Search Results (2,816)

The mineralocorticoid receptor (MR), traditionally associated with renal function, has also been identified in various extrarenal tissues, including the heart, brain, and dorsal root ganglion (DRG) neurons in rodents. Previous studies suggest a role for the MR in modulating peripheral nociception, with MR activation in rat DRG neurons by its endogenous ligand, aldosterone. This study aimed to determine whether MR, its protective enzyme 11β-hydroxysteroid dehydrogenase type 2 (11β-HSD2), its endogenous ligand aldosterone, and the aldosterone-synthesizing enzyme CYP11B2 are expressed in human DRG neurons and whether they colocalize with key pain-associated signaling molecules as potential targets for genomic regulation. To this end, we performed mRNA transcript profiling and immunofluorescence confocal microscopy on human and rat DRG tissues. We detected mRNA transcripts for MR, 11β-HSD2, and CYP11B2 in human DRG, alongside transcripts for key thermosensitive and nociceptive markers such as TRPV1, the TTX-resistant sodium channel Nav1.8, and the neuropeptides CGRP and substance P (Tac1). Immunofluorescence analysis revealed substantial colocalization of MR with 11β-HSD2 and CGRP, a marker of unmyelinated C-fibers and thinly myelinated Aδ-fibers, in human DRG. MR immunoreactivity was primarily restricted to small- and medium-diameter neurons, with lower expression in large neurons (>70 µm). Similarly, aldosterone colocalized with CYP11B2 and MR with nociceptive markers including TRPV1, Nav1.8, and TrkA in human DRG. Importantly, functional studies demonstrated that prolonged intrathecal inhibition of aldosterone synthesis within rat DRG neurons, using an aldosterone synthase inhibitor significantly downregulated pain-associated molecules and led to sustained attenuation of inflammation-induced hyperalgesia. Together, these findings identify a conserved peripheral MR signaling axis in humans and highlight its potential as a novel target for pain modulation therapies. Full article

Mechanosensitive ion channels such as OSCA1.2 enable cells to sense and respond to mechanical forces by translating membrane tension into ionic flux. While lipid rearrangement in the inter-subunit cleft has been proposed as a key activation mechanism, the contributions of other domains to OSCA gating remain unresolved. Here, we combined the genetic encoding of the photoactivatable crosslinker p-benzoyl-L-phenylalanine (BzF) with functional Ca²⁺ imaging and molecular dynamics simulations to dissect the roles of specific residues in OSCA1.2 gating. Targeted UV-induced crosslinking at positions F22, H236, and R343 locked the channel in a non-conducting state, indicating their functional relevance. Structural analysis revealed that these residues are strategically positioned: F22 interacts with lipids near the activation gate, H236 lines the lipid-filled cavity, and R343 forms cross-subunit contacts. Together, these results support a model in which mechanical gating involves a distributed network of residues across multiple channel regions, allosterically converging on the activation gate. This study expands our understanding of mechanotransduction by revealing how distant structural elements contribute to force sensing in OSCA channels. Full article

This research investigates enhancing the performance of an Archimedes screw-type hydrokinetic turbine (ASHT). A 3D transient computational model employing the six degrees of freedom (6-DOF) methodology within the ANSYS Fluent software 2022 R1, was selected for this purpose. A central composite design (CCD) methodology was applied within the response surface methodology (RSM) to enhance the turbine’s power coefficient ($C_p$). Key independent factors, including blade length (L), blade inclination angle ($\gamma$), and external diameter ($D_e$), were systematically varied to determine their optimal values. The optimization process yielded a maximum $C_p$ of 0.337 for L, $\gamma$, and $D_e$ values of 168.921 mm, 51.341°, and 245.645 mm, respectively. Experimental validation was conducted in a hydraulic channel, yielding results that demonstrated a strong correlation with the numerical predictions. This research underscores the importance of geometric design optimization in improving the energy capture efficiency of the ASHT, contributing to its potential viability as a competitive renewable energy solution in the pre-commercial phase of development. Full article

Spectral features of photoionization of various levels of C III are reported. These include characteristics of Rydberg and Seaton resonances, low and high excited levels, lifetimes, and total and partial cross sections. Calculations are performed in the relativistic Breit–Pauli R-matrix method with close-coupling approximation, including damping effects on the resonance structure associated with the core-excited states produced by the electron excitation of C IV and photoionization of C III. For bound channel contribution, the close-coupling wavefunction expansion for photoionization includes ground and 14 excited states of the target ion CIV and 105 states configurations of C III. Extensive sets of atomic data for bound fine-structure levels, resulting in 762 dipole-allowed transitions, radiative probabilities, and photoionization cross sections out of J^π = 0^± − 4^± fine-structure levels are obtained. The ground-level photoionization cross section smoothly decreases with increasing energy, showing a very narrow, strong Rydberg resonance converging to the CIV 1s²2p threshold. The work shows that prominent Seaton resonances for 2sns states with n ≥ 5, caused by photoexcitation of the core electron below the 2p threshold, visibly contribute to photoabsorption from excited states of C III. The present results provide highly accurate parameters of various model applications in plasma spectroscopy. Full article

Concrete is extensively utilized in infrastructure projects. However, issues like construction quality and external loads can lead to the formation of thin-plate-like voids with considerable aspect ratios, posing serious safety risks and highlighting the need for effective boundary detection. This paper addresses the challenges of traditional acoustic detection methods, which often suffer from low efficiency, poor adaptability to environmental conditions, and difficulties in measuring defect sizes. It explores a spatially diverse MIC Array system. Unlike single-point MIC that can only capture multi-directional sound field information from one excitation point, this array improves efficiency through simultaneous multi-channel data acquisition. This study develops a vibration model for a circular thin plate with fixed boundaries, examines the gradient relationships in various directions, and introduces a method that integrates MIC array technology with acoustic vibration techniques. The focus is on identifying concrete defect boundaries, where a single excitation at the same measurement point can yield different first-order vibration modes recorded by various MICs. A gradient-based approach is proposed to determine defect boundaries based on the locations of different MICs in the array. Experiments were carried out using circular thin-plate concrete samples with pre-existing voids. For instance, at boundary measurement point 15, the first-order modal data collected by MIC0 and MIC4 were $7.80\times {10}^{-4}\mathrm{P}\mathrm{a}$ and $5.42\times {10}^{-6}\mathrm{P}\mathrm{a}$, respectively, exhibiting a significant gradient difference, which verified the accuracy and rapidity of identifying concrete void boundaries. Full article

Penicillium expansum is an acidogenic fungal species that belongs to the phylum Ascomycota. During the infection and colonization of host fruits, P. expansum can efficiently express glucose oxidase (GOX) and oxidize β-D-glucose to generate gluconic acid (GLA). In this study, the bioinformatics analysis method was employed to predict and analyze the function of the GOX protein. In addition, a comprehensive assessment was conducted on the P. expansum GOX coding gene GOX2, and the expression response rules of GOX2 under different external stress environments were explored. The results show that GOX is an unstable hydrophilic protein. It is either an integrated membrane protein (such as a receptor or channel) that is directly anchored to the membrane through a transmembrane structure or a non-classical secreted protein that is secreted extracellularly. RNA-seq data analysis shows that the GOX2 gene is regulated by multiple environmental factors, including pH, temperature, carbon base, and chemical fungicides. The expression level of GOX2 reaches its maximum value under alkaline conditions (pH 8–10) and at approximately 10 °C. Using starch as the carbon source and adding sodium propionate or potassium sorbate has the effect of inhibiting the expression of the GOX2 gene. The analysis of the function of the GOX protein and the characteristics of the GOX2 gene in P. expansum provides new insights into the glucose oxidase-encoding gene GOX2. The research results provide significant value for the subsequent development of new disease resistance strategies by targeting the GOX2 gene and reducing post-harvest disease losses in fruits. Full article

The all-weather imaging capability of synthetic aperture radar (SAR) confers unique advantages for maritime surveillance. However, ship detection under complex sea conditions still faces challenges, such as high-frequency noise interference and the limited computational power of edge computing platforms. To address these challenges, we propose a lightweight SAR small ship detection network, LWSARDet, which mitigates feature redundancy and reduces computational complexity in existing models. Specifically, based on the YOLOv5 framework, a dual strategy for the lightweight network is adopted as follows: On the one hand, to address the limited nonlinear representation ability of the original network, a global channel attention mechanism is embedded and a feature extraction module, GCCR-GhostNet, is constructed, which can effectively enhance the network’s feature extraction capability and high-frequency noise suppression, while reducing computational cost. On the other hand, to reduce feature dilution and computational redundancy in traditional detection heads when focusing on small targets, we replace conventional convolutions with simple linear transformations and design a lightweight detection head, LSD-Head. Furthermore, we propose a Position–Morphology Matching IoU loss function, P-MIoU, which integrates center distance constraints and morphological penalty mechanisms to more precisely capture the spatial and structural differences between predicted and ground truth bounding boxes. Extensive experiments conduct on the High-Resolution SAR Image Dataset (HRSID) and the SAR Ship Detection Dataset (SSDD) demonstrate that LWSARDet achieves superior overall performance compared to existing state-of-the-art (SOTA) methods. Full article

The recently identified proton-activated chloride (PAC) channel is ubiquitously expressed, and it regulates several proton-sensitive physiological and pathophysiological processes. While the PAC channel is activated by strong acids due to the binding of protons to extracellular binding sites, here, we describe the way in which weak acids inhibit the PAC channel by a mechanism involving a distinct extracellular binding site. Whole-cell patch clamp was performed on wildtype HEK293T cells, PAC-knockout HEK293 cells expressing human (h)PAC mutant constructs, and on hiPSC-derived cardiomyocytes. Proton-induced cytotoxicity was examined in HEK293T cells. Acetic acid inhibited endogenous PAC channels in HEK 293T cells in a reversible, concentration-dependent, and pH-dependent manner. The inhibition of PAC channels was also induced by lactic acid, propionic acid, itaconic acid, and β-hydroxybutyrate. Weak acids also inhibited recombinant wildtype hPAC channels and PAC-like currents in hiPSC-derived cardiomyocytes. Replacement of the extracellular arginine 93 by an alanine (hPAC–Arg93Ala) strongly reduced the inhibition by some weak acids, including arachidonic acid. Although lactic acid inhibited PAC, it did not reduce the proton-induced cytotoxicity examined in wildtype HEK 293 cells. To conclude, weak acids inhibit PAC via an extracellular mechanism involving Arg93. These data warrant further investigations into the regulation of the PAC channel by endogenous weak acids. Full article

We present a buffer-pH-modulated electrokinetic concentration strategy in MEMS microchannels that harnesses simple pH shifts to neutralize and charge proteins, reversibly “pausing” them at a planar electric-gate electrode by tuning to their isoelectric point (pI) and mobilizing them with slight pH offsets under an applied field. This synergistic coupling of dynamic pH control and electrode-gated focusing, which requires only buffer composition changes, enables rapid and tunable protein capture and release across diverse channel geometries for lab-on-chip, preparative, and point-of-care diagnostics. Moreover, it dovetails with established MEMS biomedical platforms ranging from diagnostics to drug delivery and microsurgery to gene and cell-manipulation devices. Future work on tailored electrode coatings and optimized channel profiles will further boost selectivity, speed, and integration in sub-100 µm MEMS devices. Full article

Accurate analysis of medical videos remains a major challenge in deep learning (DL) due to the need for effective spatiotemporal feature mapping that captures both spatial detail and temporal dynamics. Despite advances in DL, most existing models in medical AI focus on static images, overlooking critical temporal cues present in video data. To bridge this gap, a novel DL-based framework is proposed for spatiotemporal feature extraction from medical video sequences. As a feasibility use case, this study focuses on gastrointestinal (GI) endoscopic video classification. A 3D convolutional neural network (CNN) is developed to classify upper and lower GI endoscopic videos using the hyperKvasir dataset, which contains 314 lower and 60 upper GI videos. To address data imbalance, 60 matched pairs of videos are randomly selected across 20 experimental runs. Videos are resized to 224 × 224, and the 3D CNN captures spatiotemporal information. A 3D version of the parallel spatial and channel squeeze-and-excitation (P-scSE) is implemented, and a new block called the residual with parallel attention (RPA) block is proposed by combining P-scSE3D with a residual block. To reduce computational complexity, a (2 + 1)D convolution is used in place of full 3D convolution. The model achieves an average accuracy of 0.933, precision of 0.932, recall of 0.944, F1-score of 0.935, and AUC of 0.933. It is also observed that the integration of P-scSE3D increased the F1-score by 7%. This preliminary work opens avenues for exploring various GI endoscopic video-based prospective studies. Full article

Infrared small-target detection encounters significant challenges due to a low image signal-to-noise ratio, limited target size, and complex background noise. To address the issues of sparse feature loss for small targets during the down-sampling phase of the traditional U-Net network and the semantic gap in the feature fusion process, a multilevel feature extraction and fusion attention network (MEFA-Net) is designed. Specifically, the dilated direction-sensitive convolution block (DDCB) is devised to collaboratively extract local detail features, contextual features, and Gaussian salient features via ordinary convolution, dilated convolution and parallel strip convolution. Furthermore, the encoder attention fusion module (EAF) is employed, where spatial and channel attention weights are generated using dual-path pooling to achieve the adaptive fusion of deep and shallow layer features. Lastly, an efficient up-sampling block (EUB) is constructed, integrating a hybrid up-sampling strategy with multi-scale dilated convolution to refine the localization of small targets. The experimental results confirm that the proposed algorithm model surpasses most existing recent methods. Compared with the baseline, the intersection over union (IoU) and probability of detection $P_d$ of MEFA-Net on the IRSTD-1k dataset are increased by 2.25% and 3.05%, respectively, achieving better detection performance and a lower false alarm rate in complex scenarios. Full article

Sediment accumulation in irrigation channels poses a significant challenge to water resource management, impacting hydraulic efficiency and agricultural sustainability. This study introduces an innovative multidisciplinary framework that integrates advanced image analysis (FIJI/ImageJ 1.54p), statistical validation (RStudio), and vector field modeling with a novel Sinusoidal Morphodynamic Bedload Transport Equation (SMBTE) to predict sediment deposition patterns with high precision. Conducted along the Malacatos River in La Tebaida Linear Park, Loja, Ecuador, the research captured a natural sediment transport event under controlled flow conditions, transitioning from pressurized pipe flow to free-surface flow. Observed sediment deposition reduced the hydraulic cross-section by approximately 5 cm, notably altering flow dynamics and water distribution. The final SMBTE model (Model 8) demonstrated exceptional predictive accuracy, achieving RMSE: 0.0108, R²: 0.8689, NSE: 0.8689, MAE: 0.0093, and a correlation coefficient exceeding 0.93. Complementary analyses, including heatmaps, histograms, and vector fields, revealed spatial heterogeneity, local gradients, and oscillatory trends in sediment distribution. These tools identified high-concentration sediment zones and quantified variability, providing actionable insights for optimizing canal design, maintenance schedules, and sediment control strategies. By leveraging open-source software and real-world validation, this methodology offers a scalable, replicable framework applicable to diverse water conveyance systems. The study advances understanding of sediment dynamics under subcritical (Fr ≈ 0.07) and turbulent flow conditions (Re ≈ 41,000), contributing to improved irrigation efficiency, system resilience, and sustainable water management. This research establishes a robust foundation for future advancements in sediment transport modeling and hydrological engineering, addressing critical challenges in agricultural water systems. Full article

The underwater environment severely degrades image quality by absorbing and scattering light. This causes significant challenges, including non-uniform illumination, low contrast, color distortion, and blurring. These degradations compromise the performance of critical underwater applications, including water quality monitoring, object detection, and identification. To address these issues, this study proposes a detail-oriented hybrid framework for underwater image enhancement that synergizes the strengths of traditional image processing with the powerful feature extraction capabilities of unsupervised deep learning. Our framework introduces a novel multi-scale detail enhancement unit to accentuate structural information, followed by a Latent Low-Rank Representation (LatLRR)-based simplification step. This unique combination effectively suppresses common artifacts like oversharpening, spurious edges, and noise by decomposing the image into meaningful subspaces. The principal structural features are then optimally combined with a gamma-corrected luminance channel using an unsupervised MU-Fusion network, achieving a balanced optimization of both global contrast and local details. The experimental results on the challenging Test-C60 and OceanDark datasets demonstrate that our method consistently outperforms state-of-the-art fusion-based approaches, achieving average improvements of 7.5% in UIQM, 6% in IL-NIQE, and 3% in AG. Wilcoxon signed-rank tests confirm that these performance gains are statistically significant (p < 0.01). Consequently, the proposed method significantly mitigates prevalent issues such as color aberration, detail loss, and artificial haze, which are frequently encountered in existing techniques. Full article

Target detection is the key technology of the apple-picking robot. To overcome the limitations of existing apple target detection methods, including low recognition accuracy of multi-species apples in complex orchard environments and a complex network architecture that occupies large memory, a lightweight apple recognition model based on the improved YOLO11 model was proposed, named YOLO-WAS model. The model aims to achieve efficient and accurate automatic multi-species apple identification while reducing computational resource consumption and facilitating real-time applications on low-power devices. First, the study constructed a high-quality multi-species apple dataset and improved the complexity and diversity of the dataset through various data enhancement techniques. The YOLO-WAS model replaced the ordinary convolution module of YOLO11 with the Adown module proposed in YOLOv9, the backbone C3K2 module combined with Wavelet Transform Convolution (WTConv), and the spatial and channel synergistic attention module Self-Calibrated Spatial Attention (SCSA) combined with the C2PSA attention mechanism to form the C2PSA_SCSA module was also introduced. Through these improvements, the model not only ensured lightweight but also significantly improved performance. Experimental results show that the proposed YOLO-WAS model achieves a precision (P) of 0.958, a recall (R) of 0.921, and mean average precision at IoU threshold of 0.5 (mAP@50) of 0.970 and mean average precision from IoU threshold of 0.5 to 0.95 with step 0.05 (mAP@50:95) of 0.835. Compared to the baseline model, the YOLO-WAS exhibits reduced computational complexity, with the number of parameters and floating-point operations decreased by 22.8% and 20.6%, respectively. These results demonstrate that the model performs competitively in apple detection tasks and holds potential to meet real-time detection requirements in resource-constrained environments, thereby contributing to the advancement of automated orchard management. Full article

In this study, we introduce a high-resolution, high-speed thermal imaging technique using Raman spectroscopy to simultaneously measure the temperature of a substrate and a channel. By modifying the Raman spectrometer, we achieved a measurement speed faster than commercial spectrometers. This system demonstrated a sub-micron spatial resolution and the ability to measure the temperatures of the Si substrate and GaN channel simultaneously. During high-current operation, we observed significant self-heating in the GaN channel, with hotspots 100 °C higher than the surroundings, while the Si substrate showed an even temperature distribution. The ability to detect hotspots can help secure the reliability of devices through early failure analysis and can also be used for improvement research to reduce hotspots. These findings highlight the potential of this technique for early defect inspection and device improvement research. This study provides a novel and effective method for measuring the sub-micron resolution temperature distribution in devices, which can be applied to various semiconductor devices, including SiC-based power devices. Full article