Search Results (1,874)

Background: Induced after-death communication (IADC) experiences have been reported to share many phenomenological features with Near-Death Experiences (NDEs). This study aimed to empirically test the hypothesis that the majority of IADC experiences manifest a phenomenology that largely overlaps with that of NDEs. Methods: A cross-sectional observational design with retrospective data collection was employed. Fifty-nine participants (M = 56.25 years, SD = 10.18) who had previously undergone IADC therapy completed the Italian version of the Near-Death Experience (NDE) Scale. Descriptive analyses and repeated measures ANOVA were conducted to examine total scores and differences across subscales. Results: A total of 51 participants (86%) exceeded the established NDE threshold (≥7), with a mean total score of 14.69. The highest scores were observed on the Transcendental and Affective subscales, whereas the Paranormal subscale showed the lowest average scores. The Cognitive subscale exhibited intermediate values. Item-level analyses confirmed the high intensity of core NDE features, such as perceiving otherworldly environments, encountering deceased loved ones, and experiencing profound peace. Conclusions: This study provides the first empirical evidence of phenomenological overlap between IADC experiences and NDEs. These results shed light on the processes underlying the effectiveness of IADC therapy. Full article

Risk zone delineation and mobility behavior control constitute critical measures in pandemic containment. Numerous studies utilize static demographic data or dynamic mobility data to calculate the high–risk zones present in cities; however, these studies fail to concurrently consider activity and mobility patterns of populations in both space and time, which results in many studies only being able to employ static geostatistical analytical methods, neglecting the transmission risks associated with human mobility. This study utilized the mobile phone signaling data of Shenzhen residents from 2019 to 2020 and developed a CP tensor decomposition algorithm to decompose the long-sequence spatiotemporal trajectory data to detect high risk zones in terms of detecting overlapped community structures. Tensor decomposition algorithms revealed community structures in 2020 and the overlapping regions among these communities. Based on the overlap in spatial distribution and the similarity in temporal rhythms of these communities, we identified regions with spatiotemporal co-location as high–risk zones. Furthermore, we calculated the degree of population mixing in these areas to indicate the level of risk. These areas could potentially lead to rapid virus spread across communities. The research findings address the shortcomings of currently used static geographic statistical methods in delineating risk zones, and emphasize the critical importance of integrating spatial and temporal dimensions within behavioral big data analytics. Future research should consider utilizing non-aggregated individual trajectories to construct tensors, enabling the inclusion of individual and environmental attributes. Full article

Background: The emergency department (ED) often represents the entry point to care for patients that require urgent medical attention or have no alternative for medical treatment. This has implications on scope of practice and how quality of care is measured. A diverse array of methodologies has been developed to evaluate the quality of clinical care and broadly includes quality improvement (QI), quality assurance (QA), observational research (OR) and clinical quality registries (CQRs). Considering the overlap between QI, QA, OR and CQRs, we conceptualized a modular framework for TECOR to effectively and efficiently streamline clinical quality evaluations. Streamlining is both appropriate and justified as it reduces redundancy, enhances clarity and optimizes resource utilization, thereby allowing clinicians to focus on delivering high-quality patient care without being overwhelmed by excessive data and procedural complexities. The objective of this study is to describe the process for designing a modular framework for ED CQRs using TECOR as a case study. Methods: We performed a scoping audit of all quality projects performed in our ED over a 1-year period (1 January 2021 to 31 December 2021) as well as data mapping and categorical formulation of key themes from the TECOR dataset with clinical data sources. Both these processes then informed the design of TECOR. Results: For the audit of quality projects, we identified 29 projects. The quality evaluation methodologies for these projects included 12 QI projects, 5 CQRs and 12 OR projects. Data mapping identified that clinical information was fragmented across 11 distinct data sources. Through thematic analysis during data mapping, we identified three extraction techniques: self-extractable, manual entry and on request. Conclusions: The modular framework for TECOR aims to enable an efficient streamlined approach that caters to all aspects of clinical quality evaluation to enable higher throughput of clinician-led quality evaluations and improvements. TECOR is also an essential component in the development of a learning health system to drive evidence-based practice and the subject of future research. Full article

The detection of plant constituents is a crucial issue in precision agriculture, as monitoring these enables the automatic analysis of factors such as growth rate, health status, and crop yield. Tomatoes (Solanum sp.) are an economically and nutritionally important crop in Mexico and worldwide, which is why automatic monitoring of these plants is of great interest. Detecting leaves on images of outdoor tomato plants is challenging due to the significant variability in the visual appearance of leaves. Factors like overlapping leaves, variations in lighting, and environmental conditions further complicate the task of detection. This paper proposes modifications to the Yolov11n architecture to improve the detection of tomato leaves in images of complex outdoor environments by incorporating attention modules, transformers, and WIoUv3 loss for bounding box regression. The results show that our proposal led to a 26.75% decrease in the number of parameters and a 7.94% decrease in the number of FLOPs compared with the original version of Yolov11n. Our proposed model outperformed Yolov11n and Yolov12n architectures in recall, F1-measure, and mAP@50 metrics. Full article

Eye movements (EMs) offer valuable insights into cognitive and perceptual processes, serving as potential biomarkers for disordered thinking. This study explores the relationship between EM indices and perception and thinking problems in the Rorschach Performance Assessment System (R-PAS). Sixty non-clinical participants underwent eye-tracking while completing the Rorschach test, focusing on variables from the Perception and Thinking Problems Domain (e.g., WSumCog, SevCog, FQo%). The results reveal that increased cognitive disturbances were associated with greater exploratory activity but reduced processing efficiency. Regression analyses highlighted the strong predictive role of cognitive variables (e.g., WSumCog) over perceptual ones (e.g., FQo%). Minimal overlap was observed between performance-based (R-PAS) and self-report measures (BSI), underscoring the need for multi-method approaches. The findings suggest that EM patterns could serve as biomarkers for early detection and intervention, offering a foundation for future research on psychotic-spectrum processes in clinical and non-clinical populations. Full article

We present an alternative to the classical SQUID magnetometric measurements for the First-Order Reversal Curve (FORC) diagram approach by employing differential scanning calorimetry (DSC) experiments. After discussing the main results, the advantages and limitations of the magnetometric FORCs, we introduce the calorimetric method. We argue that, while the results are comparable to those obtained via magnetometry, the calorimetric method not only significantly simplifies the required mathematical computations but also detects subtle or overlapping phase transitions that might be hard to distinguish magnetically. The methodology is illustrated through both experimental data and mean-field simulations. Full article

Background: Annual influenza vaccine updates target viral drift, but immune responses may be biased by original antigenic sin (OAS). Few studies have explored this across closely related strains. This study examines how OAS shapes responses to sequential influenza variants in the context of seasonal vaccination. Methods: We conducted a prospective, longitudinal study to assess the humoral immune response to the 2023–2024 seasonal influenza vaccine containing the A/Victoria/4897/2022 (H1N1) strain. Bioinformatic analyses compared the hemagglutinin (HA) sequences of A/Victoria/4897/2022 and the antigenically related A/Victoria/2570/2019 strain. B-cell epitopes were mapped with BepiPred-3.0 and BepiBlast, and their physicochemical properties analyzed via accessibility, β-turns, flexibility, and hydrophilicity. Antibody responses were measured pre- and 28 days post-Vaxigrip Tetra vaccination in young (18–35) and older (>65) adults, stratified by cytomegalovirus (CMV) serostatus. HA sequences showed >97% identity, with variations mainly in the globular head. Predicted B-cell epitopes overlapped variable sites, suggesting possible immune escape. Despite having been vaccinated against the 2022 strain, serology showed higher antibody titers against the 2019 HA strain in all participants. This pattern suggests a potential antigen imprinting effect, though confirmation awaits further analysis. Age groups differed: older adults showed greater variability, while younger CMV+ individuals tended toward stronger 2019 HA responses. Conclusions: These findings suggest a complex interplay of factors shaping immune responses, though the imprinting effect and the potential role of CMV warrant further exploration in larger, more focused studies. Full article

This study investigates the hydrodynamic performance of ships entering a ship lift compartment that is under the influence of upstream channel geometry and proposes a mechanical traction scheme to enhance operational safety and efficiency. Utilizing a Reynolds-averaged Navier–Stokes (RANS)-based computational fluid dynamics (CFD) approach with overlapping grid technology, numerical simulations were conducted for both single and grouped ships navigating through varying water depths, speeds, and shore distances. The results revealed significant transverse force oscillations near the floating navigation wall due to unilateral shore effects, posing risks of deviation. The cargo ship experienced drastic resistance fluctuations in shallow-to-very-shallow-water transitions, while tugboats were notably affected by hydrodynamic interactions during group entry. A mechanical traction system with a four-link robotic arm was designed and analyzed kinematically and statically, demonstrating structural feasibility under converted real-ship traction forces (55.1 kN). The key findings emphasize the need for collision avoidance measures in wall sections and validate the proposed traction scheme for safe and efficient ship entry/exit. This research provides critical insights for optimizing ship lift operations in restricted waters. Full article

Purpose: This pilot study aimed to evaluate how deviations in X-ray tube head angulation and digital sensor alignment affect the radiographic measurement of the profile angle in CAD-CAM abutments. Materials and Methods: A mandibular model was used with five implant positions (central, buccal, and lingual offsets). Custom CAD-CAM abutments were designed with identical bucco-lingual direction contours and varying mesio-distal asymmetry for the corresponding implant positions. Periapical radiographs were acquired under controlled conditions by systematically varying vertical tube angulation, horizontal tube angulation, and horizontal sensor rotation from 0° to 20° in 5° increments for each parameter. Profile angles, interthread distances, and proximal overlaps were measured and compared with baseline STL data. Results: Profile angle measurements were significantly affected by both X-ray tube and sensor deviations. Horizontal tube angulation produced the greatest profile angle distortion, particularly in buccally positioned implants. Vertical x-ray tube angulations beyond 15° led to progressive underestimation of profile angles, while horizontal tube head rotation introduced asymmetric mesial–distal variation. Sensor rotation also caused marked interthread elongation, in some cases exceeding 100%, despite vertical projection being maintained. Profile angle deviations greater than 5° occurred in multiple conditions. Conclusions: X-ray tube angulation and sensor alignment influence the reliability of profile angle measurements. Radiographs with > 10% interthread elongation or crown overlap may be inaccurate and warrant re-acquisition. Special attention is needed when imaging buccally positioned implants. Full article

Diagnosing and prognosing immune-mediated airway diseases, like hypersensitivity pneumonitis (HP) and sarcoidosis, is complicated due to their overlapping symptoms and the lack of definitive biomarkers. Hence, we wanted to compare bronchoalveolar lavage (BAL) cytokine and chemokine profiles from 92 patients with different immune-mediated and inflammatory airway diseases, namely, HP, sarcoidosis, non-allergic asthma, amiodarone lung, and EGPA. We also compared pulmonary function parameters, BAL’s cellularity, and lymphocyte immunophenotypes. We found significant differences across all measured lung functions (VC, VC%, FEV1, FEV1%, and Tiff%) and in the number of macrophages, lymphocytes, neutrophils, and eosinophils. Furthermore, we showed significant differences in CD4, CD8, and CD4/8 across all included ILDs and OLDs; however, no significant differences were found in CD3, CD19, NK, or NKT. We identified nine biomarkers (IL-1β, IL-6, IL-8, IL-13, VEGF, angiogenin, C4a, RANTES, and MCP-1) that significantly differ in the BAL of patients with HP and sarcoidosis and showed that RANTES and IL-6 are associated with fibrotic outcome. We have demonstrated that interstitial and obstructive lung diseases differ in cytokine and cellular lung imprint, which may, in the future, enable the determination of the disease subtype and thus the identification of targets for the treatment of individuals or subgroups within diseases. Full article

Introduction: Chronic pain affects approximately 20% of U.S. adults, imposing significant burdens on individuals and healthcare systems. Medical cannabis has emerged as a potential therapy, yet its impact on healthcare utilization remains unclear. Methods: This retrospective cohort study analyzed administrative data from a telehealth platform providing medical cannabis certifications across 36 U.S. states. Patients were classified as cannabis-exposed if they had used cannabis in the past year, while unexposed patients had no prior cannabis use. Outcomes included self-reported urgent care visits, emergency department (ED) visits, hospitalizations, and quality of life (QoL), measured using the CDC’s Healthy Days measure. Targeted Maximum Likelihood Estimation with SuperLearner estimated causal effects, adjusting for numerous covariates. Results: Medical cannabis users exhibited significantly lower healthcare utilization. Specifically, exposure was associated with a 2.0 percentage point reduction in urgent care visits (95% CI: −0.036, −0.004), a 3.2 percentage point reduction in ED visits (95% CI: −0.051, −0.012) and fewer unhealthy days per month (−3.52 days, 95% CI: −4.28, −2.76). Hospitalization rates trended lower but were not statistically significant. Covariate balance and propensity score overlap indicated well-fitting models. Conclusions: Medical cannabis use was associated with reduced healthcare utilization and improved self-reported QoL among chronic pain patients. Full article

Underground cast-in-place pipes (CIPP, Diameter of 2’–5’) are used to transport water for the Phoenix, AZ area. These pipes have developed leaks due to their age and changes in the environment, resulting in a significant waste of water. Currently, leaks can only be identified when water pools above ground occur and are then manually confirmed through the inside of the pipe, requiring the shutdown of the water system. However, many leaks may not develop a puddle of water, making them even harder to identify. The primary objective of this research was to develop an inspection method utilizing drone-based infrared imagery to remotely and non-invasively sense thermal signatures of abnormal soil moisture underneath urban surface treatments caused by the leakage of water pipelines during the regular operation of water transportation. During the field tests, five known leak sites were evaluated using an intensive experimental procedure that involved conducting multiple flights at each test site and a stringent filtration process for the measured temperature data. A detectable thermal signal was observed at four of the five known leak sites, and these abnormal thermal signals directly overlapped with the location of the known leaks provided by the utility company. A strong correlation between ground temperature and shading before sunset was observed in the temperature data collected at night. Thus, a shadow and solar energy model was implemented to estimate the position of shadows and energy flux at given times based on the elevation of the surrounding structures. Data fusion between the metrics of shadow time, solar energy, and the temperature profile was utilized to filter the existing points of interest further. When shadows and solar energy were considered, the final detection rate of drone-based infrared imaging was determined to be 60%. Full article

Background/Objectives: Multiple sclerosis (MS) is a complex neurological disease that is associated with a broad spectrum of physical and psychological symptoms. Psychosocial adjustment (PSA) refers to the ability to cope with these challenges, which influence quality of life (QoL) and depressiveness in ways not yet fully understood. This study explores the relationship of PSA and disease-specific symptoms in MS, including fatigue, a prominent MS symptom. Additionally, PSA was compared to Amyotrophic Lateral Sclerosis (ALS) to disentangle the impact of disease trajectory on PSA. Methods: We interviewed 77 MS patients using patient-reported outcome measures on QoL and depression and compared them to 30 ALS patients. Confirmatory factor analysis and regression analysis were used to identify PSA indicators and predictors in MS, while t-tests assessed PSA differences across diseases. Results: Key PSA indicators in MS included physical (PQoL), mental (MQoL), and subjective (SQoL) quality of life, as well as depressiveness, with cognitive and motor fatigue emerging as significant predictors. MS patients had higher PQoL and SQoL and lower levels of depression compared to ALS patients, while both groups were comparable with regard to MQoL. Conclusions: PSA in MS is supported by high QoL and low depression levels, with fatigue being a significant predictor. Despite different disease trajectories, patients with MS and ALS showed comparable MQoL, indicating that both diseases similarly impact mental QoL, reflecting a partial overlap in psychosocial adjustment. Overall, psychosocial adjustment was more favorable in MS, likely due to its slower disease progression compared to ALS. Full article

The construction of hydroelectric dams leads to substantial land-cover alterations, particularly through the removal of vegetation in wetland and valley areas. This results in exposed sediment that is susceptible to erosion, potentially leading to dust storms. While the reintroduction of vegetation plays a crucial role in restoring these landscapes and mitigating erosion, such efforts incur substantial costs and require detailed information to help optimize vegetation densities that effectively reduce dust storm risk. This study evaluates the performance of drones for measuring the growth of introduced low-lying grasses on reservoir beaches. A set of test flights was conducted to compare LiDAR and photogrammetry data, assessing factors such as flight altitude, speed, and image side overlap. The results indicate that, for this specific vegetation type, photogrammetry at lower altitudes significantly enhanced the accuracy of vegetation classification, permitting effective quantitative assessments of vegetation densities for dust storm risk reduction. Full article

In recent years, global agriculture has encountered several challenges exacerbated by the effects of changes in climate, such as extreme water shortages for irrigation and heat waves. Water-deficit stress adversely affects the morpho-physiology of numerous crops, including soybean (Glycine max L.), which is considered as promising crop in Bangladesh. Seaweed extract (SWE) has the potential to improve crop yield and alleviate the adverse effects of water-deficit stress. Remote and proximal sensing are also extensively utilized in estimating morpho-physiological traits owing to their cost-efficiency and non-destructive characteristics. The study was carried out to evaluate soybean morpho-physiological traits under the application of water extracts of Gracilaria tenuistipitata var. liui (red seaweed) with two varying irrigation water conditions (100% of total crop water requirement (TCWR) and 70% of TCWR). Principal component analysis (PCA) revealed that among the four treatments, the 70% irrigation + 5% (v/v) SWE and the 100% irrigation treatments overlapped, indicating that the application of SWE effectively mitigated water-deficit stress in soybeans. This result demonstrates that the foliar application of 5% SWE enabled soybeans to achieve morpho-physiological performance comparable to that of fully irrigated plants while reducing irrigation water use by 30%. Based on Pearson’s correlation matrix, a simple linear regression model was used to ascertain the relationship between unmanned aerial vehicle (UAV)-derived vegetation indices and the field-measured physiological characteristics of soybean. The Normalized Difference Red Edge (NDRE) strongly correlated with stomatal conductance (R² = 0.76), photosystem II efficiency (R² = 0.78), maximum fluorescence (R² = 0.64), and apparent transpiration rate (R² = 0.69). The Soil Adjusted Vegetation Index (SAVI) had the highest correlation with leaf relative water content (R² = 0.87), the Blue Normalized Difference Vegetation Index (bNDVI) with steady-state fluorescence (R² = 0.56) and vapor pressure deficit (R² = 0.74), and the Green Normalized Difference Vegetation Index (gNDVI) with chlorophyll content (R² = 0.73). Our results demonstrate how UAV and physiological data can be integrated to improve precision soybean farming and support sustainable soybean production under water-deficit stress. Full article