Search Results (18,527)

Natural bioactive compounds play an important role in regulating inflammatory processes and mechanisms of carcinogenesis. In view of the growing interest in their therapeutic properties, particularly in the treatment of chronic inflammation, cancer, and related diseases, this study reviews the molecular mechanisms of action of selected groups of compounds, namely polyphenols and carotenoids. The analysis is based on current scientific literature and evidence from in vitro and in vivo studies, with particular attention being paid to their effects on the NF-κB, STAT3, and MAPK regulatory pathways, as well as their role in modulating pro-inflammatory cytokine expression, apoptosis, and oxidative stress. These findings indicate that bioactive compounds represent a promising group of substances with a broad spectrum of biological activity. Nevertheless, their potential in combination therapy and in preventive strategies against cancer and inflammation requires further clinical investigation to clarify their bioavailability, safety, and therapeutic effectiveness. Full article

Speech recognition based on Electroencephalogram (EEG) has attracted considerable attention due to its potential in communication and rehabilitation. Existing methods typically process spatial and temporal features with sequential, parallel, or constrained feature fusion strategies. However, the intricate cross-relationships between spatial and temporal features remain underexplored. To address these limitations, we propose a spatial–temporal coupled cross-attention mechanism through a hierarchical network, named STCCA. The proposed STCCA consists of three key components: local feature extraction module (LFEM), coupled cross-attention (CCA) fusion module, and global feature extraction module (GFEM). The LFEM employs CNNs to extract local temporal and spatial features, while the CCA fusion module leverages a dual-directional attention mechanism to establish deep interactions between temporal and spatial features. The GFEM uses multi-head self-attention layers to model long-range dependencies and extract global features comprehensively. STCCA is validated on three EEG-based speech datasets, achieving accuracies of 45.45%, 25.91%, and 29.07%, corresponding to improvements of 1.95%, 3.98%, and 1.98% over the comparison models. Full article

Water hyacinth (Eichhornia crassipes) is one of the most invasive plants around the world. In the state of Hidalgo, Mexico it has invaded several water bodies. Nevertheless, its management is an ongoing challenge because of its rapid growth and the expensiveness of its removal. Therefore, alternatives to valorize its biomass are needed. One of them is the production of optical materials from it. Past reports have demonstrated the viability to obtain fluorophores from lignin and that it is present in E. crassipes biomass. Nevertheless, most works focus on its extraction using harsh process conditions and strong acids or alkalis. No reports about the use of E. crassipes in such processes exist. As the demand for more environmentally friendly processes increases, avoidance of such chemicals is needed. Therefore, in this work the extraction of fluorophores directly from biomass of E. crassipes via a hydrothermal process using water as the sole solvent and catalyzer was studied. The liquid to solid ratio (LSR) varied from 25 to 50 and time from 8 to 16 h. Biomass was almost completely dissolved. Fluorophores with different photoluminescent emissions were obtained. Their extraction was confirmed by photoluminescence spectroscopy. The emission of the obtained materials could be tuned by changing processing time and LSR. Full article

Kudzu (Pueraria spp.) starch, valued for its transparency, viscosity, and stability, has broad potential in functional and instant food applications. However, its limited cold-water solubility and inconsistent functional performance across cultivars hinder wider utilization. To improve its processability and nutritional functionality, this study aimed to elucidate how the degree of gelatinization (DG)—a structural indicator of starch transformation—can be precisely controlled and used to modulate starch properties. Starches from two typical kudzu cultivars, K10 (Pueraria thomsonii) and K27 (Pueraria lobata), were subjected to hydrothermal treatment (45–95 °C) to obtain samples with defined DG levels. DG was quantitatively determined by enzymatic assay, differential scanning calorimetry (DSC), and iodine-binding analysis, enabling method cross-validation. Increasing DG enhanced iodine complexation capacity, elevated gelatinization temperatures, and reduced enthalpy change and crystallinity. K27 exhibited more pronounced physicochemical transitions at lower DG than K10, indicating cultivar-specific sensitivity. In vitro digestion revealed that hydrolysis kinetics gradually approached and eventually conformed to a first-order model as DG increased, confirming a DG-dependent shift in digestibility. These results establish DG—rather than processing temperature—as the primary factor governing kudzu starch functionality and provide a methodological basis for designing starch-based foods with tailored glycemic and textural properties. Full article

With the rapid development of 3D sensor technology, point cloud semantic segmentation has found widespread applications in autonomous driving, remote sensing, mapping, and industrial manufacturing. However, outdoor traffic scenes present significant challenges: point clouds are inherently disordered, unevenly distributed, and unstructured. As a result, traditional point cloud semantic segmentation methods often suffer from low accuracy and unstable performance in complex tasks such as semantic segmentation and object detection. To address these limitations, this paper proposes an improved point cloud semantic segmentation method based on Cylinder3D. The proposed approach integrates the PointMamba and MS3DAM modules, which enhance the model’s ability to capture global features while preserving local details, thereby improving adaptability and recognition across multiple feature scales. Furthermore, leveraging the linear computational complexity of Mamba enables the method to maintain high efficiency when processing large-scale point cloud data. In addition, incorporating the KAT module into the encoder improves the model’s perceptual capacity and robustness in handling point clouds. Experimental results on the SemanticKITTI dataset demonstrate that the proposed method achieves a mean Intersection over Union (mIoU) of 64.98%, representing a 2.81% improvement over Cylinder3D, thereby confirming its superior segmentation accuracy compared with existing models. Full article

Cisplatin is a widely used chemotherapy drug for the treatment of various cancers; however, its clinical use is often accompanied by skeletal muscle atrophy, which not only impacts patients’ physical health but also significantly diminishes their quality of life. The mechanisms underlying cisplatin-induced muscle atrophy are complex and involve a series of molecular biological processes, including oxidative stress, inflammation, protein degradation, and muscle cell apoptosis. Recent studies have suggested that exercise intervention can significantly alleviate cisplatin-induced muscle damage by modulating exercise-induced myokines. Myokines, such as muscle-derived cytokines (e.g., IL-6, irisin) and other related factors, can mitigate muscle atrophy through anti-inflammatory, antioxidative, and muscle-synthesis-promoting mechanisms. This review explores the molecular mechanisms of cisplatin-induced skeletal muscle atrophy, examines the potential protective effects of exercise intervention, and highlights the role of exercise-induced myokines in this process. The findings suggest that exercise not only alleviates chemotherapy-induced muscle atrophy by improving metabolic and immune status but also activates myokines to promote muscle regeneration and repair, offering a promising adjunctive therapy for cisplatin-treated patients. Full article

Burst traffic radio systems use short signal bursts, which are prepended with an a priori known preamble sequence. The burst receivers exploit these preamble sequences for burst start detection. The process of burst start detection is commonly known as Packet Detection (PD), which employs preamble sequence cross-correlation and threshold detection. One major figure of merit for PD performance is the so-called ROC—receiver operating characteristics. ROC describes the trade-off between the probability of missed detection vs. the probability of false alarm. This article describes how to calculate the ROC for specified preamble sequences by deriving the probability density function (PDF) of the cross-correlation metric. We address this long-standing problem in the context of LEO (low Earth orbit) satellite systems, where differentially modulated PN (pseudo-noise) sequences are used for packet detection. For this particular class of preamble signals, the standard Ricean PDF assumption no longer holds and needs to be revised accordingly within this article. Full article

Glucosinolates (GSLs) are nitrogen- and sulfur-containing secondary metabolites central to the defense, development, and environmental responsiveness of Brassicaceae species. While the enzymatic steps and transcriptional networks underlying GSL biosynthesis have been extensively characterized, mounting evidence reveals that chromatin-based processes add a critical, yet underexplored, layer of regulatory complexity. Recent studies highlight the roles of DNA methylation, histone modifications, and non-coding RNAs in modulating the spatial and temporal expression of GSL biosynthetic genes and their transcriptional regulators in response to developmental cues and environmental signals. This review provides a comprehensive overview of GSL classification, biosynthetic pathway architecture, transcriptional regulation, and metabolite transport, with a focus on emerging epigenetic mechanisms that shape pathway plasticity. We also discuss how these insights may be leveraged in precision breeding and epigenome engineering, including the use of CRISPR/dCas9-based chromatin editing and epigenomic selection, to optimize GSL content, composition, and stress resilience in cruciferous crops. Integrating transcriptional and epigenetic regulation thus offers a novel framework for the dynamic control of specialized metabolism in plants. Full article

This study numerically investigates wave transformation and setup processes across fringing reefs, focusing on artificial reef configuration effects under varying tidal conditions and incident wave parameters. The OpenFOAM-based waves2Foam model simulates hydrodynamic processes along reef profiles containing a fore-reef slope and reef flat. Following validation against laboratory data, the model simulates cross-shore wave height attenuation and setup within fringing reef systems. The results demonstrate that reef flat water depth substantially modulates wave dynamics: during low tide, intensified wave breaking elevates the maximum wave height and setup by up to 45.7% and 78.5%, respectively, compared to high-tide conditions. Furthermore, this water depth critically governs the reef configuration’s influence on wave energy dissipation efficiency. Under high tide, additional reef rows increase the peak wave height by 5.2% while reducing wave setup by 10.5%. In contrast, expanded reef spacing reduces the peak wave height by 2.1% and decreases the peak wave setup by 2.4%. The temporal evolution of wave reflection ($K_R$) and transmission ($K_T$) coefficients reveals that shallow-water conditions amplify wave reflection while diminishing transmission capacity, as tidal variations directly regulate wave propagation mechanisms through water depth modulation. At the outer reef flat boundary, $K_R$ and $K_T$ values for existing artificial reefs exhibit variations below 5% across all tidal phases, row configurations, and spacing combinations. Consequently, current reef structures provide limited control over wave transmission in fringing reef terrains, indicating that structural modifications such as increasing reef elevation or deploying reefs on the fore-reef slope could enhance attenuation performance. Full article

This article addresses the problem of predicting the workload of the repair department in a company manufacturing electronic modules. The number of modules needing repair is called a repair stack. A deterministic algorithm and a machine learning-based algorithm are proposed to predict the repair stack for subsequent weeks based on historical data, current yield data, and planned production. These methods allow for estimation of the repair stack and appropriate selection of repair department staff to ensure the ongoing repair of defective products. The proposed algorithms are described and the results of their practical verification based on historical data from a large enterprise are presented. The practical utility of both algorithms is demonstrated and the impact of selected factors on their accuracy is analyzed. It is shown that using the proposed algorithms, it is possible to predict the repair stack for the coming week with a relative error not exceeding a few percentages on the basis of historical data from the previous 8 weeks. These algorithms were successfully implemented in industrial practice. Full article

C1q/TNF-related protein 6 (CTRP6) is emerging as a critical regulator of cancer biology with direct implications for clinical outcomes. Across a wide spectrum of malignancies, CTRP6 plays a central role in coordinating key oncogenic processes and linking metabolic, inflammatory, and signaling pathways that drive tumor progression. While CTRP6 generally promotes oncogenic behavior in cancers such as hepatocellular carcinoma, lung cancer, and clear cell renal cell carcinoma, conflicting findings have been reported in gastric cancer and oral or head and neck squamous cell carcinoma, where its tumor-promoting versus tumor-suppressive roles remain unresolved. CTRP6 has been shown to modulate fundamental processes including angiogenesis, ferroptosis, proliferation, apoptosis, migration, invasion, and inflammation. These effects are primarily mediated through activation of the PI3K/AKT and MEK/ERK signaling pathways, which are central to tumor growth, metastasis, and therapeutic resistance. Beyond its mechanistic roles, CTRP6 demonstrates potential as a diagnostic and prognostic biomarker, with altered expression patterns linked to cancer initiation, progression, and patient survival. Inhibition of CTRP6 in preclinical models enhances ferroptotic cell death and suppresses tumor progression, highlighting its promise as a therapeutic target. By consolidating current evidence from multiple cancer models, this review provides a comprehensive overview of CTRP6’s contributions to oncogenesis and underscores its dual potential as both a biomarker and a therapeutic target. Advancing a deeper understanding of CTRP6 in specific tumor contexts will be critical for unlocking its clinical utility and may open new opportunities to improve diagnosis, optimize therapeutic strategies, and ultimately enhance patient outcomes. Full article

One-dimensional (1D) nanowires represent a critical class of nanomaterials with extensive applications in biosensing, biomedicine, bioelectronics, and energy harvesting. In materials science, accurately extracting their morphological and structural features is essential for effective image segmentation. However, 1D nanowires frequently appear in dispersed or entangled configurations, often with blurred backgrounds and indistinct boundaries, which significantly complicates the segmentation process. Traditional threshold-based methods struggle to segment these structurally complex nanowires with high precision. To address this challenge, we propose a wavelet-based Bilateral Segmentation Network named WaveBiSeNet, to which a Dual Wavelet Convolution Module (DWCM) and a Flexible Upsampling Module (FUM) are introduced to enhance feature representation and improve segmentation accuracy. In this study, we benchmarked WaveBiSeNet against ten segmentation models on a peptide nanowire image dataset. Experimental results demonstrate that WaveBiSeNet achieves, mIoU of 77.59%, an accuracy of 89.95%, an F1 score of 87.22%, and a Kappa coefficient of 74.13%, respectively. Compared to other advanced models, our proposed model achieves better segmentation performance. These findings demonstrate that WaveBiSeNet is an end-to-end deep segmentation network capable of accurately analyzing complex 1D nanowire structures. Full article

Today’s rapidly increasing number and performance of Remotely Piloted Aircraft Systems (RPASs) and sensors allows for an innovative approach in monitoring, mitigating, and responding to natural disasters and risks. At present, there are 100s of different RPAS platforms and smaller and more affordable payload sensors. As natural disasters pose ever increasing risks to society and the environment, it is imperative that these RPASs are utilized effectively. In order to exploit these advances, this study presents the development and validation of a Natural Disaster Information System (NDIS), a geospatial decision-support framework for RPAS-based natural hazard missions. The system integrates a global geohazard database with specifications of geophysical sensors and RPAS platforms to automate mission planning in a generalized form. NDIS v1.0 uses decision tree algorithms to select suitable sensors and platforms based on hazard type, distance to infrastructure, and survey feasibility. NDIS v2.0 introduces a Random Forest method and a Critical Path Method (CPM) to further optimize task sequencing and mission timing. The latest version, NDIS v3.8.3, implements a staggered decision workflow that sequentially maps hazard type and disaster stage to appropriate survey methods, sensor payloads, and compatible RPAS using rule-based and threshold-based filtering. RPAS selection considers payload capacity and range thresholds, adjusted dynamically by proximity, and ranks candidate platforms using hazard- and sensor-specific endurance criteria. The system is implemented using ArcGIS Pro 3.4.0, ArcGIS Experience Builder (2025 cloud release), and Azure Web App Services (Python 3.10 runtime). NDIS supports both batch processing and interactive real-time queries through a web-based user interface. Additional features include a statistical overview dashboard to help users interpret dataset distribution, and a crowdsourced input module that enables community-contributed hazard data via ArcGIS Survey123. NDIS is presented and validated in, for example, applications related to volcanic hazards in Indonesia. These capabilities make NDIS a scalable, adaptable, and operationally meaningful tool for multi-hazard monitoring and remote sensing mission planning. Full article

The rapid spread of unmanned aerial vehicles (UAVs) has created new challenges for airspace security, as drones are increasingly used for surveillance, smuggling, and potentially for attacks near critical infrastructure. A key difficulty lies in reliably distinguishing UAVs from visually similar birds in electro-optical surveillance channels, where complex backgrounds and visual noise often increase false alarms. To address this, we investigated recent YOLO architectures and developed an enhanced model named YOLOv12-ADBC, incorporating an adaptive hierarchical feature integration mechanism to strengthen multi-scale spatial fusion. This architectural refinement improves sensitivity to subtle inter-class differences between drones and birds. A dedicated dataset of 7291 images was used to train and evaluate five YOLO versions (v8–v12), together with the proposed YOLOv12-ADBC. Comparative experiments demonstrated that YOLOv12-ADBC achieved the best overall performance, with precision = 0.892, recall = 0.864, mAP50 = 0.881, mAP50–95 = 0.633, and per-class accuracy reaching 96.4% for drones and 80% for birds. In inference tests on three video sequences simulating realistic monitoring conditions, YOLOv12-ADBC consistently outperformed baselines, achieving a detection accuracy of 92.1–95.5% and confidence levels up to 88.6%, while maintaining real-time processing at 118–135 frames per second (FPS). These results demonstrate that YOLOv12-ADBC not only surpasses previous YOLO models but also offers strong potential as the optical module in multi-sensor fusion frameworks. Its integration with radar, RF, and acoustic channels is expected to further enhance system-level robustness, providing a practical pathway toward reliable UAV detection in modern airspace protection systems. Full article

The objective of person re-identification (Re-ID) is to recognize a given target pedestrian across different cameras. However, perspective variations, resulting from differences in shooting angles, often significantly impact the accuracy of person Re-ID. To address this issue, this paper presents an attention-enhanced person Re-ID algorithm based on appearance–gait information interaction. Specifically, appearance features and gait features are first extracted from RGB images and gait energy images (GEIs), respectively, using two ResNet-50 networks. Then, a multimodal information exchange module based on the attention mechanism is designed to build a bridge for information exchange between the two modalities during the feature extraction process. This module aims to enhance the feature extraction ability through mutual guidance and reinforcement between the two modalities, thereby improving the model’s effectiveness in integrating the two types of modal information. Subsequently, to further balance the signal-to-noise ratio, importance weight estimation is employed to map perspective information into the importance weights of the two features. Finally, based on the autoencoder structure, the two features are weighted and fused under the guidance of importance weights to generate fused features that are robust to perspective changes. The experimental results on the CASIA-B dataset indicate that, under conditions of viewpoint variation, the method proposed in this paper achieved an average accuracy of 94.9%, which is 1.1% higher than the next best method, and obtained the smallest variance of 4.199, suggesting that the method proposed in this paper is not only more accurate but also more stable. Full article