Search Results (2,504)

Intrusion detection serves as a core technology for securing heterogeneous edge networks, including IoT, industrial edges, and 5G networks. However, existing federated learning-based intrusion detection systems suffer from environmental heterogeneity, limited sample availability, and severe class imbalance—issues that result in inefficient resource allocation and compromised detection performance against rare attacks. In this paper, we propose a novel lightweight intrusion detection model for heterogeneous edge networks, named FedNIDS-CNN, which is based on dynamic distillation-aided federated learning with a CNN backbone. In the data preprocessing phase, a two-level class balancing strategy integrating nearest-neighbor interpolation augmentation and adaptive synthetic sampling is employed to ensure distortion-free sample synthesis. For feature and model optimization, principal component analysis (PCA) is used to reduce the dimensionality of traffic features, while a lightweight 1D-CNN is adopted as the base model to alleviate computational overhead on edge devices. During federated training and knowledge aggregation, a dynamic weight distillation loss mechanism is designed to enhance the model’s ability to recognize minority-class attacks. Meanwhile, the federated framework supports client-side local training and server-side weighted soft-label aggregation, enabling effective knowledge fusion across heterogeneous models. Experimental results on the CICIDS2017 dataset demonstrate that the proposed method achieves an accuracy of 98.55% and an F1-score of 98.40%. Benefiting from the soft-label transmission and parameter-free aggregation design, the framework gets rid of the constraint of homogeneous model architecture and natively supports heterogeneous network models and edge devices with different computing capabilities. It also significantly reduces communication traffic and per-round training latency, confirming its excellent real-time performance and applicability in resource-constrained edge environments. Full article

This study presents an intelligent framework for the analysis of multidimensional geomechanical states in underground mining systems based on numerical simulation and machine learning methods. A three-dimensional geomechanical model of the Zholymbet deposit was developed in the RS3 environment using the generalized Hoek–Brown failure criterion. Numerical simulations were performed for representative mining scenarios characterized by complex excavation interaction and stress redistribution. The modelling results were transformed into a multidimensional geomechanical dataset containing stress, deformation, displacement, and yielding parameters. Principal component analysis (PCA) was applied to investigate the internal structure of the geomechanical state space and identify dominant patterns controlling the rock mass behavior. Clustering analysis revealed several geomechanical regimes corresponding to stable, transitional, and instability-prone conditions. Isolation Forest anomaly detection demonstrated that atypical geomechanical states are not randomly distributed but spatially localized near excavation systems and mining horizons. The obtained results indicate that hazardous geomechanical conditions are governed by complex interactions between stress concentration, deformation intensity, yielding processes, and excavation geometry. The proposed approach provides a basis for intelligent interpretation of large-scale numerical modelling results and may support geomechanical risk assessment in underground mining operations. Full article

Point clouds acquired by low-cost laser scanning systems have a problem of high noise, which makes the point cloud appear as thick and geometric features blurred, while existing denoising algorithms either fail to maintain a balance between denoising and shape preservation or incur excessive computational cost. To address this issue, this paper proposes a shape-preserving denoising algorithm based on normal-guided cylindrical neighborhood and bilateral weighting. Specifically, the proposed method first optimizes the PCA-initialized normals of the point cloud by integrating curvature-based feature detection and bilateral weighting. Subsequently, a cylindrical neighborhood is constructed for each point along the optimized normal direction. Finally, a bilateral weighted projection mechanism that jointly incorporates spatial and normal features is employed, whereby the aggregated projection of neighboring points drives the displacement of the central point along the normal direction, thereby achieving point cloud denoising. Experiments are conducted on synthetic datasets and real scanned datasets. The results show that, for synthetic data denoising, the proposed method achieves the best or second-best performance in 25 out of 30 experiment cases across different models and different noise levels. For real scanned data, the section views and reconstructed mesh models demonstrate that the proposed method outperforms popular algorithms in removing complex noise while preserving geometric features. In addition, the proposed method demonstrates excellent computational efficiency, capable of denoising at a speed of processing one million points every 2.4 s, and achieves acceleration of processing speed by six times compared to the fastest competitive algorithms. Full article

Cadmium (Cd) contamination in agricultural soil poses a severe threat to rice growth and food safety worldwide. Seedling-stage Cd tolerance directly determines rice establishment and subsequent yield under Cd stress, but its genetic basis remains largely unclear. In this study, a genome-wide association study (GWAS) was conducted using 490 diverse accessions from the 3000 Rice Genome Project (3K RGP). Three biomass-related traits, shoot height (SH), shoot dry weight (SDW), and root dry weight (RDW), were measured under control and Cd stress conditions, along with their relative values. A total of 3,196,134 high-quality SNPs were used for genetic analysis, and population structure was corrected by principal component analysis (PCA) and kinship matrix. In total, 39 stable QTLs were detected, including 19 for RDW, 18 for SDW, and 2 for SH, most of which were specifically identified under Cd stress. Three major QTLs (qSDW1.1, qRDW3.2, qRDW5.2) were prioritized for candidate gene mining. Combining LD block analysis, gene annotation, Cd-responsive transcriptome data, and haplotype analysis, OsAKR2 (LOC_Os01g62870), OsAS1 (LOC_Os03g18130), and LOC_Os05g11320 were identified as key candidate genes regulating seedling Cd tolerance, and superior haplotypes of these genes were identified. This study reveals the genetic architecture of rice seedling Cd tolerance and provides elite QTLs, genes, and haplotypes for molecular breeding of Cd-resilient rice varieties. Full article

Background: Heterogeneous data integration remains a major challenge in intelligent information systems, particularly under missing-modality and cross-domain conditions. Existing multimodal fusion approaches often rely on complete datasets and weak alignment mechanisms, limiting their robustness and practical applicability. Objectives: This study aims to develop and evaluate a genomics-guided multimodal representation learning framework that enables robust heterogeneous data fusion, reliable cross-modal correspondence, and accurate prediction under incomplete-data conditions. Methods: We propose a multimodal learning architecture that models genomics as the primary biological anchor and learns conditional projections to imaging modalities, including multiparametric MRI and whole-slide histopathology (WSI). The framework formulates multimodal fusion as a genomics-guided contrastive learning problem, incorporates domain-specific optimization constraints, and learns a latent shared-state representation to support inference without requiring fully paired datasets. Evaluation was conducted using public datasets, including TCGA-PRAD and TCIA, across low-risk versus higher-risk/clinically significant prostate cancer (csPCa) discrimination, Gleason-based risk stratification, and clinically significant outcome prediction tasks under realistic multimodal and missing-modality scenarios. Results: In the adequately powered $\mathrm{G}\mathrm{e}\mathrm{n}\mathrm{o}\mathrm{m}\mathrm{i}\mathrm{c}\mathrm{s}+\mathrm{W}\mathrm{S}\mathrm{I}$ cohort (n = 486), the framework achieved an AUROC of 0.985 ± 0.005 for low-risk versus higher-risk/csPCa discrimination (p < 0.001). Exploratory analysis in a small, matched $\mathrm{G}\mathrm{e}\mathrm{n}\mathrm{o}\mathrm{m}\mathrm{i}\mathrm{c}\mathrm{s}+\mathrm{M}\mathrm{R}\mathrm{I}$ cohort (n = 28) yielded an AUROC of 0.980 ± 0.006 for the same endpoint; these findings are reported descriptively with bootstrap confidence intervals due to limited sample size. Because the negative reference group consisted of low-risk prostate cancer cases rather than cancer-free controls, results are interpreted as within-cancer risk discrimination rather than de novo cancer detection. The framework achieved weighted accuracy up to 92.1%, Cohen’s κ up to 0.86, and reduced critical decision errors by 58%. Calibration remained strong (ECE 0.021–0.024), and decision-curve analysis indicated improved utility with reduced unnecessary invasive workups in retrospective modeling. Robustness analysis demonstrated AUROC degradation below 0.04 under domain shifts. Single-modality inference using genomics alone maintained AUROC > 0.90. Interpretability analysis revealed feature attributions aligned with domain-relevant genomic markers. Conclusions: The proposed framework provides a scalable and generalizable solution for heterogeneous multimodal data fusion, supporting reliable prediction, robustness to missing modalities, and applicability to complex information systems beyond the studied domain. Full article

Introduction: Lymph nodes (LN) in the anterior periprostatic fat (APPF) may harbour metastatic disease in patients with Prostate Cancer (PCa). We investigated the incidence and significance of LN in APPF tissue removed during robotic and laparoscopic radical prostatectomy (RP). Patients and Methods: We retrospectively analysed RP performed by a single surgeon from 2013 to 2023. A total of 670 patients underwent RP, with 407 procedures conducted laparoscopically and 263 robotically. Histological results were available for 509 patients, who were examined for the presence of LN and any evidence of metastatic involvement. Results: LN were detected in the periprostatic fat of eighty patients; however, only twelve had lymph node metastasis. Seven of the twelve patients presented with prostate-specific antigen (PSA) levels greater than 10 ng/mL. All LN-positive patients had a Gleason score of seven or higher. On MRI, all patients had a PIRADS score of four or higher, and eleven were staged at T3 or higher. Additionally, all twelve patients had a Briganti score exceeding twenty. Conclusions: Our series indicates that the APPF contains LN that can harbour metastatic disease. Patients can have LN involved in APPF without the involvement of pelvic LN. Therefore, our study suggests that routine excision of APPF should be considered for appropriate LN staging and to avoid missing any metastasis, and that scoring systems like Briganti should be used to help identify this high-risk group. Full article

Background/Objectives: Prostate cancer (PCa) detection via a conventional systematic biopsy (SB) may miss cancer lesions, promoting the exploration of alternative methods. Multiparametric MRI demonstrates high sensitivity and specificity for the detection of clinically significant prostate cancer and an MRI-directed three-dimensional fusion biopsy (MFB) strategy may improve cancer detection rates. There are limited data available in Taiwan regarding this novel technique. This study aimed to compare the accuracy of cancer detection rates between MFB and SB. Methods: From January, 2021 through October, 2023, patients with PSA levels of 4–20 ng/mL and no palpable prostate nodules were retrospectively reviewed. They were categorized into the MFB and SB groups. Clinical parameters and PCa detection rates were compared between the two groups. Multivariable logistic regression analyses were performed to identify independent predictors of cancer detection. A separate subgroup analysis was conducted within the MFB cohort to evaluate the association between the PI-RADS category and biopsy outcomes. Results: A total of 262 patients (89 in MFB and 173 in SB) were included in the final analysis. MFB demonstrated significantly a higher detection rate in detecting PCa compared to SB (45.0% vs. 31.8%, p = 0.036). Furthermore, MFB exhibited superior rates in detecting clinically significant PCa (csPCa) and high-grade PCa compared to SB (40.4% vs. 22.5%, p = 0.002; 20.2% vs. 5.8%, p ≤ 0.001, respectively). The MRI PIRADS score exhibited a positive correlation with the detection of PCa, csPCa, and high-grade PCa (p ≤ 0.001, p ≤ 0.001, and p = 0.015, respectively). After an adjustment for age, PSA level, and PSAD, MFB remained independently associated with overall PCa detection (adjusted OR 2.01, 95% CI 1.16–3.48, p = 0.013), csPCa detection (adjusted OR 2.83, 95% CI 1.58–5.08, p < 0.001), and high-grade PCa detection (adjusted OR 6.15, 95% CI 2.49–15.19, p < 0.001). PSAD was also independently associated with all cancer outcomes. Within the MFB cohort, PI-RADS 5 lesions demonstrated significantly higher odds of overall PCa (adjusted OR 7.38, 95% CI 2.18–24.98, p = 0.001), csPCa (adjusted OR 8.42, 95% CI 2.49–28.49, p < 0.001), and high-grade PCa detection (adjusted OR 6.93, 95% CI 1.82–26.41, p = 0.005) compared with PI-RADS 3 lesions. Conclusions: In this retrospective cohort, the MFB strategy was associated with higher detection rates of PCa, csPCa, and high-grade PCa compared with conventional SB. PSAD and MRI findings independently contributed to cancer prediction, supporting the integration of clinical and imaging parameters in prostate biopsy decision-making. These findings support the clinical value of MRI-directed biopsy strategies but should be interpreted cautiously because of the non-randomized allocation and the differences in biopsy route and sampling strategy. Full article

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. Full article

Antioxidant activity of plant extracts is commonly interpreted as a directly comparable property despite substantial methodological differences among analytical assays and extraction systems. This study investigated how extraction selectivity and assay chemistry influence antioxidant-associated responses in hop (Humulus lupulus L.) extracts by integrating experimental and literature-derived datasets. Extracts obtained using different solvents and extraction techniques were evaluated using ABTS, DPPH, and Folin–Ciocalteu (TPC) systems. Multivariate statistical analyses, including principal component analysis (PCA), correlation analysis, and non-parametric comparisons, were applied to normalized datasets to assess assay-dependent variability and cross-study comparability. The results suggested substantial divergence between ABTS- and DPPH-associated responses, including a statistically significant negative correlation between both assay systems. PCA indicated assay-selective separation patterns, while TPC values did not consistently correlate with antioxidant-associated responses. Different extraction conditions were associated with distinct antioxidant response profiles, suggesting selective redistribution of analytically detectable antioxidant fractions rather than uniform changes in antioxidant capacity. Based on these observations, this study proposes the Assay–Extraction Interaction Framework (AEIF), an interpretative framework that views antioxidant activity as a context-dependent analytical response rather than a universal intrinsic property of the extract. Full article

The majority of currently available hand kinematic databases have been gathered using expensive marker-based systems or are restricted to a particular gesture-recognition task, failing to capture the dynamic nature of joints when the hand is engaged with an object. To address this gap, we introduce the RGB-based Hand Joint Kinematics (RGB-HJK) dataset, a publicly available collection of continuous, frame-level 3D joint angle trajectories, recorded while ten healthy adults (six male, four female; age $25.8\pm 3.2$ years; BMI $22.8\pm 2.0$ kg/m²) performed five standardized object interaction grasps: Power Grasp (cylindrical bottle), Tripod Grasp (pen), Static Power Hold (smartphone), Precision Pinch (thin paper), and Lateral Pinch (book). Data were collected using a standard RGB camera and the MediaPipe Hands markerless pipeline at $26.95\pm 0.29$ Hz, a rate that was stable across all subjects. Each participant completed five trials for each grasp type. After filtering using active hold, 28,111 validated frames remained, with a 100% detection rate for all 250 trials. Intra-subject repeatability was good (mean SD $\le 7.9°$ across all joint grasp combinations) and inter-subject variability was within the range expected based on normal anatomical diversity. Importantly, kinematic validation of the Index Proximal Interphalangeal (PIP) joint (61.8° ± 18.4°) showed values consistent with ranges reported in previous studies using instrumented gloves and depth sensors. Principal Component Analysis (PCA) confirmed clear linear separability among the five grasp configurations. Unlike existing datasets, the RGB-HJK method does not compromise the natural sense of touch and is free of hardware occlusions, thereby providing an easily accessible ecological baseline. Full article

The expanding capabilities of deep learning-based media synthesis have intensified concerns regarding the authenticity of digital content and the reliability of forensic analysis tools. In response to these challenges, this work introduces DeepFakeX, a collection of 800 synthetically generated videos available under controlled access for research purposes. The dataset encompasses four distinct categories of AI-driven synthesis: facial identity replacement, audio track substitution, neural voice cloning, and combined audiovisual alteration. Unlike existing deepfake datasets that predominantly focus on facial synthesis, DeepFakeX covers a broader range of manipulation modalities, reflecting the diversity of synthetic media encountered in real-world settings. All deepfakes were generated using state-of-the-art, publicly available tools. Standardized post-processing procedures were applied to each video to ensure uniformity in terms of quality, duration and encoding format. DeepFakeX also emphasizes diversity in gender, age, ethnicity, and language. Video contexts span speeches, informational videos, movie clips, news broadcasts, and interviews that reflect content scenarios commonly encountered in real-world online environments. The dataset includes videos in both English and Urdu. The dataset’s quality and structural variability were assessed through visual and audio analyses using the Structural Similarity Index Measure (SSIM), Mel-Frequency Cepstral Coefficients (MFCCs), and Principal Component Analysis (PCA). The evaluation results revealed substantial variability within each manipulation category, along with clearly distinguishable patterns specific to each modality. DeepFakeX has been developed to facilitate rigorous and transparent research in deepfake detection, cross-modal forensic analysis, and AI-driven media forensics. It is hosted on Zenodo under controlled access for research use. Full article

Precision medicine in prostate cancer (PCa) is increasingly best understood as a continuum linking risk-adapted detection, multimodal diagnosis and phenotyping, and implementation-ready decision pathways. Contemporary clinical guidelines emphasize structured diagnostic strategies, appropriate use of advanced imaging, and selective deployment of biomarkers when results can alter management. Upstream risk enrichment using polygenic risk scores and multivariable prediction models may improve the yield of clinically significant disease while mitigating harms related to overdiagnosis. At the point of suspicion, magnetic resonance imaging-first pathways and reflex biomarker testing provide practical tools to reduce unnecessary biopsy while maintaining safeguards for the detection of clinically important disease. Beyond diagnosis, prostate-specific membrane antigen positron emission tomography refines disease-state phenotyping in initial staging, biochemical recurrence, and limited-burden presentations, while standardized acquisition and reporting improve reproducibility and multidisciplinary communication. Germline and tumor-based molecular profiling should be operationalized as a longitudinal care process with clear consent, turnaround targets, and test-to-action rules that define what each result enables at specific decision nodes. Finally, longitudinal monitoring approaches, including liquid biopsy and artificial intelligence-enabled pathology, are evolving rapidly and require transparent reporting and rigorous risk-of-bias appraisal before broad clinical adoption. This narrative review synthesizes key evidence across the precision continuum and outlines a decision-node-based, test-to-action framework for maximizing clinical benefit, maintaining quality, and ensuring equitable access. Full article

Background: The residues of Camphora officinarum c.t. borneol after essential oil extraction are often discarded, causing resource waste and environmental pollution, while the edible fungi industry is facing a shortage of traditional cultivation substrates. Methods: This study integrated UPLC-MS/MS and GC-MS to comprehensively profile volatile and non-volatile metabolites. Samples included fresh branches and leaves (ZSXY) and residues after steam distillation (ZSZL), boiling combined with distillation (ZSSZ), and sun-drying after distillation (ZSSG). Results: In total, 2454 metabolites across 25 categories were detected. PCA revealed clear separation between fresh samples and all processed samples, with ZSZL and ZSSZ exhibiting similar metabolic profiles that were distinctly separated from ZSSG. Compared with ZSXY, most metabolites decreased after processing. ZSSG exhibited the strongest degradation, with 1408 down-regulated and only 146 up-regulated metabolites, and total terpenoid content decreased by 92.27%. ZSZL retained the highest levels of nutrients (e.g., amino acids and nucleotides) and bioactive compounds (e.g., phenolic acids, flavonoids, terpenoids), with 322 up-regulated metabolites. Among the specific comparisons, 113, 212, and 487 differentially accumulated metabolites were identified in ZSXY vs. ZSZL, ZSXY vs. ZSSZ, and ZSXY vs. ZSSG, respectively. KEGG enrichment revealed distinct pathway alterations: monoterpenoid degradation and biosynthesis pathways were activated in ZSZL, nitrogen metabolism-related pathways were disturbed in ZSSZ, and both limonene and pinene degradation and aminoacyl-tRNA biosynthesis pathways were enriched in ZSSG. Conclusions: Based on metabolomic profiling, steam distillation residues exhibited favorable retention of nutrients and bioactive compounds, whereas sun-drying led to excessive metabolite loss. These findings support the valorization of processing residues and promote circular agriculture. However, whether these residues can serve as effective substrates for edible fungi cultivation remains to be tested in dedicated cultivation trials. Full article

Oxalic acid is extensively used in industrial chemical processes, purification systems, hydrometallurgical operations, and advanced oxidation environments where rapid and environmentally sustainable analytical methodologies are increasingly required for process monitoring and quality control. In this study, a micro-Raman spectroscopy methodology was developed for the direct quantification of oxalic acid in aqueous systems at moderate-to-high concentrations (0.079–0.793 M). The analytical strategy was based on the integrated Raman response of the carbonyl stretching region (1700–1750 cm⁻¹), selected due to its strong concentration-dependent behavior, spectral definition, and reduced interference from the aqueous matrix. The proposed methodology demonstrated excellent analytical performance, including high linearity (R² > 0.998), satisfactory precision, and reliable concentration-dependent reproducibility throughout the evaluated concentration range. To evaluate operational robustness, matrix-matched standards incorporating temperature variation (25–40 °C), turbidity (0–57 mg/L), dissolved Ca²⁺ (0–58 mg/L), and dissolved Fe³⁺ (0–7 mg/L) were prepared to simulate chemically perturbed industrial environments. Principal Component Analysis (PCA) demonstrated that the carbonyl vibrational region retained organized concentration-dependent spectral behavior despite operational perturbations. Partial Least Squares (PLS) regression models developed under these matrix-informed conditions preserved strong predictive capability (R² ≈ 0.997), while preliminary prediction of process-related samples yielded excellent agreement between predicted and reference concentrations (R² = 0.990). Although operational perturbations produced substantial attenuation of Raman intensity, particularly at lower concentration levels, the carbonyl Raman band remained spectrally detectable and analytically interpretable throughout all evaluated conditions. Electronic-structure analysis using Natural Bond Orbital (NBO) and Atoms-in-Molecules (AIM) methodologies demonstrated that the strong analytical behavior of the ν(C=O) vibrational mode is associated with enhanced electron-density localization, covalent stabilization, and favorable polarizability characteristics of the carbonyl bond. The combined experimental, chemometric, and computational results demonstrate the feasibility of matrix-informed micro-Raman spectroscopy as a rapid, reagent-free, and operationally robust methodology for oxalic acid monitoring in chemically perturbed aqueous industrial systems. Full article

Optimizing nutrient management is critical for enhancing crop productivity and grain nutritional quality in reclaimed soils, where poor soil fertility and salinity often limit barley cultivation. In that context, this study evaluated the effects of NPK fertilization on barley grain metabolism in reclaimed soil, using four barley cultivars (Betaone, Heuknuri, Nurichal, and Sogang) under fertilized (F) and non-fertilized (NF) conditions. Chemical fertilization (N–P₂O₅–K₂O = 88–72–36 kg ha⁻¹) increased crude protein (CP) concentrations in Heuknuri and Sogang by over 30%, while reducing the soluble sugar content by 15–24%. In contrast, starch content remained relatively stable across all cultivars. Gas chromatography–mass spectrometry (GC–MS) profiling revealed that fertilization caused only modest changes in grain primary metabolism, including increased fatty acids (oleate, linoleate), alongside consistent accumulation of amino acids related to nitrogen assimilation (asparate, asparagine, glutarate, proline). Two-way ANOVA and principal component analysis (PCA) revealed that the cultivar identity, rather than fertilization, was the dominant factor shaping metabolic variation, affecting 23 of 28 detected metabolites. Notably, Betaone and Heuknuri exhibited higher overall metabolite accumulation and stable metabolic profiles across treatments, suggesting better physiological adaptation to nutrient-deficiency stress. These results indicate that NPK fertilization under reclaimed soil conditions promotes nitrogen assimilation more than carbon storage, and grain metabolic changes are largely cultivar-dependent. However, the underlying regulatory mechanisms controlling carbon–nitrogen allocation and lipid metabolism under fertilization were not fully investigated and require further multi-omics and long-term field studies. Full article