Search Results (116,282)

The current study focuses on a preliminary evaluation of the use of solid residues produced from the distillation of selected medicinal and aromatic plants (MAP) as fertilizers for alkaline soils. Specifically, the residues of hemp (Cannabis sativa L.), helichrysum (Helichrysum Italicum (Roth) G. Don), lavender (Lavandula angustifolia Mill.), oregano (Origanum vulgare L.), rosemary (Rosmarinus officinalis L.) and sage (Salvia officinalis L.) were added in an alkaline and calcareous soil at the rates of 0 (control), 1, 2, 4 and 8%, in three replications (treatments), and the treated soils were analyzed. The results showed that upon application of the residues, soil electrical conductivity (EC), organic C, total N and the C/N ratio significantly increased, especially at the 4 and 8% rates. The same was found for soil available P, K, B, Cu and Mn. The effects of the residues on soil pH, cation exchange capacity (CEC) and available Zn and Fe were rather inconclusive, whereas soil available N significantly decreased, which was somewhat unexpected. From the different application rates tested, it seems that all residues could improve soil fertility (except N?) when they were applied to soil at rates of 2% and above, without exceeding the 8% rate. The reasons for the latter statement are soil EC and available Mn: the doubling of EC upon application of the residues and the excessive increase in soil available Mn in treatments with 8% residues raise concerns of soil salinization and Mn phytotoxicity risks, respectively. This work provides the first step towards the potential agronomic use of solid residues from MAP distillation in alkaline soils. However, for the establishment of such a perspective, further research is needed in respect to the effect of residues on plant growth and soil properties, by means of at least pot experiments. Based on the results of the current study, the undesirable effect of residues on soil available N should be investigated in depth, since N is the most important essential element for plant growth, and possible risks of micronutrient phytotoxicities should also be studied. In addition, application rates between 2 and 4% should be studied extensively in order to recommend optimum application rates of residues to producers. Full article

Antimicrobial peptides (AMPs) are a primary defense against pathogens. Here, we examined the interaction of two BP100 analogs, R²R⁵-BP100 (where Arg substitutes Lys 2 and 5) and R²R⁵-BP100-A-NH-C₁₆ (where an Ala and a C₁₆ hydrocarbon chain are added to the R²R⁵-BP100 C-terminus), with membrane models. Large unilamellar vesicles (LUVs) and giant unilamellar vesicles (GUVs) were prepared with the major lipids in Gram-positive (GP) and Gram-negative (GN) bacteria, as well as red blood cells (RBCs). Fluorescence data, dynamic light scattering (DLS), and zeta potential measurements revealed that upon achieving electroneutrality through peptide binding, vesicle aggregation occurred. Circular dichroism (CD) spectra corroborated these observations, and upon vesicle binding, the peptides acquired α-helical conformation. The peptide concentration, producing a 50% release of carboxyfluorescein (C₅₀) from LUVs, was similar for GP-LUVs. With GN and RBC-LUVs, C₅₀ decreased in the following order: BP100 > R²R⁵-BP100 > R²R⁵BP100-A-NH-C₁₆. Optical microscopy of GP-, GN-, and RBC-GUVs revealed the rupture or bursting of the two former membranes, consistent with a carpet mechanism of action. Using GUVs, we confirmed RBC aggregation by BP100 and R²R⁵-BP100. We determined the minimal inhibitory concentrations (MICs) of peptides for a GN bacterium (Escherichia coli (E. coli)) and two GP bacteria (two strains of Staphylococcus aureus (S. aureus) and one strain of Bacillus subtilis (B. subtilis)). The MICs for S. aureus were strain-dependent. These results demonstrate that Lys/Arg replacement can improve the parent peptide’s antimicrobial activity while increasing hydrophobicity renders the peptide less effective and more hemolytic. Full article

Deer tick virus (DTV) is a Tick-Borne Orthoflavivirus endemic to the United States, transmitted to humans through bites from the deer tick, Ixodes scapularis, which is also the primary vector of Borrelia burgdorferi s.l., the causative agent of Lyme disease. Human infection with DTV can result in acute febrile illness followed by central nervous system complications, such as encephalitis and meningoencephalitis. Currently, there are mouse models established for investigating the pathogenesis and clinical outcomes of DTV that mimic human infections, but the strains of mice utilized are refractory to infection with B. burgdorferi s.l. Here, we describe the pathogenesis and clinical outcomes of DTV infection in C3H/HeJ mice. Neurological clinical signs, mortality, and weight loss were observed in all DTV-infected mice during the investigation. Infected animals demonstrated consistent viral infection in their organs. Additionally, neuropathology of brain sections indicated the presence of meningoencephalitis throughout the brain. This data, along with the clinical outcomes for the mice, indicates successful infection and showcases the neuroinvasive nature of the virus. This is the first study to identify C3H/HeJ mice as an appropriate model for DTV infection. As C3H/HeJ mice are already an established model for B. burgdorferi s.l. infection, this model could serve as an ideal system for investigating disease progression and pathogenesis of co-infections. Full article

This study proposes CEL-DL-Bagging (Cross-Entropy Loss-optimized Deep Learning Bagging), a multi-model fusion framework that integrates cross-entropy loss-weighted voting with Bootstrap Aggregating (Bagging). First, we develop a lightweight recognition architecture by embedding a salient position attention (SPA) mechanism into four base networks (YOLOv5s-cls, EfficientNet-B0, MobileNetV3, and ShuffleNetV2), significantly enhancing discriminative feature extraction for disease patterns. Our experiments show that these SPA-enhanced models achieve consistent accuracy gains of 0.8–1.7 percentage points, peaking at 97.86%. Building on this, we introduce DB-CEWSV—an ensemble framework combining Deep Bootstrap Aggregating (DB) with adaptive Cross-Entropy Weighted Soft Voting (CEWSV). The system dynamically optimizes model weights based on their cross-entropy performance, using SPA-augmented networks as base learners. The final integrated model attains 98.33% accuracy, outperforming the strongest individual base learner by 0.48 percentage points. Compared with single models, the ensemble learning algorithm proposed in this study led to better generalization and robustness of the ensemble learning model and better identification of rice diseases in the natural background. It provides a technical reference for applying rice disease identification in practical engineering. Full article

The inability of existing super-resolution methods to jointly model short-range and long-range spatial dependencies in remote sensing imagery limits reconstruction efficacy. To address this, we propose LSTMConvSR, a novel framework inspired by top-down neural attention mechanisms. Our approach pioneers an LSTM-first–CNN-next architecture. First, an LSTM-based global modeling stage efficiently captures long-range dependencies via downsampling and spatial attention, achieving 80.3% lower FLOPs and 11× faster speed. Second, a CNN-based local refinement stage, guided by the LSTM’s attention maps, enhances details in critical regions. Third, a top-down fusion stage dynamically integrates global context and local features to generate the output. Extensive experiments on Potsdam, UAVid, and RSSCN7 benchmarks demonstrate state-of-the-art performance, achieving 33.94 dB PSNR on Potsdam with 2.4× faster inference than MambaIRv2. Full article

The direct fitting of sea level anomaly (SLA) using satellite along-track data provides a critical approach for monitoring mesoscale ocean dynamics. While bicubic quasi-uniform B-spline surface fitting has demonstrated feasibility in localized sea areas, its applicability to basin-scale regions remains underexplored. This study focuses on the northern Atlantic Ocean, employing B-spline surface fitting to derive SLA fields from satellite along-track data. The results show strong agreement with in situ measurements, yielding a mean absolute error (MAE) of 1.89 cm and a root mean square error (RMSE) of 3.02 cm. Comparative analysis against the Copernicus Marine Environment Monitoring Service (CMEMS) Level-4 gridded SSH data reveals nearly equivalent accuracy (MAE: 1.95 cm; RMSE: 3.06 cm). The relationship between the order of fitting and the spatial extent of the fitting domain is also examined. Furthermore, the influence of the coastline on the fitting results is investigated in detail. As the coastline area expanded, the MAE and RMSE for the entire region increased. But the maximum increase in MAE was only 1.20 cm, and the maximum increase in RMSE was only 2.49 cm. Notably, there was no upward trend in MAE and RMSE in the mesoscale vortex dense area, which highlights the advantage of B-spline’s local support. Geostrophic flow and vertical component of relative vorticity are computed from the satellite along-track SLA data, with results showing agreement with Level-4 gridded geostrophic flow and vertical component of relative vorticity data. Full article

Background: Multi-epitope vaccines have become the preferred strategy for protection against infectious diseases by integrating multiple MHC-restricted T-cell and B-cell epitopes that elicit both humoral and cellular immune responses against pathogens. Computational methods address various aspects independently, yet their orchestration is technically challenging, as most bioinformatics tools are accessible through heterogeneous interfaces and lack interoperability features. The present work proposes a novel framework for rationalized multi-epitope vaccine design that streamlines end-to-end analyses through an integrated web-based environment. Results: VaccineDesigner is a comprehensive web-based framework that streamlines the design of protective epitope-based vaccines by seamlessly integrating computational methods for B-cell, CTL, and HTL epitope prediction. VaccineDesigner incorporates single-epitope prediction and evaluation as well as additional analyses, such as multi-epitope vaccine generation, estimation of population coverage, molecular mimicry, and proteasome cleavage. The functionalities are transparently integrated into a modular architecture, providing a single access point for rationalized, multi-epitope vaccine generation in a time- and cost-effective manner. Conclusions: VaccineDesigner is a web-based tool that identifies and evaluates candidate B-cell, CTL, and HTL epitopes and constructs a library of multi-epitope vaccines that combine strong immunogenic responses, safety, and broad population coverage. The source code is available under the academic license and freely accessible. Full article

Extracts, fractions and the pure compound epifriedelanol of the medicinal plant Synadenium glaucescens have antibacterial properties. Herbal products are generally considered less prone to resistance development than conventional antimicrobials, as they contain multiple compounds, which makes bacteria less likely to develop resistance. However, data supporting this notion are lacking. This study evaluated the development of resistance in Staphylococcus aureus subjected to extract, fractions and epifriedelanol of S. glaucescens. It also identified S. aureus fitness genes contributing to intrinsic resistance to extract of S. glaucescens. Fluctuation and gradient concentration assays were used to determine mutation rates and growth adaptation, respectively, which were lower following exposure to growth in crude extract than the pure compound epifriedelanol. By subjecting 1920 single gene mutants from the Nebraska Transposon Mutant Library to growth in the presence of extract of S. glaucescens, 12 genes were identified as important for natural resistance in S. aureus JE2; however, only mutation in the hemB gene decreased the minimum inhibitory concentration by greater than 4-fold (64-fold). In conclusion, purifying active antimicrobial compounds from S. glaucescens and using them as antibacterial substances as an alternative to crude extract increased the risk of resistance development. Further, the gene hemBappears to have a significant role in the natural resistance to the extracts obtained from S. glaucescens in this study. Full article

To address the demand of equivalent self-noise suppression in a distributed hydroacoustic sensing system, this study proposes a method to enhance the acoustic sensitivity and signal-to-noise ratio (SNR) using space division multiplexed (SDM) technology based on multi-core fiber (MCF). Specifically, a dual-channel demodulation system for distributed acoustic sensing is designed using MCF. The responses of different cores in MCF are almost consistent under external acoustic pressure, while their self-noise is inconsistent. Accordingly, the acoustic pressure phase sensitivity (APPS) and SNR gain based on the accumulation of dual-channel signals are analyzed, which are verified by experiments. It is shown that the self-noise correlation coefficient between the two cores is 0.11, increasing the noise power by 3.46 dB. The APPS is increased by 5.97 dB re 1 rad/μPa after the accumulation of two-core signals, which is close to the theoretical value (6 dB). The equivalent self-noise is reduced by 2.54 dB. The experimental results reveal that the enhancement of acoustic pressure phase shift sensitivity and SNR can be achieved by the space division multiplexing (SDM) of multi-core signals, which is of great significance for suppressing the equivalent self-noise of the system and realizing the acoustic pressure detection of weak underwater signals. Full article

Endotoxins, lipopolysaccharides released from the outer membrane of Gram-negative bacteria, pose a significant risk in healthcare environments, particularly in Central Sterile Supply Departments (CSSDs), where the delivery of sterile pyrogen-free medical devices is critical for patient safety. Traditional methods for controlling endotoxins in water systems, such as ultraviolet (UV) disinfection, have proven ineffective at reducing endotoxin concentrations to comply with regulatory standards (<0.25 EU/mL). This limitation presents a significant challenge, especially in the context of reverse osmosis (RO) permeate used in CSSDs, where water typically has very low conductivity. Despite the established importance of endotoxin removal, a gap in the literature exists regarding effective chemical-free methods that can meet the stringent endotoxin limits in such low-conductivity environments. This study addresses this gap by evaluating the effectiveness of the eBooster™ electrochemical technology—featuring proprietary electrode materials and a reactor design optimized for potable water—for endotoxin removal from water, specifically under the low-conductivity conditions typical of RO permeate. Laboratory experiments using the B250 reactor achieved >90% endotoxin reduction (from 1.2 EU/mL to <0.1 EU/mL) at flow rates ≤5 L/min and current densities of 0.45–2.7 mA/cm². Additional real-world testing at three hospitals showed that the eBooster™ unit, when installed in the RO tank recirculation loop, consistently reduced endotoxin levels from 0.76 EU/mL (with UV) to <0.05 EU/mL over 24 months of operation, while heterotrophic plate counts dropped from 190 to <1 CFU/100 mL. Statistical analysis confirmed the reproducibility and flow-rate dependence of the removal efficiency. Limitations observed included reduced efficacy at higher flow rates, the need for sufficient residence time, and a temporary performance decline after two years due to a power fault, which was promptly corrected. Compared to earlier approaches, eBooster™ demonstrated superior performance in low-conductivity environments without added chemicals or significant maintenance. These findings highlight the strength and novelty of eBooster™ as a reliable, chemical-free, and maintenance-friendly alternative to traditional UV disinfection systems, offering a promising solution for critical water treatment applications in healthcare environments. Full article

Image dehazing is an effective approach for enhancing the quality of images captured under foggy or hazy conditions. Although existing methods have achieved certain success in dehazing performance, many rely on deep network architectures, leading to high model complexity and computational costs. To address this issue, this study aims to compare and optimize existing algorithms to improve dehazing performance. For this purpose, we innovatively propose a multi-scale feature-coordinated composite loss mechanism, integrating perceptual loss, Mean Squared Error, and L1 regularization to optimize two dehazing methods: AOD-Net and DehazeFormer. Extensive experiments demonstrate significant performance improvements under the multi-objective loss mechanism. For AOD-Net, the PSNR increased by 22.40% (+4.17 dB), SSIM by 3.62% (+0.0318), VSNR by 43% (+1.54 dB), and LPIPS decreased by 56.30% (−0.1161). Similarly, DehazeFormer showed notable enhancements: the PSNR improved by 11.43% (+2.45 dB), SSIM by 0.8% (+0.008), VSNR by 2.6% (+0.23 dB), and LPIPS decreased by 5.5% (−0.0104). These results fully validate the effectiveness of the composite loss mechanism in enhancing the feature representation capability of the models. Full article

Enhancing forage protein is key to sustainable ruminant nutrition. The nutritive value of Hedysarum coronarium L. was investigated by studying different preservation systems (fresh vs. dehydrated pellet vs. hay) (Exp. 1) and morphological fractions (flowers vs. leaves vs. stems) (Exp. 2). For the fresh and pelleted systems, two cuts were used. Proximately, total polyphenols and condensed tannins were detected. In vitro fermentation characteristics were studied by incubating samples with buffered sheep rumen fluid, estimating methane production by volatile fatty acids. Fresh and pelleted sulla were more nutritionally advantageous than hay, in terms of metabolizable energy and protein-to-fiber ratio. Pelleting at the beginning of flowering proved to be a suitable forage for sheep feeding due to protein (15.1% DM), metabolizable energy (9.64 MJ/kg DM), structural carbohydrates (39.5% DM), and total polyphenols (13.5 GAE g/kg DM) content. This cut showed an in vitro fermentation rate (9.86 mL/h), organic matter degradability (55.7%), and volatile fatty acids (87.3 mmoL/g) that were higher (p < 0.05) than hay. Flowers and leaves showed higher (p < 0.05) levels of secondary metabolites than stems as well as lower methane production. These results suggest the potential influence of these compounds in reducing rumen emissions. Dehydration and pelleting resulted in an effective preservation method for maintaining nutrients in sulla forage. Full article

The anisotropic nature of cup ears formed during the deep drawing of sheet metals is governed by the distribution of crystallographic orientation in interaction between earing. In this study, we examined the orientation development of a cube-oriented aluminum single crystal to couple the deep drawing kinematics with the formation of anisotropic orientations. A quarter model of a circular deep-drawn blank was simulated in the finite element software using a user-defined material subroutine. A cube-oriented aluminum single crystal was designed to serve as a reference and trace the orientation evolution in the deep drawing process. After the deep drawing, the bottom, wall, and flange of the drawn cup were investigated at azimuthal angles ($\alpha$ ) of 0° and 45° with respect to the radial direction (RD) in terms of the orientation. Our findings show that the change in the lattice orientation could be attributed to the rotation induced by drawing and bending processes under kinematic constraints. Thus, the initial cube orientation developed into different orientations during the deep drawing. The type-A slip system mainly contributed to the radial strain at α = 0°, and type-B and C slip systems accounted for the longitudinal and circumferential strains at α = 45°. Full article

A one-pot synthetic methodology that combines an Ugi-Zhu three-component reaction (UZ-3CR) with a cascade sequence (intermolecular aza Diels–Alder cycloaddition/intramolecular N-acylation/decarboxylation/dehydration) using microwave-heating conditions, ytterbium (III) triflate (Yb(OTf)₃) as the catalyst, and chlorobenzene (for the first time in a multi-component reaction (MCR)) as the solvent, was developed to synthesize twelve new fluorinated-pyrrolo[3,4-b]pyridin-5-ones containing a 4-amino-7-chloroquinoline moiety, yielding 50–77% in 95 min per product, with associated atom economies around 88%, also per product. Additionally, by in vitro tests, compounds 19d and 19i were found to effectively stop early SARS-CoV-2 replication, IC₅₀ = 6.74 µM and 5.29 µM, at 0 h and 1 h respectively, while cell viability remained above 90% relative to the control vehicle at 10 µM. Additional computer-based studies revealed that the most active compounds formed strong favorable interactions with important viral proteins (M_pro, NTDα and NTDo) of coronavirus, supporting a two-pronged approach that affects both how the virus infects the cells and how it replicates its genetic material. Finally, quantum chemistry analyses of non-covalent interactions were performed from Density-Functional Theory (DFT) to better understand how the active compounds hit the virus. Full article

This paper presents a disaster detection framework based on aerial imagery, utilizing a Branch Convolutional Neural Network (B-CNN) to enhance feature learning efficiency. The B-CNN architecture incorporates branch training, enabling effective training and inference with reduced model parameters. To further optimize resource usage, the framework integrates DoReFa-Net for weight quantization and fixed-point parameter representation. An early exit mechanism is introduced to support low-latency, energy-efficient predictions. The proposed B-CNN hardware accelerator is implemented using TSMC 16 nm CMOS technology, incorporating power gating techniques to manage memory power consumption. Post-layout simulations demonstrate that the proposed hardware accelerator operates at 500 MHz with a power consumption of 37.56 mW. The system achieves a disaster prediction accuracy of 88.18%, highlighting its effectiveness and suitability for low-power, real-time applications in aerial disaster monitoring. Full article