Search Results (245)

The autism spectrum disorder-associated SCN2A, ANK2, and SHANK-family genes encode molecularly distinct proteins that converge functionally on dendritic integration. Recent work established that ankyrin-B, encoded by ANK2, acts as an obligate dendritic scaffold for NaV1.2, encoded by SCN2A, in neocortical pyramidal neurons. Loss of this module mislocalizes dendritic NaV1.2, reduces dendritic Na⁺ influx, weakens backpropagating action potentials, and impairs synaptic maturation and long-term potentiation. SHANK proteins organize a complementary postsynaptic receptor scaffold within dendritic spines, coupling N-methyl-D-aspartate (NMDA), α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA), and metabotropic glutamate receptor (e.g., mGluR5) signaling to the actin cytoskeleton through layered PSD-95/GKAP/Homer interactions. Disruption of this scaffold can destabilize excitatory transmission, spine morphology, and plasticity. We propose that these dendritic shaft and spine-associated modules jointly regulate dendritic input–output gain and that their disruption may contribute to autism spectrum disorder by destabilizing, rather than uniformly shifting, excitatory integration across cortico-subcortical circuits relevant to sensory reactivity, behavioral flexibility, and social-valence processing. Here, we review the cortical evidence for this layered dendritic convergence and evaluate its potential relevance beyond the cortex. We assess the striatum, thalamus, and amygdala as subcortical sites where related dendritic scaffolding mechanisms may operate. The striatum provides the strongest current test case, with established roles for both NaV1.2 and SHANK3 in medium spiny neuron physiology and corticostriatal connectivity. Thalamic and amygdalar extensions are supported mainly by SHANK-related circuit and channelopathy data but lack direct evidence for ANK2–SCN2A involvement. The framework is experimentally testable: conditional Ank2 deletion in striatal, thalamic, and amygdalar cell types; dendritic Na⁺/Ca²⁺ imaging across Scn2a, Ank2, and Shank3 models; adult rescue experiments; and genetic-interaction designs would determine whether ankyrin-B supports dendritic excitability beyond the cortex and whether these genes converge on, rather than merely parallel, dendritic input–output gain. Validation in human subcortical tissue would then establish whether this dendritic scaffolding logic represents a shared point of convergence through which genetically distinct autism spectrum disorder-risk variants alter circuit function. Full article

UAV-assisted communication is widely regarded as a key component of next-generation Space-Air-Ground Integrated Networks (SAGINs), where integrated sensing and communication (ISAC) further drives the demand for accurate and reliable channel modeling. Terahertz (THz) communications are particularly attractive for UAV platforms, offering ultra-high data rates and physically secure transmission. However, the physical heterogeneity between reflection and scattering mechanisms in THz UAV channels poses significant modeling challenges, as conventional unified approaches tend to introduce energy distribution distortion and non-stationary prediction errors. To address this, we propose a 3D non-stationary geometry-based stochastic model (GBSM) based on an ellipse-sphere hierarchical geometric framework, where reflection paths are confined to ground-plane ellipses and scattering paths are distributed over spatial spheres. The model accounts for atmospheric molecular absorption, multipath fading, and non-stationarity induced by random 3D UAV trajectories. A cluster birth-death mechanism is introduced to capture the time-varying evolution of scattering clusters. Key statistical properties, including the temporal auto-correlation function (T-ACF), spatial cross-correlation function (S-CCF), and Doppler power spectral density (DPSD), are derived and analyzed. Simulation results agree well with theoretical derivations, validating the proposed model and providing practical guidance for THz UAV-ISAC system design. Full article

Super high-rise buildings are increasingly equipped with structural monitoring systems to track deformation responses during construction and operation, thereby supporting structural condition assessment and engineering management. To address key monitoring challenges, including GNSS multipath interference, insufficient vertical accuracy, accelerometer integration drift, and high-frequency noise, this study proposes a GNSS/accelerometer fusion monitoring method based on whale optimization algorithm–optimized variational mode decomposition (WOA-VMD) and adaptive robust Kalman filtering (ARKF). Continuous three-hour GNSS and accelerometer observations collected from a super high-rise building under construction are used for fusion validation. The results show that WOA-VMD effectively separates noise from deformation-related signals and outperforms conventional EMD and standard VMD in denoising performance. Compared with the raw observations, the fused east, north, and vertical displacement RMSEs are reduced by 68.84%, 75.97%, and 60.22%, respectively; the SNRs increase to 22.03 dB, 21.38 dB, and 16.74 dB, respectively; the STDs decrease by 72.58%, 75.62%, and 68.39%, respectively; and the PSDs increase to 9.47 dB, 9.02 dB, and 8.31 dB, respectively. The proposed framework exhibits sub-centimeter-level displacement monitoring performance in the horizontal directions and significantly enhances the monitoring capability of the vertical component. The field validation results demonstrate the feasibility and effectiveness of the proposed framework for short-term deformation monitoring of super high-rise buildings under practical monitoring conditions and indicate its potential for structural health monitoring applications. Full article

Not all particle-size fractions in widely graded granular materials contribute equally to compaction densification. For non-ideal particle-size distributions (PSDs) with local deviations or fine-end disturbances, the full-range fractal index may be influenced by particle-size fractions that contribute weakly to densification and, therefore, may not consistently represent the maximum dry density response. To address this problem, this study proposes a response-constrained truncation framework to identify a more effective PSD fitting range for fractal characterization. First, 20 concave and S-shaped PSDs from previous experiments were re-analyzed to compare full-range and truncated indices. Then, 21 progressively truncated specimens derived from three standard fractal PSDs were tested by relative density experiments. A unit-mass densification contribution coefficient, η_j, was defined from adjacent maximum dry density differences and particle-fraction mass contents. The η_j-d responses exhibited unimodal patterns, and the transition diameter d_c shifted with PSD coarseness. For the two material sources, replacing the full-range index with the truncated index increased the R² values between the fractal index and maximum dry density from 0.195 to 0.886 and from 0.191 to 0.856, respectively. A continuous percentile search showed that the optimal characteristic scale was concentrated near q ≈ 30, with a robust common optimum of q = 30.53. Sensitivity analysis for β = 0.85–0.95 indicated that 0.225d₃₀ falls within the transition region from highly effective filling to reduced densification efficiency. Accordingly, d_L = 0.225d₃₀ is proposed as a preliminary engineering estimate of the lower fitting limit for non-ideal PSDs. The framework is intended for widely graded materials whose full-range fractal parameters are inconsistent with compaction response. Full article

Alpha/beta (α/β) pulse shape discrimination (PSD) in liquid scintillation counting (LSC) is fundamentally limited by the charge comparison method (CCM) at low energies, where the entire tritium (³H) beta spectrum resides (0–18.6 keVee). The CCM figure-of-merit drops below 0.6 in this region, rendering it inadequate for simultaneous tritium and natural uranium alpha monitoring in nuclear power plant (NPP) liquid effluents. We present a one-dimensional convolutional neural network (1D-CNN) trained on an 80,000-waveform physics-based simulation dataset using established scintillation parameters for Ultima Gold AB. The proposed network achieves 97.4% overall classification accuracy and an area under the receiver operating characteristic curve (AUC) of 0.9981 on the held-out test set, representing improvements of 13.8 percentage points and 0.046 AUC over CCM. In the critical 0–18.6 keVee region, CNN accuracy exceeds 95% compared to below 60% for CCM—a greater than 35 percentage point improvement. Pulse amplitude discrimination (PAD), evaluated as a preliminary screening method, exhibits a 6.3% alpha spillover rate into the beta window, exceeding the regulatory limit of 3%. Gradient-weighted class activation maps (Grad-CAM) confirm that the network exploits physically meaningful pulse features rather than simulation artefacts. A comprehensive background suppression strategy combining dual-SiPM coincidence (24× reduction), anti-coincidence guard detector (5.8× reduction), composite passive shielding (10× reduction), and CNN-assisted discrimination reduces the system equivalent background to 1.83 ± 0.12 cpm, yielding a tritium minimum detectable activity (MDA) of 0.21 Bq/mL (10 mL sample, 30 min count), which satisfies the GB 14587 reference limit of 0.5 Bq/mL. After 8-bit post-training quantisation, the model achieves sub-microsecond inference latency on an embedded Xilinx Artix-7 Field-programmable gate array(FPGA), enabling real-time deployment in portable online monitoring systems. Full article

We present a novel algorithm for the retrieval of non-spherical particle microphysical parameters (PMP) from 3β + 2α + 3δ optical data taken with multiwavelength lidar. The 3β + 2α + 3δ optical datasets describe particle backscatter coefficients (β) at three wavelengths, λ = 355, 532, and 1064 nm, particle extinction coefficients (α) at two wavelengths, λ = 355 and 532 nm, and particle linear depolarization ratios (PLDR, δ) at three wavelengths, λ = 355, 532, and 1064 nm. The algorithm can be used for retrieving bimodal particle size distributions (PSDs). The PSDs can comprise mixtures of spheres and spheroids (SS). One or both modes can comprise spheroid-shaped particles or spherically shaped particles. The spheroids are used for approximating an arbitrary ensemble of non-spherical particles. The algorithm works on the basis of a combination of direct and analytical inversion methods. The algorithm uses the spheroid reference look-up table (RLUT) we developed and presented in part 1 of our research work. The algorithm uses constraints regarding the particle complex refractive index (CRI) and information on relative humidity (RH) in the atmosphere (in the case of aerosol lidar observation) for suppressing retrieval uncertainties. We carried out a numerical simulation study to evaluate the algorithm’s performance. In these numerical simulations, we considered perturbed synthetic 3β + 2α + 3δ optical data that mimic different organic carbon (OC)–dust (D) mixtures. Such mixtures are suitable examples for describing bimodal PSDs that consist of a fine mode of spherical particles and a coarse mode of non-spherical particles. The results of the numerical simulation show that (1) the PMPs of each mode of these particle mixtures can be found separately, (2) the mean retrieval errors of the effective radius, number, surface-area, and volume concentrations of these mixtures are 25%, 52%, 9%, and 28%, respectively, and (3) the mean retrieval error of single-scattering albedo (SSA) at 355 nm of these mixtures is as low as ±0.02. SSA retrieval accuracies at 532 and 1064 nm degrade because the complex refractive index (CRI) of OC and D particles depends on the measurement wavelength. In future studies, we will upgrade the algorithm such that it takes into account a spectrally dependent CRI. We also compare the results of our novel algorithm with our TiARA2.1 algorithm. The errors obtained from the TiARA2.1 algorithm are approximately three times larger compared to the errors we obtain with our novel ATLAS algorithm for the case of the OC-D mixtures considered in the present study. We explain the higher accuracy of the PMP retrievals by the use of three PLDRs and the extra constraints placed on CRI and RH. Full article

In rock tunnel engineering, the discontinuity roughness plays a crucial role in rock mass stability. Nevertheless, no suitable parameter is currently available to describe the multi-scale morphological complexity associated with varying shear directions. Improved 1D and 3D fractal dimensions are proposed and systematically evaluated for their ability to characterize the anisotropy and morphology complexity. The results show that the trends of improved 1D and 3D fractal dimensions are consistent with the Grasselli parameter GP(θ) along the orthogonal direction of the major and minor axes, which effectively characterizes the anisotropy. Meanwhile, they effectively characterize the profile and discontinuity morphology complexity, respectively. On the basis of the evaluation results, a comprehensive parameter is proposed to quantify directional multi-scale morphological complexity, which characterizes the discontinuity anisotropy, local roughness, and global morphological complexity simultaneously. Furthermore, the proposed parameter is compared with GP(θ) based on the Huashansong tunnel, using the concordance correlation coefficient and macro-trend similarity as evaluation criteria. The results show a moderate correlation between the two parameters, with a concordance correlation coefficient of 0.716. The overall similarity score of 85.8 and the macroscopic trend similarity of 71.0 indicate strong consistency in morphological features. In addition, the RMSE and MAE of γ(θ) are 0.25 and 0.19, which is lower than that of the existing GP(θ) and PSD-based methods in the study of characterizing the dominant morphology complexity direction. Full article

Primary neuronal cultures from the brain are critical for investigating disease-specific cellular and molecular mechanisms in mouse models. Current methods for obtaining primary cultures require embryonic brains that are affected by embryonic lethality and genotypic characterization in severe disease models such as sickle cell disease (SCD). Furthermore, these neuronal cultures require about 14 days in vitro (DIVs) for neurite outgrowth to mature. We adapted and optimized a relatively simplified and reproducible method using brains from postnatal day 1 mouse pups for isolating and culturing hippocampal and cortical neurons. This approach produces viable neurons that attach, extend neurites, and express key synaptic markers by 7 DIV and also minimizes glial outgrowth. We successfully applied this approach to isolating and culturing hippocampal and cortical neurons from the brains of one-day-old (P1) pups of humanized transgenic homozygous BERK sickle cell and control mice. Morphological observations at 3, 7, and 14 DIVs demonstrated robust neuronal attachment, neurite outgrowth, and overall structural development in both male and female hippocampal and cortical neurons. Neurons in culture expressed key markers including neuronal nuclear protein (NeuN/Rbfox3), neurofilament 200 (NF200), microtubule-associated protein 2 (MAP2), vesicular glutamate transporter 1 (VGLUT1), postsynaptic density protein 95 (PSD 95), and glutamate N-methyl-D-aspartate receptor subunit 2B (GluN2B). Notably, male SCD hippocampal neurons evinced a higher density of PSD 95 puncta on dendritic spines compared to controls on 7 as well as 14 DIVs. Incubation of male hippocampal neurons in a sickle cell-like microenvironment with TNF-α and heme further increased the density of PSD 95 puncta and colocalization of GluN2B with PSD 95, supporting the utility of this culture system for examining disease-relevant structural and molecular responses. This optimized culture system provides a simplified and reproducible platform to investigate the mechanisms involving neuronal dysfunction in challenging mouse models of brain disorders. Full article

Coalbed methane (CBM) development is strongly controlled by pore structure evolution in deformed coals and its influence on hydraulic fracturing behavior. To clarify the multifractal characteristics of cross-scale pores and their control on fracturing effectiveness, this study investigated eight different deformation coals from the Ordos Basin using low-temperature CO₂/N₂ adsorption (LT-CO₂A/LT-N₂A) and high-pressure mercury intrusion porosimetry (HMIP). Micropores (<2 nm), mesopores (2–50 nm), and macropores (>50 nm) were systematically characterized, and their pore size distributions (PSDs) were quantitatively analyzed using the Coal Structure Index (CSI) and multifractal theory. The results indicate that the multifractal parameters of macropores are significantly distinct from those of mesopores and micropores, exhibiting lower H (0.824–0.893) and D₁ (0.766–0.853), and higher α₀ (1.422–1.541), ΔD (1.230–1.408), and Δα (1.459–1.642). Macropores controlled by tectonic deformation exhibit stronger heterogeneity compared to mesopores and micropores in local parts of the coal mass; PSD varies significantly with deformation rising, derived from the differential pore structure evolution during brittle–ductile transition and the multi-scale synergistic effects including maturity and composition. Combined with field fracturing curves, the results further indicate that the α₀, ΔD, and Δα of macropores are negatively correlated with breakdown pressure, with correlation coefficients of 0.51, 0.61, and 0.59, respectively, and that strong local heterogeneity of macropores favors fracture initiation and propagation and reduces breakdown pressure. Cataclastic coal is the most favorable for hydraulic fracturing, followed by undeformed coal, whereas granulated coal shows the poorest fracturing performance. Full article

The aim of this study is to evaluate the biomechanical response of a seated operator subjected to whole-body vibrations generated by two agricultural tractors with different propulsion systems: a diesel model (TD80D) and an electric prototype (TE-0). An integrated experimental–numerical approach was employed, combining triaxial accelerometer measurements under real operating conditions (constant speed of 5 km/h on unprepared terrain) with random vibration response simulations performed in Altair SimSolid. The excitation input for the numerical model was defined using frequency-dependent power spectral density (PSD) functions derived from experimentally measured acceleration signals and scaled to a representative global RMS value. The analysis focused on the distribution of mechanical stress in key anatomical regions of a virtual human dummy in a seated posture, including the foot sole, knee, lumbar region, and head. The results indicate that, under the analysed conditions, the electric tractor (TE-0) exhibits improved vibration attenuation, leading to significant reductions in mechanical stress across all analysed regions, with decreases of up to 56.3% at the foot sole, 50.0% at the knee, 53.3% in the lumbar region, and 91.1% at the head compared to the diesel tractor (TD80D). These findings highlight the relevance of integrating experimental measurements with numerical simulation for assessing operator exposure to vibrations and suggest that electric tractor configurations may provide improved biomechanical comfort under the analysed operating conditions. Full article

In this study, an active vibration control (AVC) strategy is effectively used on a system made of a honeycomb polymer–paper core and carbon fiber-reinforced polymer (CFRP) plates. A cost-effective and practical solution based on an AVC system has been developed and tested using a microcontroller unit (MCU) from Texas Instruments. The control system is studied by applying out-of-plane disturbances to the composite panel via an electrodynamic shaker, by exciting the identified mode shapes obtained through experimental modal analysis, i.e., impact tests. The actuator chosen for the AVC system is a Macro Fiber Composite (MFC) patch. Multiple analog signal processing circuits were developed to scale and shift the signal at the input and output of the MCU. The proposed control algorithm is based on a positive position feedback (PPF) technique. Modal analysis was performed to identify the natural frequencies and mode shapes of the structure, which are essential for the design and tuning of the modal-based PPF controller. This analysis also enabled optimal sensor and actuator placement, ensuring effective targeting and control of the dominant vibration modes. Then, a series of tests were performed using pure sine excitations at frequencies of interest, close to the 2nd and 8th mode at 25.13 Hz and 129 Hz, respectively. The results of the experiments revealed a velocity attenuation of 55.8% to 76.9% and a Power Spectral Density (PSD) attenuation of 5.8 dB to 12.8 dB, depending on the mode under study. Owing to the size and mass properties of the Macro Fiber Composite (MFC) patches, the control system is very much suitable for automobile and aerospace applications. Full article

The coupling of non-stationary marine noise and complex ship-radiated signals makes high-fidelity signal recovery exceptionally difficult. Existing deep learning methods often prioritize objective metrics, such as the Scale-Invariant Signal-to-Noise Ratio (SI-SNR), but fail to maintain the integrity of narrow-band line spectral data. We propose FocuS-MN, an end-to-end framework that combines learnable resampling with Feedforward Sequential Memory Network (FSMN)-based temporal modeling for precise waveform reconstruction. The model is optimized using a two-stage training strategy to ensure stable magnitude estimation and waveform consistency. On the ShipsEar dataset, FocuS-MN shows strong generalization to unseen vessel types. At a −5 dB Signal-to-Noise Ratio (SNR), it achieves a Signal-to-Distortion Ratio (SDR) of 3.77 dB and a Segmental Signal-to-Noise Ratio (SSNR) of 3.83 dB. Power Spectral Density (PSD) analysis further confirms that FocuS-MN recovers fine-grained line spectral structures, proving its effectiveness in both noise suppression and signal fidelity. Full article

Traumatic brain injury (TBI) can lead to persistent adverse outcomes, including cognitive and emotional dysfunction, with recent estimates indicating that up to 50% of individuals with mild TBI experience long-term symptoms. Growing evidence suggests that biological sex influences TBI outcomes and recovery trajectories; however, the molecular underpinnings driving these sex-specific differences remain poorly understood. In this study, a preclinical TBI model was used to directly compare chronic glutamatergic alterations in adult male and female Sprague Dawley rats. To define frontocortical molecular signatures associated with sex-specific glutamatergic dysfunction, proteomic analyses were conducted. Proteomic data revealed dysregulation of key pathways, cellular processes, and molecular regulators involved in excitatory signaling and synaptic function in both sexes. Biomarker profiling identified a single common biomarker between males and females, along with multiple biomarkers unique to each sex. Furthermore, two key brain regions highly susceptible to TBI, the prefrontal cortex and hippocampal subregions, were examined for chronic alterations in key glutamatergic signaling proteins, including N-methyl-D-aspartate (NMDA) receptors and the excitatory synaptic marker postsynaptic density protein 95 (PSD95). Immunofluorescence analyses revealed both sex- and region-specific alterations in the expression of NMDA receptor subunits, as well as in PSD95. Notably, many of these changes were concentrated within the hippocampal subregions, suggesting long-term dysregulation of hippocampal glutamatergic circuitry following injury. Together, these findings indicate the emergence of chronic sex-specific pathophysiology in glutamate signaling after TBI and highlight the importance of incorporating sex as a biological variable in the development of precision medicine-based therapeutic strategies for TBI. Full article

Noise degrades both EEG and gait signals, and classical IIR filters (Butterworth, Chebyshev, elliptic) involve trade-offs between passband flatness, ripple, and roll-off. This study compared a novel exponential “Reza” filter with these designs for neural and locomotor data. We analyzed an open-source mobile brain–body imaging dataset with EEG and gait data from 49 healthy adults (EEG: $256$-channel, $512 \mathrm{H}\mathrm{z}$; IMUs: six APDM Opals, $128 \mathrm{H}\mathrm{z}$). EEG channels were grand-averaged and band-pass filtered at $0.5–50 \mathrm{H}\mathrm{z}$, while IMU axes were averaged and band-pass filtered at $0.5–5 \mathrm{H}\mathrm{z}$. The outcomes were signal-to-noise ratio SNR (dB) and band-integrated Welch PSD ($\mathrm{E}\mathrm{E}\mathrm{G}:0.5–50 \mathrm{H}\mathrm{z}; \mathrm{I}\mathrm{M}\mathrm{U}:0.5–5 \mathrm{H}\mathrm{z}$). Repeated-measures ANOVAs tested the effect of filter types (Butterworth, Chebyshev I, elliptic, Reza) with Bonferroni-adjusted post hoc tests for the six pairwise filter comparisons (${\alpha }_{adj} = 0.0083$). We reported partial eta-squared ($\eta p^2$) as the ANOVA effect size. For EEG, PSD did not differ among filters $(p = 0.146)$, whereas SNR differed strongly $(p<0.001)$: Chebyshev and elliptic yielded the highest mean SNR and did not differ from each other, while both exceeded Butterworth, Reza was the lowest. For IMU, both SNR $(p< 0.001$) and PSD $(p< 0.001)$ differed: Reza produced the highest mean SNR (significantly exceeding elliptic and Chebyshev), while Butterworth exceeded Chebyshev; meanwhile, IMU PSD showed a clear ordering with Reza retaining the most motion-band power, followed by Butterworth, then Chebyshev, with elliptic retaining the least. These results showed that filter choice materially shapes EEG and gait outcomes. For EEG, Chebyshev maximized SNR, while elliptic and Reza maintained comparable fidelity. For IMU gait signals, Reza matched Butterworth for denoising and preserved more signal power. Therefore, filter choice should be guided by the target outcome (SNR vs. band power) rather than a single default design. Full article

Background/Objectives: Alzheimer’s disease (AD) involves amyloid-β (Aβ) deposition, oxidative stress, and neuroinflammation, leading to cognitive decline. Nobiletin, a citrus-derived polymethoxylated flavonoid, exerts antioxidant and anti-inflammatory effects. This study explored its neuroprotective mechanisms in the 5XFAD mouse model. Methods: Male 5XFAD and C57BL/6J mice received oral nobiletin (20 or 40 mg/kg/d) for 4 weeks. Cognitive function was assessed by the Y-maze test. Amyloid-β burden was quantified by Congo red staining and ELISA. Serum cytokine levels and antioxidant enzyme activities were measured by ELISA. Western blotting and RT-PCR were used to assess proteins and genes related to amyloidogenesis, inflammation (TLR4/MyD88/NF-κB), mitochondrial biogenesis (AMPK/SIRT1/PGC-1α), and synaptic plasticity (PI3K/Akt–CREB–BDNF). Results: Nobiletin improved working memory, reduced amyloid-β40/42 deposition, and downregulated APP, BACE1, and PS1 expression, while enhancing ADAM10 expression. It lowered serum IL-6, IL-1β, and TNF-α, increased SOD, CAT, and GPx activities, and suppressed TLR4/MyD88/NF-κB signaling. Furthermore, it activated AMPK/SIRT1/PGC-1α and NRF2 pathways, enhancing antioxidant defenses, and promoted PI3K/Akt–CREB–BDNF signaling, increasing PSD95 and synaptophysin. Conclusions: Nobiletin exerts strong neuroprotective and antioxidant effects by targeting multiple signaling cascades, mitigating amyloid pathology and neuroinflammation, and improving synaptic plasticity. It represents a promising therapeutic agent against AD. Full article