Search Results (568)

Cold seep ecosystems harbor unique microbial communities with potential for producing secondary metabolites. However, the metabolic potential of cold seep microorganisms in the South China Sea remains under-recognized. This study employed both culture-dependent and culture-independent approaches, including 16S rRNA amplicon sequencing and metagenomics, to investigate microbial communities and their potential for secondary metabolite production in the South China Sea cold seep. The results indicate microbial composition varied little between two non-reductive sediments but differed significantly from the reductive sediment, primarily due to Planctomycetes and Actinobacteria. Predicting the Secondary Metabolism Potential using Amplicon (PSMPA) predictions revealed 115 strains encoding more than 10 biosynthetic gene clusters (BGCs), with lower BGC abundance in reductive sediment. Culture-dependent studies showed Firmicutes as the dominant cultivable phylum, with strains from shallow samples encoding fewer BGCs. Metagenomic data confirmed distinct microbial compositions and BGC distributions across sediment types, with cold seep type having a stronger influence than geographic location. Certain BGCs showed strong correlations with sediment depth, reflecting microbial adaptation to nutrient-limited environments. This study provides a comprehensive analysis of the metabolic capabilities of South China Sea cold seep microorganisms and reveals key factors influencing their secondary metabolic potential, offering valuable insights for the efficient exploration of cold seep biological resources. Full article

Al/AgO seawater-activated batteries with high specific energy and high specific power are widely used at present. The AgO electrode determines the performance of the battery, with its active material utilization rate having a significant impact on the specific capacity, energy density and discharge capacity of the battery. Therefore, this study briefly introduces the structure and working principle of Al/AgO seawater-activated batteries. Starting from the AgO material itself, common preparation methods for such positive electrode materials—including sintered silver oxide electrodes, pressed silver oxide electrodes and thin-film silver oxide electrodes—are introduced, and the factors influencing their electrochemical performance are analyzed in depth. We elaborate on the relevant research progress regarding AgO electrodes in terms of improving battery performance, detailing the effects of the silver powder’s morphology, porosity, purity, ordered structure, surface treatment and doping modification methods on silver oxide electrodes. Finally, various methods for improving the electrochemical performance of silver oxide electrodes are detailed. Current challenges and possible future research directions are analyzed, and prospects for the future development of high-specific-energy batteries based on AgO electrode materials are discussed. Overall, this review highlights the characteristics of Al/AgO batteries, providing a theoretical basis for the development of high-performance Al/AgO batteries. Full article

Although age-related changes in the gut microbiome of pigs have been extensively studied, the dynamic patterns of fecal microbiota and SCFAs during the gestation-to-weaning period in sows remain poorly characterized. We aim to characterize the changes in fecal microbiota and SCFAs from pregnancy to weaning, and to investigate their associations with maternal weight gain during gestation. We systematically collected 100 fecal samples at four time points (day 30 of pregnancy (T1), 1–2 days before delivery (T2), day 10 after delivery (T3), and day 21 of weaning stage (T3)), and measured the body weight of sows at T1 (132 kg ± 10.8) and T2 (205 kg ± 12.1). The primary nutrient components of the diets during the gestation and lactation periods are summarized. All fecal samples were subjected to 16S rRNA gene sequencing. We found that a high proportion of crude fiber (bran) is a key feature of the gestation diet, which may affect enterotype shifts and gut microbial composition. Sows fed a high-fiber diet showed significant enrichment of gut microbiota, including genera such as Prevotellaceae_UCG-003, Prevotellaceae_NK3B31_group, and Prevotella_9 during the gestational period (LDA score > 2). Moreover, Eubacterium_coprostanoligenes_group (average relative abundance: 5.5%) and Lachnospiraceae_NK4A136_group (average relative abundance: 2.5%) were the dominant bacteria during the lactation stage. Fecal propionate and butyrate levels were lowest in late gestation, and propionate negatively and acetate positively correlated with body weight change (p < 0.05). Additionally, certain Prevotella taxa were associated with arachidonic acid metabolism and acetate production (p < 0.05). Our study identified key microbial communities across four stages from gestation to weaning and revealed that dietary patterns can shape the sow gut microbiota. Furthermore, we observed significant correlations between SCFAs and body weight change during pregnancy. These findings provide a scientific basis and theoretical support for future strategies aimed at modulating gut microbiota and targeting SCFAs to improve maternal health and productivity throughout the gestation-to-weaning period. Full article

Electrospinning has emerged as a highly effective technique for fabricating micro- and nanofibers, which are characterized by high porosity, large surface area, and structural mimicry of the extracellular matrix (ECM). These properties render it particularly suitable for biomedical applications. This review provides a comprehensive overview of recent developments in electrospinning-based strategies across various biomedical fields, including tissue engineering, drug delivery, wound healing, enzyme immobilization, biosensing, and protective materials. The distinctive advantages of electrospun fibers—such as excellent biocompatibility, tunable architecture, and facile surface functionalization—are discussed, alongside challenges such as the toxicity of organic solvents and limitations in scalability. Emerging approaches, including environmentally benign electrospinning techniques and integration with advanced technologies such as 3D printing and microfluidics, present promising solutions for intelligent and personalized biomedical applications. Full article

In this paper, we investigate the regularization of the backward problem for a diffusion process with a time-fractional derivative. We propose a novel double-parameter regularization scheme that integrates the quasi-reversibility method for the governing equation with the quasi-boundary method. Theoretical analysis establishes the regularity and the convergence analysis of the regularized solution, along with a convergence rate under an a-priori regularization parameter choice rule in the general-dimensional case. Finally, numerical experiments validate the effectiveness of the proposed scheme. Full article

Synthetic aperture radar (SAR) image target recognition has important application values in security reconnaissance and disaster monitoring. However, due to speckle noise and target orientation sensitivity in SAR images, traditional spatial domain recognition methods face challenges in accuracy and robustness. To effectively address these challenges, we propose a dual-branch spatial-frequency domain fusion recognition method with cross-attention, achieving deep fusion of spatial and frequency domain features. In the spatial domain, we propose an enhanced multi-scale feature extraction module (EMFE), which adopts a multi-branch parallel structure to effectively enhance the network’s multi-scale feature representation capability. Combining frequency domain guided attention, the model focuses on key regional features in the spatial domain. In the frequency domain, we design a hybrid frequency domain transformation module (HFDT) that extracts real and imaginary features through Fourier transform to capture the global structure of the image. Meanwhile, we introduce a spatially guided frequency domain attention to enhance the discriminative capability of frequency domain features. Finally, we propose a cross-domain feature fusion (CDFF) module, which achieves bidirectional interaction and optimal fusion of spatial-frequency domain features through cross attention and adaptive feature fusion. Experimental results demonstrate that our method achieves significantly superior recognition accuracy compared to existing methods on the MSTAR dataset. Full article

To address challenges of background interference and limited multi-scale feature extraction in infrared small target detection, this paper proposes a YOLO-HVS detection algorithm inspired by the human visual system. Based on YOLOv8, we design a multi-scale spatially enhanced attention module (MultiSEAM) using multi-branch depth-separable convolution to suppress background noise and enhance occluded targets, integrating local details and global context. Meanwhile, the C2f_DWR (dilation-wise residual) module with regional-semantic dual residual structure is designed to significantly improve the efficiency of capturing multi-scale contextual information by expanding convolution and two-step feature extraction mechanism. We construct the DroneRoadVehicles dataset containing 1028 infrared images captured at 70–300 m, covering complex occlusion and multi-scale targets. Experiments show that YOLO-HVS achieves mAP50 of 83.4% and 97.8% on the public dataset DroneVehicle and the self-built dataset, respectively, which is an improvement of 1.1% and 0.7% over the baseline YOLOv8, and the number of model parameters only increases by 2.3 M, and the increase of GFLOPs is controlled at 0.1 G. The experimental results demonstrate that the proposed approach exhibits enhanced robustness in detecting targets under severe occlusion and low SNR conditions, while enabling efficient real-time infrared small target detection. Full article

Astrocyte activation and gender differences play critical roles in the prognosis following stroke. Recent studies have shown that optogenetic technology can promote brain repair after stroke by activating astrocytes in male rats. However, it remains unclear whether gender differences influence the efficacy of optogenetic activation of astrocytes in regulating post-stroke brain repair and its underlying mechanisms. In this study, we activated astrocytes in the ipsilateral cortex of adult glial fibrillary acidic protein-channelrhodopsin 2-enhanced yellow fluorescent protein (GFAP-ChR2-EYFP) transgenic Sprague Dawley rats using optogenetic stimulation at 24, 36, 48, and 60 h after inducing photothrombosis stroke. Neurobehavioral tests, cresyl violet staining, RT-qPCR, Western blot, and immunofluorescence analysis were performed on both female and male rats. Our results showed that male rats exhibited significant improvements in behavioral scores and reduction in infarct size after optogenetic activation of astrocytes at three days post-stroke (p < 0.05), whereas no significant changes were observed in female rats. Additionally, in female rats, the expression of basic fibroblast growth factor (bFGF) increased after ischemic stroke and astrocytic optogenetic stimulation (p < 0.05), leading to enhanced endothelial cell proliferation compared to male rats (p < 0.05). In vitro experiments further demonstrated that the astrocyte activation was inhibited in the presence of bFGF (p < 0.05). These findings suggest that the increase in bFGF levels in females following stroke may inhibit the optogenetic activation of astrocytes, thereby attenuating the therapeutic effect of astrocyte activation on post-stroke brain repair. This study provides important insights into the gender-specific roles of astrocytes in the acute phase of ischemic stroke. Full article

High aspect ratio (HAR) sample-induced aberrations seriously affect the topography measurement for the bottom of the microstructure by coherence scanning interferometry (CSI). Previous research proposed an aberration compensating method using deformable mirrors at the conjugate position of the pupil. However, it failed to compensate for the shift-variant aberrations introduced by the HAR hybrid trench array composed of multiple trenches with different parameters. Here, we propose a computational aberration correction method for measuring the topography of the HAR structure by the particle swarm optimization (PSO) algorithm without constructing a database and prior knowledge, and a phase filter in the spatial frequency domain is constructed to restore interference signals distorted by shift-variant aberrations. Since the aberrations of each sampling point are basically unchanged in the field of view corresponding to a single trench, each trench under test can be considered as a separate isoplanatic region. Therefore, a multi-channel aberration correction scheme utilizing the virtual phase filter based on isoplanatic region segmentation is established for hybrid trench array samples. The PSO algorithm is adopted to derive the optimal Zernike polynomial coefficients representing the filter, in which the interference fringe contrast is taken as the optimization criterion. Additionally, aberrations introduce phase distortion within the 3D transfer function (3D-TF), and the 3D-TF bandwidth remains unchanged. Accordingly, we set the non-zero part of the 3D-TF as a window function to preprocess the interferogram by filtering out the signals outside the window. Finally, experiments are performed in a single trench sample and two hybrid trench array samples with depths ranging from 100 to 300 μm and widths from 10 to 30 μm to verify the effectiveness and accuracy of the proposed method. Full article

In response to the challenges posed by complex and variable operating conditions of rolling bearings and the limited availability of labeled data, both of which hinder the effective extraction of key fault features and reduce diagnostic accuracy, this study introduces a model that combines a spatial attention (SA) mechanism with a multi-scale depthwise separable convolution module. The proposed approach first employs the Gramian angular difference field (GADF) to convert raw signals. This conversion maps the temporal characteristics of the signal into an image format that intrinsically preserves both temporal dynamics and phase relationships. Subsequently, the model architecture incorporates a spatial attention mechanism and a multi-scale depthwise separable convolutional module. Guided by the attention mechanism to concentrate on discriminative feature regions and to suppress noise, the convolutional component efficiently extracts hierarchical features in parallel through the multi-scale receptive fields. Furthermore, the trained model serves as a pre-trained network and is transferred to novel variable-condition environments to enhance diagnostic accuracy in few-shot scenarios. The effectiveness of the proposed model was evaluated using bearing datasets and field-collected industrial data. Experimental results confirm that the proposed model offers outstanding fault recognition performance and generalization capability across diverse working conditions, small-sample scenarios, and real industrial environments. Full article

With the increasing proportion of grid-connected capacity of new energy units, such as wind power and photovoltaics, accurately constructing simulation models of these units is of great significance to the study of new power systems. However, the actual control strategies and parameters of many new energy units are often unavailable due to factors like outdated equipment or commercial confidentiality. This unavailability creates modeling challenges that compromise accuracy, ultimately affecting grid-connected power generation performance. Aiming at the problem of accurate modeling of fault ride-through control of new energy turbine “black box” controllers, this paper proposes an accurate identification method of fault ride-through control characteristics of doubly fed wind turbines based on hardware-in-the-loop testing. Firstly, according to the domestic and international new energy turbine fault ride-through standards, the fault ride-through segmentation control characteristics are summarized, and a general structured model for fault ride-through segmentation control of doubly fed wind turbines is constructed; Secondly, based on the measured hardware-in-the-loop data of the doubly fed wind turbine black box controller, the method of data segmentation preprocessing and structured model identification of the doubly fed wind turbine is proposed by utilizing statistical modal features and genetic Newton’s algorithm, and a set of generalized software simulation platforms for parameter identification is developed by combining Matlab and BPA; lastly, using the measured data of the doubly fed wind turbine in the black box and the software platform, the validity and accuracy of the proposed parameter identification method and software are tested in the simulation. Finally, the effectiveness and accuracy of the proposed parameter identification method and software are simulated and tested by using the measured data of black box doubly fed wind turbine and the software platform. The results show that the method proposed in this paper has higher recognition accuracy and stronger robustness, and the recognition error is reduced by 2.89% compared with the traditional method, which is of high value for engineering applications. Full article

Amidst the swift progression of urban expansion, transformations in land utilization have become increasingly pronounced, posing significant threats to ecosystem coherence and continuity. Establishing a well-designed ecological security pattern (ESP) framework proves essential for preserving environmental equilibrium and enhancing species diversity. This investigation centers on the Fuzhou urban agglomeration as its primary study zone, employing the patch-oriented land utilization simulation (PLUS) approach to forecast 2030 land cover modifications under environmentally conscious conditions. By integrating morphological spatial configuration assessment (MSPA) with habitat linkage evaluation, critical ecological hubs were pinpointed. Subsequent application of electrical circuit principles alongside the minimal cumulative resistance (MCR) methodology enabled the identification of vital ecological pathways and junctions, culminating in the development of a comprehensive territorial ESP framework. Key findings reveal the subsequent outcomes: (1) the main land use type in the Fuzhou metropolitan area is woodland, which accounts for over 80% of its area, and under the ecological priority scenario for 2030, woodland fragmentation was significantly improved; (2) ecological sources are mainly distributed in the northwest, northeast, and central regions, with their total area proportion increasing to 40.49% by 2030; (3) we constructed 35 ecological corridors and 42 ecological nodes, including 14 key ecological pinch points, 9 potential ecological pinch points, and 4 ecological barrier points; and (4) the final ESP formed the pattern of “three cores, three areas, multiple corridors, and multiple sources,” providing strong support for ecological protection and regional sustainable development in the Fuzhou metropolitan area. In this research, we explore the coupled methods of land use simulation and ecological network construction, offering insights for optimizing ESPs in other rapidly urbanizing areas. Full article

Tuberculosis (TB) remains one of the most significant challenges to global public health. Vaccine development is a critical strategy for the prevention and control of TB. However, evaluating the protective efficacy of TB vaccines faces numerous challenges, particularly in the selection of animal models and bacterial strains. Variations in animal models, challenge strains, challenge routes, and doses can significantly impact the outcomes of preclinical evaluations. This article highlights the importance of standardizing preclinical evaluation models, summarizes the animal models and challenge strains used in novel TB vaccine candidates, efficacy studies, and discusses the advantages and limitations of commonly used animal models in TB vaccine research. It also points out the differential performance of various animal models in simulating protection and pathology. Given the current limitations of using a narrow range of challenge strains and the lack of standardized infection routes and doses, this article calls for the establishment of more standardized challenge strains and the development of standardized evaluation models to improve the reliability and generalizability of new TB vaccine efficacy assessments. Full article

The electrolyte flow field plays a pivotal role in determining the electrochemical performance of aqueous AgO-Al batteries. However, traditional flow field structures often suffer from the formation of dead zones, leading to uneven mass transport and side reactions. In this study, a flow field optimization strategy incorporating dead-zone compensation is proposed, which identifies localized dead zones and implements structural corrections to enhance electrolyte distribution. Numerical simulations reveal improved flow uniformity and reduced concentration polarization, while experimental validation confirms enhanced battery performance under the optimized configuration. This work provides a generalizable approach for electrolyte flow field design that improves mass transfer and electrochemical efficiency, offering practical insights for the development of high-performance aqueous batteries. Full article

Objectives: Patients with interstitial lung disease (ILD) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are at high risk of severe coronavirus disease 2019. It is unclear whether anti-viral cellular and humoral immunity is impacted in patients with ILD in the presence of immune disorders and immunosuppressive therapy. This results in poor control of viral infections following SARS-CoV-2 infection. We aimed to highlight the clinical management of patients with ILD with regard to the adjustment of anti-inflammatory therapy during SARS-CoV-2 infection. Methods: We compared viral clearance, antibody levels, and T-cell immune response between healthy controls and patients with connective tissue disease-related ILD (CTD-ILD) or interstitial pneumonia with autoimmune features (IPAF). Results: Patients with ILD exhibited a higher viral load than the control group (1.58 × 10⁶ vs. 2.37 × 10³ copies/mL, p = 0.018), as well as a significantly lower level of neutralizing antibodies against the wild-type (WT) virus (7.01 vs. 625.6, p < 0.0001) and Omicron BA.5 (7.19 vs. 128.4, p < 0.001). Similarly, a lower virus-specific T-cell (VST) immune response was observed 14 days post-symptom onset in the ILD group (CD4⁺ VSTs: 0.018 vs. 0.082, p = 0.005; CD8⁺ VSTs: 0.0008 vs. 0.047, p = 0.004). The ILD group had no other heightened inflammatory biomarkers compared with the control group. Conclusions: Our study provides novel evidence of the underlying interaction between virus clearance and host immune status and sheds light on the clinical management of patients with ILD with regard to the adjustment of anti-inflammatory therapy during SARS-CoV-2 infection. Full article