Search Results (2,040)

Regular bathing is a common habit worldwide, yet bath additives have been characterized almost exclusively at the physiological level, while their molecular effects on human cells remain largely unexplored. We previously showed by RNA sequencing (RNA-seq) that the complex bath additive Karada Totonou ProBath (KTPB) induced the expression of EGR1 and hyaluronic acid synthase genes in human keratinocytes and fibroblasts, which represent the vascular endothelial growth factor (VEGF)-producing side of the cutaneous angiogenic axis; whether the VEGF-receiving endothelium responds to KTPB was unknown. Here, human vascular endothelial cells were exposed to KTPB and profiled by RNA-seq, and cytotoxicity was assessed using resazurin and Hoechst assays. KTPB altered gene expression in a time-dependent manner, and the differentially expressed genes were classified into three clusters with distinct temporal profiles: a late-repressed cluster, a transiently induced cell cycle-associated cluster, and a late-induced cluster enriched for blood vessel development and VEGFA-VEGFR2 signaling. The angiogenesis-related genes ID1, ID3, and EDN1 were markedly upregulated, peaking at 1–2 h. KTPB showed no detectable cytotoxicity in this cell model at any of the tested concentrations, which spanned the recommended use range. These results indicate that KTPB elicits an angiogenesis-associated transcriptional program in vascular endothelial cells without compromising viability and highlight transcriptomics’ value for characterizing bath additives. Full article

Background/Objectives: Universal newborn hearing screening (UNHS) has significantly improved early detection of congenital hearing loss; however, discrepancies persist between prevalence at birth and latter childhood. These findings suggest the presence of hidden or delayed-onset forms of hearing impairments as a consequence of false-negative UNHS results. Methods: A bibliographic search was conducted in PubMed and Scopus for the period 2000–2026. A total of 103 papers met the inclusion criteria. This narrative review synthesizes recent literature on the limitations of current hearing screening strategies, focusing on false-positive and false-negative screening results, auditory neuropathy spectrum disorder (ANSD), genetic etiologies, and congenital cytomegalovirus (cCMV) infection as potential causes. Emphasis is placed on mechanisms underlying missed diagnoses and their clinical implications. Results: Current screening protocols, particularly those based on otoacoustic emissions (OAE), may fail to detect mild, progressive, or neural hearing loss. False-negative results are frequently associated with genetic mutations (e.g., GJB2), cCMV infection, and ANSD, whereas false-positive findings are often associated with transient middle ear conditions and early timing of testing. Automated auditory brainstem responses (AABR)-based approaches for hearing screening improve detection of neural dysfunction but are influenced by auditory pathway maturation, which may complicate interpretation of results in early life. Evidence indicates that a substantial proportion of sensorineural hearing loss identified later in childhood may not be detectable at birth using existing protocols. Conclusions: Hidden and delayed-onset sensorineural hearing loss represent significant challenges and limitations of current UNHS programs. Optimization of screening strategies—including broader implementation of AABR, integration of genetic and cCMV testing, and careful consideration of screening timing—may improve early detection of hearing loss and reduce long-term developmental consequences. Future updates of international guidelines should address these gaps to enhance diagnostic accuracy and clinical outcomes. Full article

This paper presents methods and results on optimizing the functionality of pressurized sewerage systems, specifically wastewater pumping stations. A holistic approach is introduced to address major issues in wastewater transport such as sedimentation in suction chambers and fiber-induced issues in wastewater pumps, as well as their effects and detection. A conceptual suction chamber is scaled using hydraulic similarity and is experimentally investigated regarding its susceptibility to sedimentation. Optimization of inlets and manifolds, pumps, and sloped walls contribute to minimizing sedimentation. In terms of wastewater pumps, a semi-open two-channel wastewater impeller is optimized for its efficiency via response surface optimization. The subsequent optimization for functionality showcases the nexus of the two characteristics by means of cut-back and thickened leading edges, which significantly reduce the susceptibility to clogging. Another important consideration in wastewater pump design is the back shroud cavity. Through iterative experiments with different housing recess configurations, the back shroud cavity is optimized to minimize fiber entry and protect the mechanical seal. Lastly, the demonstration of the effects of clogging in the form of transient instationarities in the torque of a wastewater pump underline the importance of optimizing wastewater pumps for their functionality and low susceptibility to clogging. Full article

Multi-angle measurements provide essential data on the anisotropic reflectance properties of vegetation, enabling more robust retrievals of leaf area index (LAI), clumping index (CI), and canopy gap fraction compared to conventional single-view remote sensing. While Unmanned Aerial Vehicles (UAVs) offer unprecedented centimeter-level spatial resolution and flexible deployment, their application exposes a fundamental scale mismatch between ultra-high-resolution imagery and traditional bidirectional reflectance distribution function (BRDF) models. This review explicitly identifies that conventional 1D radiative transfer models (RTMs), which rely on the assumption of a statistically homogeneous canopy, suffer from severe scale-dependent biases, such as systematically underestimating hotspot reflectance (e.g., observed biases of 25% to 40% in 5-cm resolution UAV studies over specific vegetation canopies), and structural-optical confounding when directly applied to UAV data. At centimeter scales, macroscopic structural heterogeneity disrupts this homogeneity, necessitating the use of 3D RTMs that can explicitly simulate geometric occlusion and complex multiple scattering processes in highly heterogeneous environments. To bridge these theoretical and operational gaps, this review uniquely synthesizes UAV-specific multi-angle methodologies, systematically correlating canopy architectural types with optimal sensor configurations, flight strategies, and BRDF modeling frameworks. By evaluating recent advancements in multimodal data fusion, physics-informed machine learning, and physiological parameter retrieval, this review provides a comprehensive roadmap for decoupling structural and biochemical traits, highlighting how multi-angle directional signatures can substantially elevate classification accuracy, with specific experiments on spectrally similar crops and mixed tree species demonstrating improvements from roughly 40% to over 89%. Ultimately, it establishes practical, decision-oriented guidelines for overcoming transient illumination and co-registration errors, advancing high-fidelity quantitative monitoring and stress detection in complex forest ecosystems. Full article

Granulation is a major physiological disorder that compromises sweet orange quality. Conventional destructive detection methods lead to food waste and cannot be used for batch inspection. This study proposes a nondestructive approach for detecting granulation in sweet oranges using air-jet transient excitation and a laser Doppler vibrometer (LDV). Acoustic vibration spectra were acquired from 640 sweet orange samples. Using both competitive adaptive reweighted sampling (CARS)-extracted feature parameters and raw acoustic vibration spectra as inputs, an ISNet-1D model integrating a multi-scale Inception module and a squeeze-and-excitation (SE) attention mechanism was developed, and its performance was compared against those of partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), K-nearest neighbor (KNN), random forest (RF), and baseline deep learning models including one-dimensional convolutional neural network (1D-CNN), Visual Geometry Group network 16 (VGG16), and residual network v1 (ResNet-v1). The results demonstrated that the ISNet-1D model trained on the full raw vibration spectrum achieved the best performance, with an overall test set accuracy, recall, and specificity of 92.97%, 95.00%, and 91.18%, respectively. Ablation experiments revealed that removal of the Inception branches and the SE module reduced the overall test accuracy by 5.47% and 4.69%, respectively, indicating that their combination effectively extracts multi-scale acoustic vibration features and enhances model precision. Gradient-weighted class activation mapping further identified the critical frequency bands primarily relied upon by the model for prediction. Collectively, noncontact acoustic vibration detection combined with ISNet-1D provides a viable method for nondestructive granulation detection in sweet oranges. Full article

Temperature-dependent characterization of nanomaterials is commonly performed through electrical measurements, and many temperature-sensing applications similarly rely on electrical readout signals. Although these approaches are simple and widely used, they require direct sample contact and may lose sensitivity when the electrical response is weak, saturated, or only weakly dependent on temperature. In this study, a photoacoustic (PA) measurement system was used to evaluate the temperature-dependent response of AgNW networks. PA measurement system detects ultrasonic waves generated through transient thermoelastic expansion after optical absorption, which enables sensitive probing of temperature-dependent optothermal responses beyond conventional electrical measurements. Silver nanowire (AgNW) films with different surface coverage were fabricated through one to four repeated spin-coating cycles using a 1% AgNW ethanol dispersion containing nanowires with diameters of 20–40 nm and lengths of 10–20 µm. The photoacoustic (PA) signals of the fabricated films were measured at various temperatures using a customized PA measurement system and were compared with sheet resistance values obtained using a four-point probe. Both sheet resistance and PA signals increased with increasing temperature, regardless of AgNW surface coverage. Notably, AgNW films with higher surface coverages showed only weak temperature dependence in sheet resistance but pronounced temperature-dependent PA response. These results indicate that PA analysis is particularly useful for characterizing AgNW networks with higher surface coverage, including those used in stretchable electrodes. All samples showed linear relationships for both sheet resistance and PA signals, with coefficients of determination (R²) exceeding 0.9. Overall, these findings suggest that PA measurement may provide complementary information on temperature-dependent behavior that is not fully reflected in sheet-resistance measurements. Full article

Impedance flow cytometry (IFC) is a label-free technique for high-throughput electrical characterization of individual cells and other micron-scale particles based on transient impedance changes during passage through microfluidic sensing electrodes. Although lock-in demodulation and low-pass filtering are commonly used for signal conditioning, post-demodulated IFC signals can still contain residual noise, baseline drift, periodic interference, and event-like artifacts that reduce event detectability and distort quantitative features. Here, we present an event-preserving neural network-based denoising framework for post-demodulation IFC signal enhancement. The novelty of this work lies in an IFC-specific event-preserving denoising strategy that combines event-focused sampling and an event-weighted loss to suppress noise while preserving sparse bipolar particle events. A lightweight multilayer perceptron (MLP) was trained and evaluated using paired synthetic noisy and clean IFC signals generated with representative noise and artifact components, and further tested on experimental IFC measurements under challenging noise conditions. Neural network denoising improved event detection, reduced amplitude-estimation error under added white noise, and suppressed experimental baseline and background fluctuations while preserving bipolar event morphology. These results suggest that lightweight neural network denoising can serve as a practical optional enhancement step for noisy post-demodulated IFC signals, potentially supporting more reliable electrical detection and analysis of micro- and submicron-scale particles in impedance-based biosensing applications. Full article

Background/Objectives: Prostate cancer (PCa) has a high incidence worldwide, yet reliable prognostic biomarkers to distinguish indolent from aggressive forms are lacking. MicroRNAs (miRNAs), a class of small non-coding RNAs, are stable molecules that play an important role in cancer development and can be detected in circulation by non-invasive methods. This study aimed to examine the influence of the cell-free (cf) miRNAs levels on PCa risk and aggressiveness using plasma samples and cell models. Methods: Four overexpressed miRNAs in prostate cancer tissue were selected from The Cancer Genome Atlas database: miR-25-3p, miR-92a-1-5p, miR-92a-2-5p, and miR-148a-3p. Cf-miRNA levels were validated via quantitative reverse transcription polymerase chain reaction (RT-qPCR) in plasma samples from 80 PCa patients and 40 controls in a Brazilian cohort. Clinicopathological associations were analyzed to determine prognostic value. Subsequently, functional analyses were performed using transient transfection of miR-25-3p in the LNCaP cell line, assessing proliferation, migration, invasion, and target gene regulation. Results: Among the miRNAs analyzed, only miR-25-3p was upregulated in the plasma of PCa patients compared to controls (p = 0.013). Patients with International Society of Urological Pathology (ISUP) grade ≥2 presented higher expression levels of miR-25-3p (p = 0.020) compared to patients with ISUP grade 1. Overexpression of miR-25-3p significantly increased cell proliferation, colony formation, migration, and invasion capacity and modulated the expression of BCL2L11, CDH1, CDKN1C, EHZ2, and TP53 genes. Conclusions: miR-25-3p had an oncogenic role in PCa, showing a potential marker to assess PCa development and progression using liquid biopsy. Full article

Underground petrochemical pipelines face severe risks from unreported third-party damage during excavation. Existing monitoring systems based on supervised deep learning or distributed acoustic sensing often require expensive hardware and large volumes of labeled defect data, and they exhibit high false-alarm rates in noisy urban environments. To overcome these limitations, this study proposes a cost-effective, field-feasible early warning framework utilizing attached acceleration sensors. High-frequency transient vibrations from physical impacts are demodulated into low-frequency rhythms using a Hilbert transform-based envelope algorithm, and these rhythms are then converted into 2D spectrograms via the Short-Time Fourier Transform. A 2D Convolutional Neural Network-Variational Autoencoder (2D CNN-VAE) is employed for unsupervised anomaly detection, trained specifically on normal background data. Furthermore, a hierarchical alarm classification logic is implemented to evaluate the reconstruction error, incorporating impulse noise rejection and temporal continuity checks. In a field demonstration, the proposed framework effectively suppressed false alarms caused by severe continuous noise, such as ground compacting, while reliably detecting sustained asphalt-breaking threats. This methodology demonstrates practical feasibility for isolating genuine excavation activities from transient environmental noise, offering a promising predictive maintenance approach that reduces alarm fatigue without requiring labeled defect data. Full article

Background/Objectives: Müllerian duct anomalies (MDAs) are congenital structural abnormalities of the female reproductive tract with heterogeneous and incompletely defined genetic contributions. PAX8 is a developmental transcription factor implicated in thyroid and urogenital development. This study assessed the functional consequences of a rare PAX8 variant identified by whole-exome sequencing (WES) in an MDA cohort, without presuming a causal relationship. Methods: WES was performed in 150 patients with MDAs. The PAX8 c.791T>C (p.Leu264Pro) variant was evaluated using computational predictions, cellular assays, and RNA sequencing in a 293FT transient-overexpression model. Results: The heterozygous p.Leu264Pro variant was identified in one patient with a complex septate uterine, cervical, and vaginal anomaly and was absent from 120 controls. Parental samples were unavailable. No statistically significant difference in construct-derived PAX8 mRNA abundance was detected between PAX8-WT and PAX8-L264P, and both proteins showed predominantly nuclear localization. In the direct PAX8-WT-versus-L264P RNA-seq comparison, 76 transcripts had nominal p values below 0.05, but none remained significant after Benjamini–Hochberg correction, and no gene met the prespecified differential-expression criteria. AK5, RCBTB2, and IFT88 were identified as candidate PAX8-responsive genes in this experimental system. Conclusions: No major functional difference was detected between PAX8-WT and L264P under the tested conditions; however, these findings do not establish functional equivalence or exclude smaller, tissue-specific, or developmental-stage-specific effects. The p.Leu264Pro variant remains a variant of uncertain significance with respect to MDAs, and the functional results were not used as benign evidence under ACMG/AMP criterion BS3. Full article

Background/Objectives The evaluation of treatment effectiveness largely depends on the outcome measures used in the study. Patient-reported outcome measures (PROMs), such as the visual analog scale (VAS), and more objective measures, such as pressure pain thresholds (PPT), are commonly employed to capture pain-sensory profiles and hypoalgesic effects. The NOD is a novel digital algometer designed to assess the PPT. This study aimed to evaluate the validity, reliability, and responsiveness of the NOD algometer using a conventional algometer as a reference instrument for PPT assessment. Methods: A prospective longitudinal study with repeated measures was conducted following the COSMIN recommendations for studies evaluating measurement properties. Healthy participants with latent trigger points were recruited from the University of Valencia, where an experimental pain induction procedure was used to generate transient changes in PPT values and evaluate the responsiveness of the NOD device. The PPT was assessed at four time points: baseline, immediately after the intervention, and at 30 and 60 min post-intervention. Results: A total of 40 participants were included in the study (mean age: 22.0 ± 3.35 years; F = 35%). For concurrent validity, a strong positive association was observed between the reference algometer and NOD (Spearman’s ρ = 0.90, p < 0.001), and the limits of agreement ranged from −1.13 to 1.34 (−25.45% to 30.18%). For reliability, intra-session reliability was good to excellent in both sessions (ICC = 0.825 and 0.859), with a standard error of measurement (SEM = 0.63 to 0.82) and minimal detectable change (MDC = 1.75 to 2.26). For responsiveness, significant reductions in NOD values were observed at all post-intervention time points compared to baseline (p ≤ 0.005). The magnitude of change increased over time, with a standardized response mean (SRM = −0.57 to −0.74) and moderate-to-large effect sizes (Cohen’s d = 0.57–0.73). Conclusions: Our findings indicate that the NOD may be a valid and reliable tool for measuring PPT at latent trigger points in healthy participants. The NOD showed a strong association with a reference algometer and good relative reliability under standardized experimental conditions. The device was also able to detect experimentally induced changes in PPT. Nevertheless, further studies are required to establish its interchangeability with conventional algometers, responsiveness to clinically meaningful change, and applicability in clinical populations. Full article

Canopy occlusion, dynamic vegetation, structural degeneracy, and implicit terrain risks make stable localization and task-adaptive planning difficult for resource-constrained orchard robots. This paper proposes CitraNav, a lightweight navigation method for global navigation satellite system (GNSS)-denied orchards. It separates stable geometric evidence for localization from short-lived semantic evidence for planning, preventing semantic observations from accumulating in the global map. For localization, a hierarchical voxel map selects its resolution according to local structure and light detection and ranging (LiDAR) sampling characteristics, while cross-frame reliability and observability constraints suppress updates from transient vegetation and weakly observable directions. For planning, synchronized color and depth observations form a local semantic risk point cloud. A model predictive path integral (MPPI) planner combines task-dependent semantic costs with exact-footprint collision checking against currently detected obstacles. In simulation, CitraNav achieved a mean translational localization root mean square error (RMSE) of 0.075 m. Compared with geometric point-cloud planning, semantic planning reduced the collision rate by 71.4% and increased weed coverage 4.72-fold. Across 14 real-world sequences spanning farm-road, lawn, forest, and orchard environments, CitraNav achieved mean translational and heading RMSEs of 0.151 m and 1.13°, respectively, while using 72.3–87.4% fewer geometric map cells than the comparison methods. The complete perception–planning pipeline operated at 20.3–32.7 frames per second on an edge platform. These results suggest that CitraNav offers a balanced approach to localization stability, task-adaptive planning, and computational efficiency in complex orchard navigation. Full article

Conductive polymer nanocomposites (CPC) are widely investigated as chemo-resistive materials for the detection of volatile organic compounds (VOC). However, the physical mechanisms governing their transient electrical response remain only partially understood, limiting the development of predictive models and highly selective sensors. In this work, the chemo-resistive behaviour of carbon nanoparticle-filled poly(ethylene-co-ethyl acrylate) (EEA-CNP) was investigated through a multiphysics experimental approach combining simultaneous measurements of solvent uptake, dimensional changes, temperature and electrical resistance during toluene sorption and desorption. Thick specimens were deliberately employed to amplify transient diffusion phenomena and enable direct observation of the coupling between mass transport, polymer swelling and conductive network evolution. The results demonstrate that electrical resistance cannot be interpreted solely from the average solvent concentration within the material. Instead, the transient response is primarily governed by solvent concentration gradients, which continuously modify the connectivity of the conductive nanoparticle network during diffusion. This mechanism explains the pronounced hysteresis observed between sorption and desorption, the transient resistance overshoot during sorption, and the absence of a unique relationship between resistance and solvent content under dynamic conditions. A dedicated quasi-static desorption protocol was therefore developed to minimise concentration gradients and establish the intrinsic correlation between electrical resistivity and solvent fraction. The experiments further show that a solvent content of approximately 6 wt% is sufficient to completely disrupt the conductive percolation network. These findings provide new insights into the multiphysics mechanisms governing chemo-resistive sensing and establish an experimental basis for the development and validation of predictive models for conductive polymer nanocomposites. The proposed methodology is expected to contribute to the optimisation of next-generation VOC sensors and electronic noses with improved selectivity and predictive capability. Full article

Software-Defined Networking (SDN) provides fine-grained control over network architectures, yet integrating intrusion detection systems (IDSs) into the control plane frequently introduces prohibitive computational overhead. This issue is compounded by the fact that existing machine learning models, typically trained on static benchmark datasets, often degrade under real-time polling conditions and unpredictable traffic bursts. To bridge this gap, this paper evaluates an ultra-compact five-feature polling scheme (F1–F5) designed to preserve statistical symmetry between control-plane monitoring and telemetry overhead within a dynamic Mininet–Ryu testbed. The experimental framework incorporates 15% background noise, and a 10% stealth attack overlaps across a 120 s dynamic trace. Four distinct classifiers—Random Forest (RF), Decision Tree (DT), Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM)—were evaluated across frame-by-frame snapshot and windowed prediction tasks. Empirical findings reveal that tree-based ensembles consistently outperform deep learning approaches, with RF attaining an overall accuracy of 97.57% and DT achieving 96.74%, compared to 90.77% for MLP and 90.73% for LSTM. Analysis of the time-series logs demonstrates that RF’s orthogonal decision boundaries successfully isolate transient, high-intensity threats such as WebAttack and PortScan vectors without needing memory-intensive recurrent architectures. Ultimately, pairing minimal feature extraction with lightweight tree ensembles offers an optimal balance between low control-plane latency and high detection efficacy. Full article

Edible lily bulbs (Lilium lancifolium Thunb.) are highly valued for their rich nutritional profile and significant medicinal properties. To further enhance these beneficial attributes, this study investigates a novel, green post-harvest enhancement strategy using low-dose ⁶⁰Co-γ irradiation (2–5 Gy) followed by immediate freeze-drying. Experimentally, fresh bulbs were exposed to varying irradiation doses, after which physiological parameters (enzyme activities, phytochemical contents) and metabolic profiles (via LC-MS/MS) were systematically evaluated. The results demonstrated that 4 Gy represented the optimal dose; compared to the non-irradiated control group (0 Gy), the 4 Gy treatment significantly enhanced the antioxidant defense system by eliciting oxidative stress responses, increasing superoxide dismutase (SOD) and catalase (CAT) activities by 1.55- to 1.86-fold, and elevating total phenolics and flavonoids by 1.31- and 2.69-fold, respectively. Untargeted metabolomic analysis identified 42 differential metabolites, revealing that 4 Gy irradiation primarily upregulated the phenylpropanoid and flavonoid biosynthetic pathways. Key antioxidants, specifically rosmarinic acid and the newly detected alkaloid jurubine, exhibited significant accumulation. Integrated correlation analysis and molecular docking simulations elucidated the underlying mechanisms, providing theoretical insights into their transient interactions with DPPH radicals. In conclusion, low-dose ⁶⁰Co-γ irradiation serves as a sustainable, green post-harvest processing strategy to improve the nutritional quality of edible lily bulbs by activating intrinsic metabolic defense mechanisms. Full article