Search Results (1,739)

Background: Cardiovascular diseases are the leading cause of global mortality, with 80% of coronary heart disease in patients over 65. Understanding aging cardiovascular structures is crucial. Photon-counting computed tomography (PCCT) offers improved spatial and temporal resolution and better signal-to-noise ratio, enabling texture analysis in clinical routines. Detecting structural changes in aging left-ventricular myocardium may help predict cardiovascular risk. Methods: In this retrospective, single-center, IRB-approved study, 90 patients underwent ECG-gated contrast-enhanced cardiac CT using dual-source PCCT (NAEOTOM Alpha, Siemens). Patients were divided into two age groups (50–60 years and 70–80 years). The left ventricular myocardium was segmented semi-automatically, and radiomics features were extracted using pyradiomics to compare myocardial texture features. Epicardial adipose tissue (EAT) density, thickness, and other clinical parameters were recorded. Statistical analysis was conducted with R and a Python-based random forest classifier. Results: The study assessed 90 patients (50–60 years, n = 54, and 70–80 years, n = 36) with a mean age of 63.6 years. No significant differences were found in mean Agatston score, gender distribution, or conditions like hypertension, diabetes, hypercholesterolemia, or nicotine abuse. EAT measurements showed no significant differences. The Random Forest Classifier achieved a training accuracy of 0.95 and a test accuracy of 0.74 for age group differentiation. Wavelet-HLH_glszm_GrayLevelNonUniformity was a key differentiator. Conclusions: Radiomics texture features of the left ventricular myocardium outperformed conventional parameters like EAT density and thickness in differentiating age groups, offering a potential imaging biomarker for myocardial aging. Radiomics analysis of left ventricular myocardium offers a unique opportunity to visualize changes in myocardial texture during aging and could serve as a cardiac risk predictor. Full article

A maximum entropy (ME) methodology was used to infer the Hubble constant from the temperature anisotropies in cosmic microwave background (CMB) measurements, as measured by the Planck satellite. A simple cosmological model provided physical insight and afforded robust statistical sampling of a parameter space. The parameter space included the spectral tilt and amplitude of adiabatic density fluctuations of the early universe and the present-day ratios of dark energy, matter, and baryonic matter density. A statistical temperature was estimated by applying the equipartition theorem, which uniquely specifies a posterior probability distribution. The ME analysis inferred the mean value of the Hubble constant to be about 67 km/sec/Mpc with a conservative standard deviation of approximately 4.4 km/sec/Mpc. Unlike standard Bayesian analyses that incorporate specific noise models, the ME approach treats the model error generically, thereby producing broader, but less assumption-dependent, uncertainty bounds. The inferred ME value lies within 1σ of both early-universe estimates (Planck, Dark Energy Signal Instrument (DESI)) and late-universe measurements (e.g., the Chicago Carnegie Hubble Program (CCHP)) using redshift data collected from the James Webb Space Telescope (JWST). Thus, the ME analysis does not appear to support the existence of the Hubble tension. Full article

Decomposing Electrodermal Activity (EDA) into phasic (short-term, stimulus-linked responses) and tonic (longer-term baseline) components is essential for extracting meaningful emotional and physiological biomarkers. This study presents a comparative analysis of knowledge-driven, statistical, and deep learning-based methods for EDA signal decomposition, with a focus on in-the-wild data collected from wearable devices. In particular, the authors introduce the Feel Transformer, a novel Transformer-based model adapted from the Autoformer architecture, designed to separate phasic and tonic components without explicit supervision. The model leverages pooling and trend-removal mechanisms to enforce physiologically meaningful decompositions. Comparative experiments against methods such as Ledalab, cvxEDA, and conventional detrending show that the Feel Transformer achieves a balance between feature fidelity (SCR frequency, amplitude, and tonic slope) and robustness to noisy, real-world data. The model demonstrates potential for real-time biosignal analysis and future applications in stress prediction, digital mental health interventions, and physiological forecasting. Full article

Background/Objectives: Accurately predicting the arrival position of a moving target is essential in sports and daily life. While predictive saccades are known to enhance performance, the neural mechanisms underlying the timing of these strategies remain unclear. This study investigated how the timing of saccadic strategies—executed early versus late—affects cortical activity patterns, as measured by electroencephalography (EEG). Methods: Sixteen participants performed a task requiring them to predict the arrival position and timing of a parabolically moving target that became occluded midway through its trajectory. Based on eye movement behavior, participants were classified into an Early Saccade Strategy Group (SSG) or a Late SSG. EEG signals were analyzed in the low beta band (13–15 Hz) using the Hilbert transform. Group differences in eye movements and EEG activity were statistically assessed. Results: No significant group differences were observed in final position or response timing errors. However, time-series analysis showed that the Early SSG achieved earlier and more accurate eye positioning. EEG results revealed greater low beta activity in the Early SSG at electrode sites FC6 and P8, corresponding to the frontal eye field (FEF) and middle temporal (MT) visual area, respectively. Conclusions: Early execution of predictive saccades was associated with enhanced cortical activity in visuomotor and motion-sensitive regions. These findings suggest that early engagement of saccadic strategies supports more efficient visuospatial processing, with potential applications in dynamic physical tasks and digitally mediated performance domains such as eSports. Full article

This study investigates the influence of key fused deposition modeling (FDM) process parameters, namely, print speed, infill percentage, layer thickness, and layer width, on the tensile properties of PLA specimens produced using 3D printing technology. A Taguchi L9 orthogonal array was employed to design the experiments efficiently, enabling the systematic evaluation of parameter effects with fewer tests. Tensile strength and elongation at break were measured for each parameter combination, and statistical analyses, including the signal-to-noise (S/N) ratio and analysis of variance (ANOVA), were conducted to identify the most significant factors. The results showed that infill percentage significantly affected tensile strength, while layer thickness was the dominant factor influencing elongation. The highest tensile strength (47.84 MPa) was achieved with the parameter combination of 600 mm/s print speed, 100% infill percentage, 0.4 mm layer thickness, and 0.4 mm layer width. A linear regression model was developed to predict tensile strength with an R² value of 83.14%, and probability plots confirmed the normal distribution of the experimental data. This study provides practical insights into optimizing FDM process parameters to enhance the mechanical performance of PLA components, supporting their use in structural and functional applications. Full article

Agriculture relies on the widespread application of nitrogen fertilisers to improve crop yields and meet the demands of a growing population. However, the excessive use of these fertilisers has led to significant water quality challenges, posing risks to aquatic life, ecosystems, and human health. This study examines the relationship between synthetic nitrogen fertiliser usage and water pollution while identifying gaps in existing research to guide future studies. A systematic search across databases (Scopus, Web of Science, and Greenfile) identified 18 studies with quantitative data, synthesised using a single-group meta-analysis of means. As the data were continuous, the mean was used as the effect measure, and a random-effects model was applied due to varied study populations, with missing data estimated through statistical assumptions. The meta-analysis found an average nitrate concentration of 34.283 mg/L (95% confidence interval: 29.290–39.276), demonstrating the significant impact of nitrogen fertilisers on water quality. While this average remains marginally below the thresholds set by the World Health Organization (50 mg/L NO₃⁻) and EU Nitrate Directive, it exceeds the United States Environmental Protection Agency limit (44.3 mg/L NO₃⁻), signalling potential health risks, especially in vulnerable or unregulated regions. The high observed heterogeneity (I² = 100%) suggests that factors such as soil type, agricultural practices, application rate, and environmental conditions influence nitrate levels. While agriculture is a key contributor, other anthropogenic activities may also affect nitrate concentrations. Future research should comprehensively assess all influencing factors to determine the precise impact of nitrogen fertilisers on water quality. Full article

Time-frequency analysis (TFA) technology is an important tool for analyzing non-Gaussian mechanical fault vibration signals. In the complex background of infinite variance process noise and Gaussian colored noise, it is difficult for traditional methods to obtain the highly concentrated time-frequency representation (TFR) of fault vibration signals. Based on the insensitive property of fractional low-order statistics for infinite variance and Gaussian processes, robust fractional lower order adaptive linear chirplet transform (FLOACT) and fractional lower order adaptive scaling chirplet transform (FLOASCT) methods are proposed to suppress the mixed complex noise in this paper. The calculation steps and processes of the algorithms are summarized and deduced in detail. The experimental simulation results show that the improved FLOACT and FLOASCT methods have good effects on multi-component signals with short frequency intervals in the time-frequency domain and even cross-frequency trajectories in the strong impulse background noise environment. Finally, the proposed methods are applied to the feature analysis and extraction of the mechanical outer race fault vibration signals in complex background environments, and the results show that they have good estimation accuracy and effectiveness in lower MSNR, which indicate their robustness and adaptability. Full article

Parkinson’s disease (PD) is a neurodegenerative disorder commonly marked by upper limb tremors that interfere with daily activities. Wearable devices, such as smartwatches, represent a promising solution for continuous and objective monitoring in PD. This study aimed to develop and validate a tremor-detection algorithm using smartwatch sensors. Data were collected from 21 individuals with PD and 27 healthy controls using both a commercial inertial measurement unit (G-Sensor, BTS Bioengineering, Italy) and a smartwatch (Apple Watch Series 3). Participants performed standardized arm movements while sensor signals were synchronized and processed to extract relevant features. Statistical analyses assessed discriminant and concurrent validity, reliability, and accuracy. The algorithm demonstrated moderate to strong correlations between smartwatch and commercial IMU data, effectively distinguishing individuals with PD from healthy controls showing associations with clinical measures, such as the MDS-UPDRS III. Reliability analysis demonstrated agreement between repeated measurements, although a proportional bias was noted. Power spectral density (PSD) analysis of accelerometer and gyroscope data along the x-axis successfully detected the presence of tremors. These findings support the use of smartwatches as a tool for detecting tremors in PD. However, further studies involving larger and more clinically impaired samples are needed to confirm the robustness and generalizability of these results. Full article

Background and Objective: This study aimed to examine the possibility of using surface electromyography (sEMG) to aid in assessing the correctness of overhead deep squat performance. Electromyography signals were recorded for 20 athletes from the lower (rectus femoris (RF), vastus medialis (VM), biceps femoris (BF), and gluteus (GM)) and upper (deltoid (D), latissimus dorsi (L)) muscles. The sEMG signals were categorized into three groups based on physiotherapists’ evaluations of deep squat correctness. Methods: The raw sEMG signals were filtering at 10–250 Hz, and then the mean frequency, median frequency, and kurtosis were calculated. Next, the maximum excitation of the muscles expressed in percentage of maximum voluntary contraction (%MVC) and co-activation index (CAI) were estimated. To determine the muscle excitation level, the pulse interference filter and variance analysis of the sEMG signal derivative were applied. Next, analysis of variance (ANOVA) tests, that is, nonparametric Kruskal–Wallis and post hoc tests, were performed. Results: The parameter that most clearly differentiated the groups considered turned out to be %MVC. The statistically significant difference with a large effect size in the excitation of RF & GM (p = 0.0011) and VM & GM (p = 0.0002) in group 3, where the correctness of deep squat execution was the highest and ranged from 85% to 92%, was pointed out. With the decrease in the correctness of deep squat performance, an additional statistically significant difference appeared in the excitation of RF & BF and VM & BF for both groups 2 and 1, which was not present in group 3. However, in group 2, with the correctness of the deep squat execution at 62–77%, the statistically significant differences in muscle excitation found in group 3 were preserved, in contrast to group 1, with the lowest 23–54% correctness of the deep squat execution, where the statistical significance of these differences was not confirmed. Conclusions: The results indicate that sEMG can differentiate muscle activity and provide additional information for physiotherapists when assessing the correctness of deep squat performance. The proposed analysis can be used to evaluate the correctness of physical exercises when physiotherapist access is limited. Full article

Background/Objectives: Herbal extracts from Betula alba (birch) are traditionally used for their purported diuretic effects, but scientific evidence supporting these claims remains limited. In this pilot study, we evaluated the short-term effects of a standardized B. alba leaf extract in healthy adult rats using an untargeted urinary metabolomics approach based on UPLC-QTOF. Methods: Two doses, 25 or 50 mg/kg, of a standardized B. alba extract were orally administered to rats. The extract contains hyperoside (0.53%), quercetin glucuronide (0.36%), myricetin glucoside (0.32%), and chlorogenic acid (0.28%) as its main constituents. After 3 days of treatment, the 24 h urine output was measured. Results: While no statistically significant changes were observed in the 24 h urine volume or the urinary Na⁺ and K⁺ excretion, multivariate metabolomic analysis revealed treatment-induced alterations in the urinary metabolic profile. Notably, the levels of two glucocorticoids, i.e., corticosterone and 11-dehydrocorticosterone, were increased in treated animals, suggesting that the extract may influence corticosteroid metabolism or excretion, potentially impacting antidiuretic hormone signaling. Elevated bile-related compounds, including bile acids and bilin, and glucuronidated metabolites were also observed, indicating changes in bile acid metabolism, hepatic detoxification, and possibly gut microbiota activity. Conclusions: Although this study did not confirm a diuretic effect of B. alba extract, the observed metabolic shifts suggest broader systemic bioactivities that warrant further investigation. Overall, the results indicate that the approach based on urinary metabolomics may be valuable in uncovering the mechanisms of action and evaluating the bioactivity of herbal extracts with purported diuretic properties. Full article

The mode pairing quantum key distribution (MP-QKD) protocol has attracted considerable attention for its capability to ensure high secure key rates over long distances without requiring global phase locking. However, ensuring symmetric channels for the MP-QKD protocol is challenging in practical quantum communication networks. Previous studies on the asymmetric MP-QKD protocol have relied on ideal decoy state assumptions and infinite-key analysis, which are unattainable for real-world deployment. In this paper, we conduct a security analysis of the asymmetric MP-QKD protocol with the finite-key analysis, where we discard the previously impractical assumptions made in the decoy state method. Combined with statistical fluctuation analysis, we globally optimized the 10 independent parameters in the asymmetric MP-QKD protocol by employing our modified particle swarm optimization. Through further analysis, the simulation results demonstrate that our work achieves improved secure key rates and transmission distances compared to the strategy with additional attenuation. We further investigate the relationship between the intensities and probabilities of signal, decoy, and vacuum states with transmission distance, facilitating their more efficient deployment in future quantum networks. Full article

Introduction: Sepsis-associated encephalopathy (SAE) is an acute brain dysfunction secondary to systemic infection, occurring without direct central nervous system involvement. Despite its clinical relevance, reliable biomarkers for diagnosing SAE and assessing its severity remain limited. This study aimed to evaluate the feasibility of amide proton transfer-weighted (APTw) chemical exchange saturation transfer (CEST) MRI as a non-invasive molecular imaging technique for detecting metabolic alterations related to neuroinflammation in SAE. Using a lipopolysaccharide (LPS)-induced rat model, we focused on hippocampal changes associated with neuronal inflammation. Materials and Methods: Twenty-one Sprague–Dawley rats (8 weeks old, male) were divided into three groups: control (CTRL, n = 7), LPS-induced sepsis at 5 mg/kg (LPS05, n = 7), and 10 mg/kg (LPS10, n = 7). Sepsis was induced via a single intraperitoneal injection of LPS. APTw imaging was performed using a 7 T preclinical MRI system, and signal quantification in the hippocampus was conducted using the magnetization transfer ratio asymmetry analysis. Results and Discussion: APTw imaging at 7 T demonstrated significantly elevated hippocampal APTw signals in SAE model rats (LPS05 and LPS10) compared to the control (CTRL) group: CTRL (−1.940 ± 0.207%) vs. LPS05 (−0.472 ± 0.485%) (p < 0.001) and CTRL vs. LPS10 (−0.491 ± 0.279%) (p < 0.001). However, no statistically significant difference was observed between the LPS05 and LPS10 groups (p = 0.994). These results suggest that APTw imaging can effectively detect neuroinflammation-related metabolic alterations in the hippocampus. Conclusion: Our findings support the feasibility of APTw CEST imaging as a non-invasive molecular MRI technique for SAE, with potential applications in diagnosis, disease monitoring, and therapeutic evaluation. Full article

Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) are powerful techniques that have been employed to analyze foodstuffs comprehensively. These techniques offer in-depth information about the chemical composition, structure, and spatial distribution of components in a variety of food products. Quantitative NMR is widely applied for precise quantification of metabolites, authentication of food products, and monitoring of food quality. Low-field ¹H-NMR relaxometry is an important technique for investigating the most abundant components of intact foodstuffs based on relaxation times and amplitude of the NMR signals. In particular, information on water compartments, diffusion, and movement can be obtained by detecting proton signals because of H₂O in foodstuffs. Saffron adulterations with calendula, safflower, turmeric, sandalwood, and tartrazine have been analyzed using benchtop NMR, an alternative to the high-field NMR approach. The fraudulent addition of Robusta to Arabica coffee was investigated by ¹H-NMR Spectroscopy and the marker of Robusta coffee can be detected in the ¹H-NMR spectrum. MRI images can be a reliable tool for appreciating morphological differences in vegetables and fruits. In kiwifruit, the effects of water loss and the states of water were investigated using MRI. It provides informative images regarding the spin density distribution of water molecules and the relationship between water and cellular tissues. ¹H-NMR spectra of aqueous extract of kiwifruits affected by elephantiasis show a higher number of small oligosaccharides than healthy fruits do. One of the frauds that has been detected in the olive oil sector reflects the addition of hazelnut oils to olive oils. However, using the NMR methodology, it is possible to distinguish the two types of oils, since, in hazelnut oils, linolenic fatty chains and squalene are absent, which is also indicated by the ¹H-NMR spectrum. NMR has been applied to detect milk adulterations, such as bovine milk being spiked with known levels of whey, urea, synthetic urine, and synthetic milk. In particular, T2 relaxation time has been found to be significantly affected by adulteration as it increases with adulterant percentage. The ¹H spectrum of honey samples from two botanical species shows the presence of signals due to the specific markers of two botanical species. NMR generates large datasets due to the complexity of food matrices and, to deal with this, chemometrics (multivariate analysis) can be applied to monitor the changes in the constituents of foodstuffs, assess the self-life, and determine the effects of storage conditions. Multivariate analysis could help in managing and interpreting complex NMR data by reducing dimensionality and identifying patterns. NMR spectroscopy followed by multivariate analysis can be channelized for evaluating the nutritional profile of food products by quantifying vitamins, sugars, fatty acids, amino acids, and other nutrients. In this review, we summarize the importance of NMR spectroscopy in chemical profiling and quality assessment of food products employing magnetic resonance technologies and multivariate statistical analysis. Full article

Diagnosing schizophrenia using Electroencephalograph (EEG) signals is a challenging task due to the subtle and overlapping differences between patients and healthy individuals. To overcome this difficulty, deep learning has shown strong potential, especially given its success in image recognition tasks. In many studies, one-dimensional EEG signals are transformed into two-dimensional representations to allow for image-based analysis. In this work, we have used the Markov Transition Field for converting EEG signals into two-dimensional images, capturing both the temporal patterns and statistical dynamics of the data. EEG signals are continuous time-series recordings from the brain, where the current state is often influenced by the immediately preceding state. This characteristic makes MTF particularly suitable for representing such data. After the transformation, a pre-trained VGG-16 model is employed to extract meaningful features from the images. The extracted features are then passed through two separate classification pipelines. The first uses a traditional machine learning model, Support Vector Machine, while the second follows a deep learning approach involving an autoencoder for feature selection and a neural network for final classification. The experiments were conducted using EEG data from the open-access Schizophrenia EEG database provided by MV Lomonosov Moscow State University. The proposed method achieved a highest classification accuracy of 98.51 percent and a recall of 100 percent across all folds using the deep learning pipeline. The Support Vector Machine pipeline also showed strong performance with a best accuracy of 96.28 percent and a recall of 97.89 percent. The proposed deep learning model represents a biomimetic approach to pattern recognition and decision-making. Full article

Background: Endometrial cancer (EC) is the sixth most common cancer among women globally, with an estimated 420,000 new cases diagnosed annually. Methods: This study comprised patients with endometrial cancer (EC) (n = 17), hyperplasia (HY) (n = 17), and controls (CO) (n = 20). Tissue was collected from the endometrium of all 54 patients, including patients with HY, EC, and CO, who underwent total hysterectomy. EC and HY diagnoses were confirmed based on histological examination. Untargeted metabolomics profiling was conducted using LC-HRMS. The partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models were used for univariate and multivariate statistical analysis. The fitness of the model (R2Y) and predictive ability (Q2) were used to create OPLS-DA models. ROC analysis was carried out, followed by network analysis using Ingenuity Pathway Analysis. Results: The top metabolites that can discriminate EC and HY from CO were identified. This revealed a decrease in the levels of the lipid species, specifically phosphatidic acid (PA) (PA (14:1/14:0), PA(10:0/17:0), PA(18:1-O(12,13)/12:0)), PG(a-13:0/a-13:0), ganglioside GA1 (d18:1/18:1), PS(14:1/14:0), TG(20:0/18:4/14:1), and CDP-DG(PGF2alpha/18:2), while the levels of 3-Dehydro-L-gulonate, Uridine diphosphate-N-acetylglucosamine, ganglioside GT2 (d18:1/14:0), gamma-glutamyl glutamic acid and oxidized glutathione were increased in cases of EC and HY as compared to CO. Bioinformatics analysis, specifically using Ingenuity Pathway Analysis (IPA), revealed distinct pathway enrichments for EC and HY. For EC, the most highly scored pathways were associated with cell-to-cell signaling and interaction, skeletal and muscular system development and function, and small-molecule biochemistry. In contrast, HY cases showed the highest scoring pathways related to inflammatory disease, inflammatory response, and organismal injury and abnormalities. Conclusions: Developing sensitive biomarkers could improve diagnosis and guide treatment decisions, particularly in identifying which patients with HY may safely avoid hysterectomy and be managed with hormonal therapy. Full article