Search Results (264)

The aim of the present study was to characterize the chemical and quality traits of retail chicken meat in Spain. A total of 39 breast (Pectoralis major) samples were collected from large stores across three seasons in 2024 (13 samples per season). All samples were consistently sourced from the same 13 suppliers, that collectively account for more than 70% of Spain’s broiler production. Based on retail label claims, samples were grouped as either ‘non-certified’ (no claims; 7 samples per season) or ‘certified’ (certified claims regarding distinctive dietary and slaughter age practices; 6 samples per season). Proximate composition, quality traits (pH, color, water-holding capacity, texture, oxidative stability), and the profiles of fatty acids (FAs) and volatile organic compounds (VOCs) were analyzed. Meat from the certified group had a higher protein content (22.37% vs. 20.62%; p < 0.01) and lower thawing (3.22% vs. 6.59%; p < 0.001) and cooking losses (14.09% vs. 24.64%; p < 0.01). Certified meat was also darker (lower L*: 48.48 vs. 52.59; p < 0.05) and exhibited a more intense yellow color (higher b*: 18.66 vs. 4.22, hue angle: 87.63 vs. 66.59, and chroma: 18.71 vs. 4.62; all p < 0.001). The intramuscular fat of certified meat contained less monounsaturated FAs (34.72% vs. 40.32%; p < 0.001) and more polyunsaturated FAs (28.82% vs. 23.55%; p < 0.001). Eight of the thirteen nutritional indices derived from the FAs profile were more favorable in the certified group. A total of 171 VOCs were identified, with sulfur compounds being more abundant in certified meat (0.94% vs. 0.67%; p < 0.05). In conclusion, retail chicken meat grouped according to commercial labeling possesses a distinct chemical and quality profile. Full article

Osteogenesis imperfecta (OI) and Ehlers–Danlos syndrome (EDS) are inherited connective tissue disorders caused by diverse genetic defects, many of which affect collagen biosynthesis. However, the identified genetic variants do not always fully explain the clinical heterogeneity observed in patients, highlighting the need for advanced models and imaging techniques to assess collagen structure and fibroblast behavior at the microscopic level. In this study, we employed 5-week three-dimensional (3D) dermal fibroblast cultures derived from patients with haploinsufficient (HI) and dominant-negative (DN) OI, EDS, and healthy controls. Using label-free higher harmonic generation microscopy (HHGM), we visualized and quantified secreted collagen fibers and fibroblast morphology in situ. We analyzed fibroblast 3D orientation, collagen fiber diameter, collagen amount per cell, and the spatial alignment between fibroblasts and collagen fibers. HI OI fibroblasts secreted significantly less collagen than both control and EDS-derived cells, while EDS samples exhibited thinner collagen fibers compared to controls. Across all groups, collagen fiber orientation was strongly correlated with fibroblast alignment, in line with the role of fibroblasts in matrix organization. In healthy controls and HI OI samples, we observed a depth-dependent, counterclockwise rotation in fibroblast orientation from the culture bottom to the surface—a pattern that was less prominent in DN OI and EDS samples, potentially reflecting altered matrix guidance in diseased tissues. Overall, the quantity and quality of collagen, as well as fibroblast morphology and organization, were markedly altered in the OI and EDS model systems. These alterations may mirror tissue-level manifestations of the diseases, demonstrating the physiological relevance of patient-derived 3D fibroblast models for OI and EDS, as well as the power of harmonic generation microscopy in probing the cellular and extracellular consequences of disease-related gene defects in collagen or its biosynthetic pathways. Extensions of this methodological approach provide a way towards deeper understanding of tissue-level manifestations of collagen dysregulation in connective tissue disorders. Full article

Caregiver communicative behaviors are critical in supporting social and language development in children with autism, yet little is known about how these behaviors manifest among Chinese immigrant families who face unique cultural and socioeconomic challenges. This study examined the communicative strategies of 11 Chinese immigrant caregivers of preschool-aged children with autism in the US during structured caregiver–child interactions. Caregiver behaviors were coded across directive and non-directive categories, including supportive directives, directives, labeling, praise, imitation, and expansion, and joint engagement quality was rated using the Joint Engagement Rating Inventory (JERI). Results showed that supportive directives and directives were the most frequent behaviors, reflecting cultural values of parental guidance and educational scaffolding, whereas non-directive strategies such as imitation and expansion were less common and more often observed among higher-income and more acculturated families. Caregiver self-efficacy in using evidence-based strategies was positively associated with greater use of non-directive communicative strategies and higher joint engagement scores. Results suggest that providers should recognize and build on culturally grounded strengths, such as the educator role and calm authority, while introducing complementary strategies that enhance joint engagement. Culturally and linguistically responsive support is especially needed to ensure equitable access for families with limited English proficiency or lower income. Although limited by a small sample size, this exploratory study provides preliminary insight into culturally influenced caregiver–child communication patterns and offers directions for larger, more rigorous research. Full article

Fluorescence Anisotropy (FA) is a sensitive and efficient technique for quantifying biomolecular interactions, offering advantages such as minimal sample requirements and elimination of separation of bound from unbound species. Thus, it is well suited for aptamer–protein binding affinity studies. However, accurately determining equilibrium dissociation constants (K_D) in FA requires low concentrations of fluorescently labeled aptamers to prevent ligand depletion. A significant challenge arises at low aptamer concentrations due to an unexpected and physically nonmeaningful increase in apparent anisotropy, which impairs accurate data fitting. This anomalous increase in apparent anisotropy may arise from non-specific adsorption of aptamers to surfaces. In this study, we investigated the use of non-ionic surfactants to mitigate these effects and stabilize the anisotropy signal at low aptamer concentrations using the thrombin aptamer as a model system. We evaluated the impact of varying concentrations of two surfactants (Tween 20 and Triton X-100) on plots of anisotropy as a function of aptamer concentration and determined aptamer–protein binding affinities. Addition of 0.1% Tween 20 corrects the anomalous increase in anisotropy at low aptamer concentrations, enabling the use of aptamer concentrations as low as 5 nM in binding assays. Triton X-100 was less effective. By incorporating optimized concentrations of Tween 20, we demonstrated improved assay reproducibility and accuracy in K_D determination, expanding the dynamic range of usable aptamer concentrations in FA-based binding affinity studies. Similar benefits were observed with the clinically relevant aptamer s10yh2 and human serum albumin. These findings provide a practical strategy for enhancing the robustness of FA measurements and may be applicable to other aptamer–target systems and high-throughput assay formats. Full article

Short-term trading presents a high-dimensional prediction problem, where the profitability of trading signals depends not only on model accuracy but also on how financial labels are defined and aligned with market dynamics. Traditional approaches often apply uniform modeling choices across assets, overlooking the asset-specific nature of volatility, liquidity, and market response. In this work, we introduce a structured, label-aware machine learning pipeline aimed at maximizing short-term trading profitability across four major benchmarks: S&P 500 (SPX), NASDAQ-100 (NDX), Dow Jones Industrial Average (DJI), and the Tadāwul All-Share Index (TASI and twelve of their most actively traded constituents). Our solution systematically evaluates all combinations of six model types (logistic regression, support vector machines, random forest, XGBoost, 1-D CNN, and LSTM), eight look-ahead labeling windows (3 to 10 days), and four feature subset sizes (44, 26, 17, 8 variables) derived through Random Forest permutation-importance ranking. Backtests are conducted using realistic long/flat simulations with zero commission, optimizing for Percentage Profit and Profit Factor on a 2005–2021 train/2022–2024 test split. The central contribution of the framework is a labeling-aware search mechanism that assigns to each asset its optimal combination of model type, look-ahead horizon, and feature subset based on out-of-sample profitability. Empirical results show that while XGBoost performs best on average, CNN and LSTM achieve standout gains on highly volatile tech stocks. The optimal look-ahead window varies by market from 3-day signals on liquid U.S. shares to 6–10-day signals on the less-liquid TASI universe. This joint model–label–feature optimization avoids one-size-fits-all assumptions and yields transferable configurations that cut grid-search cost when deploying from index level to constituent stocks, improving data efficiency, enhancing robustness, and supporting more adaptive portfolio construction in short-horizon trading strategies. Full article

Neutrophil gelatinase-associated lipocalin (NGAL) has emerged as a critical biomarker for the early diagnosis of acute kidney injury (AKI). The development of novel detection platforms that combine rapid analysis with high sensitivity is essential for improving clinical outcomes. In this study, we established an antibody-based detection system for NGAL using biolayer interferometry (BLI), a label-free optical biosensing technique that monitors real-time interference patterns generated by white light reflected from biomolecular binding events on a biosensor surface. A panel of six anti-NGAL monoclonal antibodies was generated and characterized for its binding properties, identifying candidates with high specificity for NGAL. For robust sensor functionalization, selected monoclonal antibodies were biotinylated and immobilized onto streptavidin-coated biosensor tips, establishing a stable and efficient detection interface. The optimized BLI platform demonstrated a limit of detection (LOD) of 46.1 ng/mL with wild dynamic range of 19 to 40,000 ng/mL. The platform’s accuracy was validated using human serum samples, with spike-and-recovery experiments yielding recovery rates of 96.6–104.6%. This demonstrates the capability to accurately quantify NGAL under physiologically relevant conditions with minimal matrix interference. Furthermore, the real-time kinetic measurements enabled rapid analysis, with the entire assay completed in less than half an hour. These findings establish a proof-of-concept for a BLI-based biosensor for NGAL detection, demonstrating sensitivity and specificity that show potential for clinical applications. Full article

Multiferroic composites of xNi_0.8Zn_0.2Fe₂O₄/(1 − x)BaTiO₃ (x = 0, 0.1, 0.3, 0.5, labeled NZFO/BTO) with ~100 nm particle size were synthesized via high-energy ball milling and thermal annealing. The X-ray diffraction shows a co-existence of the ferromagnetic phase of NZFO and the ferroelectric phase of BTO. Our observations indicate that saturation, remanence, and coercivity progressively increase with increasing NFO content, specifically from x = 0 to x = 0.5. At x = 0.1, the maximum electric polarization, remanent electric polarization, coercivity and electric power loss density reach their maximum values of ~0.057 µC/cm², 0.018 µC/cm², 3.25 kV/cm and 0.222 mJ/cm³, respectively, for an applied electric field less than 10 kV/cm. These multiferroic composites demonstrate excellent electromagnetic wave absorption capabilities from 2 to 18 GHz. With BTNF1 (x = 0.1) sample thickness of 2.5–3.5 mm, a minimum reflection loss of −41.51, −37, −28.72 dB corresponds to frequencies of 12.52 GHz, 11 GHz and 9.32 GHz. The effective absorption bandwidth for this sample is 11.5–16 GHz, indicating optimal impedance and attenuation matching and effective absorption of electromagnetic waves throughout the K_u bands. These outcomes reveal the capability for wideband absorption uses in radar invisibility technology and electromagnetic insulation. Full article

Antibiotic stereoisomers as components of medicines are typically characterized by different biological activities. Because pharmaceuticals can include a racemic mixture of stereoisomers, monitoring of all forms is required. One contaminant of food products, antibiotic ofloxacin (OFL), as a chiral compound, has two enantiomers—the biologically active S-isomer and less active R-isomer. In this study, a sensitive immunochromatographic test system for simultaneous enantiospeсific detection of the two OFL isomers was developed for the first time. For this, polyclonal antibodies were produced, and conditions for a double lateral flow immunoassay (LFIA) were selected and optimized so that the cross-reactivity with another enantiomer was negligible. The LFIA was performed in a competitive format with gold nanoparticles as a label for secondary antibodies. The achieved LODs/cutoffs were 0.001/10 and 0.007/30 ng/mL for S-OFL and R-OFL detection, respectively; the assay procedure took only 15 min. A double LFIA was performed to detect S-OFL and R-OFL in milk with minimal sample pretreatment; the recoveries were 85–95%. The developed test system is an effective tool for the selective detection of both isomers of OFL, allowing for the avoidance of false negative results. This immunochromatographic approach can be promising for the control of other optically active food toxicants. Full article

The automatic identification of P-wave arrival times in microseismic signals is crucial for the intelligent monitoring and early warning of dynamic hazards in coal mines. Traditional methods suffer from low accuracy and poor stability due to complex underground geological conditions and substantial noise interference. This paper proposes a microseismic P-wave arrival time automatic picking model that integrates physical constraints with a deep learning architecture. This study trained and optimized the model using a high-quality, manually labeled dataset. A systematic comparison with the AR picker algorithm and the short-term–long-term average ratio method revealed that this model achieved a precision of 96.60%, a recall of 90.59%, and an F1 score of 93.50% on the test set, with a P-wave arrival time-picking error of less than 20 ms. The average arrival time error was only 5.49 ms, significantly outperforming traditional methods. In cross-mining area generalization tests, the model performed excellently in two mining areas with consistent sampling frequencies (1000 Hz) and high signal-to-noise ratios, demonstrating good engineering transferability. However, its performance decreased in a mining area with a higher sampling rate and stronger noise, indicating its sensitivity to data acquisition parameters. This study developed a high-precision, robust, and potentially cross-domain adaptive model for automatically picking microseismic P-wave arrival times. This model provides support for the automation, precision, and intelligence of coal mine microseismic monitoring systems and has significant practical value in promoting real-time early warning and risk prevention for mine dynamic hazards. Full article

Dietary supplements often promote their antioxidant content as an indicator of quality on the packaging. This study evaluated the redox potential and total antioxidant capacity of various dietary supplements, using different analytical methods to obtain the complexity of antioxidant measurements. A green approach for detecting total antioxidant capacity in dietary products utilized modern electrochemical techniques, including differential pulse voltammetry (DPV) and cyclic voltammetry (CV). These rapid “green” methods measure the redox potential of samples, providing information about the electron-donating ability of antioxidants without the use of harmful chemicals or sample treatments, with minimal environmental impact. ABTS and FRAP measurements were expressed as vitamin C equivalents to allow comparison with CV measurements and actual vitamin C content. This approach enabled indirect comparison of activities obtained using different standard substances through conversion to standard equivalents. The results revealed that the claims made on product labels and packaging often overestimated the antioxidant content and did not match the measured total antioxidant capacities obtained in the current study. Measured vitamin C levels in 10 samples fell within the declared ranges (0–950 mg), but six products contained 4.85% to 49.18% less, and two had significantly higher levels (4.20% and 32.22%) than their declared (p < 0.05). Total antioxidant capacity varied from the labeled values. Similar trends were observed across methods, except for DPPH. FRAP values were correlated with ABTS and CV (r = 0.797 and r = 0.757, respectively). The DPV method provided a more detailed assessment of the redox activity of selected products based on distinct oxidation peaks. The study highlights the importance of mandatory testing and quantification of antioxidants, as well as the need for regulation of antioxidant properties through normative standards. Full article

The growing global population has highlighted the need to replace animal-based meat with plant-based meat (PBM) as a protein source. Using lactic acid bacteria (LAB) offers a promising and sustainable approach to prolong PBM shelf life and maintain quality comparable to non-food additives. This study investigated the potential of LAB to improve the qualities of PBM products. Three LAB strains, Lactiplantibacillus plantarum (LM), Lactiplantibacillus pentosus (LS), and Pediococcus acidilactici (PA) were selected from vegetable sources, and their effects on PBM shelf life were monitored for 21 days at 4 °C. Results showed that PBM samples treated with both Lactiplantibacillus spp. maintained consistent color properties throughout the cold storage period. Textural analysis revealed that the control samples exhibited the lowest hardness, springiness, gumminess, and chewiness, while LS-treated samples showed the highest values. Both Lactiplantibacillus spp. treated samples had pH values at less than 5, with no statistically significant differences. Volatile organic compounds were not impacted by LAB. LM-treated PBM exhibited higher amino acid content compared to LS and non-LAB-treated samples. Our findings showed that L. plantarum improved the texture and prolonged the shelf life of PBM products at 4 °C for 21 days. Results indicated that L. plantarum could be used as an alternative sustainable green biological preservative agent, serving as a clean label product. Full article

Microplastics are plastic particles less than five millimeters in diameter that have led to serious environmental problems, and detecting these tiny particles is crucial to understanding their distribution and impact on the soil environment. In this paper, we propose the Self-Supervised Hierarchical Dilated Transformer Network (SHDTNet), an improved hyperspectral image classification model based on self-supervised contrastive learning, for identifying and detecting microplastics in soil. Currently, most hyperspectral image classifications rely on supervised methods, which perform well with rich training samples. However, pixel labeling in soil microplastic detection scenarios is a difficult and costly task. By employing the self-supervised contrastive learning technique, SHDTNet addresses the problem of insufficient training samples for hyperspectral images of soil microplastics and also enhances the feature extraction module in contrastive learning to improve the network model’s feature extraction capability. Experiments on self-constructed hyperspectral soil microplastic image datasets demonstrate that the proposed method accurately recognizes unique microplastics in the soil environment without errors or missed detections, outperforming several currently available soil microplastic detection methods. Full article

There is increasing awareness about the adverse environmental and ‘food’ animal welfare impacts associated with the production of meat-based pet food. However, little is known about cat guardians’ acceptance of more sustainable food choices for the global population of approximately 476 million pet cats. By surveying 1380 cat guardians, this study explored feeding patterns used by guardians, determinants of their cat food choices, and their acceptance levels of more sustainable cat food alternatives. The sources of information used by cat guardians to obtain information about the cat diets they chose were also investigated. Key results included: (1) 51% (620/1211) of cat guardians currently feeding meat-based cat food (raw or conventional) considered at least one or more sustainable alternatives to be acceptable, with cultivated meat-based cat food being the most popular alternative, followed by nutritionally sound vegan cat food; (2) the top five characteristics alternative diets needed to offer to be considered viable were good health outcomes, nutritional soundness, palatability, quality, and environmental sustainability; (3) diet types consumed by cat guardians and their cats were strongly associated; and (4) labels/packaging and veterinarians were the information sources most used, although veterinary staff may have been less trusted as reliable sources of dietary advice by guardians feeding unconventional diets. It should be noted that, due to the reliance on convenience sampling and the overrepresentation of respondents from the UK, of female guardians, of respondents with higher education and of vegan guardians, the reported relative frequencies of subgroups were not fully representative of the global cat guardian population. Association estimates were based on regression analyses to minimize any resultant bias effects. Full article

Urban noise levels that exceed the World Health Organization (WHO) recommendations have become a growing concern due to their adverse effects on public health. In Bogotá, Colombia, studies by the District Department of Environment (SDA) indicate that 11.8% of the population is exposed to noise levels above the WHO limits. This research aims to identify and categorize environmental noise sources in real time using an embedded intelligent system. A total of 657 labeled audio clips were collected across eight classes and processed using a 60/20/20 train–validation–test split, ensuring that audio segments from the same continuous recording were not mixed across subsets. The system was implemented on a Raspberry Pi 2W equipped with a UMIK-1 microphone and powered by a 90 W solar panel with a 12 V battery, enabling autonomous operation. The TinyML-based model achieved precision and recall values between 0.92 and 1.00, demonstrating high performance under real urban conditions. Heavy vehicles and motorcycles accounted for the largest proportion of classified samples. Although airplane-related events were less frequent, they reached maximum sound levels of up to 88.4 dB(A), exceeding the applicable local limit of 70 dB(A) by approximately 18 dB(A) rather than by percentage. In conclusion, the results demonstrate that on-device TinyML classification is a feasible and effective strategy for urban noise monitoring. Local inference reduces latency, bandwidth usage, and privacy risks by eliminating the need to transmit raw audio to external servers. This approach provides a scalable and sustainable foundation for noise management in smart cities and supports evidence-based public policies aimed at improving urban well-being. This work presents an introductory and exploratory study on the application of TinyML for acoustic environmental monitoring, aiming to evaluate its feasibility and potential for large-scale implementation. Full article

Coffee silverskin (CS), the principal solid by-product from coffee roasting, is a promising raw material for sustainable food applications aligned with circular economy principles. Due to its high flammability at roasting temperatures, effective management of CS is not only an environmental but also a safety concern in coffee processing facilities. To the best of our knowledge, this is the first study evaluating the chemical composition, bioactivity, safety, and environmental impact of torrefacto (CT) and natural (CN) coffee silverskin. CT (from Arabica–Robusta blends subjected to sugar-glazing) and CN (from 100% Arabica) were characterized in terms of composition and function. Oven-dried CT showed higher levels of caffeine (13.2 ± 0.6 mg/g vs. 8.7 ± 0.7 mg/g for CN), chlorogenic acid (1.34 ± 0.08 mg/g vs. 0.92 ± 0.06 mg/g), protein (18.1 ± 0.2% vs. 16.7 ± 0.2%), and melanoidins (14.9 ± 0.3 mg/g vs. 9.6 ± 0.2 mg/g), but CN yielded more total phenolics (13.8 ± 0.6 mg GAE/g). Both types exhibited strong antioxidant capacity (ABTS: 48.9–59.2 µmol TE/g), and all oven-dried samples met food safety criteria (microbial loads below 10² CFU/g, moisture 7.9%). Oven drying was identified as the most industrially viable, ensuring preservation of bioactives and resulting in a 19% lower greenhouse gas emissions impact compared to freeze-drying. Sun drying was less reliable microbiologically. The valorization of oven-dried CT as a clean-label, antioxidant-rich colorant offers clear potential for food reformulation and waste reduction. Renewable energy use during drying is recommended to further enhance sustainability. This study provides scientific evidence to support the safe use of coffee silverskin as a novel food, contributing to regulatory assessment and sustainable food innovation aligned with SDGs 9, 12, and 13. Full article