Search Results (1,237)

Background/Objectives: Randomized controlled trials (RCTs) are the gold standard for evaluating treatment efficacy, yet recruitment and retention remain challenging, particularly when placebo arms are required. Using historical placebo data may reduce the need for contemporaneous placebo groups, but comparability between historical and real-time placebo responses is uncertain. This study assessed the feasibility of replacing a placebo control group with a historical placebo arm in osteoarthritis (OA) RCTs using several matching approaches. Methods: Data from three published knee OA RCTs (2009, 2013, 2017) were analyzed. The study followed three steps: (1) development of matching techniques using the 2009 and 2013 trials, (2) validation in the 2017 trial, and (3) post hoc analyses comparing placebo responses across trials. Methods included direct covariate adjustment, exact and nearest-neighbor matching, and propensity score matching based on baseline characteristics (age, sex, BMI, OA duration, baseline pain). The main outcome was change in 100 mm visual analogue scale (VAS) pain. Results: Initial attempts showed moderate to good success in adjusting historical placebo response on the VAS using various adjustment methods. However, in the validation process, a significant discrepancy was observed between real placebo VAS changes data and historical placebo VAS changes data, and various matching techniques failed to sufficiently reduce this discrepancy. In the post hoc analysis, despite the application of advanced matching techniques, substantial variability in VAS placebo responses persisted across trials. Even among placebo patients with highly similar baseline characteristics, the VAS changes over time differed significantly between studies. Conclusions: The findings indicate that replacing a real placebo group with a historical placebo in osteoarthritis RCTs is hardly feasible. These results underscore the complexity of placebo effects in osteoarthritis trials and the limitations of historical control data in this context. Full article

With the high penetration of power electronic devices, modern power systems exhibit complex fractional-order dynamic characteristics. Addressing this, along with the prevalent issues of multi-modal non-Gaussian noise, outliers, and sudden load changes, a fractional-order adaptive generalized cross correlation entropy unscented Kalman filter (FO-AGCCE-UKF) method is proposed in this paper. First, acknowledging that traditional integer-order models overlook the cumulative effects of historical states, a fractional-order (FO) discrete-time state-space model is constructed based on the Grünwald–Letnikov definition. This model accurately characterizes the long-memory and non-locality properties of power systems, thereby improving modeling accuracy during transient processes. Second, to mitigate the impact of non-Gaussian noise and outliers, the generalized cross correlation entropy (GCCE) criterion is adopted to replace the traditional mean square error (MSE) criterion. Combined with statistical linearization techniques, a novel recursive filtering framework is derived to enhance robustness against heavy-tailed noise. Furthermore, to address the time-varying and unknown statistical properties of process and measurement noise, an adaptive update mechanism for noise covariance matrices is introduced, which corrects noise parameters online based on innovation sequences. Simulation experiments and comparative analysis on multiple power systems of different scales demonstrate that the proposed method not only exhibits superior anti-interference capability in mixed-Gaussian noise environments but also achieves a faster convergence speed and higher tracking accuracy during dynamic events such as sudden load changes. Full article

Background/Objectives: University students exhibit high levels of sedentary behavior and low adherence to physical activity recommendations, and immersive virtual reality (IVR) represents an innovative strategy to increase physical activity participation. The aim of this study was to evaluate the effect of a physical activity intervention using IVR on anthropometric variables, physical fitness, and blood pressure in university students. Methods: A randomized controlled trial was conducted with 60 participants (30 control, 30 intervention) over 12 weeks. The intervention group performed three weekly exercise sessions using IVR, while the control group maintained their usual activity. BMI, waist and hip circumferences, handgrip strength, cardiorespiratory fitness, and blood pressure were assessed. Baseline characteristics between groups were compared using Student’s t-test. The effect of the intervention was analyzed using analysis of covariance adjusted for baseline values. Sensitivity analyses were performed to assess between-group changes, and subgroup analyses were conducted to determine the impact of sex. Results: The intervention produced significant improvements in cardiorespiratory fitness (VO₂ and the 20 m shuttle run test); no significant changes were observed in anthropometric variables, strength, or blood pressure. Conclusions: A 12-week intervention with immersive virtual reality-based physical training improves cardiorespiratory fitness in university students, representing a promising tool for health promotion in this population. Full article

Background: Few studies have examined changes in diet quality into old age, and related these changes to musculoskeletal outcomes. We examined this among Hertfordshire Cohort Study participants. Methods: In total, 178 individuals provided diet quality scores derived in 1998–2004, 2011 and 2017 (median age 64.0, 74.7 and 80.7) using principal component analysis of food frequency questionnaires; higher scores indicated healthier diets (more fruit and vegetables, oily fish and wholemeal bread, and less white bread, added sugar, full-fat dairy products, chips and processed meat). Pearson correlations between diet quality scores at each time-point were computed. Group-based trajectory modelling of diet quality scores was implemented; trajectory groups as predictors of musculoskeletal outcomes (history of hip/knee replacement, osteoporosis, fall in previous year, low grip strength, low gait speed) in 2017 were examined using logistic regression with age and sex included as covariates. Results: Diet quality showed moderate stability over time (0.64 < r < 0.74). Three trajectory groups were identified: low (29%), medium (51%), and high diet quality (20%). A higher diet quality group was related to greater odds (95% CI) of hip/knee replacement (1.85 (1.05, 3.26) per higher category); associations with other musculoskeletal outcomes were weak (p > 0.17). Conclusions: Weak associations were observed between diet quality trajectories and musculoskeletal outcomes. However, higher diet quality was related to increased likelihood of hip/knee joint replacement, potentially due to confounding by socioeconomic position. The stability of diet quality suggests individuals with poorer diets around age 65 are likely to maintain these patterns into old age and may benefit from targeted interventions. Full article

Background: The COVID-19 pandemic disrupted access to routine dermatologic care and may have delayed melanoma diagnosis and management. Evidence on the post-pandemic period and on hospital-based referral cohorts remains limited. We assessed melanoma presentations before, during and after the pandemic in three skin cancer centers in North Rhine-Westphalia, Germany. Methods: We conducted a multicenter retrospective cohort study of inpatients with cutaneous melanoma grouped into Phase 1 (February 2017–February 2020), Phase 2 (March 2020–March 2023), and Phase 3 (April 2023–May 2024). The primary endpoint was Breslow tumor thickness (TT) among invasive melanomas, analyzed using multivariable log-linear regression adjusted for center, age, sex, anatomic site, and histologic subtype. Secondary endpoints included T category and AJCC stage distributions (including stage 0/Tis), macroscopic primary tumor specimen dimensions (area and volume; available cases), staging work-up and sentinel lymph node biopsy (SLNB) indicators, and exploratory laboratory parameters (LDH, S100, CRP) and dermal mitotic rate. Results: We included 2960 patients (Phase 1: 1162; Phase 2: 1251; Phase 3: 547). Median TT among invasive melanomas was 1.1 mm (IQR 0.6–2.3), 1.1 mm (0.5–2.4), and 1.0 mm (0.5–2.3) across phases (p = 0.037). In adjusted models among invasive tumors, TT did not increase (Phase 2 vs. Phase 1: 0.97, 95% CI 0.90–1.04; Phase 3 vs. Phase 1: 0.94, 0.86–1.03). AJCC stage 0 decreased from 7.7% and 6.1% to 2.0%; adjusted OR Phase 3 vs. Phase 1: 0.24 (95% CI 0.13–0.46). Within invasive tumors, the distribution of T categories (T1a–T4) and AJCC stages I–IV was similar across periods. Among cases with available macroscopic primary tumor specimen dimensions, median area and volume were higher during and after the pandemic (area p = 0.030; volume p = 0.042), but period effects attenuated in models adjusted for TT. Exploratory analyses suggested a higher proportion of elevated LDH and a lower proportion of elevated S100 across periods, while CRP and dermal mitotic rate showed no clear period shift. Conclusions: In this large melanoma inpatient cohort, the pandemic period was not associated with thicker invasive melanomas after covariate adjustment. However, a persistent reduction in stage 0/Tis presentations in the post-pandemic period suggests ongoing disruption or shifting of early detection and referral pathways. Exploratory increases in macroscopic tumor dimensions may point to changes not captured by thickness alone, but require cautious interpretation given missingness and potential documentation effects. Full article

Objectives: To evaluate regional anatomical differences in brain volume, surface area, and cortical thickness between children with breath-holding spells (BHSs) and a control group using morphometric MRI analyses. Methods: Three-dimensional T1-weighted cranial MRI data from 48 children with BHSs and 50 control children were retrospectively analyzed, yielding volumetric, surface area, and cortical thickness measures for 135 brain regions. All measurements were assessed relative to total intracranial volume (ICV). Group comparisons were performed using analysis of covariance with age, sex, and ICV as covariates, followed by Benjamini–Hochberg false discovery rate correction (q < 0.05). Results: The BHS group exhibited reduced bilateral amygdala volumes (left: q = 0.042; right: q = 0.038). Both cortical thickness and volume were reduced in the right anterior insula (thickness: q = 0.046; volume: q = 0.049). In addition, cortical thickness was reduced in the bilateral anterior cingulate cortices (left: p = 0.019, q = 0.045; right: p = 0.017, q = 0.043) as well as in the right medial frontal cortex (p = 0.009, q = 0.036). Subregional cerebellar analysis demonstrated volume reductions in the right lobule VI (q = 0.031), left lobule VIIA (Crus I) (q = 0.043), and vermis IX–X (q = 0.039). Conclusions: Detecting measurable morphometric changes in brain regions involved in autonomic and emotional regulation in children with BHSs will contribute to understanding the neurobiological characteristics associated with BHSs. Full article

To address the performance degradation of the traditional Extended Kalman Filter (EKF) in state estimation for sensorless brushless DC motor (BLDC) control under dynamic operating conditions, such as sudden speed and load changes—a degradation caused primarily by model mismatches—this paper proposes an Adaptive Extended Kalman Filter (AEKF) algorithm. The proposed algorithm incorporates a robust weighting strategy based on the Mahalanobis distance and a dynamically adjusted adaptive forgetting factor. This integration establishes an estimation mechanism capable of online updating of the innovation covariance, thereby enhancing the state observer’s adaptability to system uncertainties and external disturbances. Simulation results demonstrate that, compared to the traditional EKF, the designed AEKF algorithm significantly improves the estimation accuracy of rotor position and speed under various operating conditions, including low-speed start-up, speed step changes, and sudden load applications. Furthermore, it accelerates dynamic response, suppresses overshoot, and enhances the system’s disturbance rejection robustness. This work provides an effective state estimation solution for high-dynamic performance sensorless control of BLDC. Full article

Chronic liver injury is accompanied by coordinated disturbances in lipid trafficking and inflammatory–fibrogenic signaling. Transforming growth factor beta 1 (TGF-β1) signaling has been implicated in hepatic fibrogenesis and tumor-associated remodeling and may co-vary with disturbances in lipid trafficking. Lisosan G (LG), a fermented wheat-derived nutraceutical, has reported antioxidant and anti-inflammatory activity and may influence these interconnected pathways. This study evaluated whether dietary LG alters the lipid composition of plasma lipoprotein fractions and hepatic TGF-β1 levels across distinct liver contexts. Seventy-two female Wistar rats were randomized into nine groups (n = 8/group) defined by liver condition, consisting of healthy control (Control), non-neoplastic liver (PH), and neoplastic liver injury (HCC; PH followed by diethylnitrosamine, DEN), and diet (standard diet, SD + 2.5% LG, or SD + 5% LG). Plasma lipoproteins (VLDL, LDL, HDL₁, HDL₂) were isolated by stepwise KBr density-gradient ultracentrifugation, and cholesterol (TC), phospholipids (PL), and triacylglycerols (TG) were quantified in each fraction. Hepatic TGF-β1 was measured by ELISA and normalized to total protein. LG effects depended strongly on baseline liver status, with significant Condition × Diet interactions for most lipid endpoints and for hepatic TGF-β1. In healthy rats, LG produced fraction-selective remodeling rather than uniform lipid lowering, including increased VLDL-TG at both doses and non-linear changes in cholesterol distribution across LDL and HDL subfractions. After PH, LG broadened lipid remodeling, including reduced VLDL-PL, increased VLDL-TG (both doses), and an increase in LDL-TC at 5% LG, accompanied by marked changes in HDL₁/HDL₂ cholesterol partitioning. In HCC, LG induced pronounced, often dose-dependent increases in LDL-associated lipids (LDL-PL, LDL-TG, LDL-TC) and increased HDL₁-TC while decreasing HDL₂-TC. Hepatic TGF-β1 was elevated in PH and further increased in HCC versus controls; LG reduced hepatic TGF-β1 in a condition-dependent manner, with the strongest reduction at 5% LG in HCC. Dietary Lisosan G remodels circulating lipoprotein lipid composition in a liver-status-dependent manner and is associated with reduced hepatic TGF-β1 abundance in injured liver, most prominently in neoplastic injury. These findings are consistent with the notion that nutraceutical interventions may show stronger phenotypic effects under perturbed metabolic–fibrogenic states than under stable physiology, while highlighting the need for mechanistic work to distinguish altered lipoprotein secretion from changes in peripheral clearance and to assess pathway-level TGF-β signaling. Full article

Networked systems operating under uncertainty require decision making frameworks capable of balancing nominal efficiency and robustness against correlated risks. In this work, we study a distributionally robust weighted dominating set problem as a system-level model for robust network design, where node selection decisions are affected by uncertainty in costs and their correlation structure. We formulate the problem as a bi-objective optimization model that simultaneously minimizes the expected price and a risk measure derived from mean–covariance ambiguity. Rather than proposing new optimization algorithms, we conduct a systematic, methodological, and computational analysis of classical multiobjective solution approaches within this nonconvex and combinatorial setting. In particular, we compare weighted-sum, lexicographic, and $\epsilon$-constraint methods, highlighting their ability to reveal different structural properties of the Pareto Frontier. Our numerical results demonstrate that the methods that use scalarization allow us to obtain only partial insights for networked systems where robustness is inherent. However, the $\epsilon$-constraint method is highly efficient in recovering the full set of Pareto-optimal solutions. Once obtained, the Pareto Frontier exposes non-supported solutions and disruptive changes in its form. Notice that the latter is directly related to different configurations of dominating sets which are induced by the uncertainties. Consequently, these observations allow us to select from different subsets of relevant operating conditions for robust network designs that are significantly different for a decision maker. Full article

Background: Transcranial Direct Current Stimulation (tDCS) has shown potential in improving negative symptoms (NS) and Cognitive Impairment Associated with Schizophrenia (CIAS). However, heterogeneity in stimulation protocols and sample characteristics limit definitive conclusions regarding tDCS effectiveness in schizophrenia. Given the detrimental effects of cigarette smoking, particularly on cognition, this study explored the role of cigarette smoking as a modifiable individual factor potentially contributing to methodological heterogeneity by evaluating tDCS effects on NS and CIAS in Smoker (SM) and Non-Smoker (NoSM) patients. Methods: Post hoc analyses of a double-blind RCT were performed on 50 patients, randomized to 2 mA active or sham-tDCS (15 weekday sessions) with bilateral bipolar-nonbalanced prefrontal placement. The sample was divided according to the smoking status, consisting of 28 SM and 22 NoSM. Separate one-way analyses of covariance (ANCOVA) were performed within each subgroup to assess changes over time between treatment conditions. Clinical outcomes included Positive and Negative Symptoms Scale (PANSS), Brief Assessment of Cognition in Schizophrenia (BACS), Clinical Global Impression (CGI) and Calgary Depression Scale for Schizophrenia (CDSS) total scores. Results: SM exhibited baseline lower cognitive scores in verbal memory, motor speed and working memory domains. NS improved in both SM and NoSM with large effect size. Significant improvement in CIAS, specifically in working memory and verbal fluency, were found exclusively in NoSM. Conclusions: Cigarette smoking appeared to limit tDCS effectiveness in improving CIAS but not NS in schizophrenia. We suggested that the neurotoxic milieu linked to chronic exposure to neurotoxins of cigarette smoking could be responsible for these effects, counterbalancing the neuroprotective effects of tDCS. Further studies are warranted to replicate these findings. Full article

Background: The human gut microbiota plays a pivotal role in maintaining health throughout the lifespan, and age-related alterations in its composition and diversity have been implicated in numerous chronic and neurodegenerative conditions. However, the combined effects of aging, dementia, and shared living environments on gut microbial communities remain incompletely understood. Methods: This study included 56 older adults residing in a nursing home, of whom 29 had been diagnosed with dementia. Gut microbiota composition was characterized by 16S ribosomal RNA (rRNA) gene sequencing. Microbial diversity was assessed using alpha- and beta-diversity metrics, and differences in amplicon sequence variants (ASVs)/features were determined. Analyses adopted some covariates as potential confounders variables including age, sex, frailty status, drug use, and time spent in the nursing home. Results: Alpha diversity was significantly higher in older adults compared with younger, while beta-diversity analyses revealed distinct microbial community structures between age groups. In older individuals, Bacteroidota and Proteobacteria were the most abundant phyla, whereas Firmicutes and Actinobacteriota declined with advancing age. Notably, older adults exhibited an increased relative abundance of Euryarchaeota, a phylum encompassing Archaea, predominantly methanogens involved in anaerobic carbon dioxide reduction to methane. In subjects with dementia, marked compositional shifts were detected, resulting in a distinct microbial signature. Dementia was associated with a significant enrichment of Actinobacteriota, Euryarchaeota, and Proteobacteria, alongside a depletion of Bacteroidota and Firmicutes. Overall, different bacterial genera mostly belonging to the Firmicutes phylum were associated both with aging and dementia. Conclusions: Results show age-related remodeling of the gut microbiota, with a stable core of common taxa and distinct individual-specific signatures. These shifts reflect both host factors and life-long environmental conditions. Dementia-related changes seem to correlate with increased inflammatory species, thus suggesting the effect of vulnerability in microbiota changes in subjects sharing living environment and diet. Full article

Understanding long-term snow cover dynamics is essential in mountain regions with limited meteorological or in situ observations. This study examines seasonal snow cover evolution across the Romanian Carpathians (2000–2025) using daily MODIS/Terra MOD10A1 Cloud-Gap-Filled data at 500 m resolution. Snow-covered pixels were identified using an NDSI ≥ 40 threshold, and snow cover duration (SCD), snow onset date (SOD), and snow end date (SED) were analyzed in relation to elevation and aspect from the FABDEM, complemented by snow-covered area (SCA) and snowline elevation (SLE) metrics. Across the entire range, the snow season shortens mainly due to later onset (+0.28 days/year) and earlier melt (−0.78 days/year), resulting in an SCD decrease of −1.14 days/year. High-elevation (>2000 m) areas show only small changes (SCD: −0.13 days/year; SOD: +0.46 days/year; SED: +0.32 days/year), while the strongest reductions occur at low and mid elevations, where snow persistence is most sensitive to warming; consistent declines in seasonal SCA and a pronounced monthly SLE cycle further document the spatial expression of this variability. Uncertainty was assessed by comparison with station-based snow cover duration (n = 230 station-years), indicating strong agreement (r = 0.95) with a modest negative bias (median: −8 days) and a mean absolute error (MAE) of 16.7 days. Climate correlations highlight air temperature as the dominant covariate of interannual snow-phenology variability, whereas precipitation associations are weaker. Overall, these shifts in snow phenology highlight increasing instability of the Carpathian snow regime and emphasize the value of long-term MODIS observations for tracking cryospheric change in a warming southeastern European mountain system. Full article

As a critical agroforestry crop in Southern China, Moso bamboo, maintains regional timber security and bamboo shoot production, with its net ecosystem productivity (NEP) directly determining dry matter accumulation and economic yield. This study integrates 2024 continuous flux observations with XGBoost and SHAP explanations to characterize the subtropical bamboo forest carbon budget and its nonlinear driving mechanisms. The results show a weak carbon sink in 2024 with an annual cumulative NEP of 120 g C m⁻², as high respiration of 860 g C m⁻² limited organic matter conversion by consuming nearly 88% of the 980 g C m⁻² total primary production. The peak production period during May and June was offset by growth stagnation in August, caused by extreme heat and drought. Net radiation served as the primary driver, with a positive contribution threshold of 75.28 W m⁻², whereas precipitation exceeding 1.85 mm or air temperatures over 17.85 °C hindered carbon accumulation through radiation attenuation and metabolic heat loss. Strong radiation–precipitation interactions confirm that water’s impacts on yield are deeply contingent upon radiation backgrounds. These nonlinear regulatory pathways provide a scientific foundation for stabilizing bamboo forest productivity through synergistic water-radiation management and structural optimization during extreme climate events. Full article

The on-machine measurement (OMM) of aero-engine blades is a critical technology for enabling closed-loop manufacturing. However, when using line laser sensors with a fixed scanning pose to measure free-form surfaces, the variation in surface geometry leads to changing incident angles, which in turn induce non-stationary noise. To address this issue, this paper proposes a multi-sensor fusion method utilizing Adaptive Heteroscedastic Gaussian Process Regression (AHGPR) based on a Spatial-Angle-Balanced Sampling (S-ABS) strategy. The AHGPR explicitly integrates the physical mapping of incident angle errors into its covariance structure, thereby automatically adjusting observation weights according to the local geometric posture. Concurrently, the S-ABS strategy captures the high-error characteristic points with large incident angles while maintaining a globally uniform spatial distribution. The experimental data indicate that this approach addresses the sampling deficiency encountered at the leading and trailing edges and in areas with large incident angles. The proposed approach reduced the impact of optical deviations on measurement accuracy and improved the precision of the process. Full article

As bearings are critical mechanical components, their actual operating conditions exhibit notable dynamic complexity. Multiple factors—including rotational speed fluctuations, sudden load changes, and environmental disturbances—interact in a strongly coupled fashion. This imposes severe challenges on traditional fault diagnosis methods, such as limited interpretability, weak adaptive capacity, and elevated misjudgment rates. Therefore, this paper proposes an Interval Belief Rule Base model integrated with an attention mechanism (IBRB-a) under variable operating conditions. The proposed model combines expert knowledge’s ability to quantify uncertainty with a data-driven adaptation mechanism, thereby addressing the challenge of variable operating conditions in complex industrial systems. First, a novel interval rule construction method is incorporated into the traditional IBRB model, and kernel density estimation (KDE) is employed to select reference values. Second, during the model reasoning process, a two-stage fusion strategy based on Evidential Reasoning (ER) is adopted: progressive information fusion is implemented via the ER analysis algorithm and the ER rule algorithm, which effectively mitigates the interval uncertainty under variable operating conditions. Finally, the constrained projected covariance matrix adaptive evolution strategy (P-CMA-ES) is employed to optimize the model. Furthermore, experimental validation under variable operating conditions is conducted via Case Western Reserve University and Southeast University bearing datasets. The effectiveness and generalizability of the proposed method are validated by the experimental result. Full article