Search Results (5,050)

The rapid advancement of genomic technologies has significantly transformed biomedical research and clinical applications, particularly in oncology. Identifying patient-specific genetic mutations has become a crucial tool for early cancer detection and personalized treatment strategies. Detecting tumors at the earliest possible stage provides critical insights beyond traditional tissue analysis. This paper presents a novel cyber-physical system that combines high-resolution tissue scanning, laser microdissection, next-generation sequencing, and genomic analysis to offer a comprehensive solution for early cancer detection. We describe the methodologies for scanning tissue samples, image processing of the morphology of single cells, quantifying morphometric parameters, and generating and analyzing real-time genomic metadata. Additionally, the intelligent system integrates data from open-access genomic databases for gene-specific molecular pathways and drug targets. The developed platform also includes powerful visualization tools, such as colon-specific gene filtering and heatmap generation, to provide detailed insights into genomic heterogeneity and tumor foci. The integration and visualization of multimodal single-cell genomic metadata alongside tissue morphology and morphometry offer a promising approach to precision oncology. Full article

Integrating multi-source remote sensing can improve the accuracy of forest aboveground biomass (AGB) estimation. However, the accuracy and stability of the forest AGB estimation results are affected by multiple remote sensing feature variables as well as parameter tuning of machine learning algorithms. To this end, this study employed six types of remote sensing data—Landsat 8 OLI, Sentinel-2A, GEDI, ICESat-2, ALOS-2, and SAOCOM. A dual-variable selection strategy based on SHapley Additive exPlanations (SHAP) was developed, and a genetic algorithm (GA) was used to optimize the parameters of five machine learning models—elastic net (EN), least absolute shrinkage and selection operator (Lasso), support vector regression (SVR), Random Forest (RF), and Categorical Boosting (CatBoost)—to estimate the AGB of Pinus kesiya var. langbianensis forest in Wuyi Village, Zhenyuan County. The dual-variable selection strategy integrates SHAP with the Pearson correlation coefficient (PC), RF, EN, and Lasso to enhance feature screening robustness and interpretability. The results of the study showed that Lasso-SHAP dual-variate screening was more stable than SHAP univariate screening. In particular, the Lasso-SHAP strategy improved the average R² from 0.59 (using SHAP alone) to above 0.70, achieving an enhancement of 11%. Among GA-optimized parametric machine learning models, the linear GA-Lasso achieved the best performance, with an R² of 0.91 and an RMSE of 12.94 Mg/ha, followed by the GA-EN model (R² = 0.89, RMSE = 14.46 Mg/ha). For nonlinear models, GA-SVR performed the best (R² = 0.74, RMSE = 22.07 Mg/ha), surpassing the GA-CatBoost model (R² = 0.64, RMSE = 25.88 Mg/ha). In summary, the Lasso-SHAP dual-variable selection strategy effectively improves the estimation accuracy of AGB for Pinus kesiya var. langbianensis forests, while GA-optimized machine learning models demonstrate excellent performance, providing strong support for regional-scale forest resource monitoring and carbon stock assessment. Full article

Systemic lupus erythematosus (SLE) is a multisystem auto-immune disease that may affect any organ/system, including the cardiovascular system. Several studies have shown that SLE is associated with an increased risk of cardiovascular disease (CVD), even though most of the patients who have lupus are young women. In this review, we present that apart from the traditional risk factors, there are more appropriate SLE-related indices such as imaging parameters, auto-antibodies, disease manifestations, medications, and genetic factors that might represent useful tools to create an algorithm for early identification of SLE patients at increased risk of CVD. Early recognition and appropriate treatment of patients at increased CVD risk might reduce morbidity/mortality and improve the quality of life of patients with SLE. Full article

The wheat planted at the end of the rainy season in the Cerrado suffers from a strong water deficit. A selection of genetic material with drought tolerance is necessary. In improvement programs that evaluate a large number of materials, efficient, automated, and non-destructive phenotyping is essential, which requires the use of sensors. The experiment was conducted in 2016 using a phenotyping platform, where irrigation gradients ranging from 184 (WR4) to 601 mm (WR1) were created, allowing for the comparison of four genotypes. In addition to productivity, we evaluated plant height, hectoliter weight, the number of spikes per square meter, ear length, photosynthesis, and the indices calculated by the sensors. For most morphophysiological parameters, extreme stress makes it difficult to discriminate materials. WR1 (601 mm) and WR2 (501 mm) showed similar trends in almost all variables. The data validated the phenotyping platform, which creates an irrigation gradient, considering that the results obtained, in general, were proportional to the water levels. The similar trend between sensors (NDVI, PRI, and LIFT) and morphophysiological, plant growth, and crop yield evaluations validated the use of sensors as a tool in selecting drought-tolerant wheat genotypes using a non-invasive methodology. Considering that only four genotypes were used, none showed absolute and unequivocal tolerance to drought; however, each genotype exhibited some desirable characteristics related to drought tolerance mechanisms. Full article

This paper presents a technique for extracting the initial parameters of the longitudinal phase space of freshly injected bunches in an electron storage ring. This technique combines simulation of single-bunch longitudinal phase space evolution with a bunch-by-bunch data acquisition and processing system, enabling high-precision determination of initial phase space parameters during electron storage ring injection—including the initial phase, initial bunch length, initial energy offset, initial energy spread, and initial energy chirp. In our experiments, a high-speed oscilloscope captured beam injection signals, which were then processed by the bunch-by-bunch data acquisition system to extract the evolution of the injected bunch’s phase and length. Additionally, a single-bunch simulation software package was developed, based on mbtrack2 and PyQt5, that is capable of simulating the phase space evolution of bunches under different initial parameters after injection. By employing a genetic algorithm to iteratively align simulation results with experimental data, the remaining initial phase space parameters of the injected bunch can be accurately determined. Full article

Raspberry (Rubus idaeus L.) micropropagation is an alternative for obtaining plantlets with high genetic and phytosanitary quality. The objective of this study was to establish a protocol for the micropropagation of raspberry (Rubus idaeus L.) using the temporary immersion bioreactor, under intermittent immersion periods and different culture medium volumes. The effect of the liquid medium using the TIB and semisolid was evaluated. Different immersion frequencies and culture medium volumes per explant were evaluated in the TIB. In all treatments, the number of shoots per explant, shoot length, number of leaves per explant, percentage of hyperhydricity, and chlorophyll and β-carotene content at multiplication stage were evaluated. The generated shoots, without a root system, were transferred to the acclimatization stage. The results show that the TIB with an immersion frequency of 2 min every 8 h and a volume of 25 mL of culture medium per explant had the best developmental parameters, with 5.75 shoots per explant, a shoot length of 3.44 cm, and 2% hyperhydricity. The highest chlorophyll and β-carotene content was observed in the TIB at different immersion frequencies of 4, 8 and 12 h, with 25 and 50 mL per explant. Survival percentages higher than 96% were observed in all methods evaluated. In conclusion, the evaluated immersion system is an efficient alternative for R. idaeus micropropagation, without using a rooting stage. Full article

Aero-engine casings with excellent impact resistance are a practical requirement for ensuring the safe operation of aero-engines. In this paper, we report on numerical simulations of broken rotating blades impacting aluminum honeycomb sandwich casings under different temperatures and optimization of structural parameters. Firstly, an impact test system with adjustable temperature was established. Restricted by the temperature range of the strain gauge, ballistic impact tests were carried out at 25 °C, 100 °C, and 200 °C. Secondly, a finite element (FE) model including a pointed bullet and an aluminum honeycomb sandwich plate was built using LS-DYNA. The corresponding simulations of the strain–time curve and damage conditions showed good agreement with the test results. Then, the containment capability of the aluminum honeycomb sandwich aero-engine casing at different temperatures was analyzed based on the kinetic energy loss of the blade, the internal energy increment of the casing, and the containment state of the blade. Finally, with the design objectives of minimizing the casing mass and maximizing the blade kinetic energy loss, the structural parameters of the casing were optimized using the multi-objective genetic algorithm (MOGA). Full article

To address traditional methods’ limitations in neglecting the interaction between random shock loads and progressive degradation, as well as failure correlations, this study proposes a dynamic reliability framework integrating Gamma processes, homogeneous Poisson processes (HPP), and mixed Copula functions. The framework develops a wind turbine gearbox reliability model under shock–degradation coupling while quantifying failure correlations. Gamma processes characterize continuous degradation, with parameters estimated from P-S-N curves. Based on stress–strength interference theory, random shocks within damage thresholds are integrated to form a coupled reliability model. A Gumbel–Clayton–Frank mixed Copula with a multi-layer nested algorithm quantifies failure correlations, with correlation parameters estimated via the RSS principle and genetic algorithms. Validation using a 2 MW gearbox’s planetary gear-stage system covers four scenarios: natural degradation, shock–degradation coupling, and both scenarios with failure correlations. The results show that compared to independent assumptions, the model accelerates reliability decline, increasing failure rates by >37%. Relative to natural degradation-only models, failure rates rise by >60%, validating the model’s effectiveness and alignment with real-world operational conditions. Full article

Driven by the urgent demand for low-frequency vibration and noise control in engineering scenarios such as automobiles, acoustic metamaterials (AMs), as a new class of functional materials, have demonstrated significant application potential. This paper proposes a low-frequency band gap optimization design method for local resonance acoustic metamaterials (LRAMs) based on a multi-objective genetic algorithm. Within a COMSOL Multiphysics 6.2 with MATLAB R2024b co-simulation framework, a parameterized unit cell model of the metamaterial is constructed. The optimization process targets two objectives: minimizing the band gap’s deviation from the target and reducing the structural mass. A multi-objective fitness function is formulated by incorporating the band gap deviation and structural mass constraints, and non-dominated sorting genetic algorithm II (NSGA-II) is employed to perform a global search over the geometric parameters of the resonant unit. The resulting Pareto-optimal solution set achieves a unit cell mass as low as 26.49 g under the constraint that the band gap deviation does not exceed 2 Hz. The results of experimental validation show that the optimized metamaterial configuration reduces the peak of the low-frequency frequency response function (FRF) at 63 Hz by up to 75% in a car door structure. Furthermore, the simulation predictions exhibit good agreement with the experimental measurements, confirming the effectiveness and reliability of the proposed method in engineering applications. The proposed multi-objective optimization framework is highly general and extensible and capable of effectively balancing between the acoustic performance and structural mass, thus providing an efficient engineering solution for low-frequency noise control problems. Full article

Machine Learning methods are exploited to extract a universal approach for self-diffusion coefficient calculation in molecular fluids. Analytical expressions are derived through symbolic regression for fluids both in bulk and confined nanochannels. The symbolic regression framework is trained on simulation data from molecular dynamics and correlates the values of the self-diffusion coefficients with macroscopic properties, such as density, temperature, and the width of confinement. New expressions are derived for nine different molecular fluids, while an all-fluid universal equation is extracted to capture molecular behavior as well. In such a way, a highly computationally demanding property is predicted by easy-to-define macroscopic parameters, bypassing traditional numerical methods based on mean squared displacement and autocorrelation functions at the atomistic level. To achieve generalizability and interpretability, simple symbolic expressions are selected from a pool of genetic programming-derived equations. The obtained expressions present physical consistency, and they are discussed in terms of explainability. The accurate prediction of the self-diffusion coefficient both in bulk and confined systems is important for advancing the fundamental understanding of fluid behavior and leading the design of nanoscale confinement devices containing real molecular fluids. Full article

Land desertification represents a significant and sensitive global ecological issue. In the Inner Mongolia region of China, soil desertification and salinization are widespread, resulting from the combined effects of extreme drought conditions and human activities. Using Gaofen 5B AHSI imagery as our data source, we collected spectral data for seven distinct land cover types: lush vegetation, yellow sand, white sand, saline soil, saline shell, saline soil with saline vegetation, and sandy soil. We applied Particle Swarm Optimization (PSO) to fine-tune the Wavelet Packet (WP) decomposition levels, thresholds, and wavelet basis function, ensuring optimal spectral decomposition and reconstruction. Subsequently, PSO was deployed to optimize key hyperparameters of the Random Forest algorithm and compare its performance with the ResNet-Transformer model. Our results indicate that PSO effectively automates the search for optimal WP decomposition parameters, preserving essential spectral information while efficiently reducing high-frequency spectral noise. The Genetic Algorithm (GA) was also found to be effective in extracting feature bands relevant to land desertification, which enhances the classification accuracy of the model. Among all the models, integrating wavelet packet denoising, genetic algorithm feature selection, the first-order differential (FD), and the hybrid architecture of the ResNet-Transformer, the WP-GA-FD-ResNet-Transformer model achieved the highest accuracy in extracting soil sandification and salinization, with Kappa coefficients and validation set accuracies of 0.9746 and 97.82%, respectively. This study contributes to the field by advancing hyperspectral desertification monitoring techniques and suggests that the approach could be valuable for broader ecological conservation and land management efforts. Full article

Genomic score testing is increasingly being integrated into the management of prostate cancer (PCa) to overcome the limitations of traditional clinical and pathological parameters. Genomic tools will represent essential components of precision medicine, supporting risk stratification, therapeutic decision-making, and personalized screening strategies. Genomic score tests can be broadly classified into two main categories: polygenic risk scores (PRSs) and tumor-derived genomic classifiers (GCs). While not yet standard in routine practice, several international guidelines recommend their selective use when results are likely to impact clinical management. PRSs estimate an individual’s susceptibility to PCa based on the cumulative effect of multiple low-penetrance germline genetic variants. These scores show promise in enhancing early detection strategies and identifying men at higher genetic risk who may benefit from tailored screening protocols. Tumor-based GCs assays provide prognostic information that complements conventional clinical and pathological parameters, and are used to guide treatment decisions, including eligibility for active surveillance (AS) or adjuvant therapy after treatment of the primary tumor. This review summarizes and analyzes the current evidence on genomic testing in PCa, with a focus on the available assays, their clinical applications, and their predictive and prognostic value across the disease spectrum. When integrated with clinical and pathological parameters, these tools have the potential to significantly enhance personalized care and should be increasingly considered in routine clinical practice. Full article

Carcass and meat quality traits are critical in pig breeding and production. Estimating genetic parameters for these traits is a vital aspect of breeding engineering, as accurate genetic parameters are essential for estimating breeding values, predicting genetic progress, and optimizing breeding programs. This study was conducted on a population of 461 Yorkshire pigs from the same breeding farm, which were slaughtered to assess nine carcass traits and seven meat quality traits, followed by descriptive statistical analysis. Additionally, we estimated the genetic parameters of these traits using genomic information based on 50K chip data. The results indicated that sex significantly affected most carcass and meat quality traits. Carcass traits including carcass length indicators (h² = mean 0.35), backfat thickness indicators (h² = mean 0.36), eye muscle area (h² = 0.28), and the number of rib pairs (h² = 0.28) exhibited medium to high heritability. Carcass length indicators showed high genetic correlations with backfat thickness indicators (r = mean −0.49) and the number of rib pairs (r = mean 0.63), while high negative genetic correlation (r = −0.72) was noted between eye muscle area and the number of rib pairs. Meat quality traits also displayed medium to high heritability, expect for pH value measured within one hour post-slaughter (h² = 0.12). Drip loss indicators had higher genetic correlations with pH (r = mean −0.73) than with meat color indicators (r = mean 0.22). These findings may provide a theoretical reference for genetic evaluation and breeding in the Yorkshire pig population. Full article

Background: Anderson–Fabry disease (AFD) is a progressive lysosomal storage disorder characterized by systemic glycosphingolipid accumulation. While cardiac imaging plays a central role in disease monitoring, the relationship between structural myocardial changes and exercise capacity remains incompletely defined. This study aimed to evaluate functional impairment in AFD patients using cardiopulmonary exercise testing (CPET) and to determine whether limitations are primarily cardiac or extracardiac in origin. Methods: Thirty-one patients with genetically confirmed AFD were retrospectively enrolled from two tertiary centers. All underwent baseline clinical assessment, resting transthoracic echocardiography (TTE), spirometry, and symptom-limited CPET using a cycle ergometer and a 10 W/min ramp protocol. Echocardiographic parameters included the LVEF, global longitudinal strain (GLS), E/e′ ratio, TAPSE, and PASP. CPET measurements included the peak VO₂, anaerobic threshold (AT), VE/VCO₂ slope, oxygen pulse (VO₂/HR), and VO₂/watt ratio. Results: The mean age was 48.4 ± 17.6 years, with most patients classified as NYHA I. LVEF was preserved (62.3 ± 8.6%), and diastolic indices were within normal limits (E/e′ 7.1 ± 2.4), but GLS was impaired (11.3 ± 10.5%). CPET showed reduced peak VO₂ (18.6 ± 6.1 mL/kg/min; 71.4% predicted) and early AT (40.8%), with preserved ventilatory efficiency and oxygen pulse. VO₂/watt was mildly reduced, suggesting peripheral limitations despite intact central hemodynamics. Conclusions: Functional impairment is common in AFD patients, even with mild cardiac involvement. CPET reveals early systemic limitations not captured by standard imaging, supporting its role in phenotypic characterization and therapeutic decision-making. Full article

Background/Objectives: Amelogenesis imperfecta (AI) represents a heterogeneous group of inherited disorders affecting the quality and quantity of dental enamel, making clinical diagnosis challenging. This study aimed to identify genetic variants in Slovenian patients with non-syndromic AI and to evaluate enamel morphology using radiographic parameters. Methods: Whole exome sequencing (WES) was performed on 24 AI patients and their families. Panoramic radiographs (OPTs) were analyzed using Fiji ImageJ to assess crown dimensions, enamel angle (EA), dentine angle (DA), and enamel–dentine mineralization ratio (EDMR) in lower second molar buds, compared to matched controls (n = 24). Two observers independently assessed measurements, and non-parametric tests compared EA, DA, and EDMR in patients with and without disease-causing variants (DCVs). Statistical models, including bootstrap-validated random forest and logistic regression, assessed variable influences. Results: DCVs were identified in ENAM (40% of families), AMELX (15%), and MMP20 (10%), including four novel variants. AI patients showed significant enamel deviations with high reproducibility, particularly in hypomineralized and hypoplastic regions. DA and EDMR showed significant correlations with DCVs (p < 0.01). A bootstrap-validated random forest model yielded a 90% (84.0–98.0%) AUC-estimated predictive power. Conclusions: These findings highlight a novel and reproducible radiographic approach for detecting developmental enamel defects in AI and support its diagnostic potential. Full article