Search Results (130)

Children with Autism Spectrum Disorder (ASD) often experience cognitive difficulties that impact learning. This study explores the use of electroencephalogram data collected with the MUSE 2 headband during task-based cognitive sessions to understand how cognitive states in children with ASD change across three structured tasks: Shape Matching, Shape Sorting, and Number Matching. Following signal preprocessing using Independent Component Analysis (ICA), power across various frequency bands was extracted using the Welch method. These features were used to analyze cognitive states in children with ASD in comparison to typically developing (TD) peers. To capture dynamic changes in attention over time, Morlet wavelet transform was applied, revealing distinct brain signal patterns. Machine learning classifiers were then developed to accurately distinguish between ASD and TD groups using the EEG data. Models included Support Vector Machine, K-Nearest Neighbors, Random Forest, an Ensemble method, and a Neural Network. Among these, the Ensemble method achieved the highest accuracy at 0.90. Feature importance analysis was conducted to identify the most influential EEG features contributing to classification performance. Based on these findings, an ASD map was generated to visually highlight the key EEG regions associated with ASD-related cognitive patterns. These findings highlight the potential of EEG-based models to capture ASD-specific neural and attentional patterns during learning, supporting their application in developing more personalized educational approaches. However, due to the limited sample size and participant heterogeneity, these findings should be considered exploratory. Future studies with larger samples are needed to validate and generalize the results. Full article

Many studies have recognized that individual variability shapes user experience in virtual reality (VR), yet little is known about how these differences influence objective cognitive engagement and performance outcomes. This study investigates how cognitive factors (IQ, age) and technological familiarity (tech enthusiasm, tech fluency, first-time VR experience) influence EEG-derived cognitive responses (alpha and theta activity) and task performance (trial duration) during VR interactions. Sixteen autistic and sixteen neurotypical participants engaged with various VR interactions while their neural activity was recorded using a Muse S EEG. Correlational analyses showed distinct group-specific patterns: higher IQ correlated with elevated average alpha and theta power in autistic participants, while tech fluency significantly influenced performance outcomes only in neurotypical group. Prior VR experience correlated with better performance in the neurotypical group but slower adaptation in the autistic group. These results highlight the role of individual variability in shaping VR engagement and underscore the importance of personalized design approaches. This work provides foundational insights toward advancing inclusive, user-centered VR systems. Full article

For densities beyond nuclear saturation, there is still a large uncertainty in the equations of state (EoSs) of dense matter that translate into uncertainties in the internal structure of neutron stars. The MUSES Calculation Engine provides a free and open-source composable workflow management system, which allows users to calculate the EoSs of dense and hot matter that can be used, e.g., to describe neutron stars. For this work, we make use of two MUSES EoS modules, i.e., Crust Density Functional Theory and Chiral Mean Field model, with beta-equilibrium with leptons enforced in the Lepton module, then connected by the Synthesis module using different functions: hyperbolic tangent, generalized Gaussian, bump, and smoothstep. We then calculate stellar structure using the QLIMR module and discuss how the different interpolating functions affect our results. Full article

The Musée des Civilisations de l’Europe et de la Méditerranée (MuCEM) in Marseille represents a paradigm shift in sustainable architecture, integrating heritage conservation with cutting-edge material technology. Designed by Rudy Ricciotti, the museum utilizes Ultra-High-Performance Concrete (UHPC) to optimize structural efficiency, environmental resilience, and architectural aesthetics. This study highlights how UHPC contributes to reducing resource consumption and enhancing durability, in line with global sustainability goals. MuCEM’s lattice facade, modular supports, and pedestrian bridge showcase innovative engineering solutions that extend the building’s lifespan while ensuring seismic resilience and energy efficiency. Furthermore, UHPC’s longevity reduces maintenance requirements, contributing to lower life cycle costs and carbon footprint. The findings underscore how advanced materials and sustainable design principles can redefine the role of cultural landmarks in the built environment. Full article

Water quality in urban streams is critical for the health of aquatic and human life, as it impacts both the environment and water availability. The strong impacts of changing climate and land use on water quality necessitate a better understanding of how stream water quality changes over space and time. To this end, four key water-quality parameters—Escherichia coli (E. coli), nitrate (NO₃⁻), sulfate (SO₄²⁻), and chloride (Cl⁻)—were collected at 12 sites along Fall Creek and Pleasant Run streams in Indianapolis, Indiana USA from 2003 to 2021 on a seasonal basis: March, July, and October each year. Two-way ANOVA tests were used to determine the impacts of seasonality and location on these parameters. Correlation and RDA (redundancy analysis) were used to determine the importance of climatic drivers. Linear regressions were used to quantify the impacts of land-use types on water quality integrating buffer zone size and sub-watershed analysis. Strong seasonal variations of the water-quality parameters were found. March had higher levels of NO₃⁻, SO₄²⁻, and Cl⁻ than other months. July had the highest E. coli concentrations compared to March and October. Seven-days antecedent snow and precipitation were found to be significantly related to Cl⁻ and log₁₀(E. coli) and can explain up to 53% and 31% of their variations, respectively. Spatially, urban built-up land in a 1000 m buffer around the sampling sites was positively correlated with the log₁₀(E. coli) variation, while lawn cover was positively related to NO₃⁻ concentrations within 500 m buffers. Conversely, NDVI (Normalized Difference Vegetation Index) values were negatively related to all variables. In conclusion, E. coli is more impacted by higher precipitation and urban land coverage, which could be related to more combined sewer overflow events in July. Cl⁻ peaking in March and its relationship with snow indicate salt runoff during snow melting events. NO₃⁻ and SO₄²⁻ increases are likely due to fertilizer input from residential lawns near streams. This suggests that Indianapolis stream water-quality changes are influenced by both changing climate and land-cover/-muse types. Full article

Lithium has been identified more than six decades ago as a preferred treatment option for manic depression. Due to its affordability, stability, minimal side effects, and immunomodulatory effects, recent studies on lithium have focused on its potential anticancer properties and possible mechanisms of action. Lung cancer ranks the highest as the main cause of death in males and has high mortality rates with low survival rates. In this study, lung adenocarcinoma (A549) cells were treated with various concentrations of lithium chloride to evaluate its inflammatory and anticancer properties. The in vitro cytotoxic effects of lithium chloride were assessed using the MTT [3-(4, 5-dimethythiazol-2-yl)-2, 5-diphenyltetrazolium bromide] assay, Muse^® cell death, and cell cycle analysis. The nitric oxide and oxidative stress flow cytometry Muse^® assays were used to monitor inflammation profiles of lithium-treated lung adenocarcinoma cells. The MTT viability assay showed the safe use of LiCl on the noncancerous RAW 264.7 macrophage cells below a concentration of 40 mM. Lithium reduced cell viability, induced late apoptotic cell death, and disrupted normal cell cycle progression in a dose-dependent manner, leading to cell cycle arrest in the S and G2/M phases of A549 cells. The induction of cell death by lithium in A549 cells is accompanied by increased ROS and nitric oxide production. This study shows that lithium chloride possesses some immunomodulatory cytotoxic effects on A549 lung cancer cells and can be further investigated for use in lung cancer treatment. Full article

Background/Objectives: This article addresses the challenge of stress detection across diverse contexts. Mental stress is a worldwide concern that substantially affects human health and productivity, rendering it a critical research challenge. Although numerous studies have investigated stress detection through machine learning (ML) techniques, there has been limited research on assessing ML models trained in one context and utilized in another. The objective of ML-based stress detection systems is to create models that generalize across various contexts. Methods: This study examines the generalizability of ML models employing EEG recordings from two stress-inducing contexts: mental arithmetic evaluation (MAE) and virtual reality (VR) gaming. We present a data collection workflow and publicly release a portion of the dataset. Furthermore, we evaluate classical ML models and their generalizability, offering insights into the influence of training data on model performance, data efficiency, and related expenses. EEG data were acquired leveraging MUSE-S^TM hardware during stressful MAE and VR gaming scenarios. The methodology entailed preprocessing EEG signals using wavelet denoising mother wavelets, assessing individual and aggregated sensor data, and employing three ML models—linear discriminant analysis (LDA), support vector machine (SVM), and K-nearest neighbors (KNN)—for classification purposes. Results: In Scenario 1, where MAE was employed for training and VR for testing, the TP10 electrode attained an average accuracy of 91.42% across all classifiers and participants, whereas the SVM classifier achieved the highest average accuracy of 95.76% across all participants. In Scenario 2, adopting VR data as the training data and MAE data as the testing data, the maximum average accuracy achieved was 88.05% with the combination of TP10, AF8, and TP9 electrodes across all classifiers and participants, whereas the LDA model attained the peak average accuracy of 90.27% among all participants. The optimal performance was achieved with Symlets 4 and Daubechies-2 for Scenarios 1 and 2, respectively. Conclusions: The results demonstrate that although ML models exhibit generalization capabilities across stressors, their performance is significantly influenced by the alignment between training and testing contexts, as evidenced by systematic cross-context evaluations using an 80/20 train–test split per participant and quantitative metrics (accuracy, precision, recall, and F1-score) averaged across participants. The observed variations in performance across stress scenarios, classifiers, and EEG sensors provide empirical support for this claim. Full article

The Aitik mine is Sweden’s largest open-pit sulfide mine and Europe’s most important producer of gold, silver, and copper. However, the mine faces problems related to water inflow, particularly in the northern zone and western hanging wall sections of the pit, resulting from various mining activities, including blasting, loading, and hauling. The presence of fracture zones within the pit further exacerbates the issue, as continuous mining operations have aggravated the thickness of these fractures, potentially increasing the volume of water inflow. Consequently, this could lead to various geotechnical issues such as slope collapse, and increase the possibility of acid mine drainage formation. This research develops a numerical model using ModelMuse as the graphical user interface and MODFLOW to simulate groundwater flow in the mining pit under different scenarios, by considering the absence, presence, and varying thickness of fracture zones to address the issue. By analyzing these scenarios, the model estimates the volume of water inflow into the pit under steady-state conditions. The results indicate that the presence of a fracture zone plays a crucial role in controlling water inflows by significantly influencing the inflow budget—by 90% for the north shear inflow (NSI) and by 20% for the western hanging wall inflow (WHWI) at deeper depths of the pit. Variations in the fracture zone thickness result in a 15% increase in water inflow at deeper depths of the pit. These findings provide valuable insights for improving mine water management strategies and informing sustainable mine closure planning to mitigate long-term environmental risks. Full article

Controlling groundwater levels is essential for the safe construction of complex or high-rise buildings. In México, dewatering regulations lack detailed references, and piezometric data are limited, making precise groundwater control a challenge. This study aimed to develop a numerical groundwater model by translating a conceptual hydrogeological model into a calibrated MODFLOW simulation using the graphical user interface ModelMuse, developed by the United States Geological Survey (USGS). For the project “Torre Tres Ríos”, field measurements recorded a water-table level of 33 m above sea level (masl) in July, rising to 35.74 masl in October due to rainy season recharge and the influence of the Tamazula River, then decreasing to 35.20 masl in November. The model, calibrated with a mean absolute error of 0.15 m and a standard deviation of 0.174 m, effectively represented steady and transient states. A spatiotemporal analysis based on the calibrated numerical model enabled the evaluation of different dewatering scenarios. Initially, deep wells with a pumping rate of 120 L per second (lps) were required for dewatering; however, a wellpoint system was proposed, showing improved performance with a reduced impact on groundwater flow and the surrounding environment during the critical August–November period. This study highlights the importance of numerical modeling in refining dewatering system designs, ensuring adaptability to fluctuating groundwater conditions. By providing a methodology for optimizing dewatering strategies, it contributes to more efficient and sustainable construction practices in regions with complex hydrogeological conditions. Full article

Background/Objectives: This study compared the image quality of conventional multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE-DWI) and deep learning MUSE-DWI with that of vendor-specific deep learning (DL) reconstruction applied to bladder MRI. Methods: This retrospective study included 57 patients with a visible bladder mass. DWI images were reconstructed using a vendor-provided DL algorithm (AIR^TM Recon DL; GE Healthcare)—a CNN-based algorithm that reduces noise and enhances image quality—applied here as a prototype for MUSE-DWI. Two radiologists independently assessed qualitative features using a 4-point scale. For the quantitative analysis, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), signal intensity ratio (SIR), and apparent diffusion coefficient (ADC) of the bladder lesions were recorded by two radiologists. The weighted kappa test and intraclass correlation were used to evaluate the interobserver agreement in the qualitative and quantitative analyses, respectively. Wilcoxon signed-rank test was used to compare the image quality of the two sequences. Results: DL MUSE-DWI demonstrated significantly improved qualitative image quality, with superior sharpness and lesion conspicuity. There were no significant differences in the distortion or artifacts. The qualitative analysis of the images by the two radiologists was in good to excellent agreement (κ ≥ 0.61). Quantitative analysis revealed higher SNR, CNR, and SIR in DL MUSE-DWI than in MUSE-DWI. The ADC values were significantly higher in DL MUSE-DWI. Interobserver agreement was poor (ICC ≤ 0.32) for SNR and CNR and excellent (ICC ≥ 0.85) for SIR and ADC values in both DL MUSE-DWI and MUSE-DWI. Conclusions: DL MUSE-DWI significantly enhanced the image quality in terms of lesion sharpness, conspicuity, SNR, CNR, and SIR, making it a promising tool for clinical imaging. Full article

Background/objectives: Mycosis fungoides (MF) is a rare malignancy, with an indolent course in the early stages of the disease. However, due to major molecular and clinical heterogeneity, patients at an advanced stage of the disease have variable responses to treatment and considerably reduced life expectancy. Today, there is a lack of specific markers for the progression from early to advanced stages of the disease. To address these challenges, the non-interventional BIO-MUSE trial was initiated. Here, we report on a case study involving one patient, where combined omics analysis of tissue and blood was used to reveal the unique molecular features associated with the progression of the disease. Methods: We applied 10× genomics-based single-cell RNA sequencing to CD3+ peripheral T-cells, combined with T-cell receptor sequencing, to samples collected at multiple timepoints during the progression of the disease. In addition, GeoMx-based digital spatial profiling of T-helper (CD3+/CD8−), T-cytotoxic (CD3+/CD8+), and CD163+ cells was performed on skin biopsies. Results. The results pinpoint targets, such as transforming growth factor β1, as some of the mechanisms underlying disease progression, which may have the potential to improve patient prognostication and the development of precision medicine efforts. Conclusions: We propose that in patients with MF, the evolution of the malignant clone and the associated immune response need to be studied jointly to define relevant strategies for intervention. Full article

This research project aims to develop a basin-scaled 3D hydrogeological model by using Petrel E&P (Petrel 2021©) as the basis for a numerical groundwater flow model developed with “ModelMuse”. A relevant aspect of the project is the use of Petrel 2021© geologic modelling tools in the field of applied hydrogeology to improve the details of both hydrogeological and numerical groundwater flow models, and their predictive capabilities. The study area is located in South Sardinia (Campidano Plain), where previous hydrogeological and modelling studies were available. The hydrogeological model was developed by digitising and interpreting the facies in the available borehole logs; a grid was subsequently created, including the main hydrogeological surfaces and performing geostatistical modelling of the facies based on grain size percentages. Afterwards, an empiric formula, achieved from flow tests and laboratory analyses, was applied to the grain size distribution to obtain preliminary hydraulic conductivity values, calibrated during simulations. These simulations, under various groundwater head scenarios, established the boundary conditions and conductivity values needed to determine the hydrogeological balance of the study area. The probabilistic approach has produced a highly detailed model able to adequately represent the natural hydrogeological phenomena and the anthropic stresses in places underground. Full article

Electroencephalography (EEG) has emerged as a pivotal tool in both research and clinical practice due to its non-invasive nature, cost-effectiveness, and ability to provide real-time monitoring of brain activity. Wearable EEG technology opens new avenues for consumer applications, such as mental health monitoring, neurofeedback training, and brain–computer interfaces. However, there is still much to verify and re-examine regarding the functionality of these devices and the quality of the signal they capture, particularly as the field evolves rapidly. In this study, we recorded the resting-state brain activity of healthy volunteers via three consumer-grade EEG devices, namely PSBD Headband Pro, PSBD Headphones Lite, and Muse S Gen 2, and compared the spectral characteristics of the signal obtained with that recorded via the research-grade Brain Product amplifier (BP) with the mirroring montages. The results showed that all devices exhibited higher mean power in the low-frequency bands, which are characteristic of dry-electrode technology. PSBD Headband proved to match BP most precisely among the other examined devices. PSBD Headphones displayed a moderate correspondence with BP and signal quality issues in the central group of electrodes. Muse demonstrated the poorest signal quality, with extremely low alignment with BP. Overall, this study underscores the importance of considering device-specific design constraints and emphasizes the need for further validation to ensure the reliability and accuracy of wearable EEG devices. Full article

Consumer-grade EEG devices, such as the InteraXon Muse 2 headband, present a promising opportunity to enhance the accessibility and inclusivity of neuroscience research. However, their effectiveness in capturing language-related ERP components, such as the N400, remains underexplored. This study thus aimed to investigate the feasibility of using the Muse 2 to measure the N400 effect in a semantic relatedness judgment task. Thirty-seven participants evaluated the semantic relatedness of word pairs while their EEG was recorded using the Muse 2. Single-trial ERPs were analyzed using robust Yuen t-tests and hierarchical linear modeling (HLM) to assess the N400 difference between semantically related and unrelated target words. ERP analyses indicated a significantly larger N400 effect in response to unrelated word pairs over the right frontal electrode. Additionally, dependability estimates suggested acceptable internal consistency for the N400 data. Overall, these findings illustrate the capability of the Muse 2 to reliably measure the N400 effect, reinforcing its potential as a valuable tool for language research. This study highlights the potential of affordable, wearable EEG technology to expand access to brain research by offering an affordable and portable way to study language and cognition in diverse populations and settings. Full article

This article examines a single figure from Christian history, the reformed sinner known as Musa of Rome (d.c. 593). Tracing the evolution of Musa from Gregory the Great’s Dialogues to early modern pastoral texts, this study explores processes of condemnation, recalibration, and negotiation regarding dance in premodern Christianity. The first section analyzes medieval portrayals of Musa as expressions of “choreophobia,” a term borrowed from dance studies scholar Anthony Shay that denotes cultural anxiety surrounding dance. Here, I argue that choreophobic renditions of Musa sedimented medieval misogyny and conceptualized sin. The second section turns to late medieval sources that assess dance differently vis-à-vis dance studies scholar André Lepecki’s concept of “choreopolice” or “choreopolicing”. For this study, choreopolicing highlights how ecclesiastical authorities refashioned Musa as a moralizing vehicle to articulate and implement clerical agendas. The third and final section explores Musa’s inspiring aura as a sacred muse. In this vein, her kinesthetic afterlives helped Christian laity apprehend Marian piety, visualize the resurrected body, and communicate hope for redemption. Methodologically, this study embraces the frameworks of religious studies, medieval studies, and dance studies. However fictional and embellished retellings of the Musa story were, this article—the first in-depth scholarly study dedicated to Musa of Rome—demonstrates how the medieval dancing body manifested a site of political contestation, ecclesiastical control, and individual redemption. Full article