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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.

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 &#x3bc;m) are insufficient to resolve trabeculae (about 100-200 &#x3bc;m). 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 &#x3bc;m 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 &#x3bc;m), two slice thicknesses (500 &#x3bc;m and 1000 &#x3bc;m), 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.

(-)-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.

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.

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 &#x3b2;-carotene (65.88 &#xb5;g/100 g). Roasted almonds contained lower amounts of phenolic compounds (42.87 mg gallic acid/100 g), tocopherols (7.64 mg &#x3b1;-tocopherol/100 g and 1.99 mg &#x3b3;-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 &#xb5;g &#x3b2;-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.

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.

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 (R&#xb2;) value and error metrics. All models achieved R&#xb2; 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.

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.

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.

Perchlorate, thiocyanate, and nitrate are ubiquitous environmental contaminants that disrupt thyroid homeostasis primarily through inhibition of iodide uptake into the thyroid. Evidence linking prenatal exposure to these chemicals with maternal thyroid function remains limited and inconsistent. We evaluated these associations in 192 pregnant women from the MARBLES cohort (2006-2020) who contributed repeated urine and serum samples. Urinary perchlorate, thiocyanate, nitrate, and iodide concentrations were quantified, and serum total triiodothyronine (TT3), total thyroxine (TT4), free thyroxine (FT4), and thyroid-stimulating hormone (TSH) levels were assessed. The thyroid feedback quantile-based index (TFQI) was calculated from FT4 and TSH values. Single-pollutant associations were estimated using generalized estimating equation models, and mixture effects were evaluated using Bayesian kernel machine regression. Analyses were stratified by maternal urinary iodine concentration (UIC) group. In the overall population, higher urinary thiocyanate concentrations were associated with lower FT4 levels (&#x3b2;= -0.17; 95% CI: -0.30, -0.04), with stronger effects observed at lower FT4 levels. Higher nitrate concentrations were associated with higher TT4 levels. Associations were generally stronger among women with UIC <150 &#x3bc;g/L. In this subgroup, higher thiocyanate was additionally associated with lower TFQI, while higher nitrate was associated with lower TSH levels. Mixture exposure was also associated with a decreasing trend in TFQI among women with UIC <150 &#x3bc;g/L and an increasing trend among women with UIC &#x2265;150 &#x3bc;g/L. These findings suggest that prenatal individual and mixture exposure to perchlorate, thiocyanate, and nitrate may alter maternal thyroid function, particularly among women with UIC <150 &#x3bc;g/L.

The aim of this study was to optimise the acquisition and reconstruction parameters for [Formula: see text] imaging on a digital PET scanner (Discovery MI 4-ring) under high (3 GBq), intermediate (1 GBq), and low (200 MBq) activity conditions. First, quantitative linearity was evaluated. Then, NEMA IEC body phantom acquisitions were reconstructed using various scan durations and Q.Clear reconstruction parameters. Next, optimal protocols were identified by minimising the discrepancies between absorbed dose maps derived from images (using the Local Deposition Model (LDM)) and those obtained from Monte Carlo (MC) simulations. The images reconstructed with these optimal protocols were then used to evaluate effective spatial resolution and to compare the accuracy of LDM with that of the Dose Voxel Kernel (DVK) approach. Quantitative linearity analysis revealed an underestimation of phantom activity at high activity (up to -&#xa0;15.7% at 3.6 GBq) and an overestimation at lower levels (up to 52.1% at 208 MBq). At high activity, the most accurate results were produced by 15- and 20-minute acquisitions with intermediate beta values (2000-5000). In contrast, intermediate and low activity levels required longer acquisition times and higher beta values (> 6000). Effective spatial resolution ranged from 7 mm at high activity to 24.9 mm at low activity. In terms of absorbed dose accuracy, the mean absorbed dose was underestimated in all spheres of the phantom. However, the error in the mean absorbed dose could be kept within 10% in the background by applying non-linearity corrections. Using MC as reference, LDM achieved greater accuracy in terms of mean absorbed dose and Dose Volume Histogram (DVH) agreement in the spheres, while DVK performed better in the background. This study enabled the optimisation of scan duration and beta parameter values for Q.Clear reconstructions for dosimetric purposes. Although accurate dosimetry remains challenging in [Formula: see text] PET imaging due to quantitative nonlinearity and limited spatial resolution, an error of less than 10% on the mean absorbed dose can be achieved in large structures if model-specific nonlinearities are corrected for.

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.

Quaternary ammonium compounds (QACs) are frequently used chemicals in consumer, medical, and personal care products that could be detected in blood samples from the general population. Cord blood samples are frequently employed in epidemiological studies to evaluate the links between contaminant exposure and birth outcomes. However, a significant knowledge gap persists regarding the presence of QACs in cord blood and their potential associations with birth outcomes. In this study, QACs were analyzed in 235 cord blood samples collected in a hospital in Jinan, Shandong Province, from February 2017 to January 2022. QACs were detected in the investigated cord blood samples, with total concentrations reaching up to 47.3 ng/mL. Benzylalkyldimethyl ammonium compounds (BACs) accounted for the largest proportion, with BAC-C12 and BAC-C14 being the most abundant QACs. The multiple linear regression (MLR) and Bayesian kernel machine regression (BKMR) models were developed to explore the relationships between QACs exposure and newborn birth outcomes. Results showed that BAC-C10, BAC-C16, and octyl decyldimethyl ammonium-8:10 were positively associated with birth weight and ponderal index (p for trend < 0.05), suggesting an obesogenic hypothesis. Furthermore, these three QAC structures can activate peroxisome proliferator-activated receptor &#x3b3; (PPAR&#x3b3;, the core adipogenesis receptor). The margin of exposure results indicated that the obesogenic risks of QACs were limited under current exposure distributions, but ongoing attention is warranted due to uncertainties and sensitive life stages.

This study developed a novel, fully reproducible EEG-based framework for binary emotion recognition that combines phase-space reconstruction with Poincar&#xe9; sections to capture the nonlinear dynamics of brain activity during prototypical emotional states. The method was applied to the publicly available AMIGOS dataset. EEG recordings from 33 participants were downsampled to 128&#x202f;Hz, bandpass-filtered (4-45&#x202f;Hz), cleaned of ocular and muscular artifacts using independent component analysis (ICA), and segmented into 1-s non-overlapping windows. Strict labeling thresholds (valence &#x2265; 6 and arousal &#x2265; 6 for Happy; valence &#x2264; 4 and arousal &#x2264; 4 for Sad) were enforced to isolate extreme high-valence/high-arousal (HVHA) versus low-valence/low-arousal (LVLA) states. A hybrid feature set integrating Poincar&#xe9;-derived geometric measures with classical spectral power and frontal asymmetry indices underwent rigorous two-stage selection. The final support vector machine with radial basis function kernel (SVM-RBF) achieved 98.21&#x202f;&#xb1;&#x202f;0.54% accuracy, 96.42&#x202f;&#xb1;&#x202f;1.06% sensitivity, and 100% specificity in strict subject-independent 7-fold cross-validation. Symmetric selection of 14 channels significantly enhanced feature separability (paired Wilcoxon signed-rank test, Bonferroni-corrected p&#x202f;=&#x202f;7.4&#x202f;&#xd7;&#x202f;10-8). Independent validation on the DEAP dataset using the identical pipeline yielded 97.68% accuracy, confirming generalizability. The near-perfect performance is specific to binary classification of extreme affective quadrants and does not extend to standard 4-class tasks (81.7%). These findings demonstrate the physiological relevance of nonlinear geometric analysis for detecting prototypical joy versus sadness, with potential clinical utility in automated depression screening.

Engineered cementitious composite (ECC) is a high-performance strain-hardening material widely used in durable infrastructure, yet its complex multi-parameter interactions make accurate mixture design and performance prediction challenging. This study aims to establish an EDFrame, which is an integrated prediction framework for engineered cementitious composite (ECC). First, two original datasets of ECC's tensile stress and strain are collected from the comprehensive and authoritative literature, comprising 18 features and 10 categories of single or hybrid fibers. Data augmentation is then performed using a constraints-modified Conditional Tabular Generative Adversarial Network (Tuned-CTGAN), with two traditional methods for comparison. A One-Dimensional Convolutional Neural Network with a residual module (1D-Residual CNN) is developed to predict tensile stress and strain, and its performance was compared against five popular machine learning models. The interpretability of the proposed model has been achieved through Partial Dependence Plot (PDP) and Kernel SHAP analyses. The results demonstrate that Tuned-CTGAN effectively generates reliable synthetic data, significantly improving the R2 of 1D-Residual CNN from 0.8658 to 0.9128 for tensile stress and from 0.8433 to 0.9378 for tensile strain, outperforming all compared models. PDP analysis identifies optimal fiber content (1.5-2%) and fiber length (12-20 mm) ranges for enhanced tensile performance, while SHAP analysis reveals fiber length and diameter as the most critical features influencing tensile stress and strain, respectively. The proposed EDFrame provides a robust and interpretable solution for ECC performance prediction, supporting efficient and accurate mixture design in engineering practice.

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.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by progressive loss of motor neurons. Accurate and accessible blood-based diagnostics for neurodegenerative diseases, including ALS, are being progressively required. Although blood cell gene expression profiles have potential clinical utility for distinguishing ALS, robust transcriptomic biomarkers for supportive diagnosis have not yet been established. Here, we analyzed publicly available peripheral blood mononuclear cell (PBMC) transcriptomic data from ALS patients using Maximum Mean Discrepancy, a kernel-based method that captures nonlinear distributional differences in a reproducing kernel Hilbert space and enables the extraction of informative gene combinations while minimizing multicollinearity, a common issue in multiple regression models. Using this approach, we identified a nonlinear three-gene combination-PRKAR1A, QPCT, and TMEM71-that distinguished ALS from healthy controls with an area under the curve (AUC) of 0.83 in a public PBMC dataset. This achievement was confirmed in laboratory PBMC samples with an AUC of 0.85, supporting the robustness of the identified gene signature in independent samples. Furthermore, these genes also enabled ALS classification in induced pluripotent stem cell-derived motor neurons with an AUC of 0.79. Knockdown of PRKAR1A, QPCT, or TMEM71 in motor neurons increased the TDP-43 expression levels, and PRKAR1A knockdown induced the mislocalization of TDP-43, accompanied by phosphorylation, suggesting a potential link to ALS-related pathophysiology. These findings suggest that nonlinear gene combinations may provide a useful strategy for identifying blood-based biomarkers and offer insights into ALS pathogenesis. This nonlinear, data-driven analytical framework enabled the transition from unbiased gene discovery to the identification of pathophysiology-associated molecules by in vitro functional validation.