Search Results (1,140)

Background. Understanding the interference patterns of respiratory viruses could be important for shedding light on potential strategies to combat these human infectious agents. Objective. To investigate the possible interactions between adenovirus type 2 (AdV2), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza A/H1N1 pandemic (H1N1pdm09) using the A549 cell line. Methods. Single infections, co-infections, and superinfections (at 3 and 24 h after the first virus infection) were performed by varying the multiplicity of infection (MOI). Virus replication kinetics and the mRNA expression of IFN-α, IL-1α and IL-6 were assessed by real-time qPCR. Results. Co-infection experiments showed different growth dynamics, depending on the presence of the specific virus and time. AdV2 replication remained stable or possibly enhanced in the presence of co-infection with each of the two H1N1pdm09 and SARS-CoV-2 viruses used. In contrast, SARS-CoV-2 replication was facilitated by H1N1pdm09 but hindered by AdV2, indicating possible different interactions. Finally, H1N1pdm09 replication exhibited variably effectiveness in the presence of AdV2 and SARS-CoV-2. Superinfection experiments showed that the replication of all viruses was affected by time and MOI. The mRNA expression of IFN-α, IL-1α and IL-6 showed divergent results depending on the virus used and the time of infection. Conclusions. Further investigation of co-infection or superinfection may be helpful in understanding the potential relationship involved in the outcome of viral respiratory infection in the human population. Full article

In this study, 3,4-diaminobenzoic acid (DABA) was introduced into the porphyrin metal–organic framework (PCN-224) for the first time to prepare a ratiometric fluorescent probe (PCN-224-DABA) to quantitatively detect ferric iron (Fe(III)) and selenium (IV) (Se(IV)). The fluorescence attributed to the DABA of PCN-224-DABA at 345 nm can be selectively quenched by Fe(III) and Se(IV), but the fluorescence emission peak attributed to tetrakis (4-carboxyphenyl) porphyrin (TCPP) at 475 nm will not be disturbed. Therefore, the ratio of I_345nm/I_475nm with an excitation wavelength of 270 nm can be designed to determine Fe(III) and Se(IV). After the experimental parameters were systematically optimized, the developed method shows good selectivity and interference resistance for Fe(III) and Se(IV) detection, and has good linearity in the ranges of 0.01–4 μM and 0.01–15 μM for Fe(III) and Se(IV) with a limit of detection of 0.045 μM and 0.804 μM, respectively. Furthermore, the quenching pattern was investigated through the Stern–Volmer equation, and the results suggest that both Se(IV) and Fe(III) quenched on PCN-224-DABA can be attributed to the dynamic quenching. Finally, the constructed ratiometric fluorescent probe was applied in the spiked detection of lake water samples, which shows good applicability in real sample analysis. Moreover, the Fe(III) and Se(IV) contents in spinach and selenium-enriched rice were determined, respectively. Full article

We present a novel high-resolution complex field extraction technique utilizing U-Net-based architecture to effectively overcome the inherent resolution limitations of polarization cameras with micro-polarized arrays. Our method extracts high-resolution complex field information, achieving a resolution comparable to that of the original polarization camera. Utilizing the parallel phase-shifting digital holography technique, we extracted high-resolution complex field information from four high-resolution phase-shifted interference patterns predicted by our network directly at the hologram plane. Extracting the object’s complex field directly at the hologram plane rather than the object’s plane, our method eliminates the dependency on numerical propagation during dataset acquisition, enabling reconstruction of objects at various depths without DC and conjugate noise. By training the network with real-valued interference patterns and using only a single pair of low- and high-resolution input and ground truth interference patterns, we simplify computational complexity and improve efficiency. Our simulations demonstrate the network’s robustness to variations in random phase distributions and transverse shifts in the input patterns. The effectiveness of the proposed method is demonstrated through numerical simulations, showing an average improvement of over 4 dB in peak-signal-to-noise ratio and 25% in intensity normalized cross-correlation metrics for object reconstruction quality. Full article

As for the fault diagnosis process of a reciprocating compressor, vibration signals are often non-stationary, nonlinear, and multi-coupled, which makes it difficult to conduct effective fault information extraction. In this paper, a method based on optimized resonance-based sparse signal decomposition (RSSD) and refined composite multiscale dispersion entropy (RCMDE) is proposed. The quality factors in RSSD are optimized by atom search optimization (ASO) primarily, then the optimal quality factors are applied to the RSSD of reciprocating compressor fault signals. The noise interference in the original vibration signal can be effectively distinguished from the low resonance component after decomposition. The genetic algorithm (GA) is employed to optimize the core parameters of RCMDE. Finally, the RCMDE of the low-resonance component is extracted as the eigenvalue for pattern recognition. The experimental study illustrates that the spring failure, valve wear, and normal valve conditions of reciprocating compressors can be effectively distinguished by the proposed method. Full article

In volcanic regions, the analysis of Thermal InfraRed (TIR) satellite imagery for Land Surface Temperature (LST) retrieval is a valid technique to detect ground thermal anomalies. This allows us to achieve rapid characterization of the shallow thermal field, supporting ground surveillance networks in monitoring volcanic activity. However, surface temperature can be influenced by processes of different natures, which interact and mutually interfere, making it challenging to interpret the spatio-temporal variations in the LST parameter. In this paper, we use a workflow to detect the main thermal patterns in active volcanic areas by analyzing the Independent Component Analysis (ICA) results applied to satellite nighttime TIR imagery time series. We employed the proposed approach to study the surface temperature distribution at the Campi Flegrei caldera volcanic site (Southern Italy, Naples) during the 2013–2022 time interval. The results revealed the contribution of four main distinctive thermal patterns, which reflect the endogenous processes occurring at the Solfatara crater, the environmental processes affecting the Agnano plain, the unique microclimate of the Astroni crater, and the morphoclimatic aspects of the entire volcanic area. Full article

With its high area density, bit-patterned media recording (BPMR) is emerging as a leading technology for next-generation storage systems. However, as area density increases, magnetic islands are positioned closer together, causing significant two-dimensional (2D) interference. To address this, detection methods are used to interpret the received signal and mitigate 2D interference. Recently, the maximum a posteriori (MAP) detection algorithm has shown promise in improving BPMR performance, though it requires extrinsic information to effectively reduce interference. In this paper, to solve the 2D interference and improve the performance of BPMR systems, a model using low-density parity-check (LDPC) coding was introduced to supply the MAP detector with the needed extrinsic information, enhancing detection in a joint decoding model we call MAP–LDPC. Additionally, leveraging similarities between LDPC codes and graph neural networks (GNNs), we replace the traditional sum–product algorithm in LDPC decoding with a GNN, creating a new model, MAP–GNN. The simulation results demonstrate that MAP–GNN achieves superior performance, particularly when using the deep learning-based GNN approach over conventional techniques. Full article

Non-orthogonal multiple access (NOMA) provides higher spectral efficiency and access to more users than orthogonal multiple access. However, the issue of resource allocation in NOMA is dynamic and produces a high computation burden when using traditional methods. In this paper, a symmetry-aware double deep Q network (DDQN) algorithm in deep reinforcement learning is employed to allocate power to users in NOMA while guaranteeing quality of service for the weakest users. The research process is divided into two parts. Firstly, users in the communication system are grouped using a method that synergistically considers gain difference and similarity, exploiting symmetrical properties within the user groups. Secondly, the DDQN algorithm is used to allocate power to multiple users in a NOMA system, which utilizes the inherent symmetry in the signal-to-interference noise ratio of each user as an objective function. By recognizing and leveraging these symmetrical patterns, the algorithm can dynamically adjust the power allocation to optimize system performance. Finally, the proposed algorithm is compared with conventional NOMA power allocation algorithms and shows significant improvements in system performance. The results of the convergence function show that the algorithm proposed in this paper can converge in approximately 1800 iterations, which effectively solves the problem of large arithmetic and complex processes existing in the traditional method. Full article

The development of affordable and reliable methods for quantitative determination of stable atomic nuclei in aqueous solutions and adjuvant agents used in tumor chemotherapy is an important task in modern pharmaceutical chemistry. This work quantified the deuterium/prothium isotope ratio in aqueous solutions through an original two-dimensional diffuse laser scattering (2D-DLS) software and hardware system based on chemometric processing of discrete interference patterns (dynamic speckle patterns). For this purpose, 10 mathematical descriptors (d_i), similar to QSAR descriptors, were used. Correlation analysis of bivariate “log d_i—D/H” plots shows an individual set of multi-descriptors for a given sample with a given D/H ratio (ppm). A diagnostic sign (DS) of differentiation was established: the samples were considered homeomorphic if 6 out of 10 descriptors differed by less than 15% (n ≥ 180). The analytical range (r = 0.987) between the upper (D/H ≤ 2 ppm) and lower (D/H = 180 ppm) limits for the quantification of stable hydrogen nuclei in water and aqueous solutions were established. Using the Spirotox method, a «safe zone» for protozoan survival was determined between 50 and 130 ppm D/H. Here, we discuss the dispersive (DLS, LALLS) and optical properties (refractive index, optical rotation angle) of the solutions with different D/H ratios that define the diffuse laser radiation due to surface density inhomogeneities. The obtained findings may pave the way for the future use of a portable, in situ diffuse laser light scattering instrument to determine deuterium in water and aqueous adjuvants. Full article

Brain–computer interfaces (BCIs) are essential in advancing medical diagnosis and treatment by providing non-invasive tools to assess neurological states. Among these, motor imagery (MI), in which patients mentally simulate motor tasks without physical movement, has proven to be an effective paradigm for diagnosing and monitoring neurological conditions. Electroencephalography (EEG) is widely used for MI data collection due to its high temporal resolution, cost-effectiveness, and portability. However, EEG signals can be noisy from a number of sources, including physiological artifacts and electromagnetic interference. They can also vary from person to person, which makes it harder to extract features and understand the signals. Additionally, this variability, influenced by genetic and cognitive factors, presents challenges for developing subject-independent solutions. To address these limitations, this paper presents a Multimodal and Explainable Deep Learning (MEDL) approach for MI-EEG classification and physiological interpretability. Our approach involves the following: (i) evaluating different deep learning (DL) models for subject-dependent MI-EEG discrimination; (ii) employing class activation mapping (CAM) to visualize relevant MI-EEG features; and (iii) utilizing a questionnaire–MI performance canonical correlation analysis (QMIP-CCA) to provide multidomain interpretability. On the GIGAScience MI dataset, experiments show that shallow neural networks are good at classifying MI-EEG data, while the CAM-based method finds spatio-frequency patterns. Moreover, the QMIP-CCA framework successfully correlates physiological data with MI-EEG performance, offering an enhanced, interpretable solution for BCIs. Full article

With the intensification of climate change and human activities, the impacts of land use shifts on hydrological processes are becoming more pronounced, especially in regions with complex geographic, geological, and climatic conditions such as the Northeast Black Soil Region, China. This study quantitatively examines the variations in various land use types from 1980 to 2020 by means of a land use transfer matrix, and it incorporates the multi-year average runoff value to mitigate the interference of short-term climate fluctuations on the runoff trend, thereby enhancing the representativeness and stability of the simulation outcomes. The SWAT (Soil and Water Assessment Tool) model is employed to simulate land use alterations in different periods. The findings indicate that the area of farmland increased by 5.34% and the area of grassland decreased by 5.36% over 40 years. The areas of forest land and wetland have fluctuated significantly due to policy interventions and population growth. This study discovers that LUCC has resulted in a marginal increase in annual water yield. For instance, the water yield of paddy fields in 2020 amounts to 92.26 mm/year, which is 0.52–9.42% higher than the historical scenario and exhibits a notable upward trend in summer. Spatial analysis discloses regional disparities, with substantial changes in the hydrological behavior of northern watersheds (such as the Huma River) and southeastern regions (such as the Toudao River). The augmentation of wetland and forest coverage has effectively mitigated peak runoff, especially during extreme rainfall events. Wetlands have manifested strong water regulation capabilities and alleviated the impact of floods. This study quantitatively discloses the complex response pattern of LUCC to runoff by introducing a multi-scale analysis approach, which furnishes a scientific basis for flood risk assessment, land use optimization, and water resource management, and demonstrates the potential for extensive application in other countries and regions with similar climatic and topographic conditions. Full article

Litopenaeus vannamei, with an annual production of 5–6 million tons and a value of USD 50–60 billion, is a cornerstone of global aquaculture. However, molting-related losses of 5–20% significantly impact this industry, and the physiological mechanisms of molting remain unclear. This study aims to elucidate the role of eclosion hormone (EH) in molting regulation and enhances the understanding of molting physiology in L. vannamei. This study investigated the role of (EH) in L. vannamei molting regulation. Two EH cDNAs, LvEH I and LvEH II, were identified, and their expression patterns across tissues and seven molting stages (A, B, C, D0, D1, D2, and D3) were analyzed. LvEH I was predominantly expressed in the gill, epidermis, and eyestalk, while LvEH II was mainly expressed in the eyestalk and brain. LvEH I was highly expressed in the eyestalk, epidermis, and gills at the D2 and D3 stages of molting, whereas LvEH II was highly expressed in both the D2 (brain) and D3 (eyestalk) stages. RNA interference (RNAi) targeting LvEH I revealed its critical role in molting, as silencing LvEH I disrupted the expression of molting-regulation genes, ETH, CCAP, CHH, EH II, CDA, and bursicon (Burs), significantly delaying the molting process. These findings highlight both LvEH I and LvEH II as indispensable for normal molting in L. vannamei and provide a foundation for developing effective molting management strategies to reduce industry losses. Full article

Biochar has gained interest as a feed ingredient in livestock nutrition due to its functional properties, circularity, potential to reduce environmental impact, and alignment with sustainable agro-zootechnical practices. The in vivo effects of biochar are closely tied to its physical characteristics, which vary depending on the biomass used as feedstock and the production process. This variability can result in heterogeneity among biochar types used in animal nutrition, leading to inconsistent outcomes. The aim of this study was to characterize the metabolomic and functional properties of an aqueous biochar extract from vine pruning waste, in order to predict its potential in vivo effects as a functional feed ingredient. A metabolomic analysis of the biochar extracts was conducted using quadrupole time-f-light (QQTOF) high-performance liquid chromatography tandem mass spectrometry (HPLC MS/MS). Antimicrobial activity against E. coli F18+ and E. coli F4+ was assessed using standard growth inhibition assays, while quorum sensing in E. coli exposed to biochar extracts was evaluated using real-time PCR. Prebiotic activity was assessed by exposing selected Lactobacillus strains to the biochar extract, monitoring growth patterns to determine species-specific responses. The metabolomic profile revealed several distinct molecular classes, including multiple peaks for phenolic compounds. The extract significantly inhibited the growth of both E. coli pathotypes, reducing growth by 29% and 16% for the F4+ and F18+, respectively (p < 0.001). The relative expression of the genes involved in quorum sensing (MotA, FliA for biofilm formation, and FtsE, HflX for cell division) indicated that the observed inhibitory effects likely resulted from interference with flagellar synthesis, motility, and reduced cell division. The biochar extract also showed species-specific prebiotic potential. In conclusion, biochar derived from vine pruning waste represents a valuable feed ingredient with functional properties that may help to reduce antibiotic use in livestock production. Full article

Fat grafting has gained prominence in reconstructive and aesthetic surgery, necessitating accurate assessment methods for evaluating graft volume retention. This paper reviews various techniques for assessing fat and fat grafts, including their benefits and limitations. Three-dimensional (3D) scanning offers highly accurate, non-invasive volumetric assessments with minimal interference from breathing patterns. Magnetic resonance imaging (MRI) is recognized as the gold standard, providing precise volumetric evaluations and sensitivity to complications like oil cysts and necrosis. Computed tomography (CT) is useful for fat volume assessment but may overestimate retention rates. Ultrasonography presents a reliable, non-invasive method for measuring subcutaneous fat thickness. Other methods, such as digital imaging, histological analysis, and weight estimation, contribute to fat graft quantification. The integration of these methodologies is essential for advancing fat graft assessment, promoting standardized practices, and improving patient outcomes in clinical settings. Full article

Brush-like graft copolymers (A-g-B), in which linear A-blocks are randomly grafted onto the backbone of a brush-like B-block, exhibit intense strain-stiffening and high mechanical strength on par with load-bearing biological tissues such as skin and blood vessels. To elucidate molecular mechanisms underlying this tissue-mimetic behavior, in situ synchrotron X-ray scattering was measured during uniaxial stretching of bottlebrush- and comb-like graft copolymers with varying densities of poly(dimethyl siloxane) and poly(isobutylene) side chains. In an undeformed state, these copolymers revealed a single interference peak corresponding to the average spacing between the domains of linear A-blocks arranged in a disordered, liquid-like configuration. Under uniaxial stretching, the emergence of a distinct four-spot pattern in the small-angle region indicated the development of long-range order within the material. According to the affine deformation of a cubic lattice, the four-spot pattern’s interference maxima correspond to 110 reflections upon stretching along the [111] axis of the body-centered unit cell. The experimental findings were corroborated by computer simulations of dissipative particle dynamics that confirmed the formation of a bcc domain structure. Full article

This study systematically explores the sources and influencing factors of resistance encountered by magnetic flux leakage (MFL) detectors in natural gas pipelines through a theoretical analysis, experimental investigation, and numerical simulation. The research methodology involves the development of a fluid–structure interaction model using ABAQUS 2023 finite element software, complemented by the design and implementation of a pull-testing platform for MFL detectors. This platform simulates detector operation under various interference conditions and quantifies the resulting frictional resistance. The findings reveal that the primary source of frictional resistance is the contact interaction between the MFL detector and the pipeline wall. Key factors influencing the magnitude of this resistance include the detector’s mass, the structural design and materials of the sealing cups and support plates, as well as the surface roughness of the pipeline. Both experimental results and numerical simulations demonstrate a pronounced increase in frictional resistance with heightened interference levels. The theoretical model exhibits strong agreement with experimental data, though deviations are observed under conditions of severe interference. This study provides a detailed understanding of frictional resistance patterns under diverse structural and operational scenarios, offering both theoretical guidance and practical recommendations for the design of low-resistance MFL detectors. Full article