Search Results (719)

We introduce a class of specular and antispecular radially polarized partially coherent twisted beams by using a wavefront-folding interferometer and then investigate the propagation of such beams blocked by an opaque obstacle. One sees that these optical fields exhibit sharp internal spectral density with a central peak in the specular case and a central dip in the antispecular case. It is also seen that both the spectral density and the polarization feature present a good twist effect and a tendency to self-heal upon propagation. However, unlike the spectral density that can recover its profile perfectly, the self-healing ability of both the degree of polarization and the generalized Stokes parameters is only partial and not complete. While a smaller value of the twist factor enhances the self-healing performance of the beam field, it slows the beam’s rotation and degrades the degree of polarization. Moreover, the polarization distribution in the central area is mainly determined by the phase difference of the interferometer. The results of our work have important applications in the fields of free-space beam communication and particle trapping. Full article

Specific Emitter Identification (SEI) distinguishes individual emitters by extracting subtle features from intercepted radio frequency signals. This process relies on the design and extraction of specific features. Current methods for selecting and characterizing radio frequency fingerprints vary by individual, and the extraction process is closely coupled with environmental conditions. As a result, the generality of such identification algorithms is often limited, particularly when the application environment does not match the premise of feature design, leading to rapid degradation or even failure of individual identification performance. This paper proposes a deep clustering model based on polarization feature learning for identifying individual communication emitters. The approach involves constructing a guided network to extract datasets of polarization features from communication signals and utilizing a contrastive representation learning network to extract dual-polarization features from I/Q data samples. Subsequently, a Bayesian nonparametric (BNP) class mixture model algorithm, capable of inferring an unknown number of clusters, is employed to build a multi-level clustering network for clustering analysis of the extracted features. Under 5 dB conditions, the method described in this paper achieves an average recognition accuracy of 87.5%. Full article

Synthetic Aperture Radar (SAR) ship classification plays a critical role in maritime surveillance, addressing challenges such as the similarity between ship categories, as well as scarcity of annotated datasets and data imbalance. In this paper, a lightweight and real-time dual-branch architecture is proposed to effectively address the SAR ship classification task. The proposed approach integrates dual-polarization data within a hybrid convolution-transformer framework to improve classification performance. The model fuses dual-polarization modes, combining convolutional layers for local feature extraction with transformer blocks for global contextual understanding. Evaluations on the OpenSARShip 2.0 dataset show that the proposed model achieves 97.50% accuracy in the 3-class configuration and 93.28% in the 6-class configuration. For the FUSAR-Ship dataset, which does not provide dual-polarization data for the same ship target, the single branch model achieved an accuracy of 94.92% for the 7-class configuration. Despite its dual-branch design, the model maintains computational efficiency, making it suitable for real-time maritime monitoring applications. The results demonstrate the effectiveness of polarization-aware hybrid models for scalable and robust SAR ship classification. Full article

Background: The Global Positioning System (GPS) and wearable monitoring technologies are increasingly applied in sport science to quantify training load; however, data from female cross-country skiers in nations with emerging competitive programs remain scarce. This case series covering the complete national team roster analyzed the complete annual training cycle of the Korean women’s national cross-country skiing team (KCF) using GPS and heart rate-based wearable sensors. Methods: All three national team members were monitored throughout the 2022–2023 season (52 weeks), structured into General Preparation Period 1 (April–July), General Preparation Period 2 (August–November), and Competition Period (December–March). Individualized five-zone intensity thresholds were established through graded exercise testing on a roller ski treadmill with ventilatory threshold and blood lactate determination, independently assessed by two exercise physiologists (PhD level). Results: The total annual training volume was 667.72 h, comprising roller/on-snow skiing (54.0%), running (23.3%), and strength training (22.7%). The endurance-only intensity distribution demonstrated a polarized pattern (Zones 1–2: 91.5%). The total annual training distance reached 4673.30 km. The mean FIS points were 108.46 ± 38.60, and the mean VO₂max was 60.17 ± 6.11 mL·kg⁻¹·min⁻¹. Conclusions: When benchmarked against world-class female (WCF) standards (800–950 h annually), the overall training volume was approximately 18–30% lower. The relative strength training allocation (22.7%) exceeded typical WCF values (10–15%). These observations should be interpreted cautiously given the small sample size and cross-study comparison design, using published literature-based benchmarks. Full article

Cancer remains a leading cause of morbidity and mortality globally, with current therapies often limited by toxicity, drug resistance, and reduced efficacy in advanced stages. Medicinal plants represent important sources of bioactive compounds (BACs) with anticancer and chemopreventive potential; however, their successful application is strongly influenced by extraction strategies that determine phytochemical recovery and downstream biological activity. This review evaluates solvent-based extraction techniques used to extract BACs from medicinal plants with reported anticancer properties, synthesizing peer-reviewed articles from PubMed and Google Scholar published between 2020 and 2025. Solvent-based methods, including Soxhlet and maceration, were most widely applied due to their operational simplicity and the preservation of structurally diverse metabolites while percolation, decoction, infusion, and hydro-distillation were sparsely utilized. Extraction strategy and solvent polarity emerged as primary factors shaping phytochemical profiles, with phenolics, flavonoids, alkaloids, and terpenoids identified as dominant classes. Reported half maximal inhibitory concentration (IC₅₀) ranged from highly potent (0.12 µg/mL) to weak (30,000 µg/mL), reflecting variability driven by extraction parameters and plant matrix complexity. Anticancer mechanisms commonly involved apoptosis induction, cell-cycle arrest, reactive oxygen species-mediated cytotoxicity, and inhibition of proliferative signaling pathways across breast, cervical, colon, lung, liver, and prostate cancer models. Although solvent-based extraction approaches remain widely used, their context-dependent nature and lack of standardization limit reproducibility. Overall, anticancer and chemotherapeutic efficacy is primarily governed by BAC composition, while extraction methods act as upstream modulators. Future progress requires phytochemical-informed, standardized workflows supported by hybrid extraction systems, AI-assisted optimization, and advanced bioavailability and delivery systems to enable reproducible and clinically relevant translation of plant-derived chemotherapeutics. Full article

This study investigates the dynamic patterns of global financial risk transmission across 11 major economies and four key asset classes (stocks, bonds, foreign exchange, and gold) using daily data spanning 2012 to 2025. To capture the non-linearities of extreme market stress, we construct a multilayer directed network based on least absolute shrinkage and selection operator (LASSO) penalized quantile regression at the 5% lower tail. We estimate tail risk spillovers using a one-year rolling window approach and identify systemically important nodes via an extended PageRank algorithm applied to the resulting adjacency tensors. Empirical results suggest that the rankings of systemically important countries undergo significant re-orderings during crisis periods. We find robust statistical evidence that the Herfindahl–Hirschman Index (HHI) of risk concentration provides forward-looking information regarding structural polarization and systemic fragility. These observed associations remain consistent across alternative quantile thresholds, varying lag lengths, and alternative rolling window specifications. Our results provide granular insights for policymakers monitoring cross-asset contagion and provides a framework for institutional investors to assess potential tail-risk hedging strategies within an increasingly interconnected multilayer architecture. Full article

The chemotaxonomic classification of Cannabis sativa L. has historically relied on the Δ⁹-tetrahydrocannabinol (THC) to cannabidiol (CBD) ratio, yielding canonical chemotypes I, II, and III. However, this binary framework overlooks the chemical diversity contributed by the minor cannabinoids. High-performance liquid chromatography (HPLC) following the AOAC Official Method 2018.10 was employed to quantify nine cannabinoids (THCA, THC, CBDA, CBD, CBGA, CBG, CBC, CBDV, and CBN) across 36 commercially and medicinally relevant cannabis varieties. Quantitative profiling revealed substantial phytochemical heterogeneity, with total THC ranging from 0.41% to 15.64% and total CBD ranging from 0.09% to 12.32% (w/w). Unsupervised principal component analysis (PCA) demonstrated that the first two principal components explained 62.7% of the total variance. PC1 (37.6%) captured the THCA–CBDA polarity axis, while PC2 (25.1%) was dominated by minor cannabinoids (CBC; loading 0.417), CBGA (0.314), and CBG (0.258). Supervised partial least squares discriminant analysis (PLS-DA) using only the nine cannabinoid variables achieved 94.2% cross-validated accuracy and 100% test-set accuracy in predicting the chemotype class, with CBC identified as the third most discriminatory variable (variable importance in projection, VIP = 1.34). Hierarchical clustering resolved three principal clades and further subdivided THC-dominant accessions into CBC-enriched (Sour Diesel, Cinderella Jack) and CBGA-enriched (Mother Gorilla, Auto Lemon Kix) subclusters. A multivariate “metabolic coordinate” system based on PC1/PC2 scores is proposed as a quantitative and reproducible alternative to the traditional Type I/II/III and sativa/indica nomenclatures. This study introduces an empirically grounded framework for variety authentication, quality control, and enhanced precision breeding in the rapidly growing medicinal cannabis sector, for both human and veterinary applications. Full article

Pseudomonas aeruginosa causes severe healthcare-associated infections, yet no vaccine has been licenced. To circumvent the antigenic variability of classical surface antigens, we evaluated LolB—an essential outer-membrane lipoprotein whose periplasmic orientation favours T-cell-dominant mechanisms with potential antibody access via outer-membrane vesicles (OMVs) or bacteriolysis. An integrative in silico pipeline combined multi-strain conservation (20 isolates), epitope discovery (B- and T-cell), safety filters, physicochemical profiling, de novo/refined 3D modelling, molecular dynamics (MD), and docking to TLR4/MD-2. LolB was highly conserved (95–100% identity) under strong purifying selection (dN/dS = 0.15). A conformational B-cell hotspot centred on Q72 mapped to a solvent-accessible flexible loop. Two class II epitopes—LAAQNSPLT and FLGSAAAVS—showed predicted high affinity (IC₅₀ < 10 nM), non-toxicity, and broad coverage, with the pooled set achieving 98.6% global HLA coverage in silico. The final 119-aa construct (N-terminal hBD-3 adjuvant; GPGPG linkers) was compact and tractable (MW = 12.7 kDa; instability index < 40; near-neutral GRAVY) and scored higher for antigenicity than native LolB (VaxiJen 0.82 vs. 0.41). MD supported thermal stability up to 350 K, linker RMSF < 1.5 Å, and a stable 18.2 ± 2.8 Å interdomain spacing. Docking predicted a 1420 Ų interface and ΔG = −10.2 kcal·mol⁻¹ (Kd = 28 nM) with reproducible polar contacts, suggesting productive TLR4/MD-2 engagement. A conservative R42A/K variant is proposed to temper IFN-γ bias. This work therefore suggests an essentiality-anchored LolB-derived multi-epitope construct as a computational vaccine candidate against multidrug-resistant P. aaeruginosa and defines specific experimentally testable hypotheses for future in vitro/in vivo assessment. Essentiality-anchored epitope selection plus adjuvant-surface engineering yielded a structurally coherent, immunologically rational LolB-derived multi-epitope vaccine warranting experimental validation. Full article

Histone deacetylase 6 (HDAC6), a member of the class IIb HDAC family, plays a crucial role in epigenetic regulation and cytoskeletal dynamics, while participating in host anti-infective immune responses. However, its precise functions and mechanisms during Chlamydia muridarum (C. muridarum) infection remain incompletely defined. Our study demonstrated that C. muridarum respiratory infection upregulates HDAC6 expression at the infection site and in immune organs. Comparative analysis of wild-type (WT) and HDAC6-deficient (HDAC6^−/−) mice in this infection model revealed that HDAC6 deficiency exacerbates disease progression, including significant weight loss, severe pulmonary inflammation, and impaired C. muridarum clearance. Relative to WT mice, HDAC6^−/− mice exhibited elevated Signal Transducer and Activator of Transcription 6 (Stat6) and GATA Binding Protein 3 (Gata3) mRNA expression, enhanced pathological Th2 responses with increased IL-4 secretion, and no significant differences in protective Th1 or Th17 responses following C. muridarum infection. Concurrently, these mice displayed enhanced M2 macrophage polarization, as evidenced by upregulated CD206 and Arg-1 expression, whereas M1 marker expression remained unchanged. The vitro studies confirmed that HDAC6^−/− bone marrow-derived macrophages (BMDMs) promote M2 polarization, characterized by increased Arg-1, IL-10, and TGF-β production, and further co-culture experiments showed that C. muridarum -stimulated HDAC6^−/− BMDMs drive Th2 differentiation. These findings elucidate the critical role of HDAC6 in regulating Th2-M2 immune responses during C. muridarum respiratory infection and suggest targeted modulation of HDAC6 as a novel therapeutic strategy for chlamydial respiratory infection. Full article

Pesticides constitute a critical class of anthropogenic contaminants whose pervasive occurrence in surface waters, groundwater, and drinking water distribution systems poses substantial ecological and public health risks. Their pronounced structural heterogeneity, spanning highly polar herbicides to hydrophobic fungicides, together with their co-occurrence at trace levels, requires analytical methodologies capable of delivering rapid, robust, and high-resolution separations. In this study, a UHPLC-based analytical strategy is presented as a methodological framework for the development and optimization of UHPLC methods targeting multiresidue pesticide mixtures of environmental interest. The framework integrates a two-factor, three-level Design of Experiments, quadratic response surface modeling, and a multicriteria global desirability function to optimize the chromatographic resolution of 27 environmentally relevant pesticides. Statistical modeling revealed significant linear and quadratic effects of flow rate and gradient duration, highlighting the importance of multivariate optimization for complex multiresidue separations. The optimized UHPLC conditions improved simultaneous resolution, particularly for structurally similar analytes prone to coelution under conventional HPLC conditions. Overall, this work provides a statistically supported and transferable methodology for chemometric optimization of UHPLC separations and establishes a basis for extending desirability-driven optimization to additional classes of organic contaminants. Full article

Seasonal tropical wetlands such as the Brazilian Pantanal are increasingly threatened by climate variability and extreme hydrological events, creating a need for robust monitoring tools that capture flood dynamics at high spatial and temporal resolution. This study used Sentinel-1 Synthetic Aperture Radar (SAR) imagery to map and monitor flooding in the northern Pantanal, a Ramsar site renowned for its wildlife, between 2017 and 2020. Ground Range Detected (GRD) VV-polarized scenes were preprocessed using radiometric terrain normalization and speckle filtering (Lee filter, 5 × 5 window) to improve the separability of water and non-water surfaces. Flooded areas were initially extracted with Otsu’s histogram thresholding and validated using high-resolution optical imagery (PlanetScope and Landsat-8). A supervised Random Forest classifier then refined land-cover discrimination into three classes (open water/flood, open land/vegetation, and others), achieving an overall accuracy of 97.70% on the independent testing dataset (n = 6622), while temporal consistency was supported by Cuiabá River hydrological data. The results revealed strong interannual variability in flood extent, with inundation covering 34.7% of the reserve in March 2017 compared with 0.75% in March 2020 and reaching a peak of 79.9% in April 2017. Overall, Sentinel-1 SAR effectively delineated open water and flood-affected surfaces under persistent cloud cover, demonstrating its value for complementing existing products such as MapBiomas, strengthening wetland management, and supporting scalable flood monitoring in other tropical flood-prone Ramsar sites. Full article

This work presents a portable polarimetric multispectral imaging (PMSI) system operating in the visible to shortwave infrared range (VNIR–SWIR: 400–1700 nm) and its application to target detection, discrimination from aquatic backgrounds, and polymer identification. The instrument integrates two synchronized cameras with motorized bandpass filters and piezoelectric polarization control, enabling the acquisition of 48 wavelength–polarization measurements per capture. This configuration allows the extraction of both intensity-based and polarimetric features, including the degree of linear polarization (DoLP). A complete radiometric and polarimetric calibration framework is implemented, encompassing system response characterization, polarization-dependent gain correction, and reflectance normalization under variable illumination. Experiments conducted on a representative set of 16 polymer materials show that polarimetric information consistently improves class separability compared to intensity-only features, with a mean gain of 6.9 (95% CI: 6.35–8.47). Although the correlation between intensity- and DoLP-based separability is moderate (r = 0.44), the results indicate complementary identification capability. Material recoverability was further evaluated using spectral unmixing techniques (VCA, N-FINDR, and PPI), with VCA offering the best accuracy–complexity trade-off on the calibrated Stokes reflectance dataset. Despite these gains, identification among chemically similar polyethylene variants remains challenging due to limited spectral and polarimetric contrast. An underwater detectability study under natural illumination reveals strong wavelength-dependent constraints: SWIR penetration is limited to 4 cm, whereas VNIR bands (430–550 nm) preserve detectability up to 20 cm, with DoLP enhancing edge visibility. These results motivate future validation in more complex aquatic conditions and with increased spectral dimensionality. Full article

The successful detection of X-ray polarization from many celestial sources belonging to different classes by the IXPE mission has opened a new window in X-ray astronomy. While an impressive number of scientific topics have already been addressed by IXPE, many of them would benefit from a new class of instrumentation that could be launched on a relatively short time scale. In this contribution, we present the development activities of a focal-plane polarimeter whose goal is to extend the energy range of IXPE up to tens of keV, with better sensitivity and lower background. Our design is based on the use of multilayer mirrors and stacked instrumentation, comprising either a low- or medium-energy imaging photoelectric polarimeter and an active Compton polarimeter. Such an approach relies on hardware with flight heritage and—although still under development for the specific application in X-ray polarimetry—it has the potential to answer compelling scientific questions and to soon become competitive from the point of view of feasibility for space applications. Full article

Background/Objectives: Epitope-based mRNA vaccines represent a promising strategy for eliciting protective T-cell immunity against SARS-CoV-2 and as well as for non-infectious mRNA-based vaccines. However, how the structural architecture of vaccine constructs (including epitope arrangement, linker composition, signal peptide presence, and the combination of MHC class I and II epitopes) shapes the quality of T-cell responses remains poorly understood. Methods: Ten tandem minigene mRNA constructs (Cons1–10) encoding different combinations of MHC class I and class II epitopes from SARS-CoV-2 proteins (S, N, M, ORF3a) were designed, encapsulated in lipid nanoparticles, and administered to C57BL/6 mice. Immunogenicity was assessed by cytokine profiling (IFN-γ, IL-2, IL-4, IL-10) and T-cell proliferation assays. Protective efficacy was evaluated in K18-hACE2 transgenic mice challenged with SARS-CoV-2. Results: Constructs lacking a signal peptide and enriched in MHC class I-restricted epitopes induced robust Th1 responses and strong CD8⁺ T-cell proliferation, achieving up to 66% survival following lethal challenge. In contrast, constructs associated with elevated IL-10 and IL-4 production conferred limited protection (11–33%), consistent with functional skewing towards regulatory or Th2-associated immune profiles. Conclusions: These findings establish a direct link between construct design parameters and T-cell polarization quality, and provide a rational framework for next-generation epitope-based mRNA vaccine development. Full article

Circularly polarized (CP) organic light-emitting diodes (CP-OLEDs) have attracted considerable attention due to their promising applications in next-generation display systems, optical data transmission, and quantum computing, and their potential roles in medical devices. Achieving efficient and tunable CP emission remains a significant challenge, prompting the development of various strategies that leverage organic semiconductors. Notably, certain classes of materials now consistently deliver CP polarization at levels suitable for technological applications. Among these, conjugated polymers, particularly the copolymer poly(9,9-dialkylfluorene-alt-benzothiadiazole) (PFBT), stand out for their exceptional optoelectronic properties, ease of processing, and adaptability to produce CP emission. PFBT has played diverse roles within CP-OLED devices, enabling innovative architectural solutions. This review explores principal strategies for integrating PFBT into CP-OLED architectures, drawing upon findings from the recent scientific literature. By consolidating current knowledge and identifying unresolved issues, this work aims to inspire further research into the development of solution-processable, high-performance and tunable CP-OLEDs based on PFBT and conjugated polymers in general. Full article