Search Results (3,196)

Gluten proteins are key components in wheat flour that determine the formation of dough and the quality of flour-based products. Upon hydration and mixing, gluten proteins undergo complex structural transformations to form a gluten network, exhibiting a hierarchical multi-scale structure spanning molecular, aggregate, and network scales. Due to the extreme complexity of gluten proteins, accurately characterizing their multi-scale structures remains challenging, requiring the combined application of multiple techniques, which are still relatively limited and thus warrant further exploration. Therefore, this review presents the principles, operational details, and result presentations of current techniques at different structural scales, including electrophoresis, high-performance liquid chromatography, proteomics, Fourier transform infrared spectroscopy, and Fourier transform Raman spectroscopy at the molecular scale; size-exclusion chromatography, asymmetrical flow field-flow fractionation, dynamic light scattering, multi-angle light scattering, differential refractive index, and ultraviolet absorbance at the aggregate scale; and confocal laser scanning microscopy, scanning electron microscopy, confocal Raman microscopy, and two-photon excitation microscopy at the network scale, among others. It further compares the advantages and disadvantages of similar techniques, facilitating their scenario-based selective utilization. Finally, it outlines the ongoing challenges and future perspectives for the development and application of techniques for the multi-scale structural characterization of gluten proteins. Full article

Mitochondria play a crucial role in cellular bioenergetics, signaling, and metabolism; yet, many fundamental mechanisms such as the proton transfer along the membranes, the link between membrane curvature and oxidative phosphorylation, and the nanoscale organization of enzyme supercomplexes remain poorly understood due to the limitations of classical biochemical approaches. This review addresses this gap by systematically analyzing the contemporary physical methods used to investigate the mitochondrial structure and function from the micro to nano scale. It covers advanced fluorescence and super-resolution microscopy, electron and volume electron microscopy, and scanning probe techniques, as well as cryo-electron tomography for resolving supramolecular assemblies in near-native conditions. The review highlights the applications of the modern fluorescent probes, expansion and phase microscopy, and machine-learning-based image analysis for a quantitative assessment of the mitochondrial morphology, membrane potential, and dynamics in living cells and tissues. Complementary spectroscopic and scattering methods, including Raman spectroscopy, NMR, and X-ray and neutron scattering, are discussed as tools for probing the redox state, metabolite composition, and membrane organization. Emphasis is placed on integrating high-resolution experimental data with advanced computational frameworks to test competing models of mitochondrial function and pathology, and to guide the development of biomimetic and biomedical technologies. Full article

The residues of veterinary drugs in the food chain are a global concern for food safety, including questions about the origin of these residues, exposure pathways, health impacts, methods for their dissolution, and accurate monitoring methods. In recent years, numerous professional studies have addressed the above concerns from various perspectives. However, these studies are relatively scattered and cannot provide a systematic and comprehensive understanding of recent developments. In this systematic review, we aim to provide a comprehensive synthesis of the current state of knowledge concerning the residues of veterinary drugs in the food chain through critical examination of their origins, exposure pathways, and associated health/environmental hazards. Investigating creative mitigation techniques to lower such residues in food products is given special attention. In summary, this research proposes a paradigm that balances the development of animal production with strict food safety governance to address productivity, consumer health, and international standards. Full article

With the growing global emphasis on space resources, the significance of space detection and surveillance technologies has escalated. Currently, space-based optical surveillance stands as the primary means for acquiring information on space objects. However, constrained by the diffraction limits of space telescopes, distant space objects are typically imaged as point sources. The resulting lack of sufficient spatial resolution renders traditional image-based recognition algorithms ineffective. In contrast, the Bidirectional Reflectance Distribution Function (BRDF) fully characterizes surface light scattering properties through four-dimensional features, significantly outperforming traditional two-dimensional spectral techniques in material identification. Consequently, leveraging BRDF signatures at varying phase angles has emerged as an effective approach for Space Object Identification. In this study, we developed an automated BRDF measurement system to characterize various typical aerospace materials and investigated the BRDF properties of mixed-material surfaces. A material composition ratio prediction model was constructed based on a One-Dimensional Convolutional Neural Network (1D-CNN). This model effectively extracts key features, including local slope variations and global waveform characteristics, from the BRDF curves. Experimental results demonstrate that the model achieves a maximum relative percentage error of 6.21%, implying a prediction accuracy for mixed-material composition ratios consistently exceeding 93.79%. Compared to image classification methods based on remote sensing imagery, the proposed approach offers higher computational efficiency, significantly reduced model complexity and computational cost, and enhanced robustness. This work provides essential data support for material identification by space-based telescopes and establishes an algorithmic and experimental foundation for intelligent space situational awareness systems. Full article

Neural rendering techniques aim to generate photorealistic images and accurate 3D geometries from multi-view images but often struggle with efficiency and geometric consistency in complex or dynamic scenes. Optimized Neural Radiance Fields (OptiNeRF) addresses these challenges through several innovations. It uses spatially optimized sampling to focus on points near object surfaces, reducing computation while improving precision. Leveraging the pre-trained Marigold model, it generates depth and normal maps as geometric priors. Sampled points are processed through a hybrid network combining an MLP and a multi-resolution feature grid (MRF), capturing fine details and large-scale structures. To handle varying illumination and complex materials, OptiNeRF introduces adaptive volume rendering (AVR), dynamically adjusting light transparency and scattering. A progressive sampling strategy further focuses computation on regions with high geometric complexity. The loss function incorporates RGB, normal, depth, boundary, and lighting optimization losses, with adaptive weight modulation for geometric priors, ensuring both visual fidelity and geometric consistency even with inaccurate depth/normal estimates. Experiments on dynamic scenes show strong performance, with a PSNR of 32.10 dB, SSIM of 0.936, Chamfer distance of $1.28\times {10}^{-3}$, training time of 12 h, and rendering speed of 25 FPS, demonstrating high geometric accuracy, realistic rendering, and computational efficiency over conventional methods. Full article

In this work, a novel nanostructured Mn₃O₄-based electrochemical sensor was developed for the determination of heavy metals in aqueous media. The Mn₃O₄ nanostructure was solvothermally synthesized in the sole presence of propylene glycol (PG). Under the specific synthetic conditions, PG provided surface coating and stabilization by decomposition products and/or residual PG molecules that have been adsorbed on Mn₃O₄ NPs surfaces, creating a thin organic layer. This imparts a negative surface charge (zeta potential), enhancing colloidal stability in dispersions and electrochemical performance. The physicochemical properties of the resulting NPs were characterized via X-ray diffraction (XRD), Fourier transform infrared (FT-IR), Thermogravimetric Analysis (TGA), and Dynamic light scattering (DLS) and ζ-potential measurements, as well as SEM imaging of the modified electrode surface, confirming its successful formation and favorable structural properties. The LODs of Cd²⁺, Pb²⁺, Zn²⁺, and Cu²⁺ for their simultaneous determination are 2.9 μg·L⁻¹, 5.2 μg·L⁻¹, 7.1 μg·L⁻¹, and 2.5 μg·L⁻¹, respectively, with relative standard deviations of about 5.24%, 4.43%, 7.74%, and 4.53%, respectively. As a result of this study, a simple, sensitive, and reproducible electrochemical sensor based on a carbon paste electrode (CPE) modified with novel synthesized manganese nanoparticles and employing voltammetric techniques was applied in water and wastewater. Full article

This paper presents the structural health monitoring (SHM) system applied to a 9 m composite outer wing box (OWB) specifically designed for a brand-new regional aircraft to detect barely visible impact damage (BVID) based on structural response data. The approach relies on different technologies to offer multilevel diagnosis, including impact detection as well as disbonding identification, localization, and sizing. The use of different sensing techniques based on piezoelectric transducers and distributed fiber optic sensors deployed all over wing structures is explored. Different features are simultaneously extracted from the propagating waves and from light scattering, able to detect low-energy BVID impact. In addition, the combined use of static and dynamic interrogation allows the estimation of the delamination surface after impact with good accuracy. The final test results on the OWB provided effectiveness in detecting, localizing, and tracking impact damage in the composite structure, ensuring long-term reliability and safety, as well as characterizing barely visible damage by a fully integrated onboard SHM system. Full article

Background: The eco-friendly synthesis of silver nanoparticles (AgNPs) utilizing medicinal flora presents a viable strategy for the development of multifunctional agents exhibiting antimicrobial, antioxidant, anti-inflammatory, and anticancer properties. This investigation aims to elucidate the phytochemical composition of Calotropis gigantea and its contribution to the synthesis of CG-AgNPs that demonstrate efficacy against Helicobacter pylori and gastric cancer cell lines. Methods: The aqueous plant leaf extract of C. gigantea underwent comprehensive analysis via gas chromatography-mass spectrometry (GC-MS), identifying a total of 25 bioactive constituents, including oleic and oxalic acid derivatives. The fabrication and analysis of silver nanoparticles (AgNPs) were performed utilizing methodologies including ultraviolet-visible (UV–Vis) spectroscopy, X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), high-resolution transmission electron microscopy (HR-TEM), dynamic light scattering (DLS), and assessments of zeta potential. Antibacterial efficacy was evaluated through methods including agar well diffusion, time-kill kinetics, and biofilm assays. The cytotoxic impact on AGS gastric cancer cells was investigated using MTT assays, DAPI staining, and acridine orange/ethidium bromide (AO/EtBr) staining techniques. The assessment of antioxidant potential was performed utilizing DPPH and ABTS assays. The anti-inflammatory properties were analyzed through protein denaturation and membrane stabilization tests. Results: CG-AgNPs exhibited a spherical morphology (11–17 nm) with commendable stability, denoted by using zeta potential analysis measurement of −30.2 mV. The antibacterial activity showed a significant inhibition zone of 16.00 ± 0.17 mm at a concentration of 50 µg/mL against H. pylori, in addition to notable biofilm disruption. The viability of AGS cells was reduced by 61% at a concentration of 100 micrograms per milliliter, with apoptosis being confirmed through relevant assays. The antioxidant potential varied from 18% to 83% (DPPH) and reached 74% (ABTS) at a concentration of 100 µg/mL. The anti-inflammatory assays indicated a BSA denaturation inhibition ranging from 45% to 80% and a membrane stabilization effect between 54% and 85%. Conclusions: CG-AgNPs exhibit substantial antibacterial, antioxidant, anti-inflammatory, and anticancer activities, underscoring their pharmaceutical potential, particularly for combating antibiotic-resistant pathogens and gastric malignancies. Full article

Time-resolved (TR) resonance Raman (RR) spectroscopy with continuous-wave excitation is a fundamental technique that has contributed substantially to the understanding of the structure and dynamics of retinal proteins. However, the underlying principles were developed about fifty years ago for instrumentation that is hardly in use anymore. Thus, the adaptation of the technique to the current state-of-the-art equipment is needed to satisfy the increasing demand for the spectroscopic characterization of novel retinal proteins. In this work, we focus on pump–probe TR RR experiments with a confocal spectrometer using a rotating cell. We define the parameters ensuring fresh-sample condition and the photochemical innocence of the probe beam as a prerequisite for studying retinal proteins that undergo a cyclic photoinduced reaction sequence. For the measurements of intermediate states and reaction kinetics, pump–probe experiments are required in which the two laser beams hit the flowing sample with a defined but variable delay time. An appropriate set-up for such two-beam experiments with a confocal spectrometer is proposed and tested in TR experiments of bacteriorhodopsin. The comparison with the results obtained with classical slit spectrometers using a 90-degree scattering illustrates the advantages and disadvantages of the confocal arrangement. It is shown that modern confocal spectrometers substantially decrease the spectra acquisition time but require a more demanding optical set-up. Furthermore, the extent of photoconversion by the pump beam is lower than for the 90-degree-scattering arrangement, which reduces the accuracy of kinetic measurements. Full article

Extracellular vesicles (EVs) are membrane-bound particles secreted by most cell types that play a pivotal role in intercellular communication via transporting protein, nucleic acid, lipid, and metabolite cargos. Among EVs, exosomes are a well-characterized subtype, typically ranging from 10–150 nm in diameter and originating from the endosomal pathway via the formation of multivesicular bodies that fuse with the plasma membrane. EVs/exosomes can be isolated from various biological fluids and cultured cells, with production and yield influenced by the cell type and culture conditions. Isolation methods, including ultracentrifugation or density-based ultracentrifugation, tangential flow filtration, size-exclusion chromatography, immunoaffinity and membrane-affinity capture, and recently developed commercial equipment, offer distinct advantages and limitations in terms of purity, scalability, and exosome integrity. Characterization techniques, such as nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), cryogenic electron microscopy (cryo-EM), atomic force microscopy (AFM), Western blotting, flow cytometry, and dynamic light scattering (DLS), assess exosome size, morphology, and biomarker expression. Given their biocompatibility and inherent targeting capabilities across a diverse range of diseases, EVs/exosomes hold clinical promise as diagnostic biomarkers, cell-free therapeutics, drug delivery vehicles, immune modulators, and in regenerative medicine. However, these emerging fields in exosome medicine continue to face challenges in standardizing EV sourcing, production, purification, yield, bio-targeting, drug loading, and drug delivery. While EVs/exosomes represent a rapidly advancing frontier in biomedical science, robust protocols for standardization and scalable production will be essential for their successful translation into clinical applications. This article provides a comprehensive overview of EV/exosome origins, their biological functions, the approaches for their isolation and characterization, and their therapeutic potential. Full article

The integration of optical-network sensing and communication (optical-network ISAC) can effectively utilize resources and meet the demands of intelligent scenarios, becoming a future development trend. This article reviews the fundamental technical principles involved in the optical-network ISAC, including three types of backward-sensing based on Rayleigh scattering, Raman scattering, and Brillouin scattering, respectively. The forward-sensing methods based on power profile estimation (PPE) and the state of polarization (SOP), as well as bidirectional sensing, are compared and analyzed. The technical difficulties and recent solutions to realize the optical-network ISAC are introduced, including the existing solutions implemented at the transmitter side or the receiver side. Finally, we discuss the new opportunities and major challenges of the optical-network ISAC technique for practical applications. Full article

Monitoring small water body coverage spatiotemporal evolution in karst areas of complex hydrogeology is pivotal for water resource management and disaster assessment. With recent infrastructure expansion, intensive tunnel excavation has occurred in Chongqing’s Geleshan, a typical karst region with fragile aquifers. It has disrupted hydrogeological systems, triggering ground subsidence, groundwater leakage, and subsequent reservoir desiccation, as well as threatening regional water security and ecology. Thus, monitoring reservoir coverage evolution is critical to clarify dynamics and driving mechanisms. Synthetic Aperture Radar (SAR) is ideal for water body mapping, enabling data acquisition independent of illumination and weather. However, traditional SAR-based water extraction methods are hampered by low-scatter noise and poor adaptability to hydrological fluctuations. To address this, a two-stage dual-polarization SAR clustering algorithm (TSDPS-Clus) was developed using 452 time-series Sentinel-1 images (7 February 2017–24 August 2025). Specifically, the Kolmogorov–Smirnov test via pixel-wise time-series statistics screened core water areas, built candidate regions, and mitigated noise. Subsequently, dual-polarization and positional features were fused via singular value decomposition (SVD) to generate a high-discrimination low-dimensional feature set, followed by the Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) clustering for high-precision extraction. Results demonstrate that the algorithm suits reservoir storage-desiccation dynamics; dual-polarization complementarity boosts accuracy and clarifies six reservoirs’ spatiotemporal evolution. Notably, post-2023, tunnel excavation-induced land subsidence increased drying frequency and duration, with a 24-month maximum cumulative desiccation period. Full article

Label-free imaging is becoming ever more important, especially in modern molecular biophysics. This method allows observation of biological structures and dynamics without the alteration caused by dyes or genetic labels. Coherent Anti-Stokes Raman Scattering (CARS) microscopy represents a unique method that utilizes the intrinsic vibrational signatures of biomolecules, thereby transforming the field. Fluorescence-based methods show marked sensitivity, but may cause photobleaching, labeling artifacts, and inadequate biochemical detection. CARS enables chemically specific, real-time imaging of molecular structures, e.g., lipids, proteins and nucleic acids, within their natural environment. Over the past decade, advances in laser technology, detection methods, and computer analysis have turned CARS from a rare optical phenomenon into a useful tool applied in many fields, from basic research on molecular structure to practical biomedical imaging. This review presents the principles of CARS microscopy and the latest achievements in this field, highlighting its impact on molecular and cellular biophysics, as well as exploring the potential of artificial intelligence and multimodal approaches to increase its applications in precision medicine. In this context, CARS serves both a state-of-the-art imaging technique and a means of transforming internal molecular vibrations into information useful in biology and biophysics. In this way, it combines the physical sciences with molecular biology, enabling innovative biomedical research. Full article

Adulteration of milk powder (MP) is performed, especially in underdeveloped countries, by adding corn starch (CS) or wheat flour (WF) without mentioning it. Multiple techniques have been established to reduce these deceptive methods. Most of these techniques require samples to be sent to the laboratory for results through a time-consuming, expert-requiring, and destructive procedure. Raman spectroscopy (RS) has seen application due to the availability of portable modalities and its non-destructive, water-insensitive nature. Using principal component analysis (PCA), the differences and similarities between MP and the adulterants (CS and WF) have been evaluated. To quantify the percentages of CS and WF binary mixtures independently with MP, partial least squares regression (PLSR) has been employed. A total of 70 MP samples independently adulterated with CS and WF were prepared. Thirteen chemometric modes were developed by combining the first and second derivatives with Standard Normal Variate (SNV) and Multiplicative Scatter Correction (MSC) to quantify adulteration. The results obtained for CS and WF mixtures show errors of 0.76 and 0.77 %w/w, respectively, with the optimized math pretreatment. These results demonstrate that the portable RS modality can be used as an effective technique for detecting adulterants in milk powder. Full article

Due to phenomena, such as refraction, reflection, and light scattering, the three-dimensional (3D) reconstruction of transparent objects with complex geometric symmetry or contours is confronted with the challenges of insufficient extraction of feature points and recognition of contour detail. To solve this challenge, a novel reconstruction method based on multi-cooperative Neural Radiance Fields (NeRF) is proposed in the paper. This method incorporates angular offset fields and local reconstruction fields, explicitly modeling the effects of refraction and reflection during light propagation. The angular offset field simulates the internal refractive deflection within transparent materials, while the localized reconstruction field performs secondary reconstruction in regions affected by specular reflection. This approach effectively captures surface contours of transparent objects and accurately reconstructs scene details. Experimental results demonstrate that our method achieves approximately 10% improvement in reconstruction accuracy compared to traditional neural radiance field techniques, with a PSNR of 25, an increased SSIM of 0.87, and a reduced LPIPS value of 0.365. The proposed method offers a new perspective for reconstructing transparent objects and scenes containing such materials, holding significant theoretical and practical value. Full article