Search Results (479)

Neurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and intellectual disability (ID) arise from disruptions of molecular programmes that coordinate neurogenesis, synaptogenesis, and circuit maturation. While genomic studies have identified numerous susceptibility loci, genetic variation alone accounts for only part of disease heritability, underscoring the importance of post-transcriptional and epigenetic regulation. Among these regulatory layers, non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), PIWI-interacting RNAs (piRNAs), and transfer RNA-derived small RNAs (tsRNAs), have emerged as central modulators of neural differentiation, synaptic plasticity, and intercellular signalling. Recent multi-omics and single-cell studies reveal that ncRNAs fine-tune chromatin accessibility, transcriptional output, and translation through tightly integrated regulatory networks. miRNAs shape neurogenic transitions and circuit refinement; lncRNAs and circRNAs couple chromatin architecture to activity-dependent transcription; and tsRNAs and piRNAs extend this regulation by linking translational control to epigenetic memory and environmental responsiveness. Spatial transcriptomics further maps ncRNA expression to vulnerable neuronal and glial subtypes across cortical and subcortical regions. Clinically, circulating ncRNAs, especially those packaged in extracellular vesicles, exhibit stable, disease-associated signatures, supporting their potential as minimally invasive biomarkers for early diagnosis and patient stratification. Parallel advances in RNA interference, antisense oligonucleotides, CRISPR-based editing, and vesicle-mediated delivery highlight emerging therapeutic opportunities. These developments position ncRNAs as both mechanistic determinants and translational targets in NDDs, offering a unifying framework that links genome regulation, environmental cues, and neural plasticity, and paving the way for next-generation RNA-guided diagnostics and therapeutics. Full article

By solving the three-dimensional Schrödinger equation with a second-order implicit Finite Difference Method (FDM), the combined effects of temperature, morphology, hydrostatic pressure, and transverse electric field on the nonlinear optical rectification (NOR) of GaAs/Al_εGa_1−εAs elliptical quantum rings are examined. The NOR amplitude is twelve times enhanced and a noticeable blue shift is induced in the THz region when the electric field is increased. Consequently, with the electric field of 1 × 10⁵ V/m, the NOR magnitude achieves its maximum value of 17.116 × 10⁵ m/V. Additionally, when the electric field is aligned along one side of the system’s in-plane cross-section, the strongest amplification takes place. However, with corresponding spectrum shifts, the NOR intensity rises with temperature and falls with hydrostatic pressure. Additionally, changing the transverse profile of the quantum ring from triangular to parabolic broadens the carrier wave functions and has a considerable impact on the NOR coefficient. These findings provide important information for the construction of high-performance, tunable THz optoelectronic devices by demonstrating effective external and structural tuning of NOR. Full article

With the rapid advancement of offshore wind power, structural vibration induced by multi-source excitations in complex marine environments is a critical concern. This study developed a multi-degree-of-freedom (MDOF) dynamic model of an offshore wind turbine using a lumped mass approach, which was then reduced to a first-order linear system to improve frequency-domain analysis and optimization efficiency. Given the non-stationary, broadband nature of wind and wave loads, a band-pass filtering technique was applied to extract dominant frequency components, enabling linear modeling of excitations within primary modal ranges. The displacement response spectrum, derived via system transfer functions, served as the objective function for optimizing tuned mass damper (TMD) parameters. Both single TMD and multiple TMD (MTMD) strategies were designed and compared. A hierarchical particle swarm optimization (H-PSO) algorithm was proposed for MTMD tuning, using the optimized single TMD as an initial guess to enhance convergence and stability in high-dimensional spaces. The results showed that the frequency-domain optimization framework achieved a balance between accuracy and computational efficiency, significantly reducing structural responses in dominant modes and demonstrating strong potential for practical engineering applications. Full article

In optical sensing, signal demodulation often degrades fine spectral data, particularly in spectroscopic measurements affected by Doppler noise, aliasing, and circuit noise. Existing algorithms often fall short in addressing these issues effectively, as they either necessitate complex parameter tuning and extensive expertise or are limited to handling simple spectral signals. To address these challenges, this study proposes an adaptive spectral extraction algorithm combining Variational Mode Decomposition (VMD) and Savitzky-Golay (SG) filtering. The algorithm optimizes parameters through an innovative adaptation strategy. By analyzing key parameters such as SG frame length, order, and VMD mode number, it leverages signal time-domain and frequency spectrum information to adaptively determine the optimal VMD modes and SG order, ensuring effective noise suppression and feature preservation. Validated through simulations and experiments, the method significantly enhances spectral signal quality by restoring absorption peaks and eliminating manual parameter adjustments. This work provides a robust solution for improving measurement accuracy and reliability in optical sensing instrumentation, particularly in applications involving complex spectral analysis. Full article

Robust control over the coupling and propagation of surface plasmon polaritons (SPPs) is essential for advancing various plasmonic applications. Traditional planar structures, commonly used to design SPP directional couplers, face limitations such as low extinction ratios and design complexities. These issues frequently hinder the dense integration and miniaturisation of photonic systems. Recently, exceptional points (EPs)—unique degeneracies within the parameter space of non-Hermitian systems—have garnered significant attention for enabling a range of counterintuitive phenomena in non-conservative photonic systems, including the non-trivial control of light propagation. In this work, we develop a rigorous temporal coupled-mode theory (TCMT) description of a non-Hermitian metagrating composed of alternating silicon–germanium nanostrips and use it to explore the unidirectional excitation of SPPs at EPs in the visible spectrum. Within this framework, EPs, typically associated with the coalescence of eigenvalues and eigenstates, are leveraged to manipulate light propagation in nonconservative photonic systems, facilitating the refined control of SPPs. By spatially modulating the permittivity profile at a dielectric–metal interface, we induce a passive parity–time ($\mathcal{PT}$)-symmetry, which allows for refined tuning of the SPPs’ directional propagation by optimising the structure to operate at EPs. At these EPs, a unidirectional excitation of SPPs with a directional intensity extinction ratio as high as 40 dB between the left and right excited SPP modes can be reached, with potential applications in integrated optical circuits, visible communication technologies, and optical routing, where robust and flexible control of light at the nanoscale is crucial. Full article

Accurate and real-time detection of polyps in colonoscopy is a critical task for the early prevention of colorectal cancer. The primary difficulties include insufficient extraction of multi-scale contextual cues for polyps of different sizes, inefficient fusion of multi-level features, and a reliance on hand-crafted anchor priors that require extensive tuning and compromise generalization performance. Therefore, we introduce a one-stage anchor-free detector that achieves state-of-the-art accuracy whilst running in real-time on a GTX 1080-Ti GPU workstation. Specifically, to enrich contextual information across a wide spectrum, our Cross-Stage Pyramid Pooling module efficiently aggregates multi-scale contexts through cascaded pooling and cross-stage partial connections. Subsequently, to achieve a robust equilibrium between low-level spatial details and high-level semantics, our Weighted Bidirectional Feature Pyramid Network adaptively integrates features across all scales using learnable channel-wise weights. Furthermore, by reconceptualizing detection as a direct point-to-boundary regression task, our anchor-free head obviates the dependency on hand-tuned priors. This regression is supervised by a Scale-invariant Distance with Aspect-ratio IoU loss, substantially improving localization accuracy for polyps of diverse morphologies. Comprehensive experiments on a large dataset comprising 103,469 colonoscopy frames substantiate the superiority of our method, achieving 98.8% mAP@0.5 and 82.5% mAP@0.5:0.95 at 35.8 FPS. Our method outperforms widely used CNN-based models (e.g., EfficientDet, YOLO series) and recent Transformer-based competitors (e.g., Adamixer, HDETR), demonstrating its potential for clinical application. Full article

This study develops a semi-discretized time system from the continuous-time Rosenzweig–-MacArthur model via the method of piecewise constant argument—a discretization approach that preserves both mathematical rigor and biological interpretability. For the proposed system incorporating constant-effort harvesting on both prey and predator populations, we present rigorous quantitative derivations for the existence and local stability of non-negative equilibrium. Furthermore, we investigate complex dynamical behaviors, including transcritical and Neimark–Sacker bifurcations, induced by parameter variations. We specifically focus on calculating the first Lyapunov coefficient to determine the stability of closed orbits emerging from the Neimark–Sacker bifurcation. Numerical validation of chaotic dynamics is conducted using the computed Maximum Lyapunov Exponent spectrum. Numerical simulations not only confirm consistency with analytical results but also reveal key ecological dynamics of the system: (i) the paradox of enrichment—a classic ecological phenomenon—persists even under constant-effort harvesting; (ii) appropriate tuning of harvesting parameters enables the coexistence of prey and predator populations in a stable closed orbit, resulting in cyclic coexistence. Full article

Active metasurfaces, whose optical properties can be tuned by an external stimulus such as electric or laser pulses, have attracted great research interest recently. The phase change material (PCM), antimony sulfide (Sb₂S₃), has been reported to modulate resonance wavelengths from the visible to the infrared. Here, we present a purely numerical study of an active and nonvolatile narrow-band perfect absorber in the infrared region based on a nanostructured metal–insulator–metal (MIM) metasurface incorporating Sb₂S₃. The proposed absorber exhibits a high quality factor and achieves near-unity absorption at resonance wavelengths. In addition, the absorption spectrum can be dynamically modulated by the phase transition of Sb₂S₃, with a modulation range approaching 1 μm. Moreover, the designed absorber shows insensitivity to the angle of incidence. This study offers a feasible strategy for developing Sb₂S₃-integrated metasurface perfect absorbers with potential applications in selective thermal emitters and bolometers. Full article

Achieving controllable wavelength tuning in optofluidic whispering gallery mode microcavity lasers is crucial for high-throughput, multi-sample, multiplexed biochemical sensing and multifunctional integrated photonic devices. This paper develops a bidirectionally wavelength-tunable optofluidic fiber whispering gallery mode microcavity laser driven by Rhodamine 6G co-doped with different acceptor dyes. Experimentally, a thin-walled silica ring inside a hollow-core anti-resonant fiber served as the optical microcavity, with a fixed 2.5 mM Rhodamine 6G co-doped with other dyes as the gain medium. The results revealed that when co-doped with Rhodamine B or Cy3, the single-longitudinal-mode laser emission wavelength exhibited a red shift with increasing co-dopant concentration. Conversely, when co-doped with Cy5, the laser output wavelength showed a distinct blue shift. This unique bidirectional tuning characteristic originates from the different fluorescence resonance energy transfer efficiencies between the co-dopants and Rhodamine 6G, and their competitive modulation of the system’s effective gain spectrum. The study offers a novel and flexible strategy for achieving wide-range, controllable wavelength tuning on a single laser platform, with significant potential for applications in biochemical sensing and multifunctional integrated photonic devices. Full article

The rapid and accurate identification of radionuclides and the quantitative analysis of their activities have long been key research areas in the field of nuclear spectrum data processing. Traditional nuclear spectrum analysis methods heavily rely on manual feature extraction, making them highly susceptible to interference from factors such as energy resolution, calibration drift, and spectral peak overlap when dealing with complex mixed-radionuclide spectra, ultimately leading to degraded identification performance and accuracy. Based on multi-nuclide energy spectral data acquired via Geant4 simulation, this study compares the performance of partial least squares regression (PLSR), random forest (RF), a convolutional neural network (CNN), and a hybrid CNN–Meta model for radionuclide identification and quantitative activity analysis under conditions of raw energy spectra, Z-score normalization, and min-max normalization. To maximize the potential of each model, principal component selection, Bayesian hyperparameter optimization, iteration tuning, and meta-learning optimization were employed. Model performance was comprehensively evaluated using the coefficient of determination (R²), root mean square error (RMSE), mean relative error (MRE), and computational time. The results demonstrate that deep learning models can effectively capture nonlinear relationships within complex energy spectra, enabling accurate radionuclide identification and activity quantification. Specifically, the CNN achieved a globally optimal test RMSE of 0.00566 and an R² of 0.999 with raw energy spectra. CNN–Meta exhibited superior adaptability and generalization under min-max normalization, reducing test error by 70.8% compared to RF, while requiring only 49% of the total computation time of the CNN model. RF was relatively insensitive to preprocessing but yielded higher absolute errors, whereas PLSR was limited by its linear nature and failed to capture the nonlinear characteristics of complex energy spectra. In conclusion, the CNN–Meta hybrid model demonstrates superior performance in both accuracy and efficiency, providing a reliable and effective approach for the rapid identification of radionuclides and quantitative analysis of activity in complex energy spectra. Full article

Chemical modification is a valuable strategy for tuning enzyme functionality by introducing new reactive groups without disrupting the overall fold. Conventional amination using ethylenediamine (EDA) is effective, but the resulting modified proteins show limited reactivity for conjugation at neutral pH, and the modifier itself poses safety concerns due to its volatility and corrosive nature. Dihydrazides, in contrast, offer a safer and more versatile alternative: they operate through the same carboxyl-activation mechanism while enabling systematic investigation of chain-length effects. In this study, Thermomyces lanuginosus lipase (TLL) and Myceliophthora thermophila laccase (MTL) were modified using dihydrazides with different alkyl chain lengths (carbonyl (CZ), oxalyl (OX), succinyl (SC), and adipic (AA)), and compared to EDA-modified and unmodified enzymes to evaluate their effects on catalytic performance. Hydrazide-modified variants exhibited enhanced catalytic performance, reaching up to 2.5-fold (TLL-CZ) and 4.2-fold (MTL-AA and MTL-OX) higher efficiencies than unmodified and EDA-modified enzymes. Notably, AA provided the most consistent improvement across both enzymes (1.3-fold in TLL and the best in MTL). Molecular dynamics and docking analyses supported these findings, linking increased flexibility (higher RoG and RMSF) with higher k_cat, and changes in substrate binding with lower k_m. Overall, hydrazide-based modification broadens the spectrum of enzyme variants attainable through amination, while offering safer procedures, thus representing an alternative that overcomes the limitations of using EDA as a conventional aminating agent. Full article

Recent advances in microbial bioremediation have significantly enhanced the effectiveness of wastewater management, offering innovative and sustainable alternatives to conventional treatment methods. Microorganisms, including bacteria, fungi, and algae, are increasingly recognized for their remarkable ability to degrade, transform, and remove a broad spectrum of pollutants such as organic compounds, heavy metals, and emerging contaminants from wastewater. Cutting-edge research has led to the development of novel approaches such as bioaugmentation, bio-stimulation, and the use of genetically engineered microbes, which have improved the efficiency, specificity, and resilience of bioremediation processes. The application of microbial consortia and advanced bioreactor designs further optimizes pollutant removal under diverse environmental conditions. Additionally, omics technologies and systems biology are providing deeper insights into microbial community dynamics and metabolic pathways, enabling the fine-tuning of bioremediation strategies for targeted outcomes. Despite ongoing challenges related to scalability, environmental variability, and regulatory considerations, these advances are paving the way for more robust, cost-effective, and eco-friendly wastewater management solutions. Overall, the integration of innovative microbial technologies holds great promise for addressing global water quality challenges and promoting environmental sustainability. Full article

Ballistocardiography (BCG) is a noninvasive modality for detecting cardiac activity. This study developed a robust atrial fibrillation (AF) detection algorithm using multiple BCG sensors at different locations and evaluated the improvement in accuracy by combining data from multiple sensors. We recorded the BCG using a piezoelectric rubber sheet sensor and an electrocardiogram in 84 participants (29 with AF and 55 without AF) in the supine position. Four BCGs (BCG1–4) were obtained using sensors placed from the head to the lumbar region (0, 25, 45, and 65 cm from the head). The BCG signals were divided into 32 s blocks and analyzed. After applying fast Fourier transform, we input the power spectrum, focusing on frequencies below 10 Hz, into machine learning (ML) classifiers to distinguish between AF and non-AF with parameter tuning. The AdaBoost classifier for BCG2 exhibited the highest accuracy (0.88) among the ML models for each sensor. When we applied the classifier to other BCGs, it achieved accuracies of 0.92, 0.73, and 0.78 for BCG1, 3, and 4, respectively. The combined model using multiple sensors exhibited an accuracy of 0.92. The optimized model for BCG2 was robust against shifts in the sensor toward the head and lumbar directions. A combined assessment using multiple sensors improved performance. Full article

The growing demand for compact, low-power infrared (IR) sensors necessitates advanced solutions for on-chip spectral selectivity, particularly for integration with Thermal Metal-Oxide-Semiconductor (TMOS) devices. This paper investigates the design and analysis of CMOS-compatible metal–insulator–metal (MIM) metamaterial absorbers tailored for selective absorption in the long-wave infrared (LWIR) region. We present a design methodology utilizing an equivalent-circuit model, which provides intuitive physical insight into the absorption mechanism and significantly reduces computational costs compared to full-wave electromagnetic simulations. An important rule in this design methodology is demonstrating how the resonance wavelength of these absorbers can be precisely tuned across the LWIR spectrum by engineering the geometric parameters of the top metallic patterns and, critically, by optimizing the dielectric substrate’s refractive index and thickness, which assist in designing small period MIM absorber units which are important in infrared thermal sensor pixels. Our results demonstrate that the resonance wavelength of these absorbers can be precisely tuned across the LWIR spectrum by engineering the geometric parameters of the top metallic patterns and by optimizing the dielectric substrate’s refractive index and thickness. Specifically, the selection of silicon as the dielectric material, owing to its high refractive index and low losses, facilitates compact designs with high-quality factors. The transmission line model provides intuitive insight into how near-perfect absorption is achieved when the absorber’s input impedance matches the free-space impedance. This work presents a new approach for the methodology of designing MIM absorbers in the mid-infrared and long-wave infrared (LWIR) regions, utilizing the intuitive insights provided by equivalent circuit modeling. This study validates a highly efficient design approach for high-performance, spectrally selective MIM absorbers for LWIR radiation, paving the way for their monolithic integration with TMOS sensors to enable miniaturized, cost-effective, and functionally enhanced IR sensing systems. Full article

Biomimetic lipid platforms provide versatile tools for mimicking various types of biological membranes and enable investigation of how industrially important amphiphiles (e.g., permeation enhancers and surfactants) interact with different membrane compositions. For example, antimicrobial lipids such as medium-chain fatty acids (FAs) and monoglycerides (MGs) are promising antibiotic alternatives that disrupt bacterial membranes and their distinct mechanisms of action are a topic of ongoing interest. The potency and targeting spectrum of individual antimicrobial lipids vary and mixing different lipids can improve functional activities. Biophysical studies indicate that optimally tuned mixtures exhibit greater disruption of synthetic lipid bilayers; however, their activity against more complex bacterial membrane compositions is largely unexplored. Herein, we applied electrochemical impedance spectroscopy (EIS) to investigate how two MG/FA pairs—composed of 10-carbon long monocaprin (MC) with capric acid (CA) and 12-carbon long glycerol monolaurate (GML) with lauric acid (LA)—disrupt tethered lipid bilayers composed of Escherichia coli bacterial lipids. While MC and CA individually inhibit E. coli, MC/CA mixtures at intermediate ratios displayed synergistic membrane-disruptive activity. Mechanistic studies showed that this synergistic activity depends on the MC/CA molar ratio rather than total lipid concentration. In contrast, GML/LA mixtures had weak membrane interactions across all tested ratios and lacked synergy, which is consistent with their low activity against E. coli. Together, the EIS results reveal that an effective disruption synergy against target membranes can arise from combining individually active antimicrobial lipids with distinct membrane-interaction profiles, laying the foundation to develop potent antimicrobial lipid formulations for tackling antibiotic-resistant bacteria. Full article