Search Results (62)

Accurate normalization of RT-qPCR data requires selecting stable internal control genes, particularly in models characterized by dynamic metabolic transitions, such as 3T3-L1 adipocytes. The current study compares the expression stability of nine widely used housekeeping genes (HKGs) (peptidylprolyl isomerase A (Ppia), glyceraldehyde-3-phosphate dehydrogenase (Gapdh), beta-2 microglobulin (B2M), ribosomal protein, large, P0 (36b4), hydroxymethylbilane synthase (Hmbs), hypoxanthine guanine phosphoribosyl transferase (Hprt), tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide (Ywhaz), 18S ribosomal RNA (18S), and β-actin (Actb)) across key stages of differentiation (days 0, 9, and 18) and under treatments with palmitic acid and docosahexaenoic acid. Stability was assessed using four classical algorithms—geNorm, NormFinder, BestKeeper, and RefFinder—supplemented by the ΔCt method, conventional statistical testing, correlation, and regression analysis relative to two target genes, fatty acid-binding protein 4 (Fabp4) and sterol regulatory element binding transcription factor 1 (Srebf1). The obtained data indicate that no single HKG remains universally stable across these experimental conditions, and the expression of traditionally used reference genes (Gapdh, Actb, Hprt, 18S) is highly influenced by both the stage of adipogenesis and exposure to lipid-modulating factors. In contrast, Ppia, 36b4, and B2M—despite some of them being underestimated in use as references—consistently display the lowest variability across most analytical tools, forming a reliable and functionally diverse normalization panel. It should be noted that our initial stability assessment revealed apparent discrepancies among mathematical evaluation methods, emphasizing the need for a holistic, multiple-level approach strategy. The applied combination of algorithmic and statistical methods provides a more rigorous and objective framework for assessing the stability of reference genes, which is highly recommended in such a complex adipocyte-based model. Full article

Background/Objectives: Advanced 3D cell culture techniques enhance the physiological relevance of in vitro models, while supporting the 3Rs principles (Reduction, Refinement, and Replacement) of animal experimentation. In this context, 3D collagen-based systems mimic key extracellular matrix properties, enabling more accurate cellular organization and phenotype. However, changes in culture dimensionality can affect RT-qPCR reference gene stability, underscoring the need for careful validation when combining 2D and 3D systems. Methods: AML12 cells were cultured for 7 days under different 2D and collagen-based 3D conditions. The expression stability of nine candidate housekeeping genes was systematically evaluated using established algorithms (BestKeeper, NormFinder, geNorm, RefFinder, and ΔCt method), followed by inter-group statistical and correlation analyses of raw Ct values. Albumin gene expression was used as a target gene. Results: Although all candidate genes initially met acceptable variability thresholds, a stepwise, exclusion-based analysis revealed distinct performance differences. Hprt, Ppia, and Actb emerged as the most stable, showing no intra-group variability or interaction with Albumin expression. Nevertheless, Ywhaz and Rplp0, despite their high stability, were compromised by significant correlation with Albumin. Furthermore, Ywhaz showed significant downregulation under 3D culture conditions. B2M, Gapdh, 18S, and Hmbs exhibited increased variability, likely reflecting metabolic and microenvironmental heterogeneity associated with prolonged 2D cultivation of AML12 cells. Conclusions: Overall, this study highlights the importance of context-dependent, exclusion-based reference gene validation when comparing 2D and 3D models, and demonstrates a new approach for reliable gene expression normalization in complex in vitro culture systems. Full article

Photosynthesis (PS) is the cornerstone of crop productivity, directly influencing yield potential. Photosynthesis remains an underexploited target in soybean breeding, partly because field-based photosynthetic traits are difficult to measure at scale. Also, it is unclear which reproductive stage(s) provide the most informative physiological signals for yield. Few studies have evaluated soybean PS in elite germplasm under field conditions, and the integration of chlorophyll fluorescence (CF) with UAV imaging for PS traits remains largely unexplored. This study evaluated genotypic variation in photosynthetic and canopy traits among elite soybean germplasm across environments and developmental stages using CF and UAV imaging. Linear mixed-model analysis revealed significant genotypic and G×E effects for yield, canopy and several photosynthetic parameters. Broad-sense heritability (H²) estimates indicated dynamic genetic control, ranging from 0.12 to 0.77 at the early stage (S1) and 0.20–0.81 at the mid-reproductive stage (S2). Phi2, SPAD and FvP/FmP exhibited the highest heritability, suggesting their potential as stable selection targets. Correlation analyses showed that while FvP/FmP and SPAD were modestly associated with yield at S1, stronger positive relationships with Phi2, PAR and FvP/FmP emerged during S2, underscoring the importance of sustained photosynthetic efficiency during pod formation. Principal component analysis identified photosynthetic efficiency and leaf structural traits as key axes of physiological variation. UAV-derived indices such as NDRE, MTCI, SARE, MExG and CIRE were significantly correlated with CF-based traits and yield, highlighting their utility as high-throughput proxies for canopy performance. These findings demonstrate the potential of integrating CF and UAV phenotyping to enhance physiological selection and yield improvement in soybean breeding. Full article

Amid rising global energy demand and Taiwan’s transition toward a non-nuclear and low-carbon future, identifying an optimal renewable energy (RE) mix has become essential. This study analyzes eight RE sources using a three-model framework—Pearson correlation, Stepwise Regression Analysis (SRA), and Principal Component Analysis (PCA)—based on 60 monthly observations from 2019 to 2023. The results show that geothermal energy (GE) and solar photovoltaics (SP) exhibit strong positive correlations with total RE generation. Both SRA and PCA consistently identify conventional hydropower (CH), SP, and offshore wind power (OSW) as Taiwan’s most effective RE combination, while PCA provides superior predictive performance and reduces multicollinearity. In contrast, OWP, SB, BG, and WTE show limited contribution to overall RE output. Policy recommendations suggest prioritizing SP under resource constraints, and jointly expanding CH, SP, and OSW when resources permit, to achieve a balanced and sustainable RE structure. Full article

Hyperthyroidism features excess thyroid hormone and a hypermetabolic state; although drugs and definitive therapies exist, mechanism-anchored options are still needed. We built a multiscale interactome and applied a biased random-walk diffusion model to prioritize herbal candidates, active ingredients, and mechanisms. Herb–compound records came from OASIS; targets from DrugBank, TTD, and STITCH; and disease genes from DisGeNET. For each herb and compound, we simulated diffusion profiles, computed the correlation with the hyperthyroidism profile, and assessed target overlap ratio. Herbs were ranked by correlation and p < 0.05 overlap, retaining those with ≥5 active compounds linked to disease targets. Top signals included Geranii Herba (0.021), Gastrodiae Rhizoma (0.012), and Veratri Rhizoma Et Radix (0.011), plus seven herbs at 0.010. Herb–disease relationships were strongly enriched. Enrichment analyses highlighted MAPK, PI3K–AKT, p53, HIF-1, and thyroid hormone signaling, with Gene Ontology terms for apoptosis/anoikis, inflammation, and RNA polymerase II-dependent transcription. Compound-level analysis recovered evidence-supported ellagic acid and diosgenin and proposed resveratrol, cardamomin, 20-hydroxyecdysone, and (Z)-anethole as novel candidates. Subnetwork mapping linked these compounds to phosphorylation, GPCR–cAMP/TSH signaling, and transcriptional control. This framework recapitulates known thyroid-modulating herbs and elevates underappreciated leads with testable mechanisms, supporting the discovery of multi-target therapeutics for hyperthyroidism. Full article

Accurate prediction of cultivar performance across diverse environments is crucial for breeding and recommendation systems, helping to reduce the yield gap, the difference between potential and actual yields, which is often widened by poor cultivar selection. This study assessed the adaptability of winter wheat (Triticum aestivum L.) cultivars using a linear mixed-model framework combined with environmental mean regression. The model was trained on yield data from 19 locations over nine years (2015–2023) and validated independently using 2024 data. To ensure robustness, outliers were removed and cultivars with fewer than 30 observations excluded. The model accounted for genotype-by-environment (G×E) interactions and produced adjusted means for each location–year–management combination. These were used in cultivar-specific regressions to estimate yield response across environments. The approach showed strong predictive performance, with a Pearson correlation of 0.958 between predicted and observed yields in the validation year. Results highlight the model’s potential to inform cultivar recommendations, including for less-tested cultivars. This framework offers a practical tool for data-driven decision-making in plant breeding and agronomy, especially under variable growing conditions. Full article

Future $e^{+}{e}^{-}$ colliders are expected to play a fundamental role in measuring Standard Model (SM) parameters with unprecedented precision and in probing physics beyond the SM (BSM). This study investigates two-particle angular correlation distributions involving final-state SM charged hadrons. Unexpected correlation structures in these distributions is considered to be a hint for new physics perturbing the QCD partonic cascade and thereby modifying azimuthal and (pseudo)rapidity correlations. Using Pythia8 Monte Carlo generator and fast simulation, including selection cuts and detector effects, we study potential structures in the two-particle angular correlation function. We adopt the QCD-like Hidden Valley (HV) scenario as implemented in Pythia8 generator, with relatively light HV v-quarks (below about 100 GeV), to illustrate the potential of this method. Full article

Banana is an important cash and food crop worldwide. Recent outbreaks of banana diseases are threatening the global banana industry and smallholder livelihoods. Remote sensing data offer the potential to detect the presence of disease, but formal analysis is needed to compare inferred disease data with observed disease data. In this study, we present a novel remote-sensing-based framework that combines Landsat-8 imagery with meteorology-informed phenological models and machine learning to identify anomalies in banana crop health. Unlike prior studies, our approach integrates domain-specific crop phenology to enhance the specificity of anomaly detection. We used a pixel-level random forest (RF) model to predict 11 key vegetation indices (VIs) as a function of historical meteorological conditions, specifically daytime and nighttime temperature from MODIS and precipitation from NASA GES DISC. By training on periods of healthy crop growth, the RF model establishes expected VI values under disease-free conditions. Disease presence is then detected by quantifying the deviations between observed VIs from Landsat-8 imagery and these predicted healthy VI values. The model demonstrated robust predictive reliability in accounting for seasonal variations, with forecasting errors for all VIs remaining within 10% when applied to a disease-free control plantation. Applied to two documented outbreak cases, the results show strong spatial alignment between flagged anomalies and historical reports of banana bunchy top disease (BBTD) and Fusarium wilt Tropical Race 4 (TR4). Specifically, for BBTD in Australia, a strong correlation of 0.73 was observed between infection counts and the discrepancy between predicted and observed NDVI values at the pixel with the highest number of infections. Notably, VI declines preceded reported infection rises by approximately two months. For TR4 in Mozambique, the approach successfully tracked disease progression, revealing clear spatial spread patterns and correlations as high as 0.98 between VI anomalies and disease cases in some pixels. These findings support the potential of our method as a scalable early warning system for banana disease detection. Full article

Understanding the influence of environmental factors on cotton performance is crucial for enhancing yield and fiber quality in the context of climate change. This study investigates genotype-by-environment (G×E) interactions in cotton, using data from 250 recombinant inbred lines (CCRI70 RILs) cultivated across 14 diverse environments in China’s major cotton cultivation areas. Our findings reveal that environmental effects predominantly influenced yield-related traits (boll weight, lint percentage, and the seed index), contributing to 34.7% to 55.7% of their variance. In contrast fiber quality traits showed lower environmental sensitivity (12.3–27.0%), with notable phenotypic plasticity observed in the boll weight, lint percentage, and fiber micronaire. Employing six machine learning models, Random Forest demonstrated superior predictive ability (R² = 0.40–0.72; predictive Pearson correlation = 0.63–0.86). Through SHAP-based interpretation and sliding-window regression, we identified key environmental drivers primarily active during mid-to-late growth stages. This approach effectively reduced the number of influential input variables to just 0.1–2.4% of the original dataset, spanning 2–9 critical time windows per trait. Incorporating these identified drivers significantly improved cross-environment predictions, enhancing Random Forest accuracy by 0.02–0.15. These results underscore the strong potential of machine learning to uncover critical temporal environmental factors underlying G×E interactions and to substantially improve predictive modeling in cotton breeding programs, ultimately contributing to more resilient and productive cotton cultivation. Full article

In this paper, we propose a deep learning model based on dual-branch learning with a hybrid attention mechanism for meeting challenges in the underutilization of features in ophthalmic image diagnosis and the limited generalization ability of traditional single modal deep learning models when using imbalanced data. Firstly, a dual-branch architecture layout is designed, in which the left and right branches use residual blocks to deal with the features of a 2D image and 3D volume, respectively. Secondly, a frequency domain transform-driven hybrid attention module is innovated, which consists of frequency domain attention, spatial attention, and channel attention, respectively, to solve the problem of inefficiency in network feature extraction. Finally, through a multi-scale grouped attention fusion mechanism, the local details and global structure information of the bimodal modalities are integrated, which solves the problem of the inefficiency of fusion caused by the heterogeneity of modal features. The experimental results show that the accuracy of MOD-Net improved by 1.66% and 1.14% over GeCoM-Net and ViT-2SPN, respectively. It can be concluded that the model effectively mines the deep correlation features of multimodal images through the hybrid attention mechanism, which provides a new paradigm for the intelligent diagnosis of ophthalmic diseases. Full article

Soil total phosphorus plays an important role in soil fertility, plant growth, and bioge-ochemical cycles. This study aims to determine the spatial distribution characteristics of soil total phosphorus and identify its main influencing factors in the study area, thereby providing a basis for the scientific management of soil total phosphorus. Here, we conducted a comprehensive analysis by combining classical statistical analysis, ge-ostatistics methods, Pearson correlation analysis, one-way analysis of variance (ANOVA), and structural equation modeling (SEM) to explore the spatial distribution patterns of soil total phosphorus and its influencing factors. The results showed that soil total phosphorus in the study area ranged from 161.00 to 991.00 mg/kg, with an average of 495.71 mg/kg. Spatially, soil total phosphorus exhibited a patchy distribu-tion pattern, with high values primarily concentrated in cultivated areas along rivers and low values mainly located in forested areas in the southeastern and central re-gions. Additionally, the nugget effect of soil total phosphorus was 71.5%, indicating a moderate level of spatial variability. The Pearson correlation analysis revealed that soil total phosphorus content was significantly correlated with multiple factors, including land use types, soil parent material, distance from settlements, slope, and soil pH. Based on these findings, we employed ANOVA to analyze the impacts of various fac-tors. The results indicated that soil total phosphorus content showed significant differences under the influence of different factors. Subsequently, we further explored in depth the action paths through which these factors affect soil total phosphorus us-ing SEM. The SEM results showed that the absolute values of the total effects of the influencing factors on soil total phosphorus, ranked from highest to lowest, were as follows: land use types (0.499) > soil parent material (0.240) > distance from settle-ments (0.178) > slope (0.161) > elevation (0.127) > soil pH (0.114) > normalized differ-ence vegetation index (0.103). These findings provide a scientific foundation for the effective management of soil total phosphorus in similar study areas. Full article

Background: Histopathological images are often used to diagnose breast cancer and have shown high accuracy in classifying cancer subtypes. Prediction of gene expression from whole-slide images and spatial transcriptomics data is important for cancer treatment in general and breast cancer in particular. This topic has been a challenge in numerous studies. Method: In this study, we present a deep learning framework called GeNetFormer. We evaluated eight advanced transformer models including EfficientFormer, FasterViT, BEiT v2, and Swin Transformer v2, and tested their performance in predicting gene expression using the STNet dataset. This dataset contains 68 H&E-stained histology images and transcriptomics data from different types of breast cancer. We followed a detailed process to prepare the data, including filtering genes and spots, normalizing stain colors, and creating smaller image patches for training. The models were trained to predict the expression of 250 genes using different image sizes and loss functions. GeNetFormer achieved the best performance using the MSELoss function and a resolution of 256 × 256 while integrating EfficientFormer. Results: It predicted nine out of the top ten genes with a higher Pearson Correlation Coefficient (PCC) compared to the retrained ST-Net method. For cancer biomarker genes such as DDX5 and XBP1, the PCC values were 0.7450 and 0.7203, respectively, outperforming ST-Net, which scored 0.6713 and 0.7320, respectively. In addition, our method gave better predictions for other genes such as FASN (0.7018 vs. 0.6968) and ERBB2 (0.6241 vs. 0.6211). Conclusions: Our results show that GeNetFormer provides improvements over other models such as ST-Net and show how transformer architectures are capable of analyzing spatial transcriptomics data to advance cancer research. Full article

This article is a numerical and experimental study of the mechanical properties of different glass, flax and hybrid composites. By utilizing hybrid composites consisting of natural fibers, the aim is to eventually reduce the percentage usage of synthetic or man-made fibers in composites and obtain similar levels of mechanical properties that are offered by composites using synthetic fibers. This in turn would lead to greener composites being utilized. The advantage of which would be the presence of similar mechanical properties as those of composites made from synthetic fibers along with a reduction in the overall weight of components, leading to much more eco-friendly vehicles. Finite element simulations (FEM) of mechanical properties were performed using ANSYS. The FEM simulations and analysis were performed using standards as required. Subsequently, actual beams/frames with a defined geometry were fabricated for applications in automotive body construction. The tensile performance of such frames was also simulated using ANSYS-based models and was experimentally verified. A correlation with the results of the FEM simulations of mechanical properties was established. The maximum tensile strength of 415 MPa was found for sample 1: G-E (glass–epoxy composite) and the minimum strength of 146 MPa was found for sample 2: F-G-E (G-4) (flax–glass–epoxy composite). The trends were similar, as obtained by simulation using ANSYS. A comparison of the results showed the accuracy of the numerical simulation and experimental specimens with a maximum error of about 8.05%. The experimental study of the tensile properties of polymer matrix composites was supplemented with interlaminar shear strength, and a high accuracy was found. Further, the maximum interlaminar shear strength (ILSS) of 18.5 MPa was observed for sample 1: G-E and the minimum ILSS of 17.0 MPa was observed for sample 2: F-G-E (G-4). The internal fractures were analyzed using a computer tomography analyzer (CTAn). Sample 2: F-G-E (G-4) showed significant interlaminar cracking, while sample 1: G-E showed fiber failure through the cross section rather than interlaminar failure. The results indicate a practical solution of a polymer composite frame as a replacement for existing heavier components in a car, thus helping towards weight reduction and fuel efficiency. Full article

Reducing the flow rate (q) of the emitter can increase the dripline laying length and reduce the engineering investment of the drip irrigation system; however, reducing q increases the risk of emitter clogging. In this study, based on the OPFN method (Optimal Latin Hypercube Experimental Design–Parametric Modeling of Emitter–Fluid Dynamics Simulation–NSGA-II Genetic Algorithm Optimization), we selected the structural parameters of channel tooth height (E), angle (A), pitch of teeth (B), and flow channel depth (D) to construct 128 emitters. Through simulation, we obtained q, the flow index (x), and the structural resistance coefficient (Cs) under the pressure (H) ranging from 0.02 to 0.15 MPa. The results showed that the rated flow rate (q_0.1) and x values of 128 emitters range from 0.50 to 0.85 L/h and 0.461 to 0.480, respectively. Since Cs is negatively correlated with x, to obtain the combination of the flow channel structural parameters with the optimal hydraulic performance (x = min f(E, A, B, D)) and the optimal anti-clogging performance (Cs = min g(E, A, B, D)), the flow channel structural parameters are optimized by using the NSGA-II genetic algorithm to obtain the Pareto frontier solution. The optimal combinations of channel structural parameters corresponding to the q_0.1 values of 0.62, 0.71, and 0.82 L/h with x of 0.470, 0.466, and 0.463 are obtained using the weighting method. Cs values are 0.131, 0.446, and 0.619, respectively. The limit laying length of the configured emitter is 150–180 m. According to the flow field cloud diagram before and after optimization, it can be found that increasing the high-velocity area and high-turbulent-kinetic-energy area in the main stream and decreasing the low-velocity area and low-turbulent-kinetic-energy area in the tooth base and downstream face can help reduce x and Cs, and thus improve the hydraulic performance and anti-clogging performance of the small flow rate emitter. Full article

Mitochondrial dysfunction is closely linked to obesity and diabetes, with declining lung function in aging increasing diabetes risk, potentially due to elevated serum levels of dioxin-like mitochondria inhibitor substances (MIS) from prolonged exposure to environmental pollutants. However, the mechanisms connecting MIS, mitochondria, lung function, and metabolic disorder remain unclear. In this study, we analyzed data from 1371 adults aged 40–69 years in the 2008 Korean Genome Epidemiologic Study (KoGES) Ansung cohort. We indirectly estimated dioxin-like MIS levels by measuring intracellular ATP (MIS_ATP) and reactive oxygen species (MIS_ROS) in cultured cells treated with the serum of participants. Using correlation analysis and structural equation modeling (SEM), we explored the relationships among MIS, mitochondrial function, body mass index (BMI), and lung function (FEV1 and FVC). Our findings revealed that MIS_ATP was associated with BMI in females and with FVC in males, while MIS_ROS correlated with both BMI and FVC in males, not in females. Significant associations between BMI and FVC were found in the highest MIS subgroup in both sexes. SEM analyses demonstrated that MIS negatively influenced mitochondrial function, which in turn affected BMI and lung function. Age-related declines in lung function were also linked to mitochondrial dysfunction. This study underscores the potential of MIS assays as alternatives for assessing mitochondrial function and highlights the importance of mitochondrial health in metabolic disorders and lung function. Full article