Search Results (41,080)

Endoreduplication and mixoploidy are widespread but poorly characterized phenomena in Cannabis sativa, a species notable for its rapid growth and high morphological variability. In this study, we analyzed 12 accessions using flow cytometry, chromosome counts, and fluorochrome banding to investigate organ-specific patterns of endopolyploidy and the occurrence of mixoploidy. Roots and cotyledons consistently exhibited high levels of endoreduplication, with substantial proportions of 8C nuclei and above, whereas the first pair of leaves displayed a profile similar to that of adult foliage, dominated by 2C nuclei. Cycle value and endocycle index calculations confirmed significant differences among organs, with roots and cotyledons showing markedly higher endoreduplication levels than leaves. Chromosome counts revealed the expected diploid number (2n = 20), but we also detected triploid and tetraploid cells in several accessions, indicating somatic mixoploidy. The presence of endoreduplication and mixoploidy suggests that C. sativa exhibits complex cell cycle regulation during early development. Fluorochrome banding revealed GC- and AT-rich DNA regions. Sex chromosome candidates are heterochromatic and contain AT-rich DNA. These findings expand the cytogenetic knowledge of the species and provide a basis for future studies on the developmental and evolutionary significance of somatic genome variation in C. sativa. Full article

Detecting discrete space-target trajectories from optical image sequences is important for ground-based space situational awareness, but direct sequence processing can be computationally expensive and sensitive to weak target responses. This paper presents LeTD, a linearity-enhanced trajectory detection framework built on compact sequence-level projections and a YOLO11n oriented bounding box detector. The aligned image sequence is reduced to maximum, median, and temporal projection maps, and sine–cosine temporal encoding is used to inject frame-index information into the standard three-channel detector input. Additional angle and endpoint supervision terms are introduced to improve localization of sparse linear trajectories while keeping the YOLO11n-OBB backbone and detection head unchanged. On an expanded real test set containing 82 sequences and 155 trajectories, LeTD achieves a track detection rate of 0.7742, a complete track rate of 0.6903, and a false-component rate of 0.2055 per frame under a unified GT-centric trajectory protocol. The method requires 0.52 s per sequence on average on the evaluated workstation platform, including representation construction and detector inference. Full article

Background: 2,3,3,3-Tetrafluoro-2-(heptafluoropropoxy)propanoic acid (GenX) is a perfluoroether carboxylic acid that has been detected in drinking water sources. Its potential hepatotoxicity has raised concern, although the associated molecular alterations remain incompletely understood. Chlorogenic acid (CGA), a naturally occurring polyphenol, has been reported to affect oxidative stress and metabolic homeostasis. Methods: Adult male C57BL/6J mice were exposed to GenX (2 mg/kg/day) with or without CGA (30 mg/kg/day) by gavage for 12 weeks. AML12 cells were treated with GenX (10–800 μM) for 24 or 48 h to assess cell viability, and intracellular lipid accumulation was evaluated after exposure to 200 μM GenX for 24 h. Results: GenX exposure induced hepatomegaly, microvesicular steatosis, inflammatory cell infiltration, and a reduction in hepatic glycogen stores. It also decreased hepatic glutathione concentrations and increased hepatic malondialdehyde concentrations. Serum alanine aminotransferase, aspartate aminotransferase, total cholesterol, and triglyceride levels were elevated. In AML12 cells, GenX increased intracellular lipid accumulation, as assessed by Oil Red O staining. Transcriptomic analysis identified significant enrichment of the peroxisome proliferator-activated receptor (PPAR) signaling pathway. Consistently, GenX altered the expression of genes and proteins involved in lipogenesis, fatty acid uptake, lipid storage, and fatty acid oxidation, suggesting disturbed PPAR-related lipid metabolic regulation. Moreover, the decreased p-mTOR/mTOR ratio, increased LC3-II/I, and overexpression of Beclin1, p62, and inflammatory mediators in the GenX group might suggest changes in autophagy-related proteins and inflammatory response. CGA coadministration partially attenuated several GenX-induced hepatic alterations, including liver enlargement, hepatic lipid accumulation, lipid peroxidation, and changes in selected autophagy- and inflammation-related proteins. Conclusions: Subchronic GenX exposure-induced adverse hepatic effects might be associated with disrupted PPAR-related lipid metabolic regulation, oxidative stress, inflammatory responses, and changes in autophagy-related proteins. CGA might exert potential modulatory effects. Full article

Background: Precise quantification of JAK2 V617F variant allele frequency (VAF) is clinically important in Philadelphia chromosome-negative myeloproliferative neoplasms (MPNs). This study evaluated the analytical performance and clinical utility of a laboratory-developed chip-based real-time digital PCR (dPCR) assay for JAK2 V617F and compared it with a commercial quantitative real-time PCR (qPCR) assay. Methods: Residual DNA extracts from 76 JAK2 V617F-positive clinical specimens collected from patients with suspected MPNs were analyzed using both qPCR and dPCR. Analytical performance was assessed according to CLSI-based approaches, including limit of blank (LoB), limit of detection (LoD), precision, linearity, and method comparison. Associations between VAF and hematologic parameters, as well as diagnostic groups, were also examined. Results: Both methods showed a LoB of 0. The LoD was 0.1167% for qPCR and 0.0846% for dPCR, and dPCR demonstrated lower variability than qPCR across high-, intermediate-, and low-concentration samples, with the largest difference observed at low VAF levels. Linearity was excellent for both assays (R² = 0.988 for qPCR and 0.999 for dPCR), although dPCR showed less low-concentration bias. The two methods were highly correlated (Pearson r = 0.9789, R² = 0.958), but dPCR yielded slightly higher VAF values overall. VAF was significantly higher in polycythemia vera than in essential thrombocythemia, and both qPCR- and dPCR-based VAFs were positively correlated with white blood cell count. Conclusions: The chip-based laboratory-developed dPCR assay showed high concordance with qPCR while providing lower LoD and better precision at low allele burden. These findings support its potential utility as a sensitive alternative for JAK2 V617F quantification in clinical laboratories, particularly in settings requiring accurate low-level detection and follow-up monitoring. Full article

Lynch syndrome (LS) is the most common hereditary colorectal cancer (CRC) syndrome, caused by germline pathogenic or likely pathogenic variants (PV/LPV) in mismatch repair (MMR) genes. Data on the spectrum of LS-associated variants in Slavic populations, including Serbia, remain limited. Given the high burden of CRC and endometrial cancer and the limited implementation of hereditary CRC screening, characterizing the spectrum of germline variants in clinically selected high-risk individuals is important for improving genetic testing strategies, risk assessment, and clinical management. Between 2018 and 2025, 176 individuals underwent germline testing for hereditary CRC syndrome based on the Amsterdam/Bethesda criteria, validated LS risk prediction models, and/or family history (FH). Next-generation sequencing (NGS) was performed using the Illumina TruSight Hereditary Cancer Panel, and variants were classified according to American College of Medical Genetics and Genomics and Association for Molecular Pathology (ACMG/AMP) guidelines. PV/LPVs in MMR genes were identified in 27/176 (15.3%) and were associated with positive FH of LS-related tumors (p = 0.0001). Most PV/LPVs were identified in MLH1 (10.2%), followed by MSH2 (4.0%) and MSH6 (1.1%), with no PV/LPVs identified in PMS2. Additionally, no pathogenic sequence-level EPCAM variants detectable by the applied panel-based NGS approach were identified. Recurrent MLH1 variants were observed in multiple families, and two previously unreported MLH1 variants were identified. This first systematic analysis of a clinically selected high-risk Serbian cohort provides novel data on the spectrum of LS-associated variants in this referral population, demonstrates the predominance of MLH1 variants, and supports broader implementation of genetic testing, tumor screening, and genetic counseling. Full article

A simple and cost-effective graphite paste electrode modified with 5-(4-aminophenyl)-4-amino-1,2,4-triazole-3(2H)-thione was developed for the selective voltammetric determination of Cu(II) ions in environmental water samples. The N,S-donor ligand was incorporated into a graphite–polystyrene matrix without the use of nanomaterials, providing a reproducible and straightforward electrode fabrication route. Scanning electron microscopy revealed a rough, porous surface morphology with an enhanced electroactive surface area of 0.065 cm², approximately twice the geometric area. Electrochemical impedance spectroscopy confirmed diffusion-controlled mass transport, while cyclic voltammetry indicated quasi-reversible behaviour of the Cu(II)/Cu(0) redox system with a linear dependence of peak current on the square root of the scan rate. Differential pulse voltammetry under optimised conditions (0.1 mol·L⁻¹ H₂SO₄, pH 1.0–1.2) yielded a linear analytical response over the concentration range of 0.01–0.4 μmol·L⁻¹ (R² = 0.99507), with a limit of detection of 0.02 μmol·L⁻¹ and a limit of quantification of 0.06 μmol·L⁻¹—well below the WHO guideline for copper in drinking water. The sensing mechanism involves selective N,S-bidentate coordination of Cu(II) at the electrode surface, followed by electrochemical reduction, as supported by FT-IR spectroscopic evidence. The sensor demonstrated good selectivity toward Cu(II) in the presence of common interfering metal ions at up to 20-fold excess. The method was successfully validated against ICP-OES (recovery 99.8%, RSD < 0.33%) and confirmed by spike–recovery experiments (99.0–99.5%), confirming its practical applicability for trace-level environmental monitoring. The modified electrode retained approximately 93% of its initial response after 30 consecutive measurements and 91% after 14 days of storage, demonstrating good operational stability. Full article

Artificial intelligence (AI) is rapidly transforming cardiovascular medicine, driven by the increasing availability of large-scale clinical data and advances in machine learning. Early computational applications in cardiology were primarily limited to rule-based electrocardiogram interpretation systems. Over time, these approaches have evolved into sophisticated deep learning models capable of analysing complex cardiovascular signals and imaging data. In parallel with the broader development of digital health technologies, including wearable devices, electronic health records, and remote monitoring systems, AI applications have expanded across multiple domains of cardiovascular care. These now include electrocardiographic (ECG) and electrophysiological analysis, cardiovascular imaging, surgical planning, and multimodal risk prediction. More recently, multimodal AI models have emerged that integrate heterogeneous data sources such as imaging, physiological signals, clinical records, and genomic information, enabling more comprehensive characterisation of cardiovascular disease. Beyond diagnostic applications, AI is increasingly influencing system-level aspects of cardiovascular medicine, including clinical decision support, workflow optimisation, medical education, and clinical trial design. This narrative review traces the historical and clinical evolution of artificial intelligence in cardiovascular medicine from early automated ECG interpretation systems to contemporary multimodal and system-level applications. It highlights key technological developments, current clinical applications, translational challenges, and the emerging role of AI within digital cardiovascular health ecosystems, with particular emphasis on early disease detection, risk stratification, prognostic modelling, and personalised cardiovascular care. Full article

Wearable inertial measurement units (IMUs) offer an objective, low-cost basis for Parkinson’s disease (PD) assessment, but multi-task clinical protocols yield heterogeneous recordings across body locations and small cohorts, and it is unclear whether such data can support reliable PD detection without training deep models from scratch. We therefore ask whether a motion-pretrained representation transfers to this setting, and quantify how much of the discriminative signal it supplies. Each subject is represented by five task-level motion embeddings, one per clinical task, produced by a frozen pretrained spatio-temporal graph convolutional network (ST-GCN) that fuses the thirteen body-worn sensors into a whole-body embedding; a three-layer Transformer with validity-mask weighting aggregates these tokens for binary PD-versus-control classification on the WearGait-PD cohort (181 subjects: 100 PD, 81 controls). Under a leakage-free nested protocol with repeated subject-disjoint stratified 5-fold cross-validation (5 seeds; 25 estimates per model) and paired significance testing, the model attains a balanced accuracy of 0.834 ± 0.087, macro-F1 of 0.842 ± 0.094, and AUC of 0.842 ± 0.103. It leads six classical baselines and a spectrogram-CNN on accuracy-based metrics, though random forest, gradient boosting, and the spectrogram-CNN edge ahead on AUC; after correction for fold correlation, none of these between-model differences is significant. The one robust finding is a transfer effect: replacing the pretrained encoder with a random one of identical architecture lowers balanced accuracy by 15.5 points when frozen (p = 0.043) and 20.4 when trained end-to-end (p = 0.014). Discrimination is preserved under 1:1 age matching (0.846) and across both genders, so it is not explained by age imbalance. Motion-pretrained skeletal encoders thus supply the majority of the discriminative signal, while the aggregator contributes gains inseparable from noise at this cohort size. Full article

Rapid detection and quantification of epidemiologically important Salmonella enterica serovars are critical for poultry surveillance, food safety monitoring, and risk-based intervention strategies. This study performed comprehensive analytical validation together with a preliminary field-based diagnostic performance evaluation of GenoPATHX™, a multiplex probe-based qPCR platform designed for the simultaneous detection and quantification of priority Salmonella serovars in poultry-associated matrices. The platform consists of two multiplex panels, designated the Chicken Key Performance Indicator (CKPI) and Turkey Key Performance Indicator (TKPI), each designed to detect priority poultry-associated Salmonella serovars together with a genus-level S. enterica marker. Analytical performance was evaluated for amplification efficiency, linearity, limit of detection (LoD₉₅), limit of quantification (LoQ), repeatability, intermediate precision, analytical specificity (inclusivity/exclusivity), robustness, matrix effects, and performance in artificially inoculated matrices. Diagnostic performance was further assessed using naturally contaminated poultry environmental samples. The assay demonstrated robust amplification performance in both singleplex and multiplex formats, with high linearity (R² = 0.987–0.999) and LoD₉₅ values ranging from 60 to 545 genome equivalents per reaction. Complete analytical inclusivity and high exclusivity were achieved for the evaluated isolate panel. In field samples, the direct GenoPATHX™ workflow demonstrated 81.0% sensitivity, 91.3% specificity, and substantial agreement with the USDA-FSIS reference culture method (κ = 0.73). Overall, GenoPATHX™ exhibited robust analytical performance and enabled rapid, same-day quantitative detection of priority Salmonella serovars in poultry-associated matrices, supporting its application for poultry surveillance and food safety monitoring. Full article

Underwater images often suffer from color distortion, low contrast, and structural blurring caused by wavelength-dependent absorption and scattering, which degrade both visual observation and downstream perception. Existing underwater image enhancement methods usually learn image-level restoration mappings, while the relationships among semantic regions, degradation patterns, and restoration responses are not fully exploited. In this paper, we propose a Semantic Guidance Network with Degradation Consistency for Underwater Image Enhancement (SGDC-UIE). Specifically, SGDC-UIE first extracts dense semantic responses from a frozen DINOv3 prior and converts them into foreground, boundary, and background region gates. These gates are then used to guide pseudo-physical degradation estimation, producing attenuation-like, transmission-like, illumination, structure, and background-light priors for region-aware restoration. These pseudo-physical priors are learned, bounded conditioning variables rather than calibrated estimates of underwater optical parameters. Based on these degradation conditions, a dual-branch restoration network corrects low-frequency color and illumination degradation while recovering high-frequency structural details through semantic-aware wavelet restoration. The color-restored and structure-restored outputs are further integrated by a degradation-consistent fusion gate, which adaptively balances visual fidelity and task-relevant structure preservation. In addition, grouped supervision with quality-anchor replay stabilizes task-aware fine-tuning and reduces visual-quality drift. Extensive experiments on paired and no-reference underwater enhancement benchmarks, semantic segmentation, and underwater object detection show that SGDC-UIE achieves competitive restoration quality and improves the usability of enhanced images for downstream perception. Full article

Background and Objectives: Baccharis dracunculifolia DC. (Asteraceae), the main botanical source of Brazilian green propolis, is recognized for its high content of bioactive secondary metabolites. Given this potential, this study aimed to characterize the chemical profile of the crude extract (CE) from the aerial parts of B. dracunculifolia and to investigate its anti-inflammatory, antiproliferative, antioxidant, and photoprotective properties. Predictive computational analyses were used to assist the interpretation of the experimental findings. Methods: The CE was obtained by dynamic maceration with ethanol and chemically characterized by UHPLC-MS/MS using external calibration curves. Anti-inflammatory activity was evaluated by inhibiting nitric oxide (NO) in RAW 264.7 macrophages, while cellular antioxidant activity (CAA) was determined in the same model. Antiproliferative activity was evaluated against the human tumor cell lines AGS, Caco-2, MCF-7, and NCI-H460, as well as non-tumor VERO cells. Additionally, antioxidant potential was investigated using classical colorimetric methods (DPPH, FRAP, and ABTS). The extract was also quantified for total phenolic and flavonoid content, as well as sun protection factor (SPF). Furthermore, complementary computational analyses included PASS prediction, SwissTargetPrediction, Gene Ontology enrichment using PANTHER, SwissADME profiling, and toxicity prediction with ProTox-III. Results: UHPLC-MS/MS analysis revealed a profile rich in flavonoids and phenolic acids and led to the identification of 14 compounds not previously reported in B. dracunculifolia according to the literature examined, notably the flavonoid morin and the phenylpropanoid coniferaldehyde detected at comparatively high concentrations (>600 µg/g). CE inhibited NO production (IC₅₀ = 62.00 µg/mL) suggesting anti-inflammatory activity, and reduced intracellular oxidation by 81% (at 2000 µg/mL) in RAW 264.7 macrophages. The extract showed total phenolic content (83.62 to 540.40 µg gallic acid equivalents/mg of CE) and high levels of flavonoids (472.00–515.00 µg quercetin equivalents/mg of CE), antioxidant activity in the DPPH assay (IC₅₀ = 0.86 mg/mL), and relevant photoprotective potential (SPF = 7.79–19.30). Antiproliferative activity was moderate to weak (GI₅₀ = 165.00–257.00 µg/mL). In silico analyses identified predicted activities, molecular targets, and enriched biological processes related to redox homeostasis, inflammation, apoptosis, and cellular responses to UV radiation, suggesting biological functions potentially associated with the identified metabolites. Conclusions: The crude extract of B. dracunculifolia demonstrated significant cellular anti-inflammatory and antioxidant activities, likely associated with its phenolic composition and the presence of metabolites reported in this study for the first time in this species, particularly morin and coniferaldehyde. These findings expand the phytochemical knowledge of B. dracunculifolia and reinforce its potential as a source of bioactive compounds for pharmaceutical, nutraceutical, and photoprotective applications. Full article

Fusarium head blight (FHB) caused by Fusarium graminearum is a major disease of wheat and barley worldwide. Besides causing yield loss, F. graminearum also contaminates infected grains with trichothecene mycotoxins such as deoxynivalenol (DON) and its acetylated derivatives. Field isolates of F. graminearum vary in the amount of mycotoxins produced, both on infected wheat heads and in controlled laboratory experiments. Genes encoding the enzymes responsible for trichothecene mycotoxin biosynthesis are already characterized, but additional genes responsible for the variation in amounts of mycotoxins detected within and among populations remain to be identified. We measured levels of trichothecenes produced in vitro in a sample of 151 F. graminearum field isolates. Genome-wide association performed with these measurements identified 10 quantitative trait loci (QTL) associated with variation in DON and/or 15ADON levels. The candidate regions contain many functionally characterized genes, including the Swr1p helicase gene, an MFS transporter, and multiple other transmembrane transporters that may relate to the fungus’ ability to transport trichothecenes across membranes for sequestration or export. These results help to characterize the genetic factors that influence variability in trichothecene levels, which contribute to our understanding of trichothecene levels on infected grain and may lead to strategies to mitigate this toxin contamination. Full article

Artificial intelligence (AI)-based 3D eye tracking is a fundamental enabling technology for human–computer interaction (HCI), extended reality (XR), and intelligent spatial computing. However, physical evaluation methodologies are heavily limited by non-repeatable human micro-movements, the lack of precise millimeter-level ground truth, and systemic camera calibration errors. Addressing these limitations, this paper delivers a methodological meta-contribution to the field by presenting a high-fidelity facial digital twin benchmark framework using NVIDIA Isaac Sim for the rigorous and repeatable evaluation of 3D eye-tracking algorithms. Rather than focusing on incremental algorithmic modifications, our framework establishes a standardized, hardware-free testing paradigm. By systematically sampling virtual facial poses under identical rendering configurations, it generates dense, fully repeatable trajectories with mathematically exact spatial ground truth across landmark-based, 3DMM-based, and direct regression architectures. To isolate intrinsic algorithmic capabilities from extrinsic calibration biases, we propose a self-referenced relative-motion evaluation protocol operating in a facial-centered local reference frame. Comprehensive diagnostics are performed across multiple key dimensions, including localization accuracy, temporal jitter, detection robustness, and pose sensitivity, culminating in a newly introduced Comprehensive Performance Index (CPI) to aggregate these multi-dimensional metrics. Statistical hypothesis testing reveals consistent performance hierarchies: landmark and 3DMM methods achieve superior geometric consistency and temporal stability by leveraging parametric shape constraints, whereas direct regression models exhibit severe tracking degradation and failure under extreme rotations. By resolving the long-standing benchmark replication bottleneck, this extensible digital twin platform establishes a standardized, reproducible methodology for developing and certifying trustworthy human-centric perception systems. Full article

Crassostrea hongkongensis is an economically important mariculture species in southern China, and hybrid breeding has been applied to improve its growth traits. However, the molecular mechanisms underlying growth heterosis remain poorly understood. In this study, inter-population hybrid (HG) and intra-population (IG) groups of C. hongkongensis were compared using integrated transcriptomic and metabolomic analyses to investigate the regulatory basis of growth heterosis. Shell length and shell height were significantly greater in HG than in IG (p < 0.01). Transcriptomic analysis identified 474 false discovery rate (FDR)-supported significant differentially expressed genes (DEGs) (233 upregulated and 241 downregulated), which were treated as the primary statistically robust findings; a broader set of 3161 candidate DEGs, including genes associated with growth regulation and shell biomineralization, was retained for exploratory functional and multi-omics analyses. Metabolomic analysis detected 345 differential metabolites (DMs), which were mainly enriched in energy metabolism, nucleotide metabolism, and arachidonic acid metabolism. Exploratory pathway-level multi-omics integration revealed significant concordance between the transcriptomic and metabolomic profiles (M² = 0.1962, p = 0.001) and highlighted the tricarboxylic acid cycle, oxidative phosphorylation, branched-chain amino acid degradation, nucleotide metabolism, and arachidonic acid metabolism. These findings suggest that growth heterosis in C. hongkongensis is associated with altered energy metabolism, biosynthetic processes, shell formation-related pathways, and potential changes in the growth–defense balance, providing molecular insights for genetic improvement and selective breeding. Full article

Defect detection in rotating propellers is critical because small defects can cause performance degradation, vibration, noise, and safety risks. Conventional frame-based cameras suffer from motion blur, whereas high-speed cameras often require costly equipment and controlled conditions. This study investigates an event-camera-based sensing framework that converts asynchronous luminance changes from high-speed rotating propellers into positive–negative (PN) composite event accumulation images and classifies normal and defective propellers using a convolutional neural network (CNN). Gradient-weighted Class Activation Mapping (Grad-CAM) visualizes class-discriminative regions and extracts candidate defect regions, while normalized cross-correlation generates event accumulation images at the same rotational phase for defect progression monitoring. Experiments with 65 mm three-blade propellers with approximately 1 mm artificial blade defects rotating at approximately 862 rpm showed that, in an exploratory accumulation-time analysis, 900 μs produced an image-level accuracy of 0.93; integrating images over one full rotation yielded no misclassifications under the present dataset. An additional experiment at 2610 rpm tracked defect progression using a quantitative indicator based on distance-transform pixel counts. These results support the feasibility of event-based sensing for defect detection, cropping candidate defect-related regions, and tracking an image-domain indicator associated with defect progression in high-speed rotating propellers under the tested conditions. Full article