Search Results (119)

Accurate, simple, cost-effective, and non-destructive estimation of heartwood content is essential for the sustainable management and commercial valuation of Pterocarpus santalinus L.f. (red sanders), one of the world’s most valuable tropical timber species. Conventional methods for heartwood assessment, including increment coring and destructive sampling, are invasive, time-consuming, and unsuitable for large-scale field applications. Electrical Resistance Tomography (ERT) offers a promising alternative by exploiting differences in electrical resistivity associated with variations in wood moisture content and anatomical characteristics. The present study standardized the application of ERT for the identification and quantification of heartwood in standing red sanders trees and validated its performance against conventional core sampling. Fifty-eight trees representing two diameter classes (10–20 cm and 20–30 cm) were evaluated using a PiCUS TreeTronic Electrical Resistance Tomograph, followed by increment core extraction at breast height for validation. Distinct resistivity gradients were observed, with higher electrical resistivity in the central heartwood region and lower resistivity in the peripheral sapwood. The resistivity values ranged from 153 to 1031 Ω in trees with diameters of 10–20 cm and from 342 to 1444 Ω in trees with diameters of 20–30 cm. Linear regression analysis showed excellent agreement between ERT-estimated and measured heartwood diameters (R² = 0.98), with an average similarity of 91.5%. The observed resistivity distribution closely reflected variations in moisture content, wood density, and anatomical structure across the stem radius. The findings demonstrate that ERT is a reliable and non-destructive technique for estimating heartwood content in standing red sanders trees. These results demonstrate that ERT can accurately estimate heartwood dimensions in standing Pterocarpus santalinus trees under the conditions of the present study and provide a reliable approach for non-destructive assessment of heartwood in this species. The technique has considerable potential for timber valuation, harvest planning, tree breeding, forest inventory, and conservation programs involving high-value tropical hardwood species. Full article

This study aimed to investigate the accumulation of aboveground biomass, organ allocation patterns, and their driving mechanisms in Moso bamboo (Phyllostachys edulis) forests along different elevational gradients and to compare regional differences in growth processes. A total of 54 sample plots were established along an elevational gradient from 50 to 550 m across three different regions, with 100 m elevational intervals. Two-way ANOVA, regression analysis, Tukey’s HSD multiple comparisons, and generalized additive models (GAMs) were used to examine distribution patterns. (1) Individual bamboo biomass followed a unimodal pattern with increasing elevation, peaking at 150 m (16.20 ± 3.88 kg culm⁻¹), which was significantly higher than at other elevations (p = 0.048). Allometric covariance analysis showed that the b value did not differ significantly among elevations (p = 0.882), indicating a stable diameter at breast height (DBH)-biomass relationship. (2) Stand biomass was highest at 50 m (34.75 ± 10.97 t·ha⁻¹) and declined with elevation to 18.54 ± 7.13 t·ha⁻¹ at 550 m, revealing a decoupling from the elevational trend of individual biomass. (3) Branch and leaf dry mass allocation exhibited a “higher at both ends, lower in the middle” pattern. Culm allocation was highest at 150 m (80.3%), though differences among elevations were not statistically significant (p = 0.591). (4) Stand density decreased with elevation, while mean DBH first increased and then decreased, reaching a maximum at 450 m (9.77 cm). Differences in stand density and DBH among elevations were highly significant (p < 0.001). (5) ANCOVA showed that after controlling for mean DBH, the effect of elevation on individual biomass was substantially weakened (p = 0.051), with partial η² declining from 0.48 to 0.21 (a 56% reduction), indicating that DBH accounted for a substantial portion of the elevation effect on individual biomass.The individual biomass of Moso bamboo in central-southern Jiangxi peaked at approximately 150 m elevation. Elevation was associated with biomass mainly through its association with DBH (a size effect), rather than through changes in allocation ratios or allometric relationships. The pathway “elevation→DBH→individual biomass” appears to be the primary mediating pathway explaining the decoupling, although a causal interpretation requires further experimental validation. These findings provide a theoretical basis for elevation-differentiated management of Moso bamboo forests. Full article

Anthropomorphic breast phantoms are increasingly used for the development and evaluation of breast imaging technologies. The aim of this study is to evaluate the impacts of the material combination and stereolithography or Standard Tessellation Language (STL) export methodology on the multimodal imaging performance of patient-derived breast phantoms. A breast model derived from segmented magnetic resonance imaging (MRI) data was used to fabricate four multi-material phantom sections representing adipose- and glandular-equivalent regions. Two material combinations—acrylic styrene acrylonitrile and high-impact polystyrene (ASA-HIPS) and acrylic styrene acrylonitrile and acrylonitrile butadiene styrene (ASA-ABS)—and two STL export procedures were evaluated, including a conventional mesh-based workflow and an in-house voxel-preserving approach designed to eliminate interface gaps. The phantoms were imaged using clinical computed tomography (CT), mammography, and digital breast tomosynthesis systems. Quantitative evaluation included contrast-based image characteristics and region of interest-based intensity distribution analysis. The results showed that ASA-HIPS phantoms demonstrated higher inter-material contrast and greater attenuation separation than ASA-ABS across all imaging modalities. The voxel-preserving STL export procedure improved physical interface integrity and eliminated visible inter-material gaps, while producing only minor differences in global radiological metrics compared with the conventional workflow. CT imaging of the breast samples acquired at 70 kVp showed attenuation characteristics consistent with clinically reported Hounsfield unit ranges for low-density breast tissues observed in clinical CT examinations. Full article

Background/Objectives: Cancer survivors admitted to inpatient rehabilitation suffer from a complex burden of interrelated physical and psychological symptoms. While mean-level improvements during rehabilitation are well-documented, it remains unknown whether rehabilitation modifies the underlying structure of symptom interconnections—the symptom network—beyond reducing individual symptom scores. This study aimed to characterize symptom network structure at admission and discharge of a 21-day inpatient cancer rehabilitation program based on cancer-related physical symptoms and psychosocial functioning, formally compare network topology across timepoints, identify structurally central treatment targets, and assess the transdiagnostic generalizability of findings. Methods: Secondary analysis of routinely collected, electronic patient-reported outcome (PRO) data from 5066 cancer survivors (mean age 60.3 years, SD 12.2; 64.2% female; most frequent diagnoses: breast cancer = 36.9%, hematological malignancies = 10.4%; prostate cancer = 8.5%) admitted to a single-center inpatient rehabilitation program was performed between January 2017 and November 2022. The EORTC QLQ-C30 and the Hospital Anxiety and Depression Scale (HADS) questionnaires were utilized. Bayesian Gaussian Graphical Models were estimated at admission (T0) and discharge (T1) across 17 symptom and functioning domains using Bayesian Model Averaging (15,000 iterations). Edge-level change was quantified via posterior distributions of pairwise differences with 95% Highest Density Intervals. Node-level changes were assessed using Bayesian paired t-tests. Centrality was quantified by Expected Influence and Bridge Expected Influence. Results: Patients showed clinically meaningful improvements across all 17 domains during rehabilitation (all Bayes Factors >10; posterior probability of direction >99.9%). The largest standardized effects were observed for emotional functioning (Cohen’s d = 0.76), global health status (d = 0.69), and fatigue (d = 0.53). These improvements were clinically meaningful for a substantial proportion of patients: 62% improved by at least the minimal important difference in fatigue and 58% in emotional functioning, and the proportion of patients with probable anxiety fell from 15% to 6% and probable depression from 10% to 4%. Emotional functioning and anxiety were the most central domains in the symptom network—most strongly connected to the rest of patients’ symptom burden—at both admission and discharge. Despite the clinical improvements, the overall architecture of symptom interconnections changed little (83% of connections were unchanged). This indicates that the severity of symptoms was mitigated while the structure linking them together remained largely intact. The one connection that strengthened was that between impaired social functioning and financial difficulties (Δ = −0.112). Structural findings were consistent across ten cancer types (leave-one-out r > 0.80 in seven of ten). Conclusions: Over the course of inpatient cancer rehabilitation, patients showed large improvements against a background of largely stable symptom network architecture. Emotional functioning and anxiety occupy structurally central positions at both admission and discharge, identifying them as candidate domains warranting further investigation for network-informed rehabilitation. These findings provide a novel structural perspective on oncological rehabilitation and a framework for developing more targeted intervention strategies. Full article

Breast density has a significant impact on how clearly masses appear in mammography. It can also introduce bias in automatic localization systems when density distributions are uneven. Although advances in deep learning-based detection methods have been made, most studies report overall performance without explicitly accounting for variability associated with breast density. Breast cancer diagnosis from mammography is strongly influenced by dataset composition, annotation variability, and breast density distribution, factors that are rarely controlled in current AI evaluations. We introduce Mass-Bench, a clinically balanced and harmonized multi-dataset benchmark that integrates CBIS-DDSM, INBREAST, VINDr-Mammo, and DMID under a unified canonical schema, with standardized ACR density and BI-RADS encoding. Using a leakage-controlled and distribution-aware evaluation protocol, density-stratified mass detection and lesion-centered regions of interest (ROIs) classification were assessed across datasets. YOLO-based detection models achieved peak area under the curve (AUC) values up to 0.943; however, performance systematically degraded with increasing ACR density, revealing limitations that are often masked in imbalanced evaluations. By enforcing clinically representative density distributions, Mass-Bench provides a more reliable estimation of localization performance, which directly impacts downstream clinical tasks. In this context, binary ACR classification achieved F1-scores up to 0.976, while binary BI-RADS discrimination reached accuracies up to 0.93. However, multi-class classification remained more challenging, showing increased sensitivity to dataset heterogeneity and contextual information. These findings demonstrate that conventional evaluations may overestimate robustness, particularly in dense breast categories, and highlight the importance of density-aware benchmarking for developing reliable and clinically applicable AI systems in mammography. Full article

High-density SNP chips have been demonstrated to be effective instruments for simultaneously genotyping large numbers of loci, thereby facilitating genome-scale analyses and advancing genomic selection (GS) in poultry and livestock. The meat-type duck, an economically valuable poultry species in China, has so far lacked precise and high-throughput genotyping systems, which has constrained the broader implementation of GS and genome-wide association analyses (GWASs) and consequently slowed genetic progress. In this study, we developed and validated a novel SNP array based on Genotyping-by-Targeted-Sequencing (GBTS) technology. The array comprises 40,875 SNP markers evenly distributed across 32 duck chromosomes. Using data generated from this array, genomic heritability estimates were obtained for six economic traits in a cultured duck population (n = 400), with values of 0.61 ± 0.09, 0.69 ± 0.08, 0.80 ± 0.08, 0.11 ± 0.09, 0.14 ± 0.08, 0.31 ± 0.09 for age at first egg (AFE), egg production number at 38 weeks (EN38w), egg weight at 38 weeks (EW38w), body weight at 35 days (BW35d), shank length at 35 days (SL35d) and thickness of breast muscle at 40 days (TB40d). A total of 163 significant SNPs associated with economic traits were identified through GWAS, and annotation revealed 28 candidate genes related to five of these traits. Moreover, the prediction accuracy of ssGBLUP for AFE, EN38w, EW38w, BW35d, SL35d, and TB40d reached 0.55 ± 0.16, 0.56 ± 0.12, 0.57 ± 0.08, 0.25 ± 0.19, 0.31 ± 0.19, and 0.47 ± 0.17, respectively—values that exceeded those obtained using BLUP. Population genomic analyses of 400 ducks demonstrated that this SNP array provides improved genomic prediction accuracy over pedigree-based BLUP for most analyzed traits. Overall, the developed SNP array provides a robust, high-efficiency, and cost-effective genotyping platform that will accelerate genetic progress and promote the sustainable development of the meat-type duck industry. Full article

Background and Objectives: Metaplastic breast carcinoma (MBC) is a rare, aggressive malignancy that is often resistant to conventional chemotherapy and characterized by a triple-negative phenotype. While immune checkpoint inhibition shows promise, the prognostic significance and distribution of programmed death-ligand 1 (PD-L1) expression within the heterogeneous architecture of MBC remain poorly understood. This study aimed to evaluate PD-L1 expression and the density of tumor-infiltrating lymphocytes (TILs) to clarify their roles in patient stratification and overall survival (OS). Materials and Methods: We retrospectively analyzed 48 MBC cases diagnosed between 2010 and 2025. PD-L1 expression was quantified using the Combined Positive Score (CPS) with the 22C3 antibody clone across diverse histological components. The density of stromal TIL density was assessed following internationally standardized guidelines. Clinical outcomes and clinicopathological parameters, including metastasis, lymphovascular invasion (LVI), and histological subtype, were correlated with biomarker status using Kaplan–Meier survival analysis and Cox proportional hazards regression models. Results: PD-L1 positivity (CPS ≥1) was identified in 72.9% of cases, one of the highest rates documented in literature. Notably, an inverse relationship was observed with PD-L1-negative tumors, which exhibited significantly higher rates of distant metastasis (46.2% vs. 17.1%; p = 0.039). Multivariate analysis confirmed that low density of TILs (HR = 9.66; p = 0.016), metastasis (HR = 4.40; p = 0.023), and the presence of LVI (HR = 3.84; p = 0.047) were strong independent predictors of mortality. While PD-L1 status alone did not directly dictate overall survival, mean overall survival was markedly reduced in the low TILs cohort (32.2 months) compared to the high TILs group (114.2 months). Conclusions: The high prevalence of PD-L1 expression supports routine screening for immunotherapy eligibility in MBC. Our findings suggest that PD-L1-negative cases represent a high-risk biological subset driven by alternative immune evasion mechanisms. Integrating TIL density with conventional pathological parameters provides a more robust prognostic framework, enabling personalized therapeutic strategies for this challenging malignancy. Full article

Amazonian large trees act as central elements of forest ecosystems, storing a disproportionate fraction of aboveground biomass. However, these trees are not randomly distributed across the landscape, and it is expected that edaphic attributes influence floristic composition, forest structure, and vegetation biomass. In this study, we investigated how variation in soil chemical and physical properties affects the diversity and biomass of large trees. Forest inventories were conducted at five sites within protected areas in the states of Pará and Amapá. Aboveground biomass was estimated using allometric equations, while soil samples were analyzed for their physical and chemical properties. Diversity indices, rarefaction, Redundancy Analysis, and Generalized Additive Models were applied. Edaphic variables such as soil pH, organic matter, phosphorus, and aluminum were associated with floristic composition and the biomass of these individuals. Trees with a diameter at breast height greater than or equal to 70 cm accounted for up to 80% of total biomass, revealing a pattern of biomass hyperdominance. The results indicate that the occurrence of large trees is related to edaphic and structural attributes, such as tree density and size distribution, suggesting that these individuals are not randomly distributed along soil gradients. Understanding these patterns is essential for improving ecological models, biomass extrapolations, and management strategies aimed at conserving the Amazon rainforest. Full article

To investigate the effects of long-term continuous rotary tillage incorporation (RT) on Fraxinus chinensis Roxb. plantations, this study compared 7- and 15-year-old stands subjected to RT since afforestation with their non-tilled counterparts (CK). Results demonstrated that RT significantly enhanced tree growth by synergistically improving soil nutrient availability, physical properties, and microbial community structure and function: (1) Compared with CK, RT increased diameter at breast height (DBH) by 28.89% in 7-year-old stands and 22.58% in 15-year-old stands, and tree height by 19.51% in 7-year-old stands and 25.00% in 15-year-old stands; (2) RT increased contents of soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP), rearranged the distribution patterns of soil particulate organic carbon (POC) and mineral-associated organic carbon (MAOC), and reduced soil bulk density (BD) and soil water content (SWC); (3) RT regulated microbial diversity, co-occurrence networks, and carbohydrate-degrading gene abundances, with more prominent effects in 15-year-old stands. This tillage practice is feasible and effective, and thus is recommended for application in F. chinensis plantation management, providing a scientific basis for refined and sustainable plantation management. Full article

Background/Objectives: Autologous fat grafting (AFG) is widely used in breast reconstruction; however, graft retention remains unpredictable due to recipient-bed variability. Photoacoustic imaging (PAI) is a contrast-free, noninvasive modality enabling visualization of vascular structures in detail. This study used PAI to visualize and quantitatively assess neovascularization and vascular structure in breasts reconstructed with AFG. Methods: In this retrospective, cross-sectional study, data from eight patients who underwent PAI of both reconstructed and contralateral breasts at least three months after their final AFG procedure for total breast reconstruction were used. Excluding the nipple–areola complex and skin markings, four 3 × 3 cm regions of interest (one per quadrant) were selected in the periareolar region. Vascular density in terms of depth from the skin surface was analyzed in five cases with adequate contact between the device and the skin. Visible vessel diameters within the regions of interest were manually measured and categorized as small, medium, or large to assess distribution patterns. Results: PAI successfully enabled visualization of vascular structures on the reconstructed side in all cases, even at depths greater than 10 mm. In five cases, vascular density in the superficial layer (0–2.5 mm) was higher on the reconstructed side than on the contralateral side. A longer postoperative interval was associated with a higher proportion of small vessels and fewer large vessels. Conclusions: PAI enabled noninvasive visualization of vascular structures consistent with neovascularization on the reconstructed side after AFG. Temporal changes in vessel diameter distribution suggest ongoing vascular remodeling, supporting the potential utility of PAI in assessing vascular structural changes in grafted tissue over time. Full article

The ecological strategies of understory shrubs are critical for maintaining the structure and function of forest understory vegetation. Understanding the assembly mechanisms of these shrub communities is a central issue in modern ecology. To address this, our study was conducted in the typical red soil regions of Jiangxi, China, focusing on secondary forests (including both broadleaved and coniferous types) of similar stand age. We aimed to assess the effects of various environmental factors—such as soil pH, total nitrogen content, bulk density, and understory temperature—along with tree-layer characteristics—including canopy closure, tree species richness, and diameter at breast height (DBH)—on the species composition, functional traits, and phylogenetic structure of the shrub layer. Results showed: One-way ANOVA revealed significant differences in functional traits between the two forest types. Specifically, leaf thickness, specific leaf area, and chlorophyll content were significantly higher in the coniferous forest, whereas leaf dry matter content was significantly lower compared to the broadleaved forest (p < 0.05). These results suggest that understory shrubs in the coniferous forest primarily adopt a resource-conservative strategy, while those in the broadleaved forest exhibit a resource-acquisitive strategy. Phylogenetic analysis further revealed that the phylogenetic diversity (PD) of coniferous forests was significantly lower than that of broadleaved forests (p < 0.05). The phylogenetic structure in coniferous forests showed a more clustered pattern (NTI > 0, NRI > 0), suggesting stronger environmental filtering. Diversity index analysis showed that the Chao1 index indicated a richer potential species pool in broadleaved forests (p < 0.05), while species distribution was more even in coniferous forests (p < 0.05). Random Forest model analysis identified the diameter at breast height (DBH) of trees as the most critical negative driver, while soil pH was the primary positive driver. Redundancy Analysis (RDA) confirmed that the community structure in coniferous forests was mainly driven by biotic competition pressure represented by DBH, whereas the structure in broadleaved forests was more closely associated with abiotic factors like soil total nitrogen and pH (R² = 0.29, p < 0.05). These environmental drivers, through strong environmental filtering, collectively resulted in a phylogenetically clustered pattern of shrub communities in both forest types. This study demonstrates that the assembly of understory shrub communities is a complex, multi-level process co-regulated by multiple factors, shaped by both the biotic pressure from the overstory structure and abiotic filtering from the soil environment. This finding deepens our understanding of the rules governing community assembly in forest ecosystems. Full article

Artificial intelligence is reshaping breast imaging, yet progress is constrained by data scarcity, privacy restrictions, and uneven representation. This narrative review synthesizes evidence (2020–April 2025) on synthetic data and generative AI—principally GANs and diffusion models—in mammography and related modalities. We examine how synthetic images enable data augmentation, class balancing, external validation, and simulation-based training; summarize reported gains in detection performance; and assess their potential to mitigate or, if misapplied, amplify bias across subgroups (age, density, ethnicity). We analyze threats to validity, including enriched cohorts, distribution shift, and unverifiable realism, and address medico-legal exposure, image provenance, and deepfake risks. Finally, we outline task-specific validation and reporting practices, equity auditing across density and demographics, and governance pathways aligned with EU/US regulatory expectations. Synthetic data and generative AI can enhance performance, training, and data sharing; however, responsible clinical adoption requires rigorous validation, transparency on failure modes, tamper-evident provenance, and shared accountability models. Full article

This paper introduces the Lambert-Lindley distribution, a two-parameter extension of the Lindley model constructed through the Lambert-F generator. The new distribution retains the non-negative support of the Lindley distribution and provides additional flexibility by incorporating a shape parameter that controls skewness and tail behavior. Structural properties are derived, including the probability density function, cumulative distribution function, quantile function, hazard rate, and moments. Parameter estimation is addressed using the method of moments and maximum likelihood, and a Monte Carlo simulation study carried out to evaluate the performance of the proposed estimators. The practical applicability of the Lambert–Lindley distribution is demonstrated with two real datasets: stress rupture times of Kevlar/epoxy composites and hospital stay durations of breast cancer patients. Comparative analyses using goodness-of-fit tests and information criteria demonstrate that the proposed model can outperform classical alternatives such as the Gamma and Weibull distributions. Consequently, the Lambert–Lindley distribution emerges as a flexible alternative for modeling positive unimodal data in contexts such as material reliability studies and clinical duration analysis. Full article

Timely and precise identification of breast cancer is crucial for enhancing clinical outcomes; however, current AI systems frequently exhibit deficiencies in transparency, trustworthiness, and the capacity to assimilate varied data modalities. We introduce a reliable, multi-agent, multimodal AI system for individualised early breast cancer diagnosis, created on the CBIS-DDSM dataset. The system consists of four specialised agents that cooperatively analyse diverse data. An Imaging Agent employs convolutional and transformer-based models to analyse mammograms for lesion classification and localisation; a Clinical Agent extracts structured features including breast density (ACR), view type (CC/MLO), laterality, mass shape, margin, calcification type and distribution, BI-RADS score, pathology status, and subtlety rating utilising optimised tabular learning models; a Risk Assessment Agent integrates outputs from the imaging and clinical agents to produce personalised malignancy predictions; and an Explainability Agent provides role-specific interpretations through Grad-CAM for imaging, SHAP for clinical features, and natural language explanations customised for radiologists, general practitioners, and patients. Predictive dependability is assessed by Expected Calibration Error (ECE) and Brier Score. The framework employs a modular design with a Streamlit interface, facilitating both comprehensive deployment and interactive demonstration. This paradigm enhances the creation of reliable AI systems for clinical decision assistance in oncology by the integration of strong interpretability, personalised risk assessment, and smooth multimodal integration. Full article

Background: In healthcare institutions, radiologists play an essential role in patients’ care, enabling them to begin treatment and start their recoveries. However, data on the characteristics and distribution of the radiology workforce in Saudi Arabia are limited. Therefore, this study aimed to conduct a comprehensive analysis of the radiology workforce in SA based on national data and identify key distributional and specialty trends relevant to workforce planning and radiology service delivery. Methods: The following data were obtained from the Saudi Commission for Health Specialties (SCFHS) Registry: total number of registered radiologists, age, subspecialty, professional classification, place of qualification, and geographical location. Descriptive statistics were used for data analysis. Additionally, the findings were compared with those of published international benchmarks. Results: There were 5150 radiologists registered with SCFHS in SA, which corresponded to 147 radiologists per 1,000,000 inhabitants. The mean age was 40.8 years (standard deviation [SD] 9.8), with 60% of them being aged 30–44 years. Most of the radiologists specialised in general diagnostic radiology (83.7%), with few of them specialising in interventional radiology (1.8%), paediatric radiology (1.1%), and breast imaging (0.9%). The workforce mainly comprised consultants (35.0%), followed by registrars (29.7%) and senior registrars (22.7%). Two-thirds (65.0%) of the radiologists had obtained their qualifications abroad. More than half of the radiologists resided in three provinces: Riyadh (29%), Mecca (23%), and the Eastern Region (15%), while several provinces had fewer than 2% of the available workforce. Conclusions: The radiology workforce in SA is relatively young and has a higher density than the average in the European Union. Further, most of the radiologists are professionally classified as consultants or registrars. However, there is a clear imbalance in their geographic distribution, which is consistent with the population sizes of the respective cities. Targeted training expansion and reduced reliance on foreign-trained professionals are warranted to meet future service demands in line with the Vision 2030 objectives. Full article