Search Results (107)

IgA nephropathy (IgAN) is the most common type of primary glomerulonephritis worldwide; however, the underlying mechanisms of this disease are not fully understood. This review explores several animal models that provide insights into IgAN pathogenesis, emphasizing the roles of aberrant IgA1 glycosylation and immune complex formation. It discusses spontaneous, immunization, and transgenic models illustrating unique aspects of IgAN development and progression. The animal models, represented by the grouped ddY (gddY) mouse, have provided guidance concerning the multi-hit pathogenesis of IgAN. In this paradigm, genetic and environmental factors, including the dysregulation of the mucosal immune system, lead to increased levels of aberrantly glycosylated IgA, nephritogenic immune complex formation, and subsequent glomerular deposition, followed by mesangial cell activation and injury. Additionally, this review considers the implications of clinical trials targeting molecular pathways influenced by IgAN (e.g., a proliferation-inducing ligand [APRIL]). Collectively, these animal models have expanded the understanding of IgAN pathogenesis while facilitating the development of therapeutic strategies that are currently under clinical investigation. Animal-model-based studies have the potential to facilitate the development of targeted therapies with reduced side effects for IgAN patients. Full article

Background: The rise of multi-drug-resistant Gram-negative bacteria necessitates the development of new antimicrobial agents. Cefiderocol shows promising activity by exploiting bacterial iron transport systems to penetrate the outer membranes of resistant pathogens. Objectives: This study evaluates the efficacy of cefiderocol testing methods and trailing effect impact using a ComASP^® Cefiderocol panel, disk diffusion (DD), and MIC test strips (MTS) compared to iron-depleted broth microdilution (ID-BMD). Methods: A total of 131 Gram-negative strains from clinical samples was tested by commercial methods and the gold standard. Results were interpreted as per 2024 and 2023 EUCAST guidelines. Results: ID-BMD revealed high cefiderocol susceptibility among Enterobacterales and Pseudomonas aeruginosa, with one Klebsiella pneumoniae isolate being resistant. Acinetobacter baumannii exhibited higher MIC values, particularly considering trailing effects that complicated MIC readings. ComASP^® showed 97% categorical agreement (CA) and 66% essential agreement (EA) with ID-BMD for Enterobacterales but failed to detect the resistant K. pneumoniae. DD tests demonstrated variable CA (72% or 93%), and 38% or 34% of strains within the ATU according to EUCAST Breakpoint Tables v13.0 and 14.0, respectively, with major errors only. MTS for P. aeruginosa had 100% CA but 44% EA, and often underestimated MIC values. Conclusions: The study emphasizes the need for standardized criteria to address trailing effects and ATU and highlights the discrepancies between testing methods. While cefiderocol resistance remains rare, accurate susceptibility testing is crucial for its effective clinical use. The findings suggest that current commercial tests have limitations, necessitating careful interpretation and potential supplementary testing to guide appropriate antibiotic therapy. Full article

Image inpainting aims to restore the damaged information in images, enhancing their readability and usability. Ancient paintings, as a vital component of traditional art, convey profound cultural and artistic value, yet often suffer from various forms of damage over time. Existing ancient painting inpainting methods are insufficient in extracting deep semantic information, resulting in the loss of high-frequency detail features of the reconstructed image and inconsistency between global and local semantic information. To address these issues, this paper proposes a Generative Adversarial Network (GAN)-based ancient painting inpainting method using multi-layer feature enhancement and frequency perception, named MFGAN. Firstly, we design a Residual Pyramid Encoder (RPE), which fully extracts the deep semantic features of ancient painting images and strengthens the processing of image details by effectively combining the deep feature extraction module and channel attention. Secondly, we propose a Frequency-Aware Mechanism (FAM) to obtain the high-frequency perceptual features by using the frequency attention module, which captures the high-frequency details and texture features of the ancient paintings by increasing the skip connections between the low-frequency and the high-frequency features, and provides more frequency perception information. Thirdly, a Dual Discriminator (DD) is designed to ensure the consistency of semantic information between global and local region images, while reducing the discontinuity and blurring differences at the boundary during image inpainting. Finally, extensive experiments on the proposed ancient painting and Huaniao datasets show that our proposed method outperforms competitive image inpainting methods and exhibits robust generalization capabilities. Full article

With the rapid development of 5G, UAV, and military communications, the data volume obtained by the non-cooperative perception system has increased exponentially, and the distributed system has become the development trend of the non-cooperative perception system. The data distribution service (DDS) produces a significant effect on the performance of distributed non-cooperative perception systems. However, the traditional DDS discovery protocol has problems such as false positive misjudgment and high flow overhead, so it can hardly adapt to a large multi-node distributed system. Therefore, the design of a DDS discovery protocol for large distributed system is technically challenging. In this paper, we proposed SDP-DCBF-SFF, a discovery protocol based on the Dynamic Counter Bloom Filter (DCBF) and Second Feedback Filter (SFF). The proposed discovery protocol coarsely filters the interested endpoints through DCBF and then accurately screens the uninterested endpoints through SFF to eliminate the connection requests of false positive endpoints and avoid extra flow overhead. The experimental results indicate that the proposed discovery protocol could effectively reduce the network overhead, and eliminate the false positive probability of endpoints in small, medium, large, and super large systems. In addition, it adopts the self-adaptive extension mechanism of BF to reduce the reconfiguration delay of BF and achieve the smallest system transmission delay. Therefore, the proposed discovery protocol has optimal comprehensive performance and system adaptability. Full article

Land use conflicts (LUCs) have become a significant global issue. Accurately identifying potential LUCs is crucial for mediating these conflicts, optimizing land use structure, and enhancing land use function. The necessary conditions of LUCs are land use multi-suitability (LUMS), land resource scarcity (LRS), and diversity of demands (DD). However, few studies have approached LUC identification from these three dimensions simultaneously. In addition, when assessing the diversity of demand, only human needs are considered and wildlife needs are ignored. In order to address this gap in the research, this paper constructs a novel framework for LUC identification and proposes an induction-oriented governance path. LUMS was evaluated from three aspects: construction suitability, cultivation suitability, and ecological suitability. LRS is measured from three dimensions: construction land, cultivated land, and ecological land scarcity. The DD is expanded into human and wildlife demand diversity. By analyzing the combination of LUMS, LRS, and DD, LUCs are classified using the potential LUC identification Rubik’s cube model, and corresponding governance paths are suggested. In Shandong Province, potential LUCs are relatively high, with strong, medium, and weak conflicts accounting for 27.39%, 57.10%, and 13.06%, respectively. Potential strong conflicts are mainly distributed in the metropolitan suburbs and in the western plain of Shandong Province. Cultivated land is the main potential land use conflict space. The new framework of LUC identification proposed in this paper can effectively identify potential LUCs. Our research provides scientific reference for sustainable land use. Full article

Recombinant adeno-associated viruses (rAAVs) play a pivotal role in the treatment of genetic diseases. However, current production and purification processes yield AAV-based preparations that often contain unwanted empty, partially filled or damaged viral particles and impurities, including residual host cell DNA and proteins, plasmid DNA, and viral aggregates. To precisely understand the composition of AAV preparations, we systematically compared four different single-stranded AAV (ssAAV) and self-complementary (scAAV) fractions extracted from the CsCl ultracentrifugation gradient using established methods (transduction efficiency, analytical ultracentrifugation (AUC), quantitative and digital droplet PCR (qPCR and ddPCR), transmission electron microscopy (TEM) and enzyme-linked immunosorbent assay (ELISA)) alongside newer techniques (multiplex ddPCR, multi-angle light-scattering coupled to size-exclusion chromatography (SEC-MALS), multi-angle dynamic light scattering (MADLS), and high-throughput sequencing (HTS)). Suboptimal particle separation within the fractions resulted in unexpectedly similar infectivity levels. No single technique could simultaneously provide comprehensive insights in the presence of both bioactive particles and contaminants. Notably, multiplex ddPCR revealed distinct vector genome fragmentation patterns, differing between ssAAV and scAAV. This highlights the urgent need for innovative analytical and production approaches to optimize AAV vector production and enhance therapeutic outcomes. Full article

Drug-resistant infectious diseases pose a substantial challenge and threat to medical regimens. While adaptive laboratory evolution provides foresight for encountering such situations, it has inherent limitations. Novel drug delivery systems (DDSs) have garnered attention for overcoming these hurdles. Multi-stimuli responsive DDSs are particularly effective due to their reduced background leakage and targeted drug delivery to specific host sites for pathogen elimination. Bacterial infections create an acidic state in the microenvironment (pH: 5.0–5.5), which differs from normal physiological conditions (pH: 7.4). Infected areas are characterized by the overexpression of hyaluronidase, gelatinase, phospholipase, and other virulence factors. Consequently, several effective stimuli-responsive DDSs have been developed to target bacterial pathogens. Additionally, biofilms, structured communities of bacteria encased in a self-produced polymeric matrix, pose a significant challenge by conferring resistance to conventional antimicrobial treatments. Recent advancements in nano-drug delivery systems (nDDSs) show promise in enhancing antimicrobial efficacy by improving drug absorption and targeting within the biofilm matrix. nDDSs can deliver antimicrobials directly to the biofilm, facilitating more effective eradication of these resilient bacterial communities. Herein, this review examines challenges in DDS development, focusing on enhancing antibacterial activity and eradicating biofilms without adverse effects. Furthermore, advances in immune system modulation and photothermal therapy are discussed as future directions for the treatment of bacterial diseases. Full article

Wildfires are one of the most devastating natural disasters in the world. This study proposes an innovative optical wildfire communication system (OWC) that leverages advanced optical technologies for wildfire monitoring and seamless communication towards the 5G and beyond (5GB) wireless networks. The multi-input–multi-output (MIMO) optical link among communication nodes is designed by gamma–gamma (GG) distribution under consideration of intensity modulation and direct-detection (IM/DD) following an on–off-keying (OOK) scheme. In this study, the performance metrics of the proposed MIMO link that enables unmanned aerial vehicles (UAVs) are analytically derived. The end-to-end (E2E) performance metrics and the novel closed-form expressions for the average BER (ABER) and outage probability ($P_{out}$) are investigated for the proposed system models. Furthermore, the simulation results are obtained based on the real experimental data. The obtained results in this study are improved spatial resolution and accuracy, enabling the detection by communication of even small-scale wildfires at their inception stages. In the further perspective of this research, the development of the proposed system holds the potential to revolutionize wildfire prevention and control efforts, making a substantial impact on safeguarding ecosystems, communities, and economies from the devastating effects of fires. Full article

The computation of the electric field in composite materials at the microscopic scale results in an immense number of degrees of freedom. Consequently, this often leads to prohibitively long computation times and extensive memory requirements, making direct computation impractical. In this study, one employs an innovative approach that integrates domain decomposition and model order reduction to retain local information while significantly reducing computation time. Domain decomposition allows for the division of the computational domain into smaller, more manageable subdomains, enabling parallel processing and reducing the overall complexity of the problem. Model order reduction further enhances this by approximating the solution in a lower-dimensional subspace, thereby minimising the number of unknown variables that need to be computed. Comparative analysis between the results obtained from the reduced model and those from direct resolution demonstrates that our method not only reduces computation time but also maintains accuracy. The new method effectively captures the essential characteristics of the electric field distribution in composite materials, ensuring that the local phenomena are accurately represented. This study provides a contribution to the field of computational electromagnetics by presenting a feasible solution to the challenges posed by the high computational demands of simulating composite materials at the microscopic scale. The proposed methodology offers a promising direction for future research and practical applications, enabling more efficient and accurate simulations of complex material systems. Full article

In the literature, the hierarchy of value retention strategies (R-strategies) is utilized to describe the impacts on various circular economy (CE) factors. However, this approach is not suitable for batteries, such as the vanadium flow battery (VFB), due to its technical complexity. The presented model primarily focuses on VFBs, as a deep technical understanding is identified as a fundamental prerequisite for a comprehensive CE analysis. Based on the R-strategies, a new model called the dynamic multi-dimensional value retention strategy model (DDS) is developed accordingly. The DDS divides the R-strategies into three dimensions, as changes in the studied object each have a unilateral influence on the underlying dimensions. In addition, interactions among the R-strategies within the dimensions are observed. Moreover, the model enables the transparent and comprehensible examination of various CE objective factors. Through the model, future adjustments to CE for batteries can be analyzed and quantified. In particular, the analysis yields new insights into individual end-of-life (EoL) strategies, based on new findings regarding the VFB. Consequently, important new perspectives on the VFB are also illuminated. The DDS model is applicable to other complex technologies as well as simple product systems. Full article

Neonatal epilepsy is an early postnatal brain disorder, and automatic seizure detection is crucial for timely diagnosis and treatment to reduce potential brain damage. This work proposes a novel Lightweight Multi-Attention Network, LMA-EEGNet, for diagnosing neonatal epileptic seizures from multi-channel EEG signals employing dilated depthwise separable convolution (DDS Conv) for feature extraction and using pointwise convolution followed by global average pooling for classification. The proposed approach substantially reduces the model size, number of parameters, and computational complexity, which are crucial for real-time detection and clinical diagnosis of neonatal epileptic seizures. LMA-EEGNet integrates temporal and spectral features through distinct temporal and spectral branches. The temporal branch uses DDS Conv to extract temporal features, enhanced by a channel attention mechanism. The spectral branch utilizes similar convolutions alongside a spatial attention mechanism to highlight key frequency components. Outputs from both branches are merged and processed through a pointwise convolution layer and a global average pooling layer for efficient neonatal seizure detection. Experimental results show that our model, with only 2471 parameters and a size of 23 KB, achieves an accuracy of 95.71% and an AUC of 0.9862, demonstrating its potential for practical deployment. This study provides an effective deep learning solution for the early detection of neonatal epileptic seizures, improving diagnostic accuracy and timeliness. Full article

This study proposes an enhanced Kepler Optimization (EKO) algorithm, incorporating fractional-order components to develop a Proportional-Integral-First-Order Double Derivative (PI–(1+DD)) controller for frequency stability control in multi-area power systems with wind power integration. The fractional-order element facilitates efficient information and past experience sharing among participants, hence increasing the search efficiency of the EKO algorithm. Furthermore, a local escaping approach is included to improve the search process for avoiding local optimization. Applications were performed through comparisons with the 2020 IEEE Congress on Evolutionary Computation (CEC 2020) benchmark tests and applications in a two-area system, including thermal and wind power. In this regard, comparisons were implemented considering three different controllers of PI, PID, and PI–(1+DD) designs. The simulations show that the EKO algorithm demonstrates superior performance in optimizing load frequency control (LFC), significantly improving the stability of power systems with renewable energy systems (RES) integration. Full article

In many technical applications, understanding the behavior of tribological contacts is pivotal for enhancing efficiency and lifetime. Traditional experimental investigations into tribology are often both costly and time-consuming. A more profound insight can be achieved through elastohydrodynamic lubrication (EHL) simulation models, such as the ifas-DDS, which determines precise friction calculations in reciprocating pneumatic seals. Similar to other distributed parameter simulations, EHL simulations require a labor-intensive resolution process. Physics-informed neural networks (PINNs) offer an innovative method to expedite the computation of such complex simulations by incorporating the underlying physical equations into the neural network’s parameter optimization process. A hydrodynamic PINN framework has been developed and validated for a variant of the Reynolds equation. This paper elucidates the framework’s capacity to handle multi-case scenarios—utilizing one PINN for various simulations—and its ability to extrapolate solutions beyond a limited training domain. The outcomes demonstrate that PINNs can overcome the typical limitation of neural networks in extrapolating the solution space, showcasing a significant advancement in computational efficiency and model adaptability. Full article

The presence of dark matter (DM) within neutron stars (NSs) can be introduced by different accumulation scenarios in which DM and baryonic matter (BM) may interact only through the gravitational force. In this work, we consider asymmetric self-interacting bosonic DM, which can reside as a dense core inside the NS or form an extended halo around it. It is seen that depending on the boson mass ($m_{\chi }$), self-coupling constant ($\lambda$) and DM fraction ($F_{\chi }$), the maximum mass, radius and tidal deformability of NSs with DM admixture will be altered significantly. The impact of DM causes some modifications in the observable features induced solely by the BM component. Here, we focus on the widely used nuclear matter equation of state (EoS) called DD2 for describing NS matter. We show that by involving DM in NSs, the corresponding observational parameters will be changed to be consistent with the latest multi-messenger observations of NSs. It is seen that for $m_{\chi }\gtrsim 200$ MeV and $\lambda \lesssim 2\pi$, DM-admixed NSs with $4\%\lesssim F_{\chi }\lesssim 20\%$ are consistent with the maximum mass and tidal deformability constraints. Full article

Diagnostic delay (DD) is associated with poor radiological and quality of life outcomes in axial spondyloarthritis (ax-SpA) and ankylosing spondylitis (AS). The female (F) population is often misdiagnosed, as classification criteria were previously studied mostly in males (M). We conducted a systematic review to investigate (i) the difference in DD between the sexes, the impact of HLA*B27 and clinical and social factors (work and education) on this gap, and (ii) the possible influence of the year of publication (before and after the 2009 ASAS classification criteria), geographical region (Europe and Israel vs. extra-European countries), sample sources (mono-center vs. multi-center studies), and world bank (WB) economic class on DD in both sexes. We searched, in PubMed and Embase, studies that reported the mean or median DD or the statistical difference in DD between sexes, adding a manual search. Starting from 399 publications, we selected 26 studies (17 from PubMed and Embase, 9 from manual search) that were successively evaluated with the modified Newcastle–Ottawa Scale (m-NOS). The mean DD of 16 high-quality (m-NOS > 4/8) studies, pooled with random-effects meta-analysis, produces results higher in F (1.48, 95% CI 0.83–2.14, p < 0.0001) but with significant results at the second analysis only in articles published before the 2009 ASAS classification criteria (0.95, 95% CI 0.05–1.85, p < 0.0001) and in extra-European countries (3.16, 95% CI 2.11–4.22, p < 0.05). With limited evidence, some studies suggest that DD in F might be positively influenced by HLA*B27 positivity, peripheral involvement, and social factors. Full article