Search Results (688)

To address the limitations of conventional conveyor-based systems in online detection and grading of orange soluble solids content (SSC), this study developed a novel gravity-driven detection device. Traditional systems are constrained by carrier-induced optical interference, complex mechanical structures, and large spatial requirements, limiting their application in small- and medium-sized enterprises. By introducing a gravity-driven paradigm, this research eliminates the need for fruit carriers and enables vertical spectral acquisition during gravitational descent, effectively overcoming carrier interference and spatial constraints. The integrated system comprises a synchronous-release feeding mechanism, a Vis/NIR detection module, and an intelligent grading unit. Through systematic optimization of disk rotation speed, integration time, and spot size, stable and efficient spectral acquisition was achieved, resulting in a throughput of one fruit per second. The optimized PLSR model, utilizing SG-SNV preprocessing and CARS feature selection, demonstrated excellent predictive performance, with an Rp² of 0.8746 and an RMSEP of 0.3001 °Brix. External validation confirmed 96.6% prediction accuracy within a ±1.0 °Brix error range and an overall grading accuracy of 86.6%. This system offers a compact, cost-effective, and high-performance solution for real-time fruit quality inspection, with potential applications to various spherical fruits. Full article

Accurate determination of Soil Organic Carbon (SOC), which is the foundation of soil health and safeguards ecological and food security, is crucial in local agricultural production. We aimed to investigate the influence of soil texture on hyperspectral models for predicting SOC content and to evaluate the role of different preprocessing methods and feature band selection algorithms in improving modeling efficiency. Laboratory-determined SOC content and hyperspectral reflectance data were obtained using soil samples from daylily cultivation areas in Yunzhou District, Datong City. Mathematical transformations, including Savitzky–Golay smoothing (SG), First Derivative (FD), Second Derivative (SD), Multiplicative Scatter Correction (MSC), and Standard Normal Variate (SNV), were applied to the spectral reflectance data. Feature bands extracted based on the successive projection algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) were used to establish SOC content inversion models employing four algorithms: partial least-squares regression (PLSR), Random Forest (RF), Backpropagation Neural Network (BP), and Convolutional Neural Network (CNN). The results indicate the following: (1) Preprocessing can effectively increase the correlation between the soil spectral reflectance process and SOC content. (2) SPA and CARS effectively screened the characteristic bands of SOC in daylily cultivated soil from the spectral curves. The SPA algorithm and CARS selected 4–11 and 9–122 bands, respectively, and both algorithms facilitated model construction. (3) Among all the constructed models, the FD-CARS-PLSR performed most prominently, with coefficients of determination (R²) for the training and validation sets reaching 0.93 and 0.83, respectively, demonstrating high model stability and reliability. (4) Incorporating soil texture as an auxiliary variable into the PLSR inversion model improved the inversion accuracy, with accuracy gains ranging between 0.01 and 0.05. Full article

The development of cancer immunotherapies has transformed cancer treatment paradigms, yet durable and tumour-specific responses remain elusive for many patients. Neoantigens, immunogenic peptides arising from tumour-specific genomic alterations, have emerged as promising cancer vaccine targets. Early-phase clinical trials using different vaccine platforms, including mRNA, peptide, DNA, and viral vector-based personalised cancer vaccines, have demonstrated the feasibility of targeting neoantigens, with early signals of prolonged survival in some patients. Most current vaccine strategies focus on canonical neoantigens, typically derived from exonic single-nucleotide variants (SNVs) and small insertions/deletions (INDELs), yet this represents only a fraction of the potential neoantigen repertoire. Evidence now shows that non-canonical neoantigens, arising mostly from alternative splicing, intron retention, translation of non-coding RNAs, gene fusions, and retroelement activation, broaden the antigenic landscape, with the potential for increasing tumour specificity and immunogenicity. In this review, we explore the biology of non-canonical neoantigens, the technological advances that now enable their systematic detection, and their potential to inform next-generation personalised cancer vaccines. Full article

This research addresses the limitations of traditional assembly line equipment, which is costly and impractical for narrow terrains, as well as the challenges of portable devices in large-scale detection. We propose a non-destructive testing method for assessing the internal quality of Sanhua Plums using a free-fall approach that integrates near-infrared spectroscopy and images. Through analysis of models created from spectral data collected under optimal conditions (motor speed: 6.6 r/min, integration time: 14 ms, spot diameter: 20 mm), we processed near-infrared data from 120 plums. The spectral data underwent preprocessing with polynomial smoothing (SG) and Standard Normal Variate (SNV) calibration, followed by feature extraction using Competitive Adaptive Reweighted Sampling (CARS), resulting in a prediction model for soluble solid content with R² of 0.8374 and RMSE of 0.5014. Simultaneously, a prediction model based solely on visual image data achieved an R² of 0.3341 and RMSE of 1.0115. We developed a multi-source information fusion model that incorporated Z-score normalization, linear weighted fusion, and Partial Least Squares Regression (PLSR), resulting in an R² of 0.8871 and RMSE of 0.4141 for the test set. This model outperformed individual spectroscopy and visual models, supporting the development of an automated non-destructive system for evaluating Sanhua Plum’s internal quality. Full article

The early detection of internal damage caused by Elasmopalpus lignosellus in fresh asparagus constitutes a challenge for the agro-export industry due to the limited sensitivity of traditional visual inspection. This study evaluated the potential of VIS–NIR hyperspectral imaging (390–1036 nm) combined with machine-learning models to discriminate between infested (PB) and sound (SB) asparagus spears. A balanced dataset of 900 samples was acquired, and preprocessing was performed using Savitzky–Golay and SNV. Four classifiers (SVM, MLP, Elastic Net, and XGBoost) were compared. The optimized SVM model achieved the best results (CV Accuracy = 0.9889; AUC = 0.9997). The spectrum was reduced to 60 bands while LOBO and RFE were used to maintain high performance. In external validation (n = 3000), the model achieved an accuracy of 97.9% and an AUC of 0.9976. The results demonstrate the viability of implementing non-destructive systems based on VIS–NIR to improve the quality control of asparagus destined for export. Full article

Hepatoblastoma (HB) is a paediatric liver malignancy arising from hepatic precursor cells, with >90% of cases harbouring a mutation in exon 3 of CTNNB1. We present a fully genetically characterised HB tumour organoid (tumoroid) biobank, which allows for in vitro studies of disease progression and clonal dynamics in vitro. We established a biobank of 14 tumoroid lines from 9 different patients. Tumours and tumoroids were characterised by whole genome sequencing (WGS) and histology, revealing strong concordance in cell morphology and β-catenin staining. In tumour—tumoroid pairs, identical pathogenic CTNNB1 variants were found, alongside shared copy number alterations (CNAs) and mutations. Variant allele frequency (VAF) was consistently higher in tumoroids, indicating increased tumour purity in vitro. In addition to CTNNB1, we frequently observed ARID1A alterations (single-nucleotide variants [SNVs] or CNAs in 56% of patients), and MYC gains as described previously. In paired pre- and post-treatment samples, we observed a clear increase in mutational load, attributed to a chemotherapy signature. Notably, from one patient, we analysed 4 tumour samples (3 post-treatment) with 4 matching tumoroid lines, all carrying a novel BCL6 mutation and loss of ARID1A. Mutational profiles varied across samples from different locations, suggesting intratumoral heterogeneity and clonal selection during tumoroid derivation. Taken together, this biobank allows detailed analysis of HB tumour biology, including treatment-induced progression and clonal dynamics across temporally and spatially distinct samples. Full article

Background/Objectives: The latest National Comprehensive Cancer Network (NCCN) Central Nervous System (CNS) Guidelines recommend utilizing next-generation sequencing (NGS) to enable comprehensive genomic profiling (CGP) as the preferred approach for molecular characterization of central nervous system (CNS) malignancies. CNS malignancies present distinct challenges due to the infeasibility of tissue-based testing for many patients and the restrictive nature of the blood–brain barrier (BBB) making plasma-based liquid biopsy an ineffective alternative. Recent advances in liquid biopsy have extended molecular testing beyond plasma to include cerebrospinal fluid (CSF), which serves as a valuable source for tumor-derived nucleic acids. Methods: The Belay Summit™ 2.0 is a high-throughput CGP assay capable of detecting multiple variant types, including single nucleotide variants (SNVs) and small insertion and deletions (Indels), copy number variations (CNVs), gene fusions, splice variants, and immunotherapy biomarkers such as microsatellite instability (MSI) and tumor mutational burden (TMB). This study details the analytical and clinical validation of Summit™ 2.0 to assess its technical performance and clinical sensitivity. Analytical validation was conducted using 68 specimens, demonstrating robust and reproducible detection of all variant types with 15 ng of CSF-derived total nucleic acid (tNA). Results: The analytical sensitivity of the Belay Summit™ 2.0 assay for SNVs and Indels was determined to be 96.7% with a 100% limit of detection (LoD) at a variant allele frequency of 0.3%. Clinical validity was evaluated across a cohort of 118 CSF specimens, including both primary and metastatic CNS tumors, demonstrating 96% sensitivity and 98% specificity. Conclusions: These findings support the use of the Belay Summit™ 2.0 assay for accurate and reproducible genomic profiling of CNS tumors using tumor-derived nucleic acids from CSF in patients for whom tissue-based testing is considered infeasible, unsafe, or not deemed by the prescribing physician to be clinically appropriate. Full article

The fermentation process of oolong tea is a critical step in shaping its quality and flavor profile. In this study, the fermentation degree of Anxi Tieguanyin oolong tea was assessed using image and hyperspectral features. Machine learning algorithms, including Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), were employed to develop models based on both single-source features and multi-source fused features. First, color and texture features were extracted from RGB images and then processed through Pearson correlation-based feature selection and Principal Component Analysis (PCA) for dimensionality reduction. For the hyperspectral data, preprocessing was conducted using Normalization (Nor) and Standard Normal Variate (SNV), followed by feature selection and dimensionality reduction with Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA), and PCA. We then performed mid-level fusion on the two feature sets and selected the most relevant features using L1 regularization for the final modeling stage. Finally, SHapley Additive exPlanations (SHAP) analysis was conducted on the optimal models to reveal key features from both hyperspectral bands and image data. The results indicated that models based on single features achieved test set accuracies of 68.06% to 87.50%, while models based on data fusion achieved 77.78% to 94.44%. Specifically, the Pearson+Nor-SPA+L1+SVM fusion model achieved the highest accuracy of 94.44%. This demonstrates that data feature fusion enables a more comprehensive characterization of the fermentation process, significantly improving model accuracy. SHAP analysis revealed that the hyperspectral bands at 967, 942, 814, 784, 781, 503, 413, and 416 nm, along with the image features H_σ and H, played the most crucial roles in distinguishing tea fermentation stages. These findings provide a scientific basis for assessing the fermentation degree of Tieguanyin oolong tea and support the development of intelligent detection systems. Full article

Background/Objectives: Variants in the lipoprotein lipase (LPL) gene have been associated with lipid level variability and obesity; however, their role in energy homeostasis remains unclear. The aim of this study was to investigate the association of LPL single-nucleotide variants (SNVs) with lipid parameters and leptin concentrations in a cohort of prepubertal children. The sample population comprised 635 boys and 631 girls, with available information on lipid profiles and leptin levels. Methods: Five LPL SNVs (rs258, rs316, rs326, rs320, and rs328) were genotyped by Real-Time PCR using predesigned TaqMan™ Genotyping Assays. Results: An association of the LPL SNV rs258 was found with non-esterified fatty acid (NEFA) levels in males and with leptin concentrations in both sexes. On the other hand, an association of the LPL SNV rs326 was observed with low-density lipoprotein cholesterol (LDL-C) and apolipoprotein B (Apo-B) levels, displaying opposite trends in males and females. No significant associations with any of the parameters under study were observed for the remaining LPL SNVs. Conclusions: These results suggest that functional differences among LPL SNVs may either be related to an enhancement of catalytic activity or modulation of lipoprotein binding affinity, influencing the efficiency of remnant lipoprotein clearance. Full article

The detection of single-nucleotide variants (SNVs) is an important challenge in modern genomics, with broad applications in medicine, diagnostics, and agricultural biotechnology. Current detection approaches include PCR-based techniques with high-affinity probes, ligase-based strategies, and sequencing approaches, each with varying degrees of sensitivity, specificity, and practicality. Despite advances in SNV analysis in the medical field, their implementation in the official control and monitoring of genetically modified organisms (GMOs) remains limited. This challenge has gained priority with the advent of new genomic techniques (NGTs), such as CRISPR-Cas nucleases, which allow precise genome editing, including subtle changes at the nucleotide level without introducing foreign DNA. Therefore, traditional methods of GMO detection targeting transgene sequences may not be sufficient to monitor such GMOs. In the European Union, GMO legislation requires distinguishing between conventionally bred and genetically modified plants. The planned introduction of new regulatory categories of NGT plants (NGT1 and NGT2) with different surveillance requirements emphasizes the need for robust, sensitive, and cost-effective SNV detection methods suitable for distinguishing between GMOs, particularly in the context of food and feed safety, traceability, and compliance. Full article

Rapid and reliable analytical methods are required to support quality control and decision-making in lithium-bearing mineral processing. In this study, the application of Fourier Transform Infrared (FTIR) spectroscopy combined with Partial Least Squares (PLS) chemometric modeling is evaluated for the simultaneous prediction of lithium oxide (Li₂O) and spodumene contents in pegmatitic samples. Two independent PLS models were developed using FTIR spectra preprocessed with first derivative and/or Standard Normal Variate (SNV). Spectral regions were selected based on the vibrational response of Al–O, Si–O, and OH⁻ groups, which are indirectly influenced by lithium-bearing phases. The spectral datasets were divided into calibration and independent external test sets, and model performance was assessed using statistical metrics and Principal Component Analysis (PCA). The Li₂O model achieved an R² of 0.9934 and an RMSEP of 0.185 in external validation, with a mean absolute error below 0.15%. The spodumene model achieved an R² of 0.9961, an RMSEP of 1.79, and a mean absolute error of 2.80%. These results demonstrate that the FTIR-PLS approach enables efficient quantitative estimation of lithium-bearing minerals, with reduced analytical time, good predictive accuracy, and suitability for application in process control and mineralogical sorting environments. PCA confirmed the statistical representativeness of the test sets, with no evidence of spectral extrapolation. Full article

Background: Several genes and genomic regions have been implicated in COVID-19 susceptibility and severity, but their clinical relevance remains uncertain. We comprehensively assessed both copy number variants (CNVs) and single-nucleotide variants (SNVs) disrupting genes implicated in COVID-19 in a Swedish cohort of ICU-treated COVID-19 patients with detailed phenotype data. Methods: Patients (n = 301) with severe COVID-19 treated in intensive care units (ICU) between March 2020 and January 2021 at two large Swedish university hospitals were included. Whole exome sequencing (WES) was performed to identify both large copy number variations (CNVs) and single-nucleotide variants (SNVs), including small indels, using the Genome Analysis Toolkit (GATK) pipelines. We focused our analyses on variants disrupting coding genes implicated in severe COVID-19, but also assessed variants known to cause human disease. Results: We identified 11 rare CNVs and several SNVs potentially linked to severe COVID-19. Patients carrying a CNV spanning a COVID-19-implicated gene had higher levels of the heart failure marker NT-proBNP (median 4440 [1558–8160] vs. 1170 [329–3152], p = 0.017), worse renal function at ICU admission (p = 0.0026), and a higher need for continuous renal replacement therapy (CRRT) (28% vs. 10%, p = 0.045) compared to patients without a potentially damaging CNV. Conclusions: Although patients with a potentially damaging CNV or SNV exhibited some differences in cardiac and renal markers, our findings do not support broad genetic screening as a predictive tool for COVID-19 severity. Full article

Soil organic carbon (SOC) is an essential indicator of soil fertility, health, and carbon sequestration capacity. Its proper management improves soil structure, productivity, and resilience to climate change, making rapid and reliable SOC assessment essential for sustainable agriculture. Visible and near-infrared (Vis–NIR) spectroscopy offers a non-destructive and cost-effective alternative to conventional laboratory analyses, allowing for the simultaneous estimation of multiple soil properties from a single spectrum. This study aimed to predict SOC content using machine learning techniques applied to Vis–NIR spectra of 860 soil samples collected in the Sierra Nevada de Santa Marta, Colombia. The spectra (400–2500 nm) were acquired using a NIR spectrophotometer, and the soil organic carbon (SOC) content was quantified using a wet oxidation method that employs dichromate in an acidic medium. A hybrid modeling framework combining Random Forest (RF) with support vector regression (SVR) and XGBoost was implemented. Spectral pretreatments (Savitzky–Golay first derivative, MSC, and SNV) were compared, and spectral bands were selected every 10 nm. The 30 most relevant wavelengths were identified using RF importance analysis. Data were divided into training (80%) and test (20%) subsets using stratified random sampling, and five-fold cross-validation was applied for parameter optimization and overfitting control. The RF–XGBoost (R² = 0.86) and RF–SVR (R² = 0.85) models outperformed the individual RF and SVR models (R² < 0.7). The proposed hybrid approach, optimized through features, and advanced spectral preprocessing demonstrate a robust and scalable framework for rapid prediction of SOC and sustainable soil monitoring. Full article

Background/Objectives: Systemic lupus erythematosus (SLE) is a complex autoimmune disease with a multifactorial origin involving genetic, epigenetic, and environmental determinants as well as some risk factors. Genetic predisposition has been quantified through polygenic risk scores (PRS), which integrate the cumulative effect of multiple single nucleotide variants (SNVs) associated with disease risk. Despite extensive research on immune and inflammatory pathways in SLE, the interplay between genetic susceptibility and metabolic dysfunction remains poorly understood. This study aimed to explore associations between SLE-related PRS and metabolic, inflammatory, and clinical parameters in adults participating in the METAINFLAMACIÓN-CM project (Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain). Methods: Ninety-three participants were included: 56 SLE patients and 37 individuals with metabolic syndrome (MetS) as a reference group. PRS were computed based on validated lupus-associated SNVs. Results: SLE patients showed a distinct metabolic profile compared with the MetS group, characterized by lower BMI, visceral fat, blood pressure, glucose, and liver enzyme levels. Within the SLE cohort, PRS values varied markedly and correlated with specific clinical and biochemical features. Linear regression models revealed a significant inverse association between PRS in SLE and ferritin levels, whereas other metabolic and inflammatory markers (glucose, IL-6, LDL, CRP, neutrophils) were directly influenced by clinical factors. Conclusions: Polygenic predisposition contributes to variability in SLE metabolic phenotype but does not independently drive most inflammatory parameters. SLE patients displayed metabolic and inflammatory alterations relevant to cardiovascular risk, highlighting the importance of comprehensive cardiometabolic assessment. Integrating PRS with metabolic profiling may support precision personalized management and improve cardiovascular risk evaluation in SLE. Full article

Dendrobium officinale is a valuable medicinal and edible homologous health food. It has immunomodulatory, antioxidant, and metabolism-regulating properties. However, its adulteration is widespread, seriously compromising product quality and safety. Traditional adulteration detection methods are complex, costly, and time-consuming, making it urgent to establish a rapid and non-destructive detection approach. This study developed a rapid identification and quantification method for adulterated D. officinale. The method combined near-infrared (NIR) spectroscopy with data-driven soft independent modeling of class analogy (DD-SIMCA) and partial least squares regression (PLSR) models. PCA, PLS-DA, and OPLS-DA were first used to visualize sample clustering and group differences. DT, SVM, ANN, and NB were used for classification. DD-SIMCA and PLSR were used for one-class modeling and quantitative analysis. Raw spectral data were preprocessed using multiplicative scatter correction (MSC), the standard normal variate (SNV), the first derivative, and Savitzky–Golay smoothing. In the identification analysis, the DD-SIMCA model achieved 100% sensitivity and 100% specificity in the validation set. Its overall accuracy in the independent test set was 99.2%, demonstrating excellent discrimination performance. In addition, SVM combined with NIR also achieved good accuracy. In the quantitative analysis of adulteration, the PLSR model predicted different adulteration levels. Most calibration and validation sets showed R² values above 0.99 and RMSE values below 0.05, indicating excellent predictive performance. The results indicate that NIR combined with DD-SIMCA and PLSR can achieve rapid identification and accurate quantification of adulterated D. officinale samples. This approach provides strong support for quality control and regulatory supervision of high-value health foods. Full article