Category: Statistics

J Environ Manage. 2026 Jun 19;412:130206. doi: 10.1016/j.jenvman.2026.130206. Online ahead of print.

ABSTRACT

Accurately apportioning organic pollution sources in mixed land-use watersheds remains challenging due to the limited tracer capacity of conventional fluorescence-based approaches. Traditional methods condense multidimensional excitation-emission matrix (EEM) spectra into a few summarized indices (e.g., BIX, HIX, FI), creating a mismatch between available fluorescence tracers and the number of pollution sources, thereby limiting source discrimination when multiple sources overlap. This study presents an analytical framework for source apportionment integrating three elements. First, full-spectrum EEM images are used as source-specific end-members to preserve spectral information for source-pattern characterization. Second, a statistical assessment of end-member independence and separability performed using SSIM and ANOSIM. Third, a CNN-unmixing architecture was developed to learn source-discriminating patterns from full-spectrum EEM images and estimate source-associated contributions in mixed-source EEMs based on candidate end-member representations. An EEM dataset was constructed from pollution source samples collected in mixed land-use watersheds. Statistical assessment of end-member similarity and separability showed high within-group similarity (Pearson’s r = 0.882-0.988; SSIM = 0.838-0.970) and significant between-group separation among the eight end-members in EEM spectral space (ANOSIM R = 0.876; PERMANOVA R² = 0.899). The framework was evaluated using ground-truth mixtures (2-5 components) and field stream samples. In controlled mixtures, CNN-unmixing showed R² > 0.7 and MAE <0.07 and generally reproduced dominant-source contributions and rankings across mixture complexities. The field application in the study watershed showed reasonable spatial consistency between the predicted dominant-source contributions and observed pollution-load distribution patterns, supporting the preliminary field-scale applicability of the framework for source-priority assessment in mixed land-use watersheds.

PMID:42320213 | DOI:10.1016/j.jenvman.2026.130206

J Environ Manage. 2026 Jun 19;412:130228. doi: 10.1016/j.jenvman.2026.130228. Online ahead of print.

ABSTRACT

Arctic lakes are exceptionally vulnerable to climate change, yet the depth-specific responses of their water quality remain largely unexplored, primarily due to the scarcity of long-term, depth-resolved water quality data. This study investigates how climatic and ice phenology conditions influence water quality in the surface and deep layers of Lake Inari, a large, oligotrophic arctic lake with minimal human-induced disturbance. Leveraging a 44-year dataset of in-situ measurements (1980-2023), we utilize canonical correlation analysis (CCA) to assess the potential interactions among key water quality parameters in the surface and deep layers of this stratified lake and climatic-ice factors. The results show a relatively robust statistical relationship between surface water quality and climatic-ice factors (R² = 0.67, p < 0.05). These climatic-ice factors account for approximately 53% of the variance in the lake’s surface water quality. In contrast, deep-water quality exhibits a relatively moderate but statistically non-significant correlation with climatic-ice factors (R² = 0.42, p > 0.05). The absence of statistical significance, likely a false negative due to limited power of our CCA analysis, suggests that deep-water quality responses may be delayed or indirect, potentially mediated by thermal stratification and reduced mixing. Both surface water quality and deep-water quality display exceptionally strong multivariate interactions (R² = 0.94, p < 0.05), driven by shared variability in turbidity, chemical oxygen demand, and electrical conductivity. The overall statistically significant redundancy between surface water quality and deep-water quality is around 60% for the latter. These findings suggest that surface-layer observations, including satellite and automated-sensor data, may help inform assessments of deep-water quality in large, oligotrophic, dimictic Arctic lakes. If further validated, this approach could support more cost-effective monitoring and strengthen climate-resilient lake-management planning.

PMID:42320202 | DOI:10.1016/j.jenvman.2026.130228

Comput Biol Chem. 2026 Jun 16;124(Pt 2):109199. doi: 10.1016/j.compbiolchem.2026.109199. Online ahead of print.

ABSTRACT

Coronavirus main protease (Mpro) is a conserved antiviral target, yet most generative AI pipelines optimize surrogate-predicted affinity without enforcing electronic plausibility or cross-target consistency. We present PRISM-Gen (Physics-guided Robust Inhibitor Selection Method – Generative Module), a multi-fidelity framework coupling fragment-tree molecular generation with a three-tier electronic screening cascade – GFN2-xTB semi-empirical descriptors, Gaussian Electronic Moderation (GEM) scoring, and B3LYP/6-31 G* DFT validation – followed by conservative worst-case docking across SARS-CoV-2, SARS-CoV-1, and MERS-CoV Mpro. Applied to 4136 generated candidates, the pipeline identifies 36 broad-spectrum-consistent inhibitor candidates whose top-ranked members exhibit predicted worst-case binding energies comparable to those of the non-covalent reference inhibitor ensitrelvir under identical docking conditions, while sharing no Bemis-Murcko scaffolds with nirmatrelvir, ensitrelvir, or GC376. Stage-wise statistical validation confirms that each tier exerts non-redundant, orthogonal selection pressure. Retrospective analysis demonstrates that replacing GEM’s continuous moderation with a conventional hard electronic cutoff would eliminate 48.0% of candidates, including 55.6% of the final 36 molecules, disproportionately depleting scaffold diversity. These results establish that continuous, physics-informed electronic moderation integrated within a multi-fidelity generative pipeline can recover structurally novel chemotypes that binary exclusion filters would irreversibly discard – a design principle applicable to generator-agnostic molecular discovery workflows.

PMID:42320197 | DOI:10.1016/j.compbiolchem.2026.109199

Comput Biol Chem. 2026 Jun 12;124(Pt 2):109184. doi: 10.1016/j.compbiolchem.2026.109184. Online ahead of print.

ABSTRACT

Pulmonary abnormality detection via analysis on respiratory sound is a novel method that shows promising results in early identification and detection of respiratory disorders. Researchers and physicians can benefit greatly from the Respiratory Sound Database that includes recordings of different respiratory sounds belonging to both healthy and unhealthy lung activities. By utilizing the latest developments in signal processing and ML methodologies, this work aims to create a new method termed as stacked ensemble model based Pulmonary abnormality detection (SEM based PAD) that can automatically identify and categorize abnormalities from the recordings of sounds. This article deploys Improved Wiener filtering (IWF) for preprocessing the sound signal. Subsequently, extraction of varied features takes place with improvement in Statistical parameter determination (ISPD). Then, Improved chi-square is used to choose the features, thereby, lessening the length of features. Finally, stacked ensemble model (SEM) that combines Squeeze Net, SVM and CNN is deployed for detecting the pulmonary abnormalities. The final outcomes are determined using Improved Score Level Fusion (ISLF).

PMID:42320196 | DOI:10.1016/j.compbiolchem.2026.109184

Poult Sci. 2026 Jun 9;105(9):107240. doi: 10.1016/j.psj.2026.107240. Online ahead of print.

ABSTRACT

Electrical water bath stunning remains the predominant method for poultry slaughter in Europe, yet its welfare implications are still debated. This study assessed the current status of stunning effectiveness in broiler chickens under commercial conditions, providing a foundation for future comparisons with controlled atmosphere stunning. Behavioral observations were made in 1 slaughterhouse over 2 study years (2021 and 2022) and were analyzed separately for each year across broilers from 4 fattening methods (LIT: light conventional fattening method, HEV: heavy conventional fattening method, LBL: label fattening method, ORG: organic fattening method). The analysis aimed to explore whether stunning performance differs according to fattening method, animal age, transport duration, carcass weight, weather conditions, and total electrical current in the electrical water bath. Stunning effectiveness was evaluated in 3 phases: pre-stunning (shackling), during stunning, and post-stunning. In each phase, key behavioral indicators, such as signs of inadequate stunning (e.g., wing flapping, body movements, rhythmic breathing), were systematically recorded. The results showed that pre-stunning behavior varied among fattening methods in 2022 (defensive reactions: ORG [0.82%] vs. LBL [0.05%]; wing flapping: LIT [29.00%] vs. OGR [17.00%], HEV [21.00%], and LBL [12.60%]), with older and lighter animals showing more pronounced defensive or flapping responses. After stunning in 2021, only ORG broilers showed cases of inadequate stunning (0.01%), whereas no incidences were observed in LIT (0.00%) and HEV broilers (0.00%). These findings highlight that even minor differences in handling and animal characteristics can significantly affect stunning effectiveness and animal welfare.

PMID:42320186 | DOI:10.1016/j.psj.2026.107240

Am J Otolaryngol. 2026 Jun 6;47(4):104868. doi: 10.1016/j.amjoto.2026.104868. Online ahead of print.

ABSTRACT

OBJECTIVE: To evaluate clinical characteristics, associated comorbidities, management strategies, and factors associated with surgical intervention in children with laryngomalacia managed at a tertiary pediatric airway referral center in Eastern India.

METHODS: This retrospective cohort study included children aged 0-60 months diagnosed with laryngomalacia between January 2019 and December 2024 at Institute of Child Health. Diagnosis was confirmed by flexible nasopharyngolaryngoscopy. Clinical features, disease severity, feeding dysfunction, gastroesophageal reflux disease (GERD), synchronous airway lesions (SALs), management details, and outcomes were analyzed. Additional variables were extracted from institutional records following peer-review recommendations to improve methodological reporting. Factors associated with surgical intervention were evaluated using univariate analysis and exploratory multivariable analysis where statistically feasible.

RESULTS: A total of 68 children were included (male:female ratio 1.9:1). Median age at symptom onset was 2.5 months (IQR 1.5-4.0 months), and median age at diagnosis was 5 months (IQR 3-7 months). Feeding difficulty occurred in 47.1%, aspiration in 17.6%, failure to thrive in 22.1%, GERD in 30.9%, and SALs in 26.5%. Most children (82.4%) were successfully managed conservatively, while 17.6% required Supraglottoplasty. Fifty-nine children (86.8%) completed at least 12 months follow-up. Complete resolution of stridor occurred in 48/59 children (81.4%), and surgical success was observed in 10/12 children (83.3%). Feeding dysfunction and SALs were significantly associated with surgical intervention.

CONCLUSIONS: Most children with laryngomalacia improve with conservative management. However, feeding dysfunction and synchronous airway lesions may identify children at higher risk of requiring surgery. Early multidisciplinary airway evaluation may improve outcomes.

PMID:42320176 | DOI:10.1016/j.amjoto.2026.104868

J Therm Biol. 2026 Jun 13;139:104507. doi: 10.1016/j.jtherbio.2026.104507. Online ahead of print.

ABSTRACT

In recent years, the automatic identification of stress and relaxation has gained attention due to the substantial impact these states have on physical and mental well-being. While incorporating physiological responses that accurately reflect emotional behavior improves the efficiency of classification models, identifying this relationship is challenging. This often leads to the use of black-box approaches that complicate physiological interpretation. This study aims to characterize the thermal behavior of fingertips in female and male participants during psychological stress and relaxation using statistical features, and to classify these states based on the most significant features. The study also evaluates virtual reality as a relaxation tool by comparing it with mindfulness-based relaxation. To this end, a virtual reality environment was developed based on scientific recommendations to support relaxation processes. The results show that automatic feature selection through linear discriminant analysis can highlight thermographic correlates of peripheral thermoregulation associated with emotional states. Findings indicate that a reduced subset of highly relevant features is sufficient for high performance, achieving high classification accuracy with the 45, 19, and 55 most relevant features for the female, male, and global datasets, respectively. However, these results should be interpreted cautiously due to the limited sample size and the exploratory nature of the study. Additionally, the study demonstrates that virtual reality is associated with relaxation-like thermal responses, supporting its potential as a complementary tool in psychological interventions. Overall, the results may contribute to improving classification performance, furthering our understanding of psychophysiological mechanisms and aiding in the development of effective systems for identifying emotional states automatically.

PMID:42320157 | DOI:10.1016/j.jtherbio.2026.104507

Eur J Radiol. 2026 Jun 17;203:113026. doi: 10.1016/j.ejrad.2026.113026. Online ahead of print.

ABSTRACT

BACKGROUND: Pediatric cardiomyopathies represent a heterogeneous group of myocardial diseases with significant morbidity and mortality. Conventional cardiovascular magnetic resonance (CMR) techniques provide functional and tissue characterization but offer limited insight into myocardial microstructural organization. Diffusion tensor imaging (DTI) enables non-invasive assessment of myocardial microstructure through quantitative metrics such as apparent diffusion coefficient (ADC) and fractional anisotropy (FA). This study aims to evaluate the utility of DTI in differentiating dilated and hypertrophic cardiomyopathies in pediatric patients through quantitative analysis of ADC and FA, hypothesizing that these metrics provide distinct microstructural biomarkers that correlate with ventricular function.

METHODS: This study employed a mixed retrospective-prospective design and included 21 pediatric patients (0-18 years) with confirmed cardiomyopathies (12 dilated, 9 hypertrophic) and 11 healthy volunteers. Cardiac DTI was performed on a 3 T MRI system using a free-breathing, motion-compensated protocol. Quantitative analysis of ADC and FA was conducted in the interventricular septum. Group comparisons were performed using non-parametric statistical tests, correlations with left ventricular ejection fraction (LVEF) were assessed using Spearman analysis, and receiver operating characteristic (ROC) curves were generated to evaluate discriminatory performance.

RESULTS: Significant differences were observed in ADC (H = 24.50, p < 0.001) and FA (H = 15.31, p < 0.001) among groups. Dilated cardiomyopathy (DCM) demonstrated the highest ADC values (0.068 ± 0.013 mm²/s) and the lowest FA (0.413 ± 0.058), whereas hypertrophic cardiomyopathy (HCM) showed moderately elevated ADC (0.044 ± 0.005 mm²/s) with preserved FA (0.527 ± 0.029), not significantly different from healthy volunteers. ADC correlated inversely with LVEF (ρ = – 0.409, p < 0.001), while FA showed a positive correlation (ρ = +0.443, p < 0.001), assessed in patients with complete LVEF data (n = 18). ROC analysis demonstrated excellent discriminatory performance for ADC in DCM identification (AUC = 1.000) and good performance for FA in both DCM (AUC = 0.912) and HCM identification (AUC = 0.813).

CONCLUSIONS: Cardiac DTI-derived metrics reveal distinct microstructural patterns in pediatric cardiomyopathies and show significant associations with ventricular function. These findings suggest that DTI may provide complementary quantitative biomarkers for myocardial characterization in pediatric populations, warranting further validation in prospective and multicenter studies.

PMID:42320145 | DOI:10.1016/j.ejrad.2026.113026

Anim Reprod Sci. 2026 Jun 10;292:108271. doi: 10.1016/j.anireprosci.2026.108271. Online ahead of print.

ABSTRACT

Captive breeding plays an important role in the conservation of large carnivores. Yet, factors influencing offspring survival remain poorly quantified. Using long-term reproductive records from the Jaguar Conservation Fund, Brazil, we examined sources of variation in cub survival within a highly productive jaguar (Panthera onca) population. We analysed 34 litters (66 cubs) produced by 10 females and 11 males between 2014 and 2025. Data were evaluated using both litter-level binomial mixed-effects models and individual-level survival analyses. Overall cub survival was high (86.4%), but varied predictably with maternal parity and management context. Litters from multiparous females showed near-complete survival, whereas primiparous litters experienced significantly lower survival probabilities. Paternal presence at birth was associated with reduced cub survival and elevated early mortality risk. In contrast, a history of maternal hand-feeding was associated with improved survival. Proximity to humans showed no statistically supported effect after accounting for other variables. Survival analyses restricted to the first 90 days post-birth confirmed that these factors influenced early-life mortality risk rather than cumulative losses over time. Random effects for dam and sire identity explained little additional variation, indicating that observed differences in survival were largely attributable to identifiable life-history and management factors rather than persistent individual effects. Together, these results demonstrate that high reproductive success in captive jaguars is compatible with structured human intervention, and that early postnatal survival is shaped primarily by maternal experience and social context. By identifying specific, actionable risk factors, this study provides evidence-based guidance for refining captive management strategies for large felids.

PMID:42320123 | DOI:10.1016/j.anireprosci.2026.108271

Urol Oncol. 2026 Jun 19;44(9):211-222. doi: 10.1016/j.urolonc.2026.05.014. Online ahead of print.

ABSTRACT

BACKGROUND: While several environmental carcinogens are well-established in the development of bladder and kidney cancer, the role of microplastic exposure remains poorly understood. Micro- and nanoplastics (MNPs), often released during plastics manufacturing and recycling, are emerging pollutants that may carry carcinogenic additives or act as chemical vectors in air and water. Ohio-a major hub for plastics production-has experienced rising rates of bladder and kidney cancer, prompting an investigation into the spatial relationship between cancer incidence and exposure to plastics-processing waste.

METHODS: We conducted a geospatial epidemiological study using data from the Ohio Cancer Incidence Surveillance System (OCISS) from 2013 to 2021, alongside environmental exposure estimates derived from the Environmental Protection Agency (EPA’s) Toxics Release Inventory (TRI) and Risk-Screening Environmental Indicators (RSEI) model. Chemicals were categorized into adjusted plastics-processing waste (APPW), known bladder and kidney carcinogens, and combined exposures. Incidence and exposure data were analyzed at the ZIP Code Tabulation Area (ZCTA) level using spatial statistics (Moran’s I, bivariate Moran’s I, and Mantel tests), local cluster detection, and spatial regression.

RESULTS: Between 2013 and 2021, both bladder and kidney cancer incidence increased across Ohio, with notable geographic clustering of cases. Spatial analysis demonstrated that regions with higher environmental exposure to chemicals used in plastics-processing, particularly via air, had significantly higher rates of both cancers. Airborne bladder carcinogens showed the strongest spatial association with cancer incidence (bivariate Moran’s I = 0.0510, P = 0.004; Mantel P < 0.001), followed by airborne microplastics (bivariate Moran’s I = 0.0589, P = 0.007). Waterborne microplastics, while not spatially clustered on their own, were significantly associated with higher bladder and kidney cancer rates (kidney: bivariate Moran’s I = 0.0940, P = 0.001). When combining all plastics-related exposures, spatial relationships remained robust, suggesting a cumulative effect. These patterns were most prominent in industrial ZIP codes, particularly in northern and southwestern Ohio.

CONCLUSIONS: Our findings reveal significant spatial associations between plastics-related industrial emissions and both bladder and kidney cancer incidence in Ohio. These patterns suggest an environmental component to urologic cancer risk, with airborne exposures showing the strongest spatial alignment. The results warrant deeper mechanistic studies and targeted epidemiological investigations in high-exposure communities to further assess causality and inform public health interventions.

PMID:42320109 | DOI:10.1016/j.urolonc.2026.05.014