Search Results (10,323)

Leaf senescence is a developmental process that allows nutrients to be remobilized and transported to sink organs. Previously, papain-like cysteine proteases (PLCPs) have been found to be highly expressed during leaf senescence in different plant species. In this study, we analyzed active PLCPs in barley (Hordeum vulgare L.) leaves during the terminal stage of natural senescence. Anion exchange chromatography of protein extracts from barley leaves, harvested six weeks after anthesis, followed by activity assays using the substrates Z-FR-AMC and Z-RR-AMC, revealed a single prominent peak corresponding to active PLCPs. This hydrolytic activity was completely inhibited by E-64, a potent and irreversible inhibitor of cysteine proteases. Fractions enriched for PLCP activity were affinity-labeled with DCG-04 and subjected to SDS-PAGE fractionation, separating two major bands at 43 and 38 kDa. These bands were analyzed using tandem mass spectrometry, allowing the identification of eleven PLCPs. Identified enzymes belong to eight PLCP subfamilies, including CTB/cathepsin B-like (HvPap-19 and -20), RD19/cathepsin F-like (HvPap-1), ALP/cathepsin H-like (HvPap-12 or aleurain), SAG12/cathepsin L-like A (HvPap-17), CEP/cathepsin L-like B (HvPap-14), RD21/cathepsin L-like D (HvPap-6 and -7), cathepsin L-like E (HvPap-13 and -16), and XBCP3 (HvPap-8). Among the identified PLCPs, HvPap-6 was the most abundant. Peptides corresponding to HvPap-6 were identified in both the 43 kDa and 38 kDa bands in approximately the same quantity based on total spectral count. Thus, our results indicate that two active HvPap-6 isoforms can be isolated from barley leaves at late senescence. Full article

Remote sensing images, which obtain surface information from aerial or satellite platforms, are of great significance in fields such as environmental monitoring, urban planning, agricultural management, and disaster response. However, due to the complex and diverse types of ground coverage and significant differences in spectral characteristics in remote sensing images, achieving high-quality semantic segmentation still faces many challenges, such as blurred target boundaries and difficulty in recognizing small-scale objects. To address these issues, this study proposes a novel deep learning model, MKF-NET. The fusion of KAN convolution and Vision Transformer (ViT), combined with the multi-scale feature extraction and dense connection mechanism, significantly improves the semantic segmentation performance of remote sensing images. Experiments were conducted on the LoveDA dataset to systematically evaluate the segmentation performance of MKF-NET and several existing traditional deep learning models (U-net, Unet++, Deeplabv3+, Transunet, and U-KAN). Experimental results show that MKF-NET performs best in many indicators: it achieved a pixel precision of 78.53%, a pixel accuracy of 79.19%, an average class accuracy of 76.50%, and an average intersection-over-union ratio of 64.31%; it provides efficient technical support for remote sensing image analysis. Full article

The flat peach, an important commercial crop in the 143rd Regiment of Shihezi, China, is overwintered using plastic film mulching. Flat peaches are cultivated to boost the local temperate rural economy. The development of accurate maps of the spatial distribution of flat peach plantations is crucial for the intelligent management of economic orchards. This study evaluated the performance of pixel-based and object-based random forest algorithms for mapping flat peaches using the GF-1 image acquired during the overwintering period. A total of 45 variables, including spectral bands, vegetation indices, and texture, were used as input features. To assess the importance of different features on classification accuracy, the five different sets of variables (5, 15, 25, and 35 input variables and all 45 variables) were classified using pixel/object-based classification methods. Results of the feature optimization suggested that vegetation indices played a key role in the study, and the mean and variance of Gray-Level Co-occurrence Matrix (GLCM) texture features were important variables for distinguishing flat peach orchards. The object-based classification method was superior to the pixel-based classification method with statistically significant differences. The optimal performance was achieved by the object-based method using 25 input variables, with an overall accuracy of 94.47% and a Kappa coefficient of 0.9273. Furthermore, there were no statistically significant differences between the image-derived flat peach cultivated area and the statistical yearbook data. The result indicated that high-resolution images based on the overwintering period can successfully achieve the mapping of flat peach planting areas, which will provide a useful reference for temperate lands with similar agricultural management. Full article

Collagen peptides derived from fish skin may be limited in food applications due to undesirable flavors. To investigate the effects of Maillard reaction modification on their physicochemical and flavor properties, collagen peptides from tilapia skin were prepared via enzymatic hydrolysis, followed by the Maillard reaction with four reducing sugars (xylose, ribose, glucose and glucosamine) through a combined procedure involving simultaneous enzyme inactivation and Maillard reaction at 100 °C. The resultant Maillard reaction products (MRPs) were characterized by analyzing free amino groups, peptide size distribution and color difference, while the reaction progression was monitored using UV absorption and fluorescence spectroscopy. The flavor profile of MRPs was analyzed through quantitative descriptive sensory evaluation and GC-MS coupled with principal component analysis. Among the four reducing sugars tested, glucosamine-induced Maillard reaction products exhibited the most pronounced physicochemical and sensory improvements. Specifically, glucosamine-MRPs showed the greatest reduction in free amino groups (0.69 μmol/L) and a notable decrease in high-molecular-weight peptides (3.31%), accompanied by an increase in low-molecular-weight fractions. Colorimetric analysis revealed a marked color change (ΔE = 31.78), and spectral analysis further confirmed intensified UV absorbance and fluorescence intensity in the glucosamine group, indicating advanced reaction progression. Sensory evaluation demonstrated a significant reduction in bitterness and enhancement of umami and saltiness. Moreover, GC-MS analysis revealed that the glucosamine-treated group exhibited the most favorable volatile profile, characterized by an increase in aromatic compounds and a substantial decrease in undesirable odorants. This study provides a theoretical basis for controlling the undesirable flavor of collagen peptides through low-extent Maillard reactions by different reducing sugars. Full article

Background: Optical coherence tomography (OCT) is the main diagnostic technology used to detect damage to the retinal ganglion cells (RGCs) in glaucoma. However, it remains unclear which OCT parameter demonstrates the best diagnostic performance for eyes with early, especially preperimetric glaucoma (PPG). We determined the diagnostic performance of ganglion cell–inner plexiform layer (GCIPL) parameters using spectral-domain OCT (SD-OCT) in primary open-angle preperimetric and early perimetric glaucoma and compared them with optic nerve head (ONH) and peripapillary retinal nerve fiber layer (pRNFL) parameters. Methods: We analyzed 101 eyes: 36 normal eyes, 33 with PPG, and 32 with early perimetric glaucoma. All patients underwent Topcon SD–OCT imaging using the Optic Disc and Macular Vertical protocols. The diagnostic abilities of the GCIPL, rim area, vertical cup-to-disc ratio (CDR), and pRNFL were assessed using the area under the receiver operating characteristic curve (AUC). Results: For PPG, the AUCs ranged from 0.60 to 0.63 (GCIPL), 0.82 to 0.86 (ONH), and 0.49 to 0.75 (pRNFL). For early perimetric glaucoma, the AUCs for GCIPL and pRNFL ranged from 0.81 to 0.88 and 0.57 to 0.91, respectively, whereas both ONH parameters demonstrated an AUC of 0.89. The GCIPL parameters were significantly lower than both ONH parameters in detecting preperimetric glaucoma (p < 0.05). For early perimetric glaucoma, comparisons between the AUCs of the best-performing mGCIPL parameters and those of the best-performing pRNFL and ONH parameters revealed no significant differences in their diagnostic abilities (p > 0.05). Conclusions: GCIPL parameters exhibited a diagnostic performance comparable to that of ONH and pRNFL parameters for early perimetric glaucoma. However, their ability to detect preperimetric glaucoma was significantly lower than the ONH parameters. Full article

Fractional heat conduction models extend classical formulations by incorporating fractional differential operators that capture multiscale relaxation effects. In this work, we introduce an electrical analogy that represents the action of these operators via generalized longitudinal impedance and admittance elements, thereby clarifying their physical role in energy transfer: fractional derivatives account for the redistribution of heat accumulation and dissipation within micro-scale heterogeneous structures. This analogy unifies different classes of fractional models—diffusive, wave-like, and mixed—as well as distinct fractional operator types, including the Caputo and Atangana–Baleanu forms. It also provides a general computational methodology for solving heat conduction problems through the concept of thermal impedance, defined as the ratio of surface temperature variations (relative to ambient equilibrium) to the applied heat flux. The approach is illustrated for a semi-infinite sample, where different models and operators are shown to generate characteristic spectral patterns in thermal impedance. By linking these spectral signatures of microstructural relaxation to experimentally measurable quantities, the framework not only establishes a unified theoretical foundation but also offers a practical computational tool for identifying relaxation mechanisms through impedance analysis in microscale thermal transport. Full article

Quantum walks are, at present, an active field of study in mathematics, with important applications in quantum information and statistical physics. In this paper, we determine the influence of basic chaotic features on the walker behavior. For this purpose, we consider an extremely simple model consisting of alternating one-dimensional walks along the two spatial coordinates in bidimensional closed domains (hard wall billiards). The chaotic or regular behavior induced by the boundary shape in the deterministic classical motion translates into chaotic signatures for the quantized problem, resulting in sharp differences in the spectral statistics and morphology of the eigenfunctions of the quantum walker. Indeed, we found, for the Bunimovich stadium—a chaotic billiard—level statistics described by a Brody distribution with parameter $\delta \simeq 0.1$. This indicates a weak level repulsion, and also enhanced eigenfunction localization, with an average participation ratio $(\mathrm{PR})\simeq 1150$ compared to the rectangular billiard (regular) case, where the average $\mathrm{PR}\simeq 1500$. Furthermore, scarring on unstable periodic orbits is observed. The fact that our simple model exhibits such key signatures of quantum chaos, e.g., non-Poissonian level statistics and scarring, that are sensitive to the underlying classical dynamics in the free particle billiard system is utterly surprising, especially when taking into account that quantum walks are diffusive models, which are not direct quantizations of a Hamiltonian. Full article

Background/Objectives: The present work deals with the sol–gel synthesis of hybrid materials based on a silica–polyvinylpyrrolidone (Si-PVP) system. Methods: The nanohybrids have been prepared using an acidic catalyst at ambient temperature. Ibuprofen (IBP) was used as a model substance in the obtained model drug systems, while tetraethyl orthosilicate (TEOS) was used as a silica precursor. Poly(vinylpyrrolidone) (PVP) and IBP were introduced into the reaction mixture as solutions in ethanol using two different approaches: (i) a direct introduction of a drug solution into the reaction mixture during sol–gel synthesis, and (ii) a solvent deposition technique. Results: XRD data provide evidence that IBP entrapped in the silica–PVP network is in an amorphous state. By SEM it was revealed that in the adsorbate, the IBP particles possess an average particle size of about 20 μm. Based on the obtained IR and UV-Vis spectral results, the existence of hydrogen bonding of IBF with silica and PVP could be suggested. Solid-state NMR analysis allowed the identification of the presence of both crystalline-like and amorphous phases in the hybrid material prepared by the sol–gel method, while it was demonstrated that in the adsorbate, the rigid crystalline dimeric structure of the drug has been preserved. Conclusions: The overall analysis of the structural characteristics of the two materials indicated that in the hybrid material obtained by the sol–gel method, the interactions between the amorphous drug, PVP, and the silica matrix are more pronounced as compared to the adsorbate. An improvement of the drug’s aqueous solubility as well of in vitro drug release profile (up to 8 h) was achieved, demonstrating the potential of the developed drug–silica–organic polymer nanohybrid as a promising drug delivery system. Full article

Coal mining disturbances in semi-arid grasslands affect land surface phenology (LSP), impacting ecosystem functions, restoration target setting, and carbon sequestration; however, the magnitude and spatial extent of these disturbances and their detectability across vegetation indices (VIs), remain insufficiently constrained. We developed and applied a streamlined quantitative framework to delineate the extent and intensity of mining-induced phenological disturbance and to compare the sensitivity and stability of commonly used VIs. Using Harmonized Landsat Sentinel (HLS) surface reflectance data over the Yimin mine, we reconstructed multitemporal VI trajectories and derived phenological metrics; directional phenology gradients were used to delineate disturbance, and VI responsiveness was evaluated via mean difference (MD) and standard deviation (SD) between affected and control areas. Research findings indicate that the impact of mining extends to an area approximately four times the size of the mining site, with the start of season (SOS) in affected areas occurring about 10 days later than in unaffected areas. Responses varied markedly among VIs, with the Modified Soil-Adjusted Vegetation Index (MSAVI) exhibiting the highest spectral stability under disturbance. This framework yields an information-rich quantification of phenological impacts attributable to mining and provides operational guidance for index selection and the prioritization of restoration and environmental management in semi-arid mining landscapes. Full article

Psychogenic non-epileptic seizures (PNES) are often misdiagnosed as epileptic seizures (ES), leading to inappropriate treatment and delayed psychological care. To address this challenge, we analysed electroencephalogram (EEG) data from 74 patients (46 PNES, 28 ES) using one-minute preictal and interictal recordings per subject. Nine entropy measures (Sample, Fuzzy, Permutation, Dispersion, Conditional, Phase, Spectral, Rényi, and Wavelet entropy) were evaluated individually to classify PNES from ES using k-nearest neighbours, Naïve Bayes, linear discriminant analysis, logistic regression, support vector machine, random forest, multilayer perceptron, and XGBoost within a leave-one-subject-out cross-validation framework. In addition, a dynamic state, defined as the entropy difference between interictal and preictal periods, was examined. Sample, Fuzzy, Conditional, and Dispersion entropy were higher in PNES than in ES during interictal recordings (not significant), but significantly lower in the preictal (p < 0.05) and dynamic states (p < 0.01). Spatial mapping and permutation-based importance analyses highlighted O1, O2, T5, F7, and Pz as key discriminative channels. Classification performance peaked in the dynamic state, with Fuzzy entropy and support vector machine achieving the best results (balanced accuracy = 72.4%, F1 score = 77.8%, sensitivity = 74.5%, specificity = 70.4%). These results demonstrate the potential of entropy features for differentiating PNES from ES. Full article

X-ray absorption spectroscopy (XAS) and X-ray emission spectroscopy (XES) are analytical techniques enabling precise analysis of the electronic structure and local atomic environment in chemical compounds and materials. Their application spans materials science, chemistry, biology, and environmental sciences, supporting studies on catalytic mechanisms, redox processes, and metal speciation. A key advantage of both techniques is element selectivity, allowing the analysis of specific elements without matrix interference. Their high sensitivity to chemical state and coordination enables determination of oxidation states, electronic configuration, and local geometry. These methods are applicable to solids, liquids, and gases without special sample preparation. Modern XAS and XES studies are typically performed using synchrotron radiation, which provides an intense, monochromatic X-ray source and allows advanced in situ and operando experiments. Sub-techniques such as XANES (X-ray absorption near-edge structure), EXAFS (Extended X-ray Absorption Fine Structure), and RIXS (resonant inelastic X-ray scattering) offer enhanced insights into oxidation states, local structure, and electronic excitations. Despite their broad scientific use, applications in pharmaceutical research remain limited. Nevertheless, recent studies highlight their potential in analyzing crystalline active pharmaceutical ingredients (APIs), drug–biomolecule interactions, and differences in drug activity. This review introduces the fundamental aspects of XAS and XES, with an emphasis on practical considerations for pharmaceutical applications, including experimental design and basic spectral interpretation. Full article

Welsh onion (Allium fistulosum L.) is a globally significant culinary vegetable with extensive cultivation and high application value. In China, Welsh onion is vulnerable to drought and strong-light stress in summer production, resulting in growth inhibition and quality decline. This study utilized LED-intelligent spectral-customized lamps to simulate high-light stress and a 10% PEG-6000 Hoagland solution to simulate drought stress. The effects of different stress treatments on the nutritional quality, volatile compounds, and mineral element composition of the edible portions were systematically analyzed. The results demonstrated that drought stress significantly promoted the accumulation of alcoholic compounds in leaf tissues while reducing the content of sulfur-containing compounds. High-light stress markedly increased the levels of hydrocarbon compounds in leaves. Sulfur-containing compounds in leaf tissues were predominantly disulfides, but under combined drought and high-light stress, their content decreased, while the proportion of trisulfides significantly increased. Volatile compounds in pseudostems were primarily composed of sulfur-containing and aldehyde compounds, yet their levels markedly declined under combined stress. Additionally, combined stress led to reductions in pyruvic acid, soluble sugars, and soluble protein content in the edible portions, while the crude fiber content increased, thereby significantly impairing nutritional quality. This study provides a scientific basis for understanding the abiotic stress response mechanisms of Welsh onion and offers valuable insights for cultivation management and quality regulation. Full article

Accurate tree health monitoring in orchards is essential for optimal orchard production. This study investigates the efficacy of a deep learning-based object detection single-step method for detecting tree health on multispectral UAV imagery. A modified Mask R-CNN framework is employed with four different backbones—ResNet-50, ResNet-101, ResNeXt-101, and Swin Transformer—on three image combinations: (1) RGB images, (2) 5-band multispectral images comprising RGB, Red-Edge, and Near-Infrared (NIR) bands, and (3) three principal components (3PCs) computed from the reflectance of the five spectral bands and twelve associated vegetation index images. The Mask R-CNN, having a ResNeXt-101 backbone, and applied to the 5-band multispectral images, consistently outperforms other configurations, with an F1-score of 85.68% and a mean Intersection over Union (mIoU) of 92.85%. To address the class imbalance, class weighting and focal loss were integrated into the model, yielding improvements in the detection of the minority class, i.e., the unhealthy trees. The tested method has the advantage of allowing the detection of unhealthy trees over UAV images using a single-step approach. Full article

Operating range is an important indicator for determining the detection capability of an infrared search and track (IRST) system. To address the deficiencies of a traditional range model, such as excessive parameters and calculations, a noise-equivalent temperature difference (NETD)-based range optimization model for staring IRST systems is derived, offering a new solution method. Compared with traditional range models, the proposed model uses fewer parameters, fully considers the spectral radiation characteristics of the object and background and separately fits the integral part into a function related only to the range, which simplifies calculation and improves accuracy. The proposed operating range model is applied to a flying object for sample calculation, and the accuracy of the model is verified by field experiments. Full article

IRS 16CC and IRS 33N are among more than 100 young, massive stars identified within 0.5 pc from the Galactic central supermassive black hole Sgr A*, where conventional star formation processes are expected to be strongly suppressed. A subset of these stars, including IRS 16CC, has been confirmed to reside in a clockwise rotating stellar disk, and is thought to have formed in a massive, gaseous disk around Sgr A*. In contrast, other young massive stars, such as IRS 33N, exhibit dynamical behaviors that deviate significantly from those of the disk population, and their formation mechanism is still uncertain. To investigate their formation mechanism, we carried out near-infrared, high-resolution spectroscopic observations of IRS 16CC and IRS 33N using the Infrared Camera and Spectrograph on the Subaru telescope, equipped with an adaptive optics system. We compared the profiles of He I absorption lines with synthetic spectra generated from model atmospheres, and then compared derived stellar parameters with stellar evolutionary tracks to estimate their ages and initial masses. Our analysis yields their effective temperatures of ∼23,000 K, surface gravities of ∼2.8, and initial masses of $37\pm 6M_{\odot }$ and ${27}_{-3}^{+4}{M}_{\odot }$, consistent with spectral types of B0.5–1.5 supergiants. The ages of IRS 16CC and IRS 33N are estimated to be $4.4\pm 0.7$ Myr and $5.3_{-0.7}^{+1.1}$ Myr, respectively. These results suggest that, despite their different dynamical properties, the two stars are likely to share a common origin. Full article