Search Results (1,154)

Previous studies have shown that hazard ratios between treatment groups estimated with the Cox model are uninterpretable because the unspecified baseline hazard of the model fails to identify temporal change in the risk-set composition due to treatment assignment and unobserved factors among multiple contradictory scenarios. To alleviate this problem, especially in studies based on observational data with uncontrolled dynamic treatment and real-time measurement of many covariates, we propose abandoning the baseline hazard and using kernel-based machine learning to explicitly model the change in the risk set with or without latent variables. For this framework, we clarify the context in which hazard ratios can be causally interpreted, then develop a method based on Neyman orthogonality to compute debiased maximum likelihood estimators of hazard ratios, proving necessary convergence results. Numerical simulations confirm that the proposed method identifies the true hazard ratios with minimal bias. These results lay the foundation for the development of a useful alternative method for causal inference with uncontrolled, observational data in modern epidemiology. Full article

Background: Psoriasis is a chronic immune-mediated disease affecting approximately 43 million individuals worldwide. While previous studies provide certain insights, there remains different conclusions and a lack of a comprehensive analysis regarding the burden of psoriasis. In response to ongoing therapeutic advances and a growing patient population, this study utilizes the Global Burden of Disease (GBD) 2021 estimates to characterize the spatiotemporal evolution of the psoriasis burden from 1990 through 2021. By integrating these biological, geographic, and socioeconomic determinants, this study aims to inform more targeted and effective health policy planning. Methods: To track changes over time, the Estimated Annual Percentage Change (EAPC) was determined using a linear regression model. In addition, a frontier analysis was utilized to investigate the link between psoriasis burden and socio-demographic progress. Furthermore, geographically weighted regression was used for the spatial econometric assessment of EAPC, age-standardized rates (ASRs), and Human Development Index (HDI) covariance structures across nation-states. Results: Between 1990 and 2021, the global burden of psoriasis increased consistently, with ASRs exhibiting a positive correlation with the Socio-demographic Index (SDI). High-SDI regions reported the highest burden, while high–middle-SDI regions experienced the steepest rise. Conclusions: This study reveals an increasing global psoriasis burden (1990–2021) through systematic analyses, indicating distinct regional progression patterns. These findings advocate for geographically tailored strategies to alleviate healthcare system pressures. Full article

The use of cover crops is one of the most effective practices for maintaining, or even improving, the carbon balance of agricultural soils, while offering various ecosystem benefits. However, replacing bare soil with cover crops can increase transpiration and potentially reduce the water available for subsequent cash crops. The study takes place in southwestern France where it is essential to strike a balance between carbon storage and water availability, and where agroecological practices are encouraged and water resources are limited and expected to diminish with climate change. In this study, estimates of cover crop biomass production, as well as of the components of the water and carbon cycles, are carried out using a hybrid approach, AgriCarbon-EO, combining modeling, remote sensing, and assimilation, with quantification of target variables and their uncertainties at decametric resolution. The SAFYE-CO₂ agrometeorological model used in AgriCarbon-EO is calibrated to represent cover crops development, and simulated variables are compared with CO₂ fluxes and evapotranspiration measured by eddy covariance (for NEE, R² = 0.57, RMSE = 0.97 gC·m⁻²; for ETR, R² = 0.42, RMSE = 0.87 mm), as well as to an extensive above-ground biomass dataset (R² = 0.71, RMSE = 93.3 g·m⁻²). Knowing the local performance of the approach, a large-scale, decametric-resolution modeling exercise was carried out to simulate winter cover crops in southwestern France, over five contrasting fallow periods. The significant variability in cover crop phenology and above-ground biomass was characterized, and estimates of the amount of humified carbon added to the soil by cover crops were quantified at the pixel level. With amounts ranging from 40 to 130 gC·m⁻² for most of the considered pixels, these new SOC values show clear trends as a function of cumulative evapotranspiration. However, the impact of cover crops on soil water content appears to be minimal due to spring precipitation. Full article

Globalisation, population growth, climate change, and energy-policy shifts have deepened interdependence between agri-food and energy systems, amplifying price volatility. This study examines the determinants of global wheat and corn price dynamics over 2000–2023, emphasising energy markets (oil and biofuels), agronomic and climatic factors, population pressure, and cross-market interdependencies. Using multiple linear regression with backward selection on annual global data from official sources (FAO, USDA, EIA and market series), we quantify the relative contributions of these drivers. The models explain most of the variation in world prices (R² = 0.89 for wheat; 0.92 for corn). Oil prices are a dominant covariate: a 1 USD/barrel increase in Brent is associated with a 1.33 USD/t rise in the wheat price, while a 1 USD/t increase in the corn price raises the wheat price by 0.54 USD/t. Lower biodiesel output per million people is linked to higher wheat prices (+0.67 USD/t), underscoring the role of biofuel supply conditions. We also document an asymmetric yield effect—higher yields correlate positively with wheat prices but negatively with corn—consistent with crop-specific market mechanisms. Although temperature and precipitation were excluded from the regressions due to collinearity, their strong correlations with yields and biofuel activity signal continuing climate risk. The contribution of this study lies in integrating energy, climate, and agricultural market factors within a single empirical framework, offering evidence of their joint role in shaping staple grain prices. These findings add to the literature on food–energy linkages and provide insights for sustainability policies, particularly the design of integrated energy–agriculture strategies and risk-management instruments to enhance resilience in global food systems. Full article

Background/Objectives: Mental health disparities across generations are a growing concern in rapidly changing societies. While health-related quality of life (HRQoL) and social networking are widely recognized as determinants of psychological well-being, less is known about how their effects differ across age groups. This study investigates the generational patterns in the relationship between HRQoL, social networking, and mental health in South Korea. Methods: We analyzed data from the nationally representative 2023 Community Health Survey (South Korea). HRQoL, social networking activity, and self-reported mental health outcomes (stress and depression) were assessed. Multivariate models were used to test main effects and interactions, with stratification by generational cohorts. Results: Across all models (i.e., all age groups), good HRQoL strongly predicts lower depression (PHQ-9 scores), showing coefficients ranging from –1.20 to –1.48, p < 0.001. Social networking activity also predicts reduced depressive symptoms, with significant effects from the thirties onward (e.g., –0.317 in the 30s, –0.507 in the 50s, –0.424 in the 70s +; all p < 0.001). However, the interaction term between HRQoL and social networking activity yields unexpected findings. The interaction becomes positive and significant, with coefficients that increase steadily by age: 0.388 in the 40s, 0.472 in the 50s, 0.533 in the 60s, and 0.638 in the 70s + (all p < 0.001). Using stress (1 = high-level, 0 = low-level) as the outcome variable, with the same set of covariates, it replicates the findings similar to those obtained when PHQ-9 as the outcome variable. Conclusions: The results suggest that the protective role of HRQoL and social networking is not uniform across generations. In South Korea, relative deprivation and social comparison may intensify with age, amplifying the psychological burden despite higher quality of life or social participation. These findings highlight the need for mental health interventions and policy responses that account for generational differences in the social determinants of well-being. Full article

Learning from demonstration with multiple executions must contend with time warping, sensor noise, and alternating quasi-stationary and transition phases. We propose a label-free pipeline that couples unsupervised segmentation, duration-explicit alignment, and probabilistic encoding. A dimensionless multi-feature saliency (velocity, acceleration, curvature, direction-change rate) yields scale-robust keyframes via persistent peak–valley pairs and non-maximum suppression. A hidden semi-Markov model (HSMM) with explicit duration distributions is jointly trained across demonstrations to align trajectories on a shared semantic time base. Segment-level probabilistic motion models (GMM/GMR or ProMP, optionally combined with DMP) produce mean trajectories with calibrated covariances, directly interfacing with constrained planners. Feature weights are tuned without labels by minimizing cross-demonstration structural dispersion on the simplex via CMA-ES. Across UAV flight, autonomous driving, and robotic manipulation, the method reduces phase-boundary dispersion by 31% on UAV-Sim and by 30–36% under monotone time warps, noise, and missing data (vs. HMM); improves the sparsity–fidelity trade-off (higher time compression at comparable reconstruction error) with lower jerk; and attains nominal 2σ coverage (94–96%), indicating well-calibrated uncertainty. Ablations attribute the gains to persistence plus NMS, weight self-calibration, and duration-explicit alignment. The framework is scale-aware and computationally practical, and its uncertainty outputs feed directly into MPC/OMPL for risk-aware execution. Full article

Background: The cerebral compliance (or compensatory reserve) index, RAP, is a critical yet underutilized physiological marker in the management of moderate-to-severe traumatic brain injury (TBI). While RAP offers promise as a continuous bedside metric, its broader cerebral physiological context remains partly understood. This study aims to characterize the burden of impaired RAP in relation to other key components of cerebral physiology. Methods: Archived data from 379 moderate-to-severe TBI patients were analyzed using descriptive and threshold-based methods across three RAP states (impaired, intact/transitional, and exhausted). Agglomerative hierarchical clustering, principal component analysis, and kernel-based clustering were applied to explore multivariate covariance structures. Then, high-frequency temporal analyses, including vector autoregressive integrated moving average impulse response functions (VARIMA IRF), cross-correlation, and Granger causality, were performed to assess dynamic coupling between RAP and other physiological signals. Results: Impaired and exhausted RAP states were associated with elevated intracranial pressure (p = 0.021). Regarding AMP, impaired RAP was associated with elevated levels, while exhausted RAP was associated with reduced pulse amplitude (p = 3.94 × 10⁻⁹). These two RAP states were also associated with compromised autoregulation and diminished perfusion. Clustering analyses consistently grouped RAP with its constituent signals (ICP and AMP), followed by brain oxygenation parameters (brain tissue oxygenation (PbtO₂) and regional cerebral oxygen saturation (rSO₂)). Cerebral autoregulation (CA) indices clustered more closely with RAP under impaired autoregulatory states. Temporal analyses revealed that RAP exhibited comparatively stronger responses to ICP and arterial blood pressure (ABP) at 1-min resolution. Moreover, when comparing ICP-derived and near-infrared spectroscopy (NIRS)-derived CA indices, they clustered more closely to RAP, and RAP demonstrated greater sensitivity to changes in these ICP-derived CA indices in high-frequency temporal analyses. These trends remained consistent at lower temporal resolutions as well. Conclusion: RAP relationships with other parameters remain consistent and differ meaningfully across compliance states. Integrating RAP into patient trajectory modelling and developing predictive frameworks based on these findings across different RAP states can map the evolution of cerebral physiology over time. This approach may improve prognostication and guide individualized interventions in TBI management. Therefore, these findings support RAP’s potential as a valuable metric for bedside monitoring and its prospective role in guiding patient trajectory modeling and interventional studies in TBI. Full article

Background/Objectives: Dutch newborn screening is an important public health program designed to detect conditions early in life, enabling timely interventions that can prevent mortality, morbidity, and long-term disabilities. However, the program also faces certain challenges. One such issue is obtaining and maintaining a high positive predictive value (PPV); another is that newborn screening (NBS) in the Netherlands is intended for all newborn babies until the age of six months. This means comparing infants at different ages may introduce variability that complicates data interpretation. To support the optimization of the program, we systematically analyzed population-level tandem mass spectrometry (MS/MS) data to explore postnatal metabolic changes. Methods: We evaluated the impact of covariates—including birth weight, gestational age, age at blood collection, and biological sex—on metabolite profiles using retrospective newborn screening (NBS) data. Special emphasis was placed on the combined effects of these covariates. The analysis was based on data from 985,629 newborns collected between 2018 and 2024. Results: Specifically, (extremely) preterm infants exhibit altered levels of several amino acids and acylcarnitines. Moreover, we observed multiplicative effects of gestational age and birth weight on several metabolic markers. Biological sex however, does not have an impact. The largest impact of the age of sampling was observed on the C0/C16+C18 ratio, which may impact screening performance for CPT1 deficiency. Conclusions: Covariate-adjusted reference values could improve the performance of the Dutch newborn screening. Full article

Anatomic and functional changes in the pelvic floor muscles (PFMs) have been associated with stress urinary incontinence (SUI). The aim of this study is to compare cough-induced contraction response time and PFM strength in women with and without SUI. This cross-sectional study evaluated 40 women (20 with and 20 without SUI) aged 20 to 60 years. PFM strength was measured using a vaginal dynamometer. The cough signal was captured using an accelerometer, and activity of the right external oblique muscle was measured using surface electromyography. All signals were synchronized and recorded using the same signal acquisition module. Analysis of covariance (ANCOVA) with the Bonferroni post hoc test was used to compare the dynamometric data between groups (control and SUI). No significant differences were found between groups regarding variables related to PFM strength, but significant differences were found for activation time of the PFMs (F = 59.42, p < 0.0001, η_p² = 0.76), activation time of the external oblique muscle (F = 6.55, p = 0.004, η_p² = 0.26), and cough pulse time (F = 3.32, p = 0.04, η_p² = 0.15). Women with stress urinary have a delayed cough-induced contraction response of the pelvic floor muscles, but no difference in the contraction force of these muscles was found in comparison to women without stress urinary incontinence. Full article

Background/Objectives: Spinal muscular atrophy (SMA) is a progressive neurodegenerative disease resulting in proximal muscle weakness and paralysis. SMA treatment has radically changed in the past 10 years thanks to the development of novel therapies such as nusinersen and onasemnogene abeparvovec. Since the advent of new treatments, the incidence and perioperative risk factors of patients with SMA undergoing longer, higher-risk surgeries are unknown. We hypothesized that patients with SMA would be at an overall elevated risk for postoperative pulmonary complications (PPC) and prolonged length of stay compared to the general population, but that this would be reduced in patients undergoing surgery in the years after the release of new therapies. Methods: Patients who underwent surgery at a continuously enrolled American College of Surgeons National Surgery Quality Improvement Program-Pediatric hospital from 1 January 2012, to 31 December 2021, were included in this study. Cases with missing covariate or primary outcome data were excluded from the analysis. Patients with ages greater than 17 years, preoperative tracheostomy, preoperative mechanical ventilation, missing covariate or primary outcome data were excluded. Patients with SMA were identified by their ICD-9 and 10 codes. A cutoff year of 2018 was chosen for analysis of the primary outcomes as this was a full year after nusinersen received FDA approval. Results: On univariable analysis, the risk for PPC in patients with SMA was reduced in patients undergoing surgery in 2018 or later compared to pre-2018 (pre-2018 OR 4.44, 95% CI 1.56–9.6, p = 0.008; post-2018 OR 3.48, 95% CI 0.84–9.12, p = 0.08). On multivariable analysis, the association between SMA and PPC substantially decreased in 2018 and after but was no longer statistically significant (pre-2018 OR 1.96, 95% CI 0.80–4.80, p = 0.14; post-2018 OR 1.03, 95% CI 0.33–3.26, p = 0.96). SMA was positively associated with LOS in the pre-2018 cohort, with a coefficient from a log linear model of 0.67 (95% CI 0.32–1.01; p < 0.001), and SMA adding an additional 1.93 days in LOS. For data post-2018, the effect of SMA on LOS was no longer statistically significant. Conclusions: Utilizing a large dataset, we found a reduced association between SMA and PPC a year following widespread implementation of SMN antisense oligonucleotide therapy, and a statistically significant reduction in LOS in patients with SMA after 2018. This may reflect improved motor outcomes and respiratory mechanics in the new treatment era. Full article

The Xinjiang Province of China, characterized as a typical arid to semi-arid region, is increasingly facing severe issues related to soil salinization. Timely and accurate estimation of soil salinization in this region is crucial for the sustainable development of agriculture and food security. However, current methods for detecting soil salinization primarily rely on various environmental covariates, which assess the extent of soil salinization by analyzing the relationship between environmental factors and the accumulation of soil salts. Nonetheless, these conventional environmental covariates often suffer from response delays, making it challenging to promptly reflect the dynamic changes in soil salinity. Solar-induced chlorophyll fluorescence (SIF) has been widely used to assess vegetation photosynthetic efficiency and is considered a direct indicator of plant photosynthetic activity. In contrast, SIF provides a timely means of monitoring the status of plant photosynthesis, indirectly reflecting the impact of soil salinization on plant growth. However, the spatial resolution of SIF products derived from satellites is typically low, which significantly limits the accurate estimation of soil salinization in Xinjiang. This study proposes a novel method for monitoring soil salinization, based on SIF data. The approach employs a Stacking ensemble learning model to downscale SIF data, thereby improving the spatial resolution of soil salinity monitoring. Using the GOSIF dataset, combined with environmental covariates, such as MODIS, the Stacking framework facilitates the fine-scale downscaling of SIF data, generating high-resolution SIF products, ranging from 0.05° to 0.005°, with a spatial resolution of 30 m. This refined SIF data is then used to predict soil electrical conductivity (EC). The experimental results demonstrate that: (1) the proposed Stacking-based SIF downscaling method is highly effective, with a high degree of fit to reference SIF data (R² > 0.85); (2) the high-resolution SIF data, after downscaling, more accurately reflects the spatial heterogeneity of soil salinization, especially in shallow soils (r < −0.6); and (3) models combining SIF and environmental covariates exhibit superior accuracy compared to models that rely solely on SIF or traditional environmental covariates (R² > 0.65). This research provides new data support and methodological advancements for precision agriculture and ecological environmental monitoring. Full article

Background: Emotional eating (EmE) is a maladaptive eating behavior that has been frequently observed among university students, possibly due to academic stress and lifestyle changes. However, its specific assessment in health science students has been poorly addressed, even though this population faces high levels of academic stress and emotional burden. Objective: This study explores the association between self-perceived health status, life satisfaction, and EmE among university students in the health field on the north coast of Peru. Methods: A cross-sectional study was conducted on a sample of 1213 students. Self-perceived health, life satisfaction, and EmE were assessed using validated instruments. In addition, sociodemographic data were considered as covariates and possible confounding factors. T-tests, chi-square tests, and Poisson regression with robust variance were applied. Results: EmE was more prevalent in women (78.0%) than in men (66.8%; p < 0.001). In addition, an inverse association was observed between self-perceived health and emotional eating: students with average self-perceived health (adjusted OR = 0.88; 95% CI: 0.83–0.94) and those with high self-perceived health (adjusted OR = 0.75; 95% CI: 0.69–0.81) showed a progressively lower prevalence of EmE compared to those with low self-perceived health. Similarly, high life satisfaction was associated with a lower prevalence of EmE (adjusted PR = 0.88; 95% CI: 0.80–0.96). Conclusions: Low self-perceived health and life dissatisfaction were significantly associated with a higher probability of EmE in medical and nursing students. These results highlight the need to strengthen university programs on mental health, emotional regulation, and subjective well-being promotion as strategies to prevent maladaptive eating behaviors in academic settings, considering gender. Full article

Divers’ navigation heavily depends on their experience and physical condition, and accidents caused by failure to return occur every year. To address this issue, we developed a navigation system for divers. This navigation system leverages Raspberry Pi and low-cost sensors, including an accelerometer, gyro sensor, geomagnetic sensor, and pressure gauge, to guide divers along predefined routes back to their starting point. The system employs a 20 Hz sampling frequency and applies high-pass filtering (HPF) to acceleration signals to eliminate gravitational interference. Velocity integration errors are corrected using the rate of pressure change, while impulse noise in accelerometer and geomagnetic sensors is removed via the Robust Kalman Filter (RKF). A time-varying system noise covariance matrix enhances accuracy during rotational states. Quaternion-based attitude avoids gimbal lock, with the Kalman Filter (KF) fusion of accelerometer/geomagnetic data mitigating gyro sensor drift. Forced oscillator trials achieved pitch/roll RMS errors of ±1.23° and ±0.26°. In Kanagawa, Japan, divers successfully navigated 44 waypoints (<5 m spacing) along a route with obstacles (30 m rope, Authors, reefs), with a start/end GNSS positioning error of 6.67 m. Full article

Fatty acid binding proteins FABP3 and FABP4 act as intracellular lipid chaperones that influence fatty acid transport and metabolism in mammary tissue, and genetic variation in these genes may affect milk composition. We examined the associations between FABP3 and FABP4 polymorphisms and milk composition and fatty acid profiles in 200 lactating Native Southern Yellow (NSY) cows. DNA from each cow was PCR-amplified and Sanger-sequenced for FABP3 and FABP4; genotypes were tested for their association with milk fatty acid concentrations and standard composition traits using linear models adjusted for relevant covariates. We detected a missense variant in FABP3 (c.3656G > A; p.Val45Met) and an intronic SNP in FABP4 (g.3509T > C). The FABP3 p.Val45Met AA genotype was associated with higher concentrations of butyric, palmitic, oleic, and α-linolenic acids. Cows with the FABP4 TC genotype exhibited elevated levels of myristoleic, γ-linolenic, conjugated linoleic, and arachidic acids, along with increased fat-free dry matter, protein, and lactose. In silico analyses provided mixed evidence for the structural effects of p.Val45Met, molecular docking suggested altered ligand affinity for several fatty acids, and splice site prediction implicated g.3509T > C in possible transcript processing changes. These variants constitute candidate markers for milk fatty acid composition in NSY cattle; replication in independent cohorts and functional validation are recommended to confirm their utility for milk quality improvement. Full article

This study investigates the spatial characteristics of daily maximum precipitation for Prince Edward Island using a max-stable process model. The ssp126, ssp245, and ssp585 climate change scenarios, indicating low/optimistic, intermediate/in-between, and worst/pessimistic emissions scenarios, respectively, were extracted from 11 global climate model ensembles. For the time periods, the reference (historical) period was from 1971 to 2000, according to the World Meteorological Organization recommendations. Other time periods considered were 2011–2040, 2041–2070, and 2071–2100 as immediate, intermediate, and far future periods, respectively. The spatial trends analysis shows a west-to-east gradient throughout the entire study area. Return levels of 25 years were predicted for all the projections using the spatial generalized extreme value model fitted to the historical period, showing that topography should be included as a covariate in the spatial extreme model. Across the 134 grid points used in the study, the predicted return level for the historical period was 94 mm. Compared with the immediate time period, there is an increase of 47%, 53%, and 50% for the low, intermediate, and worst emission scenarios, respectively. For the intermediate period, there is an increase of 43%, 59%, and 56% for the low, intermediate, and worst emission scenarios, respectively. For the far future period, there is an increase of 49%, 48%, and 84% for the low, intermediate, and worst emission scenarios, respectively. There is a systematic increase in return levels based on the different periods. This shows a high chance of increased risks of extreme events of large magnitudes for this area in the immediate future through to the far future. This study will be useful for engineers, city planners, financial officials, and policymakers tasked with infrastructure development, long-term safety protocols, and sustainability and financial risk management. Full article