Cinnamomum camphora seed kernels are a potentially valuable fatty oil resource; however, their lipid composition and dynamic changes during development remain poorly understood. In this study, morphological and anatomical observations combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based lipidomics were used to investigate lipid accumulation patterns in C. camphora seed kernels across five developmental stages. The results showed that seed development followed a distinctive pattern in which morphological maturation preceded physiological maturity. A total of 627 lipid molecules were identified and classified into 27 subclasses and 5 major classes. Among them, glycerolipids (GLs) and glycerophospholipids (GPs) were the dominant lipid classes, with triacylglycerols (TGs) representing the principal storage lipids. Approximately 84.2% of the detected lipids were unsaturated, indicating a highly unsaturated lipid profile. The fatty acid composition was enriched in medium-chain fatty acids (MCFAs), especially decanoic acid and lauric acid, suggesting that C. camphora seed kernel oil possesses distinctive compositional characteristics compared with conventional fatty oils. In addition, coenzyme Q (CoQ) showed relatively high abundance and dynamic accumulation during seed development. Differential lipid analysis further revealed that lipid remodelling occurred mainly during the early developmental stages and was significantly associated with glycerolipid and glycerophospholipid metabolism. Diacylglycerols (DGs), phosphatidylcholines (PCs), and phosphatidylethanolamines (PEs) decreased during early development, whereas TGs accumulated continuously from the middle stage onwards. Overall, this study provides a systematic characterisation of lipid composition and developmental dynamics in C. camphora seed kernels and offers a theoretical basis for their future utilisation as a novel functional fatty oil resource.
Dual-energy computed tomography (DECT) enables improved volumetric bone mineral density (vBMD) assessment by accounting for marrow alterations associated with aging, disease, and injury. However, DECT reconstruction kernels and monochromatic energy pair combinations may influence vBMD measurements and finite element model (FEM)-estimated bone stiffness. This study investigated the effects of reconstruction kernel and DECT energy pair combinations on proximal humeral vBMD and FEM-estimated stiffness in cadaveric specimens. Fourteen cadaveric shoulders from seven specimens were scanned bilaterally using DECT with a K2HPO4 calibration phantom. Images were reconstructed using standard (STD) and bone-sharpening (BONE) kernels. Simulated monochromatic images at 40, 90, and 140 keV were combined into 40/90, 40/140, and 90/140 keV energy pairs. Volumetric BMD was extracted from the humeral shaft diaphysis and anatomic neck using custom Python scripts and 3D Slicer software. Image-based FEMs were generated to estimate bone stiffness. Results were analyzed using repeated-measures analysis of variance (RM-ANOVA). In the cortical-dense humeral diaphysis, energy pair combinations had the greatest variation. Mean diaphyseal vBMD increased from 332.08 ± 102.54 mgK2HPO4/cc (40/90 keV BONE) to 406.84 ± 130.15 mgK2HPO4/cc (90/140 keV BONE), while FEM stiffness increased from 180.30 ± 47.07 kN/mm to 223.30 ± 63.91 kN/mm. Significant differences were observed across reconstruction conditions and energy pair combinations. In contrast, trabecular-rich anatomic neck regions demonstrated minimal variation in vBMD and FEM stiffness across energy pairs and kernels. These findings indicate that DECT energy pairs and reconstruction kernel substantially influence cortical bone assessments, particularly when using the 90/140 keV energy pair, while trabecular-rich regions remain comparatively unaffected.
Spodoptera frugiperda (J. E. Smith) is a major invasive pest of maize in Asia, with migration pathways extending annually in July into North China, a key maize-producing region. However, the potential utility of Bt maize for controlling this pest remains unclear. In this study, we evaluated the insecticidal activity and field control efficacy of Bt-(Cry1Ab + Cry1F) maize against the insect in North China. Bt insecticidal protein levels in Bt-(Cry1Ab + Cry1F) maize tissues followed the order: V6-8 leaves > V12 leaves > VT tassels, R4 kernels > R1 silks. Correspondingly, larval mortality was 100 ± 0% after leaf feeding for 7 days and 84.49 ± 1.71%, 75.65 ± 1.50%, and 82.57 ± 1.08% after feeding on VT tassels, R1 silks, and R4 kernels, respectively. V5 leaf feeding significantly reduced pupal weight, pupation and emergence rates, and fecundity of 1st- and 3rd-instar S. frugiperda, while prolonging larval development. Field infestation experiments showed significantly lower larval density and leaf and plant damage rates of S. frugiperda in Bt-(Cry1Ab + Cry1F) maize than those in conventional maize fields, with larval control efficacy of 61.14-100%. Field surveys in 2023-2025 revealed no S. frugiperda larvae in Bt-(Cry1Ab + Cry1F) maize fields throughout the entire growth period, with plants exhibiting no damage. In contrast, conventional maize suffered continuous damage during the reproductive growth stage, with a peak larval population of 9.33-12.67 per 100 plants and a plant damage rate of 64-74%. This study demonstrates that Bt-(Cry1Ab + Cry1F) maize can effectively control this pest in the region and provide a green tool for area-wide management of S. frugiperda. © 2026 Society of Chemical Industry.
Introduction: Detection of in-stent restenosis by cardiac CT is challenging due to blooming artifacts. The technological progress of CT scanners and especially the recent introduction of photon-counting detectors (PCDs) has led to an improvement in image quality. Several studies have analyzed the lumen visibility of coronary stents, but most studies used models which did not simulate cardiac movement. In this study we use a pulsatile heart model to simulate a heartbeat to analyze the effects of cardiac motion on image quality. Methods: Seventeen different coronary stents with an outer diameter of 3.0 mm were implanted into polyolefin tubes. The tubes were then filled with diluted contrast medium and attached to the pulsatile heart model. The stents were scanned in a third-generation dual-source CT with an energy-integrating detector (EID) and a first-generation PCD CT. Results: In motion, the mean visible stent lumen was reduced from 64.4% to 59.4% in EID CT, from 61.4% to 56.0% in PCD CT using the Bv60 kernel, and from 72.9% to 62.9% in PCD CT using the Bv72 kernel, each in standard resolution mode. Employing the ultra-high-resolution mode (UHR), stent lumen visibility was reduced from 61.3% to 57.9% with the Bv60 kernel and from 71.7% to 61.8% with the Bv72 kernel. The difference between static imaging and motion was significant in each instance (p < 0.001). Conclusions: While PCD CT and the use of sharper kernels improves the image quality in comparison with EID CT and smoother kernels, the impact of cardiac motion on the reduction in stent lumen visibility is substantial. Hence, the best image quality is achieved in patients with a normal and regular heart rate. If this is not possible to achieve, a retrospective acquisition mode should be considered.
Almonds (Prunus dulcis; family Rosaceae) contain 18-25% protein (dry weight). They are an important plant-based protein source in dairy alternatives and other functional foods. The hard and dense nature of almond kernels and the localization of proteins with lipid bodies in the cotyledons of almond seeds make it challenging to recover protein from the seed efficiently and preserve its function. Therefore, this review evaluates the influence of pretreatments, including blanching, grinding, and defatting, on almond protein recovery and functionality, and compares conventional and emerging technologies for almond protein. Traditional protein extraction techniques such as alkaline extraction-isoelectric precipitation (AE-IEP), aqueous extraction, and salt extraction provide moderate-to-high protein yields, but harsh processing conditions denature the proteins, decrease solubility, and cause functional properties to be lost. On the other hand, emerging protein extraction technologies (including enzyme-assisted aqueous extraction (EAE) ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE), high-pressure processing (HPP), and pulsed electric field (PEF) treatment) improve protein recovery, resulting in protein extract with superior functional properties and reduced allergenicity. However, their application in industry remain challenging. This review reveals that pretreatment approaches and conditions/parameters significantly influence protein extraction efficiency and the functional and structural properties of almonds, and that no single method is universally optimal. This review concludes that controlled enzymatic hydrolysis combined with physical pretreatment may be the best approach for producing high-value-added almond protein ingredients with specific techno-functional properties for use in plant-based beverages, hypoallergenic products, or nutraceuticals. More research is needed to develop an efficient, applicable, sustainable and eco-friendly almond protein extraction process, optimizing processing conditions to achieve high protein recovery while retaining desirable functional properties, and reduce operating costs.
Almond (Prunus dulcis) is a highly nutritious nut, rich in unsaturated fatty acids, fibre, and bioactive compounds such as tocopherols, phenolic compounds, and carotenoids. Processing methods commonly applied in the almond industry, including blanching and roasting, may modify the nutritional composition and bioactive profile of the kernels. Therefore, the aim of this study was to evaluate the effect of blanching and roasting on the nutritional composition and bioactive compound content of the 'Largueta' almond variety. Three forms were analysed: raw almonds with skin, blanched (peeled) raw almonds, and roasted almonds, with their chemical composition, lipid profile and bioactive compound content being examined. The data obtained indicated that raw almonds with skin showed higher levels of fibre (12.16 g/100 g), phenolic compounds (66.35 mg gallic acid/100 g), and β-carotene (65.88 µg/100 g). Roasted almonds contained lower amounts of phenolic compounds (42.87 mg gallic acid/100 g), tocopherols (7.64 mg α-tocopherol/100 g and 1.99 mg γ-tocopherol/100 g) and essential amino acids such as tryptophan (1.23 g/100 g protein) and lysine (3.22 g/100 protein). Blanching, by removing the skin, significantly reduces fibre (7.52 g/100 g) and carotenes (26.60 µg β-carotene/100 g). With regard to fatty acids, the main components of nuts, oleic acid predominated in all samples (>65%), with no significant changes due to processing. Thermal treatments modify the composition of almonds. Roasting concentrates some nutrients but reduces antioxidants, while blanching mainly affects fibre content. Therefore, the consumption of raw sweet almonds with skin is recommended to preserve their nutritional and antioxidant benefits, or to subject them to moderate heat treatment.
A Gaussian Process Regression (GPR) model was developed to predict the shear capacity of exterior reinforced concrete (RC) beam-column joints subjected to seismic loading. The model accounts for key parameters, including beam and column geometry, reinforcement detailing, axial column load, and concrete compressive strength. A database of 273 experimentally tested specimens was used, with emphasis on horizontal joint shear strength. Three kernel structures within the GPR framework-Primary, Rational Second-Order, and Combined kernels-were examined. Model predictions were evaluated against existing shear-strength formulations using deterministic metrics (MAE, RMSE, and R2) and probabilistic measures (NLPD and MSLL). The results show that kernel effectiveness depends on model formulation; however, the Combined kernel exhibited more stable predictions and improved uncertainty calibration for models with higher-dimensional input sets. Sensitivity analysis identified concrete compressive strength, beam depth, and joint transverse reinforcement as dominant variables, followed by column height, axial load ratio, and reinforcement configuration. Overall, the proposed framework enables consistent shear-strength prediction and quantifies the relative influence of geometric and material parameters, contributing to more informed assessment and design of RC beam-column joints.
(-)-Epigallocatechin has multiple bioactivities. ANR catalyzes its biosynthesis, with unclear structure and mechanism restricting enzyme modification. Here, we first screened ANRs from 21 plant species and identified Torreya grandis ANR (TgANR) as the most efficient candidate based on molecular docking and in vitro assays. Through integrated alanine scanning and computational prediction, we constructed a combinatorial mutant N15S/F106E/P182S, which exhibited a 3.88-fold higher EGC synthesis activity than the wild-type enzyme. The interaction mechanism between TgANR and the substrate delphinidin as well as the structure-activity relationship in the catalytic production of EGC were studied on the basis of protein engineering modification, thus clarifying the catalytic mechanism of TgANR. Furthermore, transient overexpression of the mutant gene in T. grandis kernels significantly increased EGC accumulation. This study not only elucidates the structure activity relationship of TgANR but also provides an efficient enzyme variant and a protein engineering framework for boosting EGC biosynthesis in plants.
Background/Objectives: Popcorn (Zea mays L. var. everta) is an important specialty maize type; however, the genetic variation underlying popping-related quality traits remains insufficiently characterized in breeding. Methods: In this study, 18 popcorn inbred lines were analyzed using 25 simple sequence repeat (SSR) markers distributed across all 10 maize chromosomes, and 16 lines were further evaluated for popping performance and image-based flake morphology. Results: Substantial phenotypic variation was observed among the tested lines, with expansion volume ranging from 173.33 to 343.33 mL and expandability ranging from 16.79- to 32.46-fold. Image-based analysis of 957 popped kernels revealed continuous variation in flake circularity, indicating that flake morphology represents a quantitative trait rather than a strictly discrete classification. SSR analysis detected 2 to 11 alleles per locus, with polymorphism information content values ranging from 0.05 to 0.85, indicating moderate-to-high genetic diversity among the tested lines. Principal component analysis (PCA), unweighted pair group method with arithmetic mean (UPGMA) clustering, and population structure analysis revealed clear genetic differentiation and heterogeneous genetic backgrounds within the germplasm collection. Marker-trait association analysis identified several putative SSR loci associated with expansion efficiency, flake morphology, pericarp retention, and popping dynamics. Notably, marker M18 was putatively associated with both expansion volume and expandability. Conclusions: Based on these results, a conceptual framework was proposed in which popping-related traits were organized into partially independent but interconnected functional modules. Overall, this study provides SSR-based genetic information for popcorn germplasm characterization and offers preliminary marker resources for quality-oriented popcorn breeding.
Predicting the human burden of vector-borne diseases from limited surveillance data remains a major challenge, particularly in the presence of nonlinear transmission dynamics and delayed effects arising from vector ecology and human behavior. We develop a data-driven framework based on an extension of Sparse Identification of Nonlinear Dynamics to systems with distributed memory, enabling discovery of transmission mechanisms directly from time series data. Using severe fever with thrombocytopenia syndrome as a case study, we show that this approach can uncover key features of tick-borne disease dynamics using only human incidence and local temperature data, without imposing predefined assumptions on human case reporting. We further ascertain the robustness of the recovered incidence-temperature model by integrating it with mechanistically derived tick-host covariates, showing that the forecasting ability does not improve. This suggests that the proposed core data-driven model already delivers strong predictions. The framework also allows for systematic sensitivity analysis of memory kernels and behavioral parameters. Although the approach prioritizes predictive accuracy over mechanistic transparency, it yields sparse, interpretable integral representations suitable for epidemiological forecasting. This methodology provides a scalable strategy for forecasting vector-borne disease risk and informing public health decision-making under data limitations.
This study aimed to characterize the stress relaxation of wheat kernels of different hardness and varying moisture contents using generalized Maxwell models with three, five, and seven elements. Single-kernel stress relaxation tests were conducted under controlled axial compression to obtain force decay data, which were fitted using non-linear regression to estimate model constants. Positive correlations between kernel moisture content and Maxwell constants were observed, indicating greater magnitude of force decay and higher relaxation rates at higher moisture levels, while the residual force decreased. Significant differences in viscoelastic parameters were also found between hard and soft wheat varieties.
The swift progression of three-dimensional (3D) concrete printing has opened the door to the creation of innovative materials, such as fiber-reinforced concrete, making it essential to develop accurate models for predicting their mechanical properties. Accurately estimating the compressive strength (CS) of such materials is required for optimizing mix designs and ensuring structural performance. In this context, the present study is the first to systematically investigate and compare three different support vector regression (SVR) kernels, namely, linear SVR (L-SVR), polynomial SVR (Poly-SVR), and radial basis function SVR (RBF-SVR), for predicting the compressive strength of normal, high, and ultra-high strength 3D-printed fiber-reinforced concrete (3DPFRC). A model was developed and validated using a dataset of 278 samples collected from the literature. In addition to SVR models, Artificial Neural Networks (ANN) and Gradient Boosting Machines (GBM) were developed for benchmarking purposes. Results from statistical evaluation revealed that the ANN model achieved the highest predictive accuracy overall, outperforming the L-SVR, Poly-SVR, and GBM models. Among the SVR-based models, the RBF-SVR demonstrated superior performance, showing competitive accuracy with the ANN while outperforming both the other SVR variants (L-SVR and Poly-SVR) as well as the GBM model in terms of coefficient of determination (R²) value and error metrics. All models achieved R² scores ranging between 0.94 and 0.86 for the training datasets and 0.93 and 0.72 in the testing datasets. The RBF-SVR model, in particular, offered an excellent balance between accuracy, training speed, and reliability, making it a strong and efficient alternative to more complex models. Additionally, SHapley Additive exPlanations (SHAP) analysis identified the water-to-cement ratio, silica fume content, and cement volume as the most influential parameters affecting compressive strength.
Single-lead electrocardiogram (ECG) is widely used in wearable devices for atrial fibrillation (AF) screening. Nevertheless, subtle pathological characteristics like P-waves and f-waves in practical signals are vulnerable to noise contamination. Meanwhile, the scarcity of high-quality annotated abnormal data instances leads to severe class imbalance. To mitigate these issues, we present an end-to-end framework designed for arrhythmia diagnosis using single-lead ECG signals, which integrates quality-aware data augmentation with a Peak-Enhanced attention mechanism. First, to mitigate the problem of data imbalance, a Quality-Aware Generative Adversarial Network (QA-GAN) is designed. This network integrates a signal quality evaluation module based on signal kurtosis, together with a dynamic soft-label training scheme, guiding the generator to prioritize learning high-quality morphological features, thereby synthesizing high-fidelity minority class samples. Second, to accurately capture subtle pathological features in electrocardiograms, a Peak-Enhanced Attention Convolutional Network (PEAC-Net) classification model is proposed. This model incorporates a Peak-Enhanced Attention (PE-Att) module, which employs learnable derivative convolutional kernels to precisely identify the transition points in the ECG signal. Furthermore, by integrating one-dimensional multi-scale dilated convolution (DSGC1D) with bidirectional LSTM, the model achieves effective capturing of both fine-grained local morphological features and long-range global rhythm patterns. Experimental results on the PhysioNet 2017 dataset indicate that the presented model attains an accuracy of 0.902 and a macro-F1 score of 0.880, respectively, outperforming other state-of-the-art models and also exhibiting robust data adaptability on the MIT-BIH dataset.
Low back pain affects hundreds of millions of people and is associated with degenerative changes in the lumbar spine. Trabecular bone microarchitecture change is a reasonable contributor to low back pain, but it remains largely invisible on routine clinical CT because typical voxel sizes (500 to 1000 μm) are insufficient to resolve trabeculae (about 100-200 μm). We present LumbarSR, a paired and registered dataset of 30 human lumbar vertebral specimens scanned with a photon-counting micro-CT (Micro-PCCT) reference at 105 μm isotropic resolution and with a standard clinical CT system under eight acquisition configurations formed by the factorial combination of two in-plane resolutions (195 and 586 μm), two slice thicknesses (500 μm and 1000 μm), and two reconstruction kernels (bone and soft tissue). Clinical CT volumes are rigidly registered to the Micro-PCCT reference using ANTs-based alignment and resampled to a common voxel grid, enabling voxel-wise evaluation and supervised learning. LumbarSR provides data in both original DICOM and registered NIfTI volumes with a consistent directory structure and specimen identifiers. We provide baseline evaluations using whole-image and masked image-quality metrics, trabecular morphometry against the Micro-PCCT reference, and super-resolution benchmarks based on interpolation and deep learning methods. LumbarSR is intended to support the development and evaluation of super-resolution methods for lumbar vertebra CT and related analyses of trabecular level structure. Because specimens were de-identified dry teaching specimens without available clinical histories or demographic metadata, LumbarSR should be interpreted as a paired imaging and benchmarking resource rather than a clinically labeled cohort.
Environmental heavy metal exposure poses significant endocrine-disrupting risks, yet evidence regarding the impact of metal mixtures on sex hormones in susceptible populations remains limited. We conducted a cross-sectional study involving 434 participants (167 males, 267 postmenopausal females) from the Dongdagou Xinglong cohort, a well-established cohort residing in a historically heavy metal-polluted region in Northwest China. Concentrations of eight metals (aluminum, chromium, copper, zinc, arsenic, selenium, cadmium, lead) in whole blood and a panel of six sex hormones (including androgens and estrogens) in serum were measured. Multiple linear regression analysis revealed significant positive associations between blood cadmium and dehydroepiandrosterone sulfate (β = 0.468), androstenedione (β = 0.571), testosterone (β = 0.680), and estradiol (β = 0.528) in males (all P < 0.05). Conversely, in females, blood cadmium was negatively associated with dehydroepiandrosterone sulfate (β = -0.221), androstenedione (β = -0.375), testosterone (β = -0.256), progesterone (β = -0.258), estradiol (β = -0.437), and estrone (β = -0.432) (all P < 0.05). Bayesian kernel machine regression (BKMR), weighted quantile sum (WQS), and quantile-based g-computation (Qgcomp) regression models consistently demonstrated that heavy metal mixture exposure elevated androstenedione and testosterone levels in males while reducing estradiol levels in females. Cadmium was consistently identified as the primary risk factor in both males and postmenopausal females. Furthermore, animal experiments corroborated above findings: Cadmium-exposed rats exhibited dose-dependent cadmium accumulation, disruption of blood metals homeostasis, elevated serum estradiol, and reduced testosterone levels (all P < 0.05). Collectively, our analyses of a metal-exposed cohort highlight that heavy metal mixtures are associated with sex-specific dysregulation of sex hormones, wherein cadmium appears to be a primary driver. This study offers insights for assessing reproductive health risks in similar metal-polluted contexts.
Acrylamide (AA), a food processing contaminant and potential carcinogen, poses a significant health risk in heat-processed snacks, particularly for children. This study evaluates the efficacy of three pre-treatments: pulsed electric fields (PEFs), ultrasound (USN), and soaking for AA mitigation in popcorn (Zea mays everta). Using liquid chromatography-tandem mass spectrometry (LC-MS/MS), AA levels were quantified across nine treatment variations. All strategies significantly reduced AA formation (p < 0.0001), with soaking (20 min) and USN (20 min) achieving the highest reductions (>82% and 82%, respectively). High-intensity PEF (3 kV cm-1, 300 kJ kg-1) yielded a 71% reduction, though it showed lower reproducibility due to the kernel's dense morphology. Crucially, while soaking and USN were superior in AA leaching, durations exceeding 20 min compromised popping expansion and sensory texture due to excessive hydration. These results define the critical processing window for industry, balancing toxicological safety with product quality.
Early and reliable crack localization in jet turbine blades is important for structural health monitoring in aerospace systems. This study presents an uncertainty-aware Deep Kernel Learning (DKL) framework that integrates a deep residual feature extractor with a Sparse Variational Gaussian Process (SVGP) model for crack location regression from modal frequency data. The model is trained end-to-end, enabling the learned latent representation to be optimized jointly with the Gaussian Process objective while providing predictive uncertainty estimates. A finite element simulation dataset consisting of 901 samples was generated by parametrically varying crack positions along the turbine blade and extracting modal frequency features. To ensure a comprehensive evaluation, robustness analyses were conducted under both Gaussian noise (σ = 0.01, 0.05, 0.10) and synthetic dataset shifts (± 5% and ± 10%). In addition, a more realistic evaluation was performed using non-uniform stochastic perturbations that simulate feature-wise variability, sensor drift, and environmental noise. The proposed DKL model was compared with classical machine learning methods (Random Forest, SVR, XGBoost) as well as modern uncertainty-aware deep learning approaches, including MC Dropout and Deep Ensemble. Results show that all models achieve high predictive accuracy under clean conditions due to the smooth and deterministic nature of the simulation dataset. Under stochastic perturbations, Deep Ensemble achieves the lowest prediction error, while the proposed DKL model maintains competitive accuracy and provides more conservative and stable uncertainty estimates. These findings indicate that the proposed DKL framework offers a balanced trade-off between predictive performance and uncertainty awareness. However, further validation on real-world sensor data is required to assess its applicability in practical structural health monitoring scenarios.
Friction at the drawbead in metal forming operations directly affects the quality of drawpieces. However, identifying the complex effect of friction process parameters on the coefficient of friction (CoF) is difficult based on experimental results. The aim of this paper is to analyze the results of a drawbead simulator test using various machine learning (ML) methods to select the most appropriate algorithm and to analyze in detail the feature importance, permutation importance, and cumulative Shapley additive explanation values of predictors. The test material was DC04 low-carbon steel sheet. Experimental tests were conducted for varying friction process conditions. Of the three different ML algorithms (support vector machine, regression trees, and ensemble tress), the support vector machine (SVM) algorithm with a cubic kernel function provided the lowest root mean square error (0.0085) and the highest correlation coefficient R2 (0.9657) for the test data. The predictors in descending order of permutation importance are friction conditions, drawbead height, sample width, Sa of countersamples, and sample orientation. A combined swarm-box chart presenting Shapley values for an SVM model with a cubic kernel function indicates that a low value of the drawbead height predictor has a strong, increasing effect on CoF. However, low values of the remaining explanatory parameters (sample width, mean roughness of countersamples, and sample orientation) have a decreasing effect on CoF.
Gene regulatory network (GRN) reconstruction is a fundamental challenge in computational biology, and is crucial for understanding gene interactions. In this study, we aim to incorporate Gene Ontology (GO) similarities into the construction of GRNs. Our key assumption is that genes with higher similarity in Molecular Function, Biological Process, or Cellular Component categories are more likely to be functionally related and, therefore, more likely to be connected in the network. We introduce SimMapNet, a Bayesian framework that estimates the precision matrix, which serves as the adjacency matrix in a Gaussian Graphical Model for undirected GRN inference. SimMapNet enhances network inference by integrating GO similarities, which inform the hyperparameters of the prior distribution through a kernel function, incorporating biological prior knowledge in a principled manner. We evaluate SimMapNet on three datasets: two datasets from the SOS DNA-repair response pathway in Escherichia coli and one dataset from Drosophila melanogaster. The results demonstrate the algorithm's superior performance compared to state-of-the-art methods such as GLASSO, GENIE3, and KBOOST in terms of F1-score. SimMapNet has low time complexity, making it suitable for constructing large networks. Our simulation results confirm that SimMapNet is particularly well-suited for scenarios with limited sample sizes, where traditional methods often struggle.
Exposure to organochlorine pesticides (OCPs) may disrupt adolescent development; however, their precise impacts remain unclear. Using data from the National Health and Nutrition Examination Survey (NHANES) 2011-2016, we examined associations between OCPs and adolescent body composition indicators, including body mass index (BMI) z_score, appendicular lean mass (ALM), trunk fat (TRF), total fat (TOF), total lean mass (TLM), and total percent fat (TPF). We fitted several statistical models including linear regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR). Mediation analysis evaluated the effect of serum albumin, while network toxicology and molecular docking identified key targets and pathways. Linear regression showed that OCPs were negatively correlated with BMI z_score, ALM, TRF, TOF, TLM, and TPF in adolescents, particularly in males. The WQS and BKMR revealed a negative relationship between OCPs mixtures and BMI z_score, TRF, TOF, and TPF, with hexachlorobenzene (HCB) as the major contributor. Albumin mediated the negative effects of HCB on all body composition indicators. Preliminary bioinformatics analyses suggested that HCB may influence body composition through inflammation, metabolic regulation, and apoptosis involving the MAPK, PI3K-Akt, and Ras signaling pathways. These findings suggest that HCB exposure may adversely affect adolescent growth and nutritional health, particularly among males.