunus species. Full article

Hederagenin, a pentacyclic triterpenoid saponin from various medicinal plants, shows immense therapeutic potential; however, its inherent low bioavailability severely hinders its clinical translation. This comprehensive review synthesizes recent studies on the health benefits of hederagenin and its glycosides, critically the chemical modification strategies and pharmacological mechanisms aimed at optimizing its bioactivity. Key findings reveal that its broad anticancer and anti-inflammatory activities largely stem from its capacity to modulate crucial cellular signaling pathways, including the NF-κB, PI3K/Akt, and MAPK. Structural modification, particularly intelligent derivatization at the C-28 position, is a central strategy to overcome its pharmacokinetic deficiencies and significantly boost cytotoxicity. Furthermore, its unique pro-oxidant function within cancer cells, achieved by inhibiting the Nrf2-ARE antioxidant pathway, offers a novel approach for selective chemotherapeutics. For the clinical translation of hederagenin, we propose a strategic focus on derivatization through multi-target hybrids and sophisticated delivery systems. This approach is essential for addressing its pharmacokinetic barriers while strategically leveraging its context-dependent pro-oxidant effects. Full article

This study aims to develop a gelatin methacryloyl (GelMA)-based ginsenoside Rb3 (G-Rb3) drug delivery system and investigate its application in the treatment of periodontitis and the underlying mechanisms. Periodontal ligament stem cells (PDLSCs) were obtained and identified. The appropriate concentration ranges of G-Rb3 and lipopolysaccharide (LPS) were investigated by the CCK-8 experiments. Quantitative RT-PCR, ELISA, and Western blot were performed to assess the effects of GelMA@G-Rb3 on LPS-treated PDLSCs. The possible mechanisms were determined through network pharmacology analysis and Western blot. The therapeutic effects of GelMA@G-Rb3 in rat periodontitis animal models were systematically evaluated using Micro-CT, H&E staining, Masson staining, and immunofluorescence staining. PDLSCs were successfully isolated and characterized. The in vitro results indicated that GelMA@G-Rb3 significantly alleviated LPS-induced inflammation in PDLSCs by inhibiting the p38/ERK signaling pathway and activating the PI3K/AKT signaling pathway. In vivo experiments confirmed that GelMA@G-Rb3 significantly reduced alveolar bone resorption, and promoted periodontal tissue regeneration, while simultaneously demonstrating significant regulatory effects on the MAPK signaling pathway. This study demonstrated the efficacy of the GelMA@G-Rb3 system in modulating the inflammatory responses of periodontitis and improving the periodontal tissue regeneration, which establish a solid foundation and proposed innovative therapeutic approaches for the treatment of periodontitis. Full article

Background: The amount of data produced from biological experiments has increased geometrically, posing a challenge for the development of new methodologies that could enable their interpretation. We propose a novel approach for the analysis of transcriptomic data derived from acute lymphoblastic leukemia (ALL) and rhabdomyosarcoma (RMS) cell lines, using bioinformatics, systems biology and geometrical approaches. Methods: The expression profile of each cell line was investigated using microarrays, and identified genes were used to create a systems pathway model, which was then simulated using differential equations. The transcriptomic profile used involved genes with similar expression levels. The simulated results were further analyzed using geometrical approaches to identify common expressional dynamics. Results: We simulated and analyzed the system network using time series, regression analysis and helical functions, detecting predictable structures after iterating the modelled biological network, focusing on TIE1, STAT1, MAPK14 and ADAM17. Our results show that such common attributes in gene expression patterns can lead to more effective treatment options and help in the discovery of universal tumor biomarkers. Discussion: Our approach was able to identify complex structures in gene expression patterns, indicating that such approaches could prove useful towards the understanding of the complex tumor dynamics. Full article

Investigating the physiological responses and resistance mechanisms in plants under drought stress provides critical insights for optimizing irrigation water utilization efficiency and promoting the development of irrigation science. In this study, cotton seedlings were cultivated in a light incubator. Three drought stress levels were applied: mild (M1, 50–55% field moisture), moderate (M2, 45–50%), and severe (M3, 40–45%). Transcriptome analysis was performed under mild and severe stress. The results revealed that differentially expressed genes (DEGs) related to proline degradation were down-regulated and proline content increased in cotton. Under different stress treatments, cotton exhibited a stress-intensity-dependent regulation of carbohydrate metabolism and soluble sugar content decreased and then increased. And the malondialdehyde content analysis revealed a dose-dependent relationship between stress intensity and membrane lipid peroxidation. Stress activated the antioxidant system, leading to the down-regulation of DEGs for reactive oxygen species production in the mitogen-activated protein kinase (MAPK) signaling pathway. Concurrently, superoxide dismutase activity and peroxidase content increased to mitigate oxidative damage. Meanwhile, the photosynthetic performance of cotton seedlings was inhibited. Chlorophyll content, stomatal conductance, the net photosynthetic rate, the transpiration rate and water use efficiency were significantly reduced; intercellular carbon dioxide concentration and leaf stomatal limitation value increased. But photosynthesis genes (e.g., PSBO (oxygen-evolving enhancer protein 1), RBCS (ribulose bisphosphate carboxylase small chain), and FBA2 (fructose-bisphosphate aldolase 1)) in cotton were up-regulated to coordinate the photosynthetic process. Furthermore, cotton seedlings differentially regulated key biosynthesis and signaling components of phytohormonal pathways including abscisic acid, indoleacetic acid and gibberellin. This study elucidates the significant gene expression of drought-responsive transcriptional networks and relevant physiological response in cotton seedlings and offers a theoretical basis for developing water-saving irrigation strategies. Full article

The lack of cultural spaces and the inadequate preservation of architectural heritage hinder the development of ecotourism in Ollantaytambo. This research aims to propose an architectural design for green infrastructure that supports the growth of ecotourism at the Ollantaytambo archeological site, located in the Urubamba Province, Peru. The study consists of three main phases: a literature review; a site analysis focusing on climate, flora, and fauna; and the development of a comprehensive architectural proposal. The process is supported by digital tools, including Google Earth Pro 2024, OpenStreetMap 2024, SketchUp 2024, Lumion 2024, Photoshop 2024, and 3D Sun-Path 2024. The resulting design includes the implementation of a sustainable cultural center, conceived to ensure seasonal thermal comfort through the use of green roofs and walls, efficient irrigation systems, and native vegetation. The proposal incorporates elements of Cusco’s vernacular architecture by combining traditional earth-based construction techniques, such as rammed earth, adobe, and quincha, with contemporary materials, such as bamboo and timber, in order to improve the energy and environmental performance of the built environment. Furthermore, the project integrates a rainwater-harvesting system and a photovoltaic lighting system. It includes 30 solar-powered luminaires with an estimated monthly output of 72 kWh, and 135 photovoltaic panels capable of generating approximately 2673 kWh per month. In conclusion, the proposed design blends naturally with the local environment and culture. It adheres to principles of sustainability and energy efficiency and aligns with Sustainable Development Goals (SDGs) 3, 6, 7, 11, and 15 by promoting heritage conservation, environmental regeneration, and responsible ecotourism. Full article

Objective: This scoping review aims to comprehensively map the existing literature on the mechanisms of action of Alamandine (ALA), a peptide within the renin–angiotensin system, and its effects across various physiological systems. Materials and Methods: Utilizing the Joanna Briggs Institute methodology, a thorough search of databases including PubMed, Embase, Scopus, and Web of Science was conducted up to 30 January 2025. The review focused on identifying studies that explore the biological and therapeutic roles of ALA in different contexts, incorporating in vivo, in vitro, and in silico research. Results: A total of 590 records were initially identified, with 25 meeting the eligibility criteria for inclusion in this review. China emerged as the leading contributor to the research in this area, with a significant focus on the cardiovascular system. The studies revealed that ALA exhibits a range of beneficial effects, including anti-inflammatory, vasodilatory, antifibrotic, and antiapoptotic actions. These effects are primarily mediated through the inhibition of the mitogen-activated protein kinase (MAPK) signaling pathway and modulation of the nitric oxide pathway. The review also highlighted AL’s potential in mitigating oxidative stress and its implications in treating cardiovascular diseases, fibrosis, and cancer. Conclusions: The findings suggest that ALA holds significant therapeutic potential, offering antihypertensive, anti-inflammatory, antifibrotic, and anticancer benefits without notable adverse effects, warranting further research to explore its full potential and mechanism of action. Full article

Plastic mulch technology plays an important role in increasing agricultural productivity and economic returns. However, residual mulch remaining in agricultural fields poses significant challenges to both crop production and environmental sustainability. Effective recovery and recycling of residual plastic mulch requires accurate detection and identification of mulch fragments, which presents a substantial technical challenge. The detection of residual plastic film is complicated by several factors: the visual similarity between residual film fragments and soil in terms of color and texture, as well as the irregular shapes and variable sizes of the target objects. To address these challenges, this study develops FreqDyn-YOLO, a detection model for residual film identification in agricultural environments based on the YOLO11 architecture. The proposed methodology introduces three main technical contributions. First, a Frequency-C3k2 (FreqC3) feature extraction module is implemented, which employs a Frequency Feature Transposed Attention (FreqFTA) mechanism to improve discrimination between residual film and soil backgrounds. Second, a High-Performance Multi-Scale Feature Pyramid Network (HPMSFPN) is developed to enable effective cross-layer feature fusion, enhancing detection performance across different target scales. Third, a Dynamic Detection Head With DCNv4 (DWD4) is introduced to improve the model’s ability to adapt to varying film morphologies while maintaining computational efficiency. Experimental findings on a self-developed agricultural field residual film dataset confirm that FreqDyn-YOLO outperforms the baseline approach, achieving improvements of 5.37%, 1.97%, and 2.96% in mAP50, precision, and recall, respectively. The model also demonstrates superior performance compared to other recent detection methods. This work provides a technical foundation for precise residual film identification in agricultural applications and shows promise for integration into automated recovery systems. Full article

On-road vision-based systems rely on object detection to ensure vehicle safety and efficiency, making it an essential component of autonomous driving. Deep learning methods show high performance; however, they often require special hardware due to their large sizes and computational complexity, which makes real-time deployment on edge devices expensive. This study proposes lightweight object detection models based on the YOLOv5s architecture, known for its speed and accuracy. The models integrate advanced channel attention strategies, specifically the ECA module and SE attention blocks, to enhance feature selection while minimizing computational overhead. Four models were developed and trained on the KITTI dataset. The models were analyzed using key evaluation metrics to assess their effectiveness in real-time autonomous driving scenarios, including precision, recall, and mean average precision (mAP). BaseECAx2 emerged as the most efficient model for edge devices, achieving the lowest GFLOPs (13) and smallest model size (9.1 MB) without sacrificing performance. The BaseSE-ECA model demonstrated outstanding accuracy in vehicle detection, reaching a precision of 96.69% and an mAP of 98.4%, making it ideal for high-precision autonomous driving scenarios. We also assessed the models’ robustness in more challenging environments by training and testing them on the BDD-100K dataset. While the models exhibited reduced performance in complex scenarios involving low-light conditions and motion blur, this evaluation highlights potential areas for improvement in challenging real-world driving conditions. This study bridges the gap between affordability and performance, presenting lightweight, cost-effective solutions for integration into real-time autonomous vehicle systems. Full article

This study presents a terrain-integrated Soil Management Unit (SMU) framework for precision agriculture in semi-arid tropical basaltic soils. Using high resolution (10-ha grid) sampling across 4627 geo-referenced locations and machine learning-enhanced integration of terrain attributes with legacy soil maps, and (3) quantitative validation of intra-SMU homogeneity, 15 SMUs were delineated based on landform, soil depth, texture, and slope. Principal Component Analysis (PCA) revealed SMU11 as the most heterogeneous (68.8%). Geo-statistical analysis revealed structured variability in soil pH (range = 1173 m) and nutrients availability with micronutrient sufficiency following Mn > Fe > Cu > Zn, (Zn deficient in SMU13). Organic carbon strongly correlated with key nutrients (AvK, r = 0.83 and Zn, r = 0.86). This represents the first systematic implementation of terrain-integrated SMU delineation in India’s basaltic landscapes, demonstrating a potential for 20–25% input savings. The spatially explicit fertility-integrated SMU framework provides a robust basis for developing decision support systems aimed at optimizing location-specific nutrient and land management strategies. Full article