Search Results (271)

Life course exposure estimates developed using geospatial datasets must address issues of individual mobility, missing and incorrect data, and incompatible scaling of the datasets. We propose methods to assess and resolve these issues by developing individual exposure histories for an adult cohort of patients with amyotrophic lateral sclerosis (ALS) and matched controls using residence history and PM_2.5, black carbon, NO₂, and traffic intensity estimates. The completeness of the residence histories was substantially improved by adding both date and age questions to the survey and by accounting for the preceding and following residence. Information for the past five residences fully captured a 20-year exposure window for 95% of the cohort. A novel spatial multiple imputation approach dealt with missing or incomplete address data and avoided biases associated with centroid approaches. These steps boosted the time history completion to 99% and the geocoding success to 92%. PM_2.5 and NO₂, but not black carbon, had moderately high agreement with observed data; however, the 1 km resolution of the pollution datasets did not capture fine scale spatial heterogeneity and compressed the range of exposures. This appears to be the first study to examine the mobility of an older cohort for long exposure windows and to utilize spatial imputation methods to estimate exposure. The recommended methods are broadly applicable and can improve the completeness, reliability, and accuracy of life course exposure estimates. Full article

This study addresses the challenge of accurate classification under missing data conditions by integrating multiple imputation strategies with discriminant analysis frameworks. The proposed approach evaluates six imputation methods (Mean, Regression, KNN, Random Forest, Bagged Trees, MissRanger) across several discriminant techniques. Simulation scenarios varied in sample size, predictor dimensionality, and correlation structure, while the real-world application employed the Cirrhosis Prediction Dataset. The results consistently demonstrate that ensemble-based imputations, particularly regression, KNN, and MissRanger, outperform simpler approaches by preserving multivariate structure, especially in high-dimensional and highly correlated settings. MissRanger yielded the highest classification accuracy across most discriminant analysis methods in both simulated and real data, with performance gains most pronounced when combined with flexible or regularized classifiers. Regression imputation showed notable improvements under low correlation, aligning with the theoretical benefits of shrinkage-based covariance estimation. Across all methods, larger sample sizes and high correlation enhanced classification accuracy by improving parameter stability and imputation precision. Full article

Embryo genomic selection (EGS) is a contemporary breeding strategy that combines genomic selection (GS) methodology with embryo biotechnology. By conducting genotyping and genomic prediction at the pre-implantation stage, embryos with superior breeding value can be identified for transfer, markedly increasing breeding efficiency while reducing the uncertainty and temporal expenditure associated with conventional GS. This study establishes a reliable embryo biopsy-based GS pipeline for sheep, incorporating optimized whole-genome amplification and microcell genotyping techniques. We developed a high-efficiency in vitro sheep embryo production platform compatible with embryo biopsy. Systematic comparison of Multiple Displacement Amplification (MDA) and Multiple Annealing and Looping Based Amplification Cycles (MALBAC) whole-genome amplification systems yielded high-quality genotypes from biopsy samples of embryos containing as few as 10 cells. Imputation using 10× whole-genome sequencing data significantly increased both genotype call rates and accuracy. High concordance was observed between embryo and lamb genotypes, and genomic estimated breeding values (GEBVs) for key growth traits exhibited strong correlations (R²: 0.91–0.98). This system enables accurate preimplantation genomic evaluation and provides an efficient strategy to accelerate genetic improvement in sheep breeding programs. Full article

Background/Objectives: This study examines the associations between household socioeconomic status (SES), child nutrition, and developmental status among children aged 24–59 months in the Mont Ngafula health zone in Kinshasa. The primary research question focuses on how SES and stunting affect developmental outcomes in early childhood. Methods: A cross-sectional analysis was conducted involving 348 children, assessing developmental outcomes using the Early Childhood Development Index (ECDI2030). Results: The study found that 70.4% of children were classified as on track, with ONTRACK prevalence increasing across SES tertiles. Children who attended preschool education had higher odds of being on track. The rich tertile had higher odds of being on track than those in the poor tertile, while the middle tertile showed a weaker association. Child age categories and stunting were inversely associated with being developmentally on track. The results are consistent with multiple imputation sensitivity analyses. Conclusions: The study concludes that preschool attendance and a higher household socioeconomic position are strongly associated with better early developmental outcomes, while an age of 48–59 months and stunting are associated with a markedly lower likelihood of being developmentally on track. Integrated policies that reduce household poverty, promote early education, and prevent/treat early faltering growth could improve early childhood developmental trajectories. Full article

The digital transformation of civic life has created new opportunities for older adults to engage in virtual volunteer activities. However, their participation still remains limited. This study investigates the factors that influence older adults’ interest in virtual volunteering. It integrated theoretical framework combining the Technology Acceptance Model (TAM), Self-Efficacy, and Digital Divide Theories to examine the drivers of virtual volunteerism interest among this target population. This study presents ordered logistic regression models with data on 814 adult volunteers in multiple imputation procedures. The final reduced model identifies two key predictors: a preference for virtual activities and interest in technology training, respectively, representing TAM and the Digital Divide Theory. While the self-efficacy-related variable showed statistical significance in earlier models, its explanatory power diminished when controlling for other factors. The findings indicate that older adults’ interest in virtual volunteering is primarily shaped by perceived usefulness of digital tools and their willingness to improve technical competence. This study confirms the relevance of the TAM and Digital Divide theories regarding virtual volunteerism. In practical terms, the findings indicate that program design should combine usability-focused platform features and targeted support that lower both technological and motivational barriers for older adults interested in virtual volunteering. Full article

Background: Blood-based comprehensive genomic profiling (CGP), a form of liquid biopsy, is often used for biliary tract cancer (BTC) when tissue-based CGP (tissue CGP) is unavailable, despite lower detection rates. This study explored factors linked to detecting actionable genomic alterations to optimize its use. Methods: We retrospectively analyzed BTC cases in Japan’s C-CAT (June 2019–January 2025), restricting panel comparisons to FoundationOne^® CDx (F1; n = 5019) and FoundationOne^® Liquid CDx (F1L; n = 1550). Missing covariates were handled by multiple imputations (m = 20). Between-panel balance used 1:1 propensity-score matching (caliper 0.2). Outcomes were modeled with logistic regression. Targets included MSI-H, TMB-H, FGFR2/RET/NTRK fusions, BRAF V600E, KRAS G12C, IDH1 mutations, and ERBB2 amplification. An exploratory analysis stratified results by the number of prespecified enrichment factors (0–4). Liquid biopsy was performed using plasma-based comprehensive genomic profiling assays (FoundationOne^® Liquid). Results: Missingness was low; after matching (n = 1549 per group) covariates were well balanced (all|SMD|≤0.05). Detection of any actionable alteration was lower with F1L than F1 (16.8% vs. 24.8%; OR 0.61, 95% CI 0.49–0.75; p < 0.001). F1L also had lower TMB-H (OR 0.62, 0.43–0.90; p = 0.01) and ERBB2 amplification (OR 0.42, 0.31–0.57; p < 0.001), with no significant differences for MSI-H, IDH1, KRAS G12C, or BRAF V600E. Within F1L, non-perihilar location (OR 2.05), liver (1.90), lymph-node (1.41), and lung metastases (1.52) predicted detection of actionable genomic alterations. F1L detection increased from 5.8% (zero factors) to 32.8% (four factors), approximating tissue at three factors. Conclusions: The utility of liquid biopsy can be maximized by carefully selecting samples on the basis of conditions that increase the detection rate. Full article

This study examines the spatio-temporal evolution of precipitation in the State of Guanajuato, Mexico, from 1981 to 2016 by analyzing monthly series from 65 meteorological stations. A rigorous data quality protocol was implemented, selecting stations with more than 30 years of continuous data and less than 10% missing values. Multiple Imputation by Chained Equations (MICE) with Predictive Mean Matching was applied to handle missing data, preserving the statistical properties of the time series as validated by Kolmogorov–Smirnov tests ($p=1.000$ for all stations). Homogeneity was assessed using Pettitt, SNHT, Buishand, and von Neumann tests, classifying 60 stations (93.8%) as useful, 3 (4.7%) as doubtful, and 2 (3.1%) as suspicious for monthly analysis. Breakpoints were predominantly clustered around periods of instrumental changes (2000–2003 and 2011–2014), underscoring the necessity of homogenization prior to trend analysis. The Trend-Free Pre-Whitening Mann–Kendall (TFPW-MK) test was applied to account for significant first-order autocorrelation (${\rho }_1 > 0.3$) present in all series. The analysis revealed no statistically significant monotonic trends in monthly precipitation at any of the 65 stations ($\alpha =0.05$). While 75.4% of the stations showed slight non-significant increasing tendencies (Kendall’s $\tau$ range: 0.0016 to 0.0520) and 24.6% showed non-significant decreasing tendencies ($\tau$ range: −0.0377 to −0.0008), Sen’s slope estimates were negligible (range: −0.0029 to 0.0111 mm/year) and statistically indistinguishable from zero. No discernible spatial patterns or correlation between trend magnitude and altitude ($\rho =-0.022$, $p>0.05$) were found, indicating region-wide precipitation stability during the study period. The integration of advanced imputation, multi-test homogenization, and robust trend detection provides a comprehensive framework for hydroclimatic analysis in semi-arid regions. These findings suggest that Guanajuato’s severe water crisis cannot be attributed to declining precipitation but rather to anthropogenic factors, primarily unsustainable groundwater extraction for agriculture. Full article

Fractional laser (FL) and fractional radiofrequency (FRF) are effective treatments for atrophic acne scars, yet comparative data in Asian populations with darker skin types remain limited. This retrospective cohort study compared the clinical effectiveness and safety of FL and FRF in Thai patients aged 18–60 years with Fitzpatrick skin types III–IV who underwent at least two treatment sessions between 2012 and 2023. Baseline characteristics were balanced using propensity score stratification, and missing data were addressed through multiple imputation with chained equations. The primary endpoint was the proportion of patients achieving ≥25% improvement in scarring at 6 months, with equivalence testing performed using a 20% margin. A total of 397 patients (254 FL, 143 FRF) were included, with balanced baseline characteristics after stratification. At 6 months, 88.1% of FRF-treated and 71.9% of FL-treated patients achieved the primary endpoint. FRF showed numerically greater mean improvement at all time points, though differences were not statistically significant. FL met the non-inferiority criterion but not equivalence. FRF was associated with significantly higher pain scores (p < 0.001), while adverse events, including post-inflammatory hyperpigmentation, were rare and similar between groups. Both modalities demonstrated meaningful clinical benefit and acceptable safety, although statistical equivalence could not be established and FRF was associated with greater procedural discomfort. Full article

Enterprise bankruptcy prediction is a critical task in financial risk management. Traditional methods, such as logistic regression and decision trees, rely heavily on structured financial data, which limits their ability to capture complex relational networks and unstructured industry information. Heterogeneous graph neural networks (HGNNs) offer a solution by modeling multiple relationships between enterprises. However, current models struggle with financial risk graph data challenges, such as the oversimplification of internal financial features and the lack of dynamic imputation for missing external topological features. To address these issues, we propose HGNN-EBP, an enterprise bankruptcy prediction algorithm that integrates both internal and external features. The model constructs a multi-relational heterogeneous graph that combines structured financial data, unstructured textual information, and real-time industry data. A multi-scale graph convolution network captures diverse relationships, while a Transformer-based self-attention mechanism dynamically imputes missing external topological features. Finally, a multi-layer perceptron (MLP) predicts bankruptcy probability. Experimental results on a dataset of 32,459 Chinese enterprises demonstrate that HGNN-EBP outperforms traditional models, especially in handling relational diversity, missing features, and dynamic financial risk data. Full article

Background: Outdoor and household PM_2.5 are established risk factors for chronic disease and early mortality. In China, high levels of outdoor PM_2.5 and solid fuel use for cooking and heating, especially in winter, pose large health risks to the country’s aging population. Hand grip strength is a validated biomarker of functional aging and strong predictor of disability and mortality in older adults. We investigated the effects of wintertime household and outdoor PM_2.5 on maximum grip strength in a rural cohort in Beijing. Methods: We analyzed data from 877 adults (mean age: 62 y) residing in 50 rural villages over three winter seasons (2018–2019, 2019–2020, and 2021–2022). Outdoor PM_2.5 was continuously measured in all villages, and household (indoor) PM_2.5 was monitored for at least two months in a randomly selected ~30% subsample of homes. Missing data were handled using multiple imputation. We applied multivariable mixed effects regression models to estimate within- and between-individual effects of PM_2.5 on grip strength, adjusting for demographic, behavioral, and health-related covariates. Results: Wintertime household and outdoor PM_2.5 concentrations ranged from 3 to 431 μg/m³ (mean = 80 μg/m³) and 8 to 100 μg/m³ (mean = 49 μg/m³), respectively. The effect of a 10 μg/m³ within-individual increase in household and outdoor PM_2.5 on maximum grip strength was 0.06 kg (95%CI: −0.01, 0.12 kg) and 1.51 kg (95%CI: 1.35, 1.68 kg), respectively. The household PM_2.5 effect attenuated after adjusting for outdoor PM_2.5, while outdoor PM_2.5 effects remained robust across sensitivity analyses. We found little evidence of between-individual effects. Conclusions: We did not find strong evidence of an adverse effect of household PM_2.5 on grip strength. The unexpected positive effects of outdoor PM_2.5 on grip strength may reflect transient physiological changes following short-term exposure. However, these findings should not be interpreted as evidence of protective effects of air pollution on aging. Rather, they highlight the complexity of air pollution’s health impacts and the value of longitudinal data in capturing time-sensitive effects. Further research is needed to better understand these patterns and their implications in high-exposure settings. Full article

Background/Objectives: Feigning spectrum behavior (FSB) is the exaggeration, fabrication, or false imputation of symptoms. It occurs in compensable injury with great cost to society by way of loss of productivity and excessive costs. The aim of this study is to identify feigning by developing cut scores on the long and short forms (SF) of the Orebro Musculoskeletal Pain Screening Questionnaire (OMPSQ and OMPSQ-SF) and the Perceived Stress Scale (PSS and PSS-4). Methods: As part of pre-screening for a support program, 40 injured workers who had been certified unfit for work for more than 2 weeks were screened once with the OMPSQ and PSS by telephone by a mental health professional. A control sample comprised of 40 non-injured community members were screened by a mental health professional on four occasions under different aliases, twice responding genuinely and twice simulating an injury. Results: Differences between the workplace injured people and the community sample were compared using ANCOVA with age and gender as covariates, and then receiver operator characteristics (ROCs) were calculated. The OMPSQ and OMPSQ-SF discriminated (ρ < 0.001) between all conditions. All measures discriminated between the simulation condition and workplace injured people (ρ < 0.001). Intraclass correlation demonstrated the PSS, PSS-4, OMPSQ, and OMPSQ-SF were reliable (ρ < 0.001). Area Under the Curve (AUC) was 0.750 for OMPSQ and 0.835 for OMPSQ-SF for work-injured versus simulators. Conclusions: The measures discriminated between injured and non-injured people and non-injured people instructed to simulate injury. Non-injured simulators produced similar scores when they had multiple exposures to the test materials, showing the uniformity of feigning spectrum behavior on these measures. The OMPSQ-SF has adequate discriminant validity and sensitivity to feigning spectrum behavior, making it optimal for telephone screening in clinical practice. Full article

Background/Objectives: Understanding the impact of dietary habits in terms of obesity, health outcomes, and functional decline is critical in Europe’s growing elderly population. This study analyzed trends in Mediterranean diet (MD) adherence, obesity prevalence, and grip strength among middle-aged and elderly Europeans using data from the Survey of Health, Ageing and Retirement in Europe (SHARE). Methods: Data from four SHARE waves (2015–2022) across 28 countries were analyzed. Dietary patterns were assessed through food frequency questionnaires classifying participants as MD-adherent or non-adherent where adherent implies daily consumption of fruits and vegetables and occasional (3–6 times/week) intake of eggs, beans, legumes, meat, fish, or poultry (an unvalidated definition of the MD pattern). Handgrip strength, a biomarker of functional capacity, was categorized into low, medium, and high groups. Body mass index (BMI), self-perceived health (SPHUS), chronic disease prevalence, and CASP-12 scores (control, autonomy, self-realization, and pleasure evaluated on the 12-item version) were also evaluated. Statistical analyses included descriptive methods, logistic regressions, and multiple imputations to address missing data. Results: A significant majority (74–77%) consumed fruits and vegetables daily, which is consistent with MD principles; however, the high daily intake of dairy products (>50%) indicates limited adherence to the MD, which advocates for moderate consumption of dairy products. Logistic regression indicated that individuals with two or more chronic diseases were more likely to follow the MD (odds ratio [OR] = 1.21, confidence interval [CI] = 1.11–1.32), as were those individuals who rated their SPHUS as very good/excellent ([OR] = 1.42, [CI] = 1.20–1.69). Medium and high maximal handgrip were also strongly and consistently associated with higher odds of MD adherence (Medium: [OR] = 1.44, [CI] = 1.18–1.74; High: [OR] = 1.27, [CI] = 1.10–1.48). Conclusions: The findings suggest that middle-aged and older adults are more likely to adhere to the MD dietary pattern if they have more than two chronic diseases, are physically active, and have a medium or high handgrip. Although an unvalidated definition of the MD dietary pattern was used, the results highlight the importance of implementing targeted dietary strategies for middle-aged and elderly adults. Full article

Single-cell RNA sequencing (scRNA-seq) has revolutionized molecular biology and genomics by enabling the profiling of individual cell types, providing insights into cellular heterogeneity. Deep learning methods have become popular in single cell analysis for tasks such as dimension reduction, cell clustering, and data imputation. In this work, we introduce DropDAE, a denoising autoencoder (DAE) model enhanced with contrastive learning, to specifically address the dropout events in scRNA-seq data, where certain genes show very low or even zero expression levels due to technical limitations. DropDAE uses the architecture of a denoising autoencoder to recover the underlying data patterns while leveraging contrastive learning to enhance group separation. Our extensive evaluations across multiple simulation settings based on synthetic data and a real-world dataset demonstrate that DropDAE not only reconstructs data effectively but also further improves clustering performance, outperforming existing methods in terms of accuracy and robustness. Full article

Background/Objectives: Accurate dietary assessment is crucial for nutritional epidemiology, but tools like 24 h recalls (24HRs) face challenges with missing or implausible data. The Automated Self-Administered 24 h Dietary Assessment Tool (ASA24) facilitates large-scale data collection, but its lack of interviewer input may lead to implausible dietary recalls (IDRs), affecting data integrity. Multiple imputation (MI) is commonly used to handle missing data, but its effectiveness in high-variability dietary data is uncertain. This study aims to assess MI’s accuracy in estimating nutrient intake under varying levels of missing data. Methods: Data from 24HRs completed by 743 adolescents (ages 13–18) in Ontario, Canada, were used. Implausible recalls were excluded based on nutrient thresholds, creating a cleaned reference dataset. Missing data were simulated at 10%, 20%, and 40% deletion rates. MI via chained equations was applied, incorporating demographic and psychosocial variables as predictors. Imputed values were compared to actual values using Spearman’s correlation and accuracy within ±10% of true values. Results: Spearman’s rho values between the imputed and actual nutrient intakes were weak (mean ρ ≈ 0.24). Accuracy within ±10% was low for most nutrients (typically < 25%), with no clear trend by missingness level. Diet quality scores showed slightly higher accuracy, but values were still under 30%. Conclusions: MI performed poorly in estimating individual nutrient intake in this adolescent sample. While MI may preserve sample characteristics, it is unreliable for accurate nutrient estimates and should be used cautiously. Future studies should focus on improving data quality and exploring better imputation methods. Full article

This study addresses the challenges of data imbalance and missing values in credit card transaction datasets by employing mode-based imputation and various machine learning models. We analyzed two distinct datasets: one consisting of European cardholders and the other from American Express, applying multiple machine learning algorithms, including Artificial Neural Networks, Convolutional Neural Networks, and Gradient Boosted Decision Trees, as well as others. Notably, the Gradient Boosted Decision Tree demonstrated superior predictive performance, with accuracy increasing by 4.53%, reaching 96.92% on the European cardholders dataset. Mode imputation significantly improved data quality, enabling stable and reliable analysis of merged datasets with up to 50% missing values. Hypothesis testing confirmed that the performance of the merged dataset was statistically significant compared to the original datasets. This study highlights the importance of robust data handling techniques in developing effective fraud detection systems, setting the stage for future research on combining different datasets and improving predictive accuracy in the financial sector. Full article