Search Results (10,891)

Background/Objectives: Patient Support Programs (PSPs) are increasingly used to support treatment adherence and continuity of care in chronic, high-cost conditions. In hemophilia A, consistent prophylaxis is essential to prevent bleeding episodes and long-term joint damage. In Mexico, disparities in access to treatment have encouraged the development of public–industry collaborative models. The objective of this study was to describe the structure, implementation, and operational characteristics of a PSP delivering home-based prophylactic treatment for individuals with hemophilia A in Mexico, and to compare annual bleeding rates according to factor VIII dosing adequacy. Methods: A cross-sectional, retrospective analysis was conducted using fully anonymized operational data from the PSP registry between January 2023 and March 2024. Variables included infusion location and administrator, prescribed and used doses, weekly infusion frequency, program incorporation and discontinuation, geographic coverage, and bleeding events. Annual bleeding rates were compared across dosing categories using Poisson regression models with patient-years as an offset. Results: A total of 1173 patients contributed 16,331 infusion records. Participants were predominantly male (99.8%), with a median age of 26 years; 71.8% had severe hemophilia. Home infusion accounted for 92.0% of administrations, primarily self-administered or caregiver-delivered. The median prescribed and used monthly doses were 18,000 IU and 16,000 IU, respectively, with dose concordance observed in 66.8% of records. Only 40.7% of patients achieved the recommended prophylactic frequency of three infusions per week. Geographic coverage increased from 62.5% to 71.9% of states. The overall annualized bleeding rate was 2.24 bleeds per patient-year. When stratified by dosing adequacy, patients receiving doses consistent with clinical recommendations showed the lowest bleeding rate (0.18 bleeds per patient-year), compared with those with overdosing (3.84) and underdosing (6.68), with statistically significant differences between groups. Knees, elbows, and ankles were the most frequently affected sites. Conclusions: This PSP achieved broad national reach and high adoption of home-based infusion. The observed dose-dependent differences in bleeding rates underscore the clinical relevance of appropriate prophylactic dosing within structured support programs and support the value of PSPs in strengthening treatment continuity in middle-income settings. Full article

This paper presents the development of a robot-oriented Distributed Acoustic Sensing (DAS) system designed for environmental monitoring and hazard detection on ground robotic platforms. Unlike conventional DAS solutions primarily intended for stationary or quasi-stationary infrastructures, the proposed approach explicitly accounts for robot-induced mechanical vibrations, mobility constraints, and limited onboard resources. A dedicated anti-jitter signal processing pipeline combined with edge-based data processing is introduced to suppress motion-induced strain components while preserving weak external acoustic signals. The system integrates optical fiber deployment along the robot structure using flexible guides and vibration-isolated clamps, ensuring stable mechanical coupling under continuous motion. Experimental validation, including laboratory tests and preliminary outdoor field trials, demonstrates reliable detection of acoustic events in the 10–200 Hz frequency range, with reduced processing latency of 80–100 ms and a detection reliability of up to 95%. Comparative analysis with conventional sensors confirms the advantages of the proposed DAS-based approach in terms of sensitivity, spatial coverage, and robustness. The results demonstrate the feasibility and effectiveness of DAS technology for real-time sensing applications on mobile robotic platforms. Full article

Background: Botulinum toxin type A (BoNT-A) is an established preventive therapy for chronic migraine (CM), yet the accompanying neurochemical changes remain incompletely characterized. Objective: To evaluate the effects of BoNT-A on plasma substance P (SP), γ-aminobutyric acid (GABA), glutamate, glutamine, and 5-hydroxytryptamine (5-HT), and on urinary 5-HT, and to explore relationships with clinical outcomes. Methods: In this prospective study, plasma neurotransmitters were analyzed in CM patients (n = 31) at baseline and one month after BoNT-A (155 U; PREEMPT protocol) and in healthy controls (n = 30). Plasma SP was measured using enzyme-linked immunosorbent assay (ELISA); plasma GABA, glutamate, and glutamine were quantified via liquid chromatography–tandem mass spectrometry (LC–MS/MS) with isotopically labeled internal standards; plasma and urinary 5-HT were determined by high-performance liquid chromatography (HPLC). Clinical outcomes included monthly headache frequency, Visual Analog Scale (VAS), and Migraine Disability Assessment (MIDAS). Statistical analyses applied appropriate parametric or non-parametric tests with p < 0.05 considered significant. Results: One month post-BoNT-A, headache frequency, MIDAS, and VAS were significantly reduced (all p < 0.001). SP levels were significantly higher after BoNT-A than at baseline and versus controls. Plasma 5-HT increased post-BoNT-A, while urinary 5-HT decreased. Plasma GABA was elevated in patients versus controls without statistical significance. Glutamine was significantly higher before treatment, whereas the Glu/Gln ratio increased after BoNT-A. Correlations revealed that higher GABA was associated with lower VAS and attack frequency post-treatment. Conclusions: BoNT-A provided short-term clinical improvement with distinct neurochemical changes, including increased plasma SP and 5-HT, decreased urinary 5-HT, reduced glutamine, and a higher Glu/Gln ratio. These biomarkers, particularly Glu/Gln, may serve as indicators of cortical excitability and therapeutic response in CM. Full article

Infrasound, physically defined as sound at frequencies below 20 Hertz, can travel long distances with minimal attenuation and permeate biological tissues due to its marked particle displacement and deep penetration. Generated by both natural phenomena and human-made systems, infrasound has drawn increasing scientific and public attention regarding its potential physiological and psychological effects. Experimental studies demonstrate that infrasound can modulate mechanosensitive structures at the cellular level, particularly pressure-sensitive ion channels such as PIEZO1 and TRPV4, leading to intracellular calcium influx, oxidative stress, altered intercellular communication, and in some settings, apoptosis. These responses vary according to sound pressure levels, frequencies, exposure duration, and tissue type. In the cardiovascular system, higher sound pressures have been associated with mitochondrial injury and fibrosis, whereas low sound pressures may exert context-dependent protective effects. In animal models, prolonged or intense exposure to infrasound has been shown to induce neuroinflammatory responses and memory impairment. Short-term studies in humans at moderate intensities have reported minimal physiological changes, with psychological and contextual factors influencing symptom perception. Occupational environments such as factories and agricultural settings may contain elevated levels of infrasound, underscoring the importance of systematic measurements and exposure assessments. At the same time, controlled infrasound stimulation has shown potential as an adjunct modality in bone repair and tissue regeneration, highlighting its dual capacity as both a biological stressor and a possible therapeutic tool. Overall, existing data indicate that infrasound may be harmful at chronic exposure depending on intensity and frequency, yet beneficial when precisely regulated. Future research should standardize exposure metrics, refine measurement technologies, and clarify dose–response relationships to better define the health risks and therapeutic applications of infrasound. Full article

Background and Objectives: Controlled breathing can influence autonomic regulation and haemodynamics; however, the role of its timing relative to exercise remains unclear. Materials and Methods: Fourteen healthy, physically active men (mean age 21.8 ± 0.7 years; body mass index within the normal range) participated in this randomised crossover study. Each session consisted of five 5 min cycling bouts at 50% of heart-rate reserve, interspersed with 3 min passive recovery periods. The three conditions were: control (no structured breathing), 30 s hyperventilation (approximately 30 breaths·min⁻¹) performed before each bout, and the same hyperventilation performed after each bout. Resting heart rate variability spectra (low-frequency [LF], high-frequency [HF]) were assessed pre- and post-session; arterial blood pressure was measured stage-wise; quadriceps muscle oxygen saturation (StO₂) was monitored using near-infrared spectroscopy; and a discriminant co-integration index (Dsk) was calculated to integrate multisystem responses. Results: Compared with baseline, LF power increased and HF power decreased after exercise in the control and post-exercise hyperventilation conditions (p < 0.05), whereas pre-exercise hyperventilation attenuated these shifts. Post-exercise hyperventilation blunted the rise in systolic blood pressure and reduced diastolic blood pressure compared with control (p < 0.05). Both breathing interventions accelerated StO₂ recovery, with higher early recovery StO₂ following pre-exercise hyperventilation and sustained advantages after post-exercise hyperventilation (moderate-to-extensive effects). Dsk values were consistently highest after exercise, indicating stronger and more coherent multisystem coupling. Conclusions: In this acute crossover study of healthy young men, hyperventilation performed before or after exercise induced distinct short-term cardiovascular and muscular responses, reflecting respiratory-driven modulation of haemodynamic and autonomic processes. The timing of hyperventilation influenced these responses, suggesting that deliberate hyperventilation may acutely modify exercise-related regulatory mechanisms. Full article

Sentiment analysis of Arabic text, particularly on social media platforms, presents a formidable set of unique challenges that stem from the language’s complex morphology, its numerous dialectal variations, and the frequent and nuanced use of emojis to convey emotional context. This paper presents SiAraSent, a hybrid framework that integrates traditional text representations, emoji-aware features, and deep contextual embeddings based on Arabic transformers. Starting from a strong and fully interpretable baseline built on Term Frequency–Inverse Definition Frequency (TF–IDF)-weighted character and word N-grams combined with emoji embeddings, we progressively incorporate SinaTools for linguistically informed preprocessing and AraBERT for contextualized encodings. The framework is evaluated on a large-scale dataset of 58,751 Arabic tweets labeled for sentiment polarity. Our design works within four experimental configurations: (1) a baseline traditional machine learning architecture that employs TF-IDF, N-grams, and emoji features with an Support Vector Machine (SVM) classifier; (2) an Large-language Model (LLM) feature extraction approach that leverages deep contextual embeddings from the pre-trained AraBERT model; (3) a novel hybrid fusion model that concatenates traditional morphological features, AraBERT embeddings, and emoji-based features into a high-dimensional vector; and (4) a fully fine-tuned AraBERT model specifically adapted for the sentiment classification task. Our experiments demonstrate the remarkable efficacy of our proposed framework, with the fine-tuned AraBERT architecture achieving an accuracy of 93.45%, a significant 10.89% improvement over the best traditional baseline. Full article

This meta-analysis investigates the acute (immediate) pre–post changes in the modulation of physiological tremor in healthy adults following physical exercise, including resistance-based protocols. Physiological tremor is characterized by low-amplitude, high-frequency oscillations during posture or movement and reflects transient changes in neuromuscular control. Background/Objectives: Quantify the pooled effect of physical exercise on physiological tremor amplitude in healthy adults using magnitude-based metrics (RMS, peak power). A secondary objective was to synthesize evidence from acute resistance-based protocols separately. Methods: This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines and followed the methodological framework outlined in the Cochrane Handbook for Systematic Reviews of Interventions. Thirteen experimental studies met the inclusion criteria, with eleven included in the general exercise analysis and eight in the acute resistance-based subset. Results: Random-effects models revealed a moderate reduction in tremor amplitude following acute exercise (Hedges’ g = −0.42, p < 0.001). The resistance-based synthesis was restricted to acute single-session protocols only and indicated a directionally consistent reduction in tremor amplitude. Conclusions: These findings suggest that physical exertion is associated with transient suppression of physiological tremor amplitude. Acute single-session resistance-based exercise protocols showed a consistent direction of effect, although pooled estimates should be interpreted cautiously due to heterogeneity. Overall, physiological tremor may serve as a sensitive, non-invasive outcome measure reflecting short-term neuromuscular state. Full article

The tropospheric zenith wet delay (ZWD) serves as a pivotal parameter for atmospheric water vapour inversion. By converting it into precipitable water vapour, high-temporal-resolution atmospheric humidity monitoring becomes feasible, providing crucial support for enhancing short-term rainfall forecast accuracy. However, ZWD exhibits significant non-stationarity due to complex influencing factors, and traditional models struggle to achieve precise predictions across all scenarios owing to limitations in local feature extraction. This article employs a ZWD prediction method based on the dynamic temporal decomposition module of TimesNet, re-constructing one-dimensional high-frequency ZWD time series into two-dimensional tensors to overcome the technical limitations of conventional models. Comprehensively considering topographical characteristics, climatic features, and seasonal factors, experiments were conducted using 30 s ZWD data from 20 IGS stations. This dataset comprised four consecutive days of PPP solutions for each season in 2023. Through comparative experiments with CNN-ATT and Informer models, the global prediction accuracy, seasonal adaptability, and topographical robustness of TimesNet were systematically evaluated. Results demonstrate that under the input-prediction window configuration where each can achieve the optimal accuracy, TimesNet achieves an average seasonal Root Mean Square Error (RMSE) of 5.73 mm across all seasonal station samples, outperforming Informer (7.89 mm) and CNN-ATT (10.02 mm) by 27.4% and 42.8%, respectively. It maintains robust performance under the most challenging conditions—including summer severe convection, high-altitude terrain, and climatically variable maritime zones—while achieving sub-5 mm precision in stable environments. This provides a reliable algorithmic foundation for short-term precipitation forecasting in Global Navigation Satellite System (GNSS) real-time meteorology. Full article

Automatic Music Transcription (AMT) plays a fundamental role in Music Information Retrieval (MIR) by converting raw audio signals into symbolic representations such as MIDI or musical scores. Despite advances in deep learning, accurately transcribing piano performances remains challenging due to dense polyphony, wide dynamic range, sustain pedal effects, and harmonic interactions between simultaneous notes. Existing approaches using convolutional and recurrent architectures, or autoregressive models, often fail to capture long-range temporal dependencies and global harmonic structures, while conventional Vision Transformers overlook the anisotropic characteristics of audio spectrograms, leading to harmonic neglect. In this work, we propose SpectTrans, a novel piano transcription framework that integrates a Spectral Gating Network with a multi-head self-attention Transformer to jointly model spectral and temporal dependencies. Latent CNN features are projected into the frequency domain via a Real Fast Fourier Transform, enabling adaptive filtering of overlapping harmonics and suppression of non-stationary noise, while deeper layers capture long-term melodic and chordal relationships. Experimental evaluation on polyphonic piano datasets demonstrates that this architecture produces acoustically coherent representations, improving the robustness and precision of transcription under complex performance conditions. These results suggest that combining frequency-domain refinement with global temporal modeling provides an effective strategy for high-fidelity AMT. Full article

Developmental dyslexia (DD) often involves difficulties in phonological processing of speech. Objectives: While underlying neural changes have been identified in terms of stimulus- and task-related responses within specific brain regions and their neural connectivity, there is still limited understanding of how these changes affect the overall organization of brain networks. Methods: This study used EEG and functional network analysis, focusing on small-world propensity across various frequency bands (from δ to γ), to explore the global brain organization during the auditory discrimination of words and pseudowords in children with DD. Results: The main finding revealed a systemic inefficiency in the functional network of individuals with DD, which did not achieve the optimal small-world propensity. This inefficiency arises from a fundamental trade-off between localized specialization and global communication. During word listening, the δ-/γ1-networks (related to impaired syllabic and phonemic processing of words) and the θ-/β-networks (related to pseudoword listening) in the DD group showed lower local clustering and connectivity compared to the control group, resulting in reduced functional segregation. In particular, the θ-/β-networks for words in the DD group exhibited a less optimal balance between specialized local processing and effective global communication. Centralized midline hubs, such as the postcentral gyrus (PstCG) and inferior frontal gyrus (IFG), which are crucial for global coordination, attention, and executive control, were either absent or inconsistent in individuals with DD. Consequently, the DD network adopted a constrained, motor-compensatory, and left-lateralized strategy. This led to the redirection of information flow and processing effort toward the left PstCG/IFG loop, interpreted as a compensatory effort to counteract automatic processing failures. Additionally, the γ1-network, which is involved in phonetic feature binding, lacked engagement from posterior sensory hubs, forcing this critical process into a slow and effortful motor loop. The γ2-network exhibited unusual activation of right-hemisphere posterior areas during word processing, while it employed a simpler, less mature routing strategy for pseudoword listening, which further diminished global communication. Conclusions: This functionality highlights the core phonological and temporal processing deficits characteristic of dyslexia. Full article

Background/Objectives: High-risk, low-frequency incidents such as building collapses and large chemical fires can result in acute, high-dose exposures to toxic agents for first responders and the surrounding community. While these exposures may last for hours to days, their contribution to firefighters’ risks for cancer and other diseases is relatively unknown. In February 2023, a freight train transporting chemicals derailed and caught fire in East Palestine, Ohio, US. More than 350 firefighters, primarily volunteer, responded to the incident. In this cross-sectional study, we evaluated epigenetic markers of toxicity in responding firefighters. We hypothesized that exposures from responding to the train derailment would alter the expression of microRNAs (miRNAs) linked to carcinogenesis. Methods: We enrolled 62 responding firefighters and a comparison group of 26 firefighters from the same region who did not respond to the incident. We measured the relative expression of 800 miRNAs in blood samples using the nCounter Human v3 miRNA expression panel. We compared the expression of miRNA between exposure groups in negative binomial regression models, adjusting for potential confounders. Results: At a false discover rate cut-off of 5% (q-value < 0.05), 16 miRNAs had significantly higher expression and one significantly lower among firefighters that responded to the incident. Top disease-related pathways in which these miRNAs were enriched included those relevant to neurodegenerative diseases, vascular disease, and multiple cancer sites. Conclusions: Overall, results suggest responding to one large incident can have non-transient impacts on miRNA expression. Whether this translates into longer-term health risks or adaptive responses to exposures is unclear. Full article

In video scene graph prediction, the aim is to capture structured object interactions that occur over time in dynamic visual content. While recent spatiotemporal attention-based models have improved performance, they often suffer from high computational costs and limited structural consistency across long sequences. Therefore, we developed a Fourier transformer mixer network (FTM-Net), a modular, frequency-aware architecture that integrates spatial and temporal modeling via spectral operations. It incorporates a resolution-invariant Fourier Mixer for global spatial encoding and a Fast Fourier Transform (FFT)-Net-based temporal encoder that efficiently represents long-range dependencies with less complexity. To improve structural integrity, we introduce a spectral consistency loss function that synchronizes high-frequency relational patterns between frames. Experiments conducted utilizing the Action Genome dataset demonstrate that FTM-Net surpasses previous methodologies in terms of both Recall@K and mean Recall@K while markedly decreasing parameter count and inference duration, providing an efficient, interpretable, and generalizable approach for structured video comprehension. Full article

Variance-driven decomposition based on the singular spectrum analysis of the European Project for Ice Coring in Antarctica (EPICA) δD, CO₂, and CH₄ records allowed a novel quantitative structural interpretation of all glacial/interglacial cycles and glacial terminations of the last 800 kyr. This bottom-up approach used the response components of EPICA stacked records to reconstruct the envelope of the thermal response through a physical interference model. The aim was to improve understanding of the intensity, amplitude, and asymmetry features of 73 marine isotope stages/substages (MISs) and seven glacial terminations. The Antarctic stack record can be described by a variance-weighted superposition of ten thermal waves of different origins (mid-term oscillation, orbitals, and suborbitals) that stochastically interfere at a given time according to their relative differences in frequency, amplitude, and polarity. Interglacial/glacial stages resulted from constructive interference and bipolar amplification of warming/cooling responses, respectively. The low-intensity MISs (including 90% of substages) and the unbiased-dated terminations fell in the low-interference regions, where dominant destructive patterns minimize the thermal envelope. The positive skewness of the EPICA stack resulted from constructive interference with a strong bias in the warming direction, especially after the Mid-Brunhes Event. Duration analysis of short eccentricity hemicycles exhibited an intrinsic unexpectedly prolonged mean cooling in the nominal solution (5.8 kyr) and its EPICA response as well (8.6 kyr), along with an interference-induced asymmetry (21.1 kyr). The overall effect has led to the saw-tooth shape of glacial cycles, which was strongly induced by interference. Full article

Digital signal processing plays a central role in intelligent railway communications under high-mobility, strong-multipath, and time-varying-channel conditions. This review surveys representative techniques for multi-carrier modulation, precoding, index modulation, and chaos-inspired physical layer security and highlights their mathematical foundations. Core themes include transform-domain representations typified by time–frequency analysis, linear-algebraic formulations of precoding and equalization, combinatorial structures underlying index mapping and spectral efficiency gains, and nonlinear dynamical systems theory of chaotic encryption. The methods are compared in terms of bit error performance, peak-to-average power ratio, spectral efficiency, computational complexity, and information security, with emphasis on railway-specific deployment constraints. The synergistic application of these methods with intelligent railway transportation systems is expected to enhance the overall performance of railway transportation systems in terms of transmission efficiency, reliability, and security. It provides critical technological support for the efficient and secure operation of next-generation intelligent transportation systems. Full article

This paper examines precise timestamping of LoRaWAN messages (particularly beacons) to enable wide-area synchronization for end devices without GNSS. The need for accuracy demands hardware-level timestamping architectures, possibly using time-domain cross-correlation (matched filtering) against internally generated chirp references. Focusing on Time-of-Arrival (TOA) estimation from raw IQ samples, the authors analyze effects of non-idealities—additive white Gaussian noise (AWGN), Carrier Frequency Offset (CFO), Sampling Phase and Frequency Offset (SPO and SFO, respectively), and radio parameters such as spreading factor (SF) and sampling rate of the baseband signals. A MATLAB (R2020) simulation mimics preamble detection and Start-of-Frame Delimiter (SFD) timestamping while sweeping SF (7, 9, 12), sampling rates (0.25–10 MSa/s), SNR (−20 to +20 dB), and CFO/SFO offsets (−10–10 ppm frequency deviation). Errors are evaluated in terms of mean and dispersion, the latter represented by the P95–P5 range metric. Results show that oversampling not only improves temporal resolution, but sub-microsecond error dispersion can be achieved with high sampling rates in favorable SNR and SF cases. Indeed, SPO and SNR greatly contribute to error dispersion. On the other hand, higher SF values increase correlation robustness at the cost of longer chirps, making SFO a dominant error source; ±10 ppm SFO can induce roughly ±3 μs SFD bias for SF12. CFO largely cancels after up-/down-chirp averaging. As a concluding remark, matched-filter hardware timestamping can ensure sub-μs errors thanks to oversampling but requires SFO compensation for accurate real-world synchronization in practice. Full article