Search Results (3,246)

Adulteration of high-value oils such as olive and camellia oil poses serious challenges to market integrity and consumer safety. This study develops a comprehensive, model-free marker library for high-throughput detection of single and multivariate adulteration across nine vegetable oils (olive, camellia, sesame, rapeseed, flaxseed, soybean, peanut, industrial hemp seed, and sunflower seed oils) using untargeted metabolomics via UHPLC-Q-TOF-MS. We identified 34 characteristic markers, including 9 confirmed by reference standards, such as hydroxytyrosol in olive oil, camelliasaponins in camellia oil, and sesamin in sesame oil, which are uniquely present in specific oils and absent in others. The method enables reliable qualitative screening of adulteration at levels as low as 5% without dependence on chemometric models. Validation using binary and multicomponent blends confirmed its robustness and specificity. In commercial sample analysis, adulteration was detected in 16.0% of olive oils (4/25) and 12.7% of camellia oils (7/55), with results consistent with regulatory findings. This work establishes the first integrated marker library for simultaneous screening of nine vegetable oils, offering a standardized, high-throughput tool for large-scale market surveillance that bridges the gap between discovery-based omics and routine regulatory practice. Full article

Texture image retrieval based on subjective visual descriptions remains a significant challenge due to the “semantic gap”, where conventional Content-Based Image Retrieval (CBIR) methods rely on low-level features or reference images that often diverge from human perception. To bridge this gap, this paper proposes a reference-free, perception-driven retrieval framework that enables users to query textures directly via abstract perceptual attributes. First, we constructed a human-centric perceptual feature space through controlled psychophysical experiments, quantifying 12 explicit texture attributes (e.g., granularity, directionality) using a 9-point Likert scale. Second, addressing the variability in visual sensitivity across user demographics, we developed a user-adaptive mechanism incorporating dual perceptual libraries tailored for art-major and non-art-major groups. Retrieval is formulated as a perception-aligned similarity optimization problem within this normalized space. Experimental evaluations on the Describable Textures Dataset (DTD) demonstrate that our method achieves superior perceptual consistency compared to both handcrafted descriptors (GLCM, LBP, HOG) and deep learning baselines (VGG16, ResNet50). Notably, the framework attained high PAP@3 performance across both user groups, validating its effectiveness in decoding fuzzy human intent without the need for query images. This work provides a robust solution for semantic-based texture retrieval in human–computer interaction scenarios. Full article

Oxidative stress and excitotoxicity are key contributors to neuronal damage in various neurodegenerative diseases. Caffeine, a widely used neuroactive compound with moderate antioxidant properties, may benefit from structural modifications to enhance its neuroprotective potential. In this study, a series of novel caffeine derivatives was synthesized and evaluated for antioxidant and potential neuroprotective relevance using in vitro models of oxidative stress and glutamate-induced excitotoxicity in SH-SY5Y human neuroblastoma cells. Antioxidant capacity was assessed using ABTS•⁺ radical cation decolorization and DPPH radical scavenging assays. Most derivatives exhibited strong free radical scavenging activity, surpassing both caffeine and the reference antioxidant Trolox at low concentrations (5 µM). Notably, compounds AL-7, AL-8, AL-9, and AL-10 demonstrated particularly high activity. Cytotoxicity evaluation using the MTT assay revealed low toxicity for all compounds, with calculated IC₅₀ values above 500 µM. Intracellular reactive oxygen species (ROS) levels measured by the DCFH-DA assay showed that several derivatives, especially AL-4, significantly reduced H₂O₂-induced oxidative stress. In neuroprotection assays, compounds AL-0, AL-1, and AL-4 markedly protected against hydrogen peroxide-induced damage, restoring cell viability up to 73%, while AL-7 achieved up to 85% protection against L-glutamate-induced excitotoxicity, outperforming caffeine. In silico SwissADME analysis indicated favorable oral bioavailability, with predicted gastrointestinal absorption and limited blood–brain barrier permeability. Overall, these findings highlight structurally modified caffeine derivatives as promising antioxidant and neuroprotective agents warranting further mechanistic and therapeutic investigation. Full article

Optical Wireless Power Transmission (OWPT) holds a significant position for enabling cable-free energy delivery in long-distance, high-energy, and mobile scenarios. However, ensuring human and equipment safety under high-power laser exposure remains a critical challenge. This study reports a vision-based OWPT safety system that implements the principle of automatic emission control (AEC)—dynamically modulating laser emission in real time to prevent hazardous exposure. While camera-based OWPT safety systems have been proposed in the concept, there are extremely limited working implementations to date. Moreover, existing systems struggle with response speed and single-object assumptions. To address these gaps, this research presents a low-latency safety architecture based on a customized deep learning-based object detection framework, a dedicated OWPT dataset, and a multi-threaded control stack. The research also introduces a real-time risk factor (RF) metric that evaluates proximity and velocity for each detected intrusion object (IO), enabling dynamic prioritization among multiple threats. The system achieves a minimum response latency of 14 ms (average 29 ms) and maintains reliable performance in complex multi-object scenarios. This work establishes a new benchmark for OWPT safety system and contributes a scalable reference for future development. Full article

This paper investigates the different relaxation channels of a single symmetric top NH₃ and a spherical top CH₄ molecule trapped at low temperature in a clathrate hydrate nano-cage in the infrared absorption domain of their vibrational degrees of freedom. The approach utilizes the Born–Oppenheimer approximation and the extended site inclusion model applied to CO₂ in a previous work, which was based on pairwise atom–atom effective interaction potentials. The calculations show that trapping the methane or ammonia molecule is energetically more favorable in a type sI clathrate structure than in an sII one, and entropic considerations show that methane can be released much more easily than ammonia from clathrate hydrate nano-cages. In the small (s) and large (l) nano-cages with the sI structure, the CH₄ molecule exhibits a more or less perturbed rotational motion, while the NH₃ molecule shows a strongly hindered orientational motion that tends to a three-dimension librational motion (oscillation motion) around its orientational equilibrium configuration. The calculated orientational energy level schemes are quite different from those of the molecular free rotation. In the static field inside the cage, degenerate ${\nu }_3$ and ${\nu }_4$ vibrational modes of methane and ammonia molecules are shifted and split. Moreover, for ammonia molecules, the ${\nu }_1$ and ${\nu }_2$ modes are shifted, and the inversion motion is no longer allowed. The non-radiative and radiative relaxation channels of CH₄, NH₃ and CO₂ in clathrate nano-cages are discussed with reference to the matrix isolation spectroscopic results. Upon laser excitation, then, from the energy levels calculated for the different degrees of freedom, NH₃ and CO₂ are expected to fluoresce, while for CH₄, non-radiative relaxation should lead to evaporation at the surface of clathrates. Experimental setups are suggested to localize and study these species underneath ice surfaces on distant planets or planetesimals from mobile detectors such as drones or CubeSats equipped with appropriate laser sources and telescopes with 2D imaging detectors. Full article

Shiqian Taicha (Camellia sinensis) is a local tea cultivar originating from Shiqian County and Guizhou (China) that is suitable for both green and black tea. The year-on-year manufacturing conditions, which affect chemical quality, were elucidated through the analysis of 78 green tea and 38 black tea commercial batches manufactured in 2021–2023. The batches were manufactured by the same process, but these naturally varied in raw-leaf status and factory parameters. The moisture content, water-soluble extract, free amino acids, tea polyphenols, caffeine, gallic acid, total ash, total catechins and individual catechins were predicted using a calibrated near-infrared (NIR) spectroscopy model and membership function evaluation, which integrated multiple indices to produce an overall quality score for each year and tea type. The amino acids of green tea peaked in the year 2022, (with 4.55%) whereas the polyphenols (which refers to carbon-based molecules) was in the year 2021, (with 24.22%), and the total catechins was in the year 2021, (with 16.71%); due to these observations, the ratio of phenol-to-amino was high in the year 2021, with (10.09); while the year 2022 had a lower ratio with (3.41). Although there were fewer differences from region to region with black tea, 2022 was better in terms of moisture control, amino acids retention and composite score with a value of 0.585. The assessment of the membership function indicated that 2022 was the most ideal tea production year for green tea (0.506) as well as black tea (0.477), with 2021 tea (0.486) and 2023 tea (0.488) following next based on type. The data presents quantitatively stable fixation and moisture/fermentation management targets to improve Shiqian Taicha value and consistency. Full article

Pseudomonas aeruginosa is a Gram-negative pathogen with a remarkable capacity to acquire multiple resistance mechanisms, severely limiting current therapeutic options. Consequently, the identification of new antimicrobial agents remains a critical priority. In this study, an integrated in silico-guided strategy was applied to identify small molecules with antibacterial potential against P. aeruginosa, targeting the quorum-sensing regulator LasR (PDB ID: 2UV0) and elastase (PDB ID: 1U4G). Pharmacophore modeling was performed for both targets, followed by ligand-based virtual screening, structure-based virtual screening (SBVS), and MM-GBSA (Molecular Mechanics-Generalized Born Surface Area) binding free energy calculations. Top-ranked compounds based on predicted binding affinity were selected for in vitro cytotoxicity and antibacterial evaluation. Antimicrobial activity was assessed against three P. aeruginosa strains: an American Type Culture Collection (ATCC) reference strain, a clinically susceptible isolate, and an extensively drug-resistant (XDR) clinical isolate. SBVS yielded docking scores ranging from −6.96 to −12.256 kcal/mol, with MM-GBSA binding free energies between −18.554 and −88.00 kcal/mol. Minimum inhibitory concentration (MIC) assays revealed that MolPort-001-974-907, MolPort-002-099-073, MolPort-008-336-135, and MolPort-008-339-179 exhibited MIC values of 62.5 µg/mL against the ATCC strain, indicating weak-to-moderate antibacterial activity consistent with early-stage hit compounds. MolPort-008-336-135 showed the most favorable activity against the clinically susceptible isolate, with an MIC of 62.5 µg/mL, while maintaining HepG2 cell viability above 70% at this concentration and an half-maximal inhibitory concentration (IC₅₀) greater than 500 µg/mL. In contrast, all tested compounds displayed MIC values above 62.5 µg/mL against the XDR isolate, reflecting limited efficacy against highly resistant strains. Overall, these results demonstrate the utility of in silico-driven approaches for the identification of antibacterial hit compounds targeting LasR and elastase, while highlighting the need for structure–activity relationship optimization to improve potency, selectivity, and activity against multidrug-resistant P. aeruginosa. Full article

Respiratory infections cause substantial morbidity and mortality in older adults and other at-risk adult populations. Despite the availability of effective vaccines, adult vaccination coverage remains suboptimal. This narrative review examines strategies designed to improve vaccine uptake among non-pregnant adults aged ≥18 years and inform future adult vaccination strategies. We conducted a targeted literature search using keywords for vaccination, respiratory diseases, strategy/program/implementation, and adults in PubMed database and CDC, WHO, and ECDC websites, between 2014 and 2024. A snowball search of literature reviews and key references was also performed to identify additional relevant studies. Eligible publications focused on vaccination strategies against influenza, COVID-19, and pneumococcal disease targeting non-pregnant adults (≥18 years). We categorized the strategies by intervention type to describe their influence on vaccination campaigns and vaccine uptake/coverage. We included 45 publications, encompassing strategies focused on individual decision-making, healthcare system functions, and national policy. Educational and awareness interventions (such as healthcare worker/provider recommendations during consultation, phone calls, letters, text messages, and social media outreach) reportedly raised vaccination rates. Access-related factors, including convenient vaccination sites and free or subsidized vaccines, were reported to be important factors in improving coverage in underserved communities. Within healthcare settings, strategies such as continuous vaccine provider training and workflow/process optimization were shown to enhance vaccination delivery. At the local or national policy levels, legislation governing program targets shaped immunization efforts and facilitated collaborations and partnerships to expand campaign reach. The findings may inform policymakers and public health/immunization practitioners in designing context-specific immunization initiatives that effectively reach adult populations. Full article

The rise of SIM cloning, identity spoofing, and covert manipulation in mobile and IoT networks has created an urgent need for continuous post-registration verification. This work introduces an unsupervised deep learning framework for detecting behavioral anomalies in SIM-tagged network flows by modeling the intrinsic structure of benign behavioral descriptors (TTL, timing drift, payload statistics). A Temporal Deep Autoencoder (TDAE) combining Conv1D layers and an LSTM encoder is trained exclusively on normal traffic and used to identify deviations through reconstruction error, enabling one-class (label-free) training. For deployment, alarms are set using an unsupervised quantile threshold ${\tau }_{\alpha }$ calibrated on benign traffic with a false-alarm budget; ${\tau }^{*}$ is reported only as a diagnostic reference for model comparison. To ensure realism, a large-scale corpus of 3.6 million SIM-tagged flows was constructed by enriching public IoT traffic with pseudo-operator identifiers (synthetic SIM tags derived from device identifiers) and controlled anomaly injections. Cross-domain experiment transfer under SIM-grouped protocol: Training on clean Cassavia-like traffic and testing on attack-rich Guarascio-like flows yields a PR-AUC of 0.93 for the proposed Conv-LSTM Temporal Deep Autoencoder, outperforming Dense Autoencoder, Isolation Forest, One-Class SVM, and LOF baselines. Conversely, the reverse direction collapses to PR-AUC $\approx 0.5$, confirming the absence of data leakage and the validity of one-class behavioral learning. Sensitivity analysis shows that performance is stable around the unsupervised quantile operating point. Overall, the proposed framework provides a lightweight, interpretable, and data-efficient behavioral verification layer for detecting cloned or unauthorized SIM activity, complementing existing registration mechanisms in next-generation telecom and IoT ecosystems. Full article

Pseudomonas aeruginosa elastase LasB accelerates refrigerated food spoilage through proteolytic degradation of muscle and milk proteins. While Marrubium vulgare essential oil terpenes exhibit antimicrobial activity, their weak potency and nonspecificity limit direct food preservation applications. This computational study aimed to rationally redesign terpene scaffolds into predicted selective LasB inhibitors. A virtual library of 635 terpene–peptide–phosphinic acid hybrids (expanded to 3940 conformers) was evaluated using consensus molecular docking (Glide/Flare) against LasB (PDB: 3DBK) and three human off-target proteases. Top candidates underwent duplicate 150 ns molecular dynamics simulations with MM/GBSA binding free-energy calculations. Computational screening identified thymol–Leu–Trp–phosphinic acid as the lead candidate with predicted binding affinity of −12.12 kcal/mol, comparable to reference inhibitor phosphoramidon (−11.87 kcal/mol), and predicted selectivity index of +0.12 kcal/mol representing a 2.3 kcal/mol advantage over human proteases. Molecular dynamics simulations indicated exceptional stability (98.7% stable frames, 0.12 Å inter-replica RMSD) with consistent zinc coordination. Structure–activity analysis revealed phosphinic zinc-binding groups (+1.57 kcal/mol), Leu–Trp linkers (+2.47 kcal/mol), and phenolic scaffolds (+1.35 kcal/mol) as predicted optimal structural features. This in silico study provides a computational framework and prioritized candidate set for developing natural product-derived food preservatives. All findings represent computational predictions requiring experimental validation through enzymatic assays, food model studies, and toxicological evaluation. Full article

This study evaluates the swelling potential in clayey soils of the Paleogene Brebi, Mera, and Moigrad formations in the Transylvanian Basin (Romania) by integrating direct free-swelling tests (FS; STAS 1913/12-88) with indirect index-property diagrams and semi-quantitative X-ray diffraction (XRD; RIR method). The indirect analysis combines three swelling-susceptibility classification charts—Seed et al. (AI–clay), Van der Merwe (PI–clay), and Dakshanamurthy and Raman (LL–PI)—with mineralogical trends from the Casagrande plasticity chart, complemented by Holtz and Kovacs’s clay-mineral reference fields and Skempton’s activity concept (AI = PI/% < 2 µm). The geotechnical dataset comprises 88 Brebi, 46 Mera, and 263 Moigrad specimens (with parameter counts varying by test), an XRD was performed on a representative subset. The free swell (FS) results indicate that Brebi soils range from low to active behavior (50–135%) without reaching the very active class; most Brebi specimens fall in the medium-activity range. Moigrad spans the full FS spectrum (20–190%) but is predominantly in the medium-to-active range. In contrast, Mera soils exhibit predominantly active behavior, covering the full range of activity classes (30–170%). The empirical classification charts diverge systematically: clay-sensitive schemes tend to assign higher swell susceptibility than the LL–PI approach, especially in carbonate-influenced soils. XRD results corroborate these patterns: Brebi is calcite-rich (mean ≈ 53.5 wt% CaCO₃) with minor expandable minerals (mean ≈ 3.1 wt%); Mera is feldspathic (orthoclase mean ≈ 55.3 wt%) with variable expandable phases; and Moigrad has a higher clay-mineral content (mean ≈ 38.8 wt%). Overall, swelling is controlled by the combined effects of clay-fraction reactivity, clay volume continuity, and carbonate-related microstructural constraints. Full article

Background/Objectives: Energy availability (EA) is associated with Relative Energy Deficiency in Sport syndrome. This study assessed the EA, body composition, and phase angle (φ) of adolescent artistic gymnasts during a competitive season. Methods: Thirty non-elite artistic gymnasts aged 11–14 years participated in this cross-sectional study. Anthropometric data were collected and body mass index (BMI) was assessed using the World Health Organization growth charts. Bioelectrical impedance analysis was performed and diet and physical activity were recorded for three days. Dietary and physical activity records were analyzed to estimate energy intake, total energy expenditure (TEE), and exercise energy expenditure, from which energy balance (EB) and EA were calculated. The 95% confidence ellipses of the impedance (Z) vectors were compared with a reference population using the two-sample Hotelling’s T² test. Correlations between variables were examined by Pearson’s or Spearman’s correlation analysis. Statistical significance was set at α = 0.05. Results: All participants were classified within the normal BMI category, except for one who was classified as being overweight. Mean (± SD) fat mass, fat-free mass (FFM), and φ were 16.1 ± 3.4%, 83.9 ± 3.4%, and 6.0 ± 0.6°, respectively. The 95% confidence ellipses of Z vectors differed significantly from the reference population. Energy balance was 32 ± 223 kcal/day and EA was 49.2 ± 11.4 kcal/kg FFM/day. Energy availability was significantly correlated with EB, TEE, and body composition variables. Conclusions: Adolescent non-elite artistic gymnasts showed no clear indications of LEA and exhibited a normal body composition and φ during the competitive season, consistent with their EA. Full article

The monitoring of perennial and annual crops requires different analytical approaches due to their contrasting phenological dynamics and management practices. This study investigates the temporal behavior of the Normalized Difference Vegetation Index (NDVI) derived from Harmonized Landsat and Sentinel-2 (HLS) imagery to characterize apple, grape, soybean, and maize crops in Vacaria, Southern Brazil, between January 2024 and April 2025. NDVI time series were extracted from cloud-free HLS observations and analyzed using raw, interpolated, and Savitzky–Golay, smoothed data, supported by field reference points collected with the AgroTag application. Distinct NDVI temporal patterns were observed, with apple and grape showing higher stability and soybean and maize exhibiting stronger seasonal variability. Descriptive statistics derived from 112 observation dates confirmed these differences, highlighting the ability of HLS-based NDVI time series to capture crop-specific phenological patterns at the municipal scale. Complementary analysis using the SATVeg platform demonstrated consistency in long-term vegetation trends while evidencing scale limitations of coarse-resolution data for small perennial plots. Overall, the findings demonstrate that the NDVI enables robust monitoring of mixed agricultural landscapes, with complementary spatial resolutions and analytical tools enhancing crop-specific phenological analysis. Full article

The conformation of a glycosaminoglycan (GAG) carbohydrate biopolymer is dependent upon the ring puckering states of its constituent monosaccharide residues and the dihedral angles (φ, ψ) of the glycosidic linkages connecting these residues. In the context of GAGs, the monosaccharide residue iduronate (IdoA; the conjugate base of iduronic acid) is able to take on both chair and boat-like ring pucker states. All-atom explicit-solvent molecular dynamics simulations were applied to determine the extent to which IdoA ring pucker state affects the conformational preferences of (φ, ψ) in 16 different IdoA-containing disaccharides derived from the GAGs heparin/heparan sulfate and dermatan sulfate. Using the extended-system adaptive biasing force (eABF) method, the complete free-energy surface ΔG(φ, ψ) was computed for each disaccharide with its IdoA ring restrained separately to the ¹C₄, ²S_O, B_3,O, or ⁴C₁ ring pucker state. Global-minimum ΔG(φ, ψ) values resided within broad ΔG(φ, ψ) basins, and both ring pucker state and sulfation status influenced basin shape and size. Various sulfoforms of the disaccharide IdoAα1–4GlcNS had prominent secondary-minimum basins distinct from the global-minimum basins, and these secondary-minimum basins may manifest as metastable states in standard (nonbiased) molecular dynamics simulations on the 1-microsecond timescale. As such, the present results provide a reference for assessing (φ, ψ) sampling in nonbiased molecular dynamics simulations of GAGs and demonstrate the interplay between IdoA ring puckering, glycosidic linkage dihedral rotation, and sulfation status in contributing to GAG conformational preferences. Full article

Background: Age-related macular degeneration (AMD) is a major cause of vision loss, yet publicly available Optical Coherence Tomography (OCT) datasets lack demographic diversity, particularly from South Asian populations. Existing datasets largely represent Western cohorts, limiting AI generalizability. Moreover, raw OCT volumes contain redundant spatial information and speckle noise, hindering efficient analysis. Methods: We introduce BanglaOCT2025, a retrospective dataset collected from the National Institute of Ophthalmology and Hospital (NIOH), Bangladesh, using Nidek RS-330 Duo 2 and RS-3000 Advance systems. We propose a novel preprocessing pipeline comprising two stages: (1) A constraint-based centroid minimization algorithm automatically localizes the foveal center and extracts a fixed 33-slice macular sub-volume, robust to retinal tilt and acquisition variability; and (2) A self-supervised volumetric denoising module based on a Flip-Flop Swin Transformer (FFSwin) backbone suppresses speckle noise without requiring paired clean reference data. Results: The dataset comprises 1585 OCT volumes (202,880 B-scans), including 857 expert-annotated cases (54 DryAMD, 61 WetAMD, and 742 NonAMD). Denoising quality was evaluated using reference-free volumetric metrics, paired statistical analysis, and blinded clinical review by a retinal specialist, confirming preservation of pathological biomarkers and absence of hallucination. Under a controlled paired evaluation using the same classifier with frozen weights, downstream AMD classification accuracy improved from 69.08% to 99.88%, interpreted as an upper-bound estimate of diagnostic signal recoverability rather than independent generalization. Conclusions: BanglaOCT2025 is the first clinically validated OCT dataset representing the Bengali population and establishes a reproducible fovea-centric volumetric preprocessing and restoration framework for AMD analysis, with future validation across independent and multi-centre test cohorts. Full article