Search Results (735)

TasA gene, encoding a functional amyloid protein critical for biofilm formation and antimicrobial activity, was cloned from the endophytic strain Bacillus amyloliquefaciens BS-3, isolated from rubber tree roots. This study identified the shortest functional TasA variant (483 bp, 160 aa) reported to date, featuring unique amino acid substitutions in conserved domains. Bioinformatics analysis predicted a signal peptide (1–27 aa) and transmembrane domain (7–29 aa), which were truncated to optimize heterologous expression. Two prokaryotic vectors (pET28a and pCZN1) were constructed, with pCZN1-TasA expressed solubly in Escherichia coli Arctic Express at 15 °C, while pET28a-TasA formed inclusion bodies at 37 °C. Purified recombinant TasA exhibited potent antifungal activity, achieving 98.6% ± 1.09 inhibition against Colletotrichum acutatum, 64.77% ± 1.34 against Alternaria heveae. Notably, TasA completely suppressed spore germination in C. acutatum and Oidium heveae Steinmannat 60 μg/mL. Structural analysis via AlphaFold3 revealed that truncation enhanced protein stability. These findings highlight BS-3-derived TasA as a promising biocontrol agent, providing molecular insights for developing protein-based biopesticides against rubber tree pathogens. Full article

The rapid evolution of pathogens and the limited genetic diversity of hosts are two major factors contributing to the plant pathogenic phenomenon known as the loss of disease resistance in maize (Zea mays L.). It has emerged as a significant biological stressor threatening the global food supplies and security. Based on previous cross-species homologous gene screening assays conducted in the laboratory, this study identified the maize disease-resistance candidate gene ZmNLR-7 to investigate the maize immune regulation mechanism against Bipolaris maydis. Subcellular localization assays confirmed that the ZmNLR-7 protein is localized in the plasma membrane and nucleus, and phylogenetic analysis revealed that it contains a conserved NB-ARC domain. Analysis of tissue expression patterns revealed that ZmNLR-7 was expressed in all maize tissues, with the highest expression level (5.11 times) exhibited in the leaves, and that its transcription level peaked at 11.92 times 48 h post Bipolaris maydis infection. Upon inoculating the ZmNLR-7 EMS mutants with Bipolaris maydis, the disease index was increased to 33.89 and 43.33, respectively, and the lesion expansion rate was higher than that in the wild type, indicating enhanced susceptibility to southern corn leaf blight. Physiological index measurements revealed a disturbance of ROS metabolism in ZmNLR-7 EMS mutants, with SOD activity decreased by approximately 30% and 55%, and POD activity decreased by 18% and 22%. Moreover, H₂O₂ content decreased, while lipid peroxide MDA accumulation increased. Transcriptomic analysis revealed a significant inhibition of the expression of the key genes NPR1 and ACS6 in the SA/ET signaling pathway and a decrease in the expression of disease-related genes ERF1 and PR1. This study established a new paradigm for the study of NLR protein-mediated plant immune mechanisms and provided target genes for molecular breeding of disease resistance in maize. Overall, these findings provide the first evidence that ZmNLR-7 confers resistance to southern corn leaf blight in maize by synergistically regulating ROS homeostasis, SA/ET signal transduction, and downstream defense gene expression networks. Full article

The aim was to investigate the impact of laser parameters on the surface morphology of ablated graphene and elucidate the interaction mechanism between carbon materials and femtosecond lasers. A pulsed laser with a wavelength of 1030 nm is employed to infer the ablation threshold of the surface and interface of graphene coatings formed through ultrasonic spraying. The ablation threshold of the coating–substrate interface is verified by numerical simulation. Incorporating the data of groove width and depth obtained from a three-dimensional profilometer and finite element simulation, an in-depth analysis of the threshold conditions of laser ablation in coating materials is accomplished. The results indicate that when the femtosecond laser frequency is 10 kHz, the pulse width is 290 fs, and the energy density reaches 0.057 J/cm², the graphene material can be effectively removed. When the energy density is elevated to 2.167 J/cm², a complete ablation of a graphite coating with a thickness of 1.5 μm can be achieved. The findings of this study validate the evolution law and linear relationship of ablation crater morphology, offering new references for microstructure design and the selection of controllable laser processing parameters. Full article

Accurate identification of active-layer compositions in organic photovoltaic (OPV) devices often relies on invasive techniques such as electrical measurements or material extraction, which risk damaging the device. In this study, we propose a non-invasive classification approach based on simulated full-device absorption spectra. To account for fabrication-related variability, the active-layer thickness varied by over ±15% around the optimal value, creating a realistic and diverse training dataset. A multilayer perceptron (MLP) neural network was applied with various activation functions, optimization algorithms, and data split ratios. The optimized model achieved classification accuracies exceeding 99% on both training and testing sets, with minimal sensitivity to random initialization or data partitioning. These results demonstrate the potential of applying deep learning to spectral data for reliable, non-destructive OPV composition classification, paving the way for integration into automated manufacturing diagnostics and quality control workflows. Full article

The image style transfer task aims to apply the style characteristics of a reference image to a content image, generating a new stylized result. While many existing methods focus on designing feature transfer modules and have achieved promising results, they often overlook the entanglement between content and style features after transfer, making effective separation challenging. To address this issue, we propose a Dual-Branch Decoupled Image Style Transfer Network (DBDST-Net) to better disentangle content and style representations. The network consists of two branches: a Content Feature Decoupling Branch, which captures fine-grained content structures for more precise content separation, and a Style Feature Decoupling Branch, which enhances sensitivity to style-specific attributes. To further improve the decoupling performance, we introduce a dense-regressive loss that minimizes the discrepancy between the original content image and the content reconstructed from the stylized output, thereby promoting the independence of content and style features while enhancing image quality. Additionally, to mitigate the limited availability of style data, we employ the Stable Diffusion model to generate stylized samples for data augmentation. Extensive experiments demonstrate that our method achieves a better balance between content preservation and style rendering compared to existing approaches. Full article

Gene therapy for hemophilia B offers the advantage of a single administration with sustained therapeutic effects. This study evaluated the systemic safety, efficacy, biodistribution, and immunogenicity of AAV8-FIX-TripleL, a recombinant adeno-associated virus type 8 (AAV8) vector encoding a modified factor IX (FIX) variant with increased activity. In this good laboratory practice (GLP)-compliant study, 180 male FIX-knockout hemophilia B mice were randomized into 12 groups (n = 15) and received intravenous AAV8-FIX-TripleL at therapeutic (5 × 10¹¹ VG/kg) or supraphysiological (5 × 10¹² VG/kg) doses on Day 1. The mice were sacrificed on Days 2, 15, 28, and 91 for comprehensive evaluations, including hematological and biochemical assessments, histopathological examination, FIX protein/activity analysis, immunogenicity assessment, and vector biodistribution via quantitative polymerase chain reaction (qPCR) in major organs. AAV8-FIX-TripleL demonstrated dose-dependent increases in FIX activity and protein levels, with FIX activity exceeding physiological levels and the maintenance of a favorable safety profile. Biodistribution analysis confirmed predominant hepatic accumulation and vector persistence up to 91 days post-injection, with minimal off-target distribution. These findings indicate that AAV8-FIX-TripleL is a promising gene therapy candidate for hemophilia B, as it has robust expression, sustained efficacy, and a favorable safety profile, and that further translational studies are warranted. Full article

The increasing adoption of DNNs in critical domains such as healthcare, finance, and autonomous systems underscores the growing importance of explainable artificial intelligence (XAI). In these high-stakes applications, understanding the decision-making processes of models is essential for ensuring trust and safety. However, traditional DNNs often function as “black boxes,” delivering accurate predictions without providing insight into the factors driving their outputs. Expected gradients (EG) is a prominent method for making such explanations by calculating the contribution of each input feature to the final decision. Despite its effectiveness, conventional baselines used in state-of-the-art implementations of EG often lack a clear definition of what constitutes “missing” information. This study proposes DeepPrior-EG, a deep prior-guided EG framework for leveraging prior knowledge to more accurately align with the concept of missingness and enhance interpretive fidelity. It resolves the baseline misalignment by initiating gradient path integration from learned prior baselines, which are derived from the deep features of CNN layers. This approach not only mitigates feature absence artifacts but also amplifies critical feature contributions through adaptive gradient aggregation. This study further introduces two probabilistic prior modeling strategies: a multivariate Gaussian model (MGM) to capture high-dimensional feature interdependencies and a Bayesian nonparametric Gaussian mixture model (BGMM) that autonomously infers mixture complexity for heterogeneous feature distributions. An explanation-driven model retraining paradigm is also implemented to validate the robustness of the proposed framework. Comprehensive evaluations across various qualitative and quantitative metrics demonstrate its superior interpretability. The BGMM variant achieves competitive performance in attribution quality and faithfulness against existing methods. DeepPrior-EG advances the interpretability of complex models within the XAI landscape and unlocks their potential in safety-critical applications. Full article

After being subjected to mechanical damage, plants trigger changes in primary and secondary metabolites to enhance their resistance or defenses. However, there are limited studies on the joint use of transcriptomics and metabolomics in investigating leaf damage-related defense mechanisms and their regulation in woody plants. This study investigated the leaf damage defense mechanisms of Populus talassica × Populus euphratica at the molecular level using transcriptome and secondary metabolome analyses. In total, 4078 differentially expressed genes (DEGs; 1207 up-regulated and 2871 down-regulated) and 30 differential secondary metabolites (DSMs; 21 up-regulated and nine down-regulated) were identified from a transcriptome analysis of controls (CK) and CL₇₅-treated leaves after 24 h. Plant–pathogen interactions and the MAPK signaling pathway were important defense pathways that synergized in the early stages of leaf damage in P. talassica × P. euphratica. There were 44 DEGs enriched in the KEGG pathways that encoded 21 WRKY transcription factors. Flavonoid genes were the most abundant. They were mainly enriched in the flavone and flavonol biosynthesis and flavonoid biosynthesis pathways. Sakuranetin and pinocembrin were most frequently associated with the differential metabolites and may be the main flavonoids involved in responding to leaf damage in P. talassica × P. euphratica. This study has far-reaching theoretical and practical significance for understanding the response strategies of P. talassica × P. euphratica to leaf damage and for achieving sustainable management and accurate predictions of artificial forests. Full article

Accurate photovoltaic (PV) power forecasting is essential to mitigating the security and stability challenges associated with PV integration into power grids. Ground-based sky images can quickly reveal cloud changes, and the spatiotemporal feature information extracted from these images can improve PV power forecasting. Therefore, this paper proposes a hybrid framework based on shifted windows Transformer (Swin Transformer), convolutional neural network, and long short-term memory network to comprehensively extract spatiotemporal feature information, including global spatial, local spatial, and temporal features, from ground-based sky images and PV power data. The mean absolute error and root mean squared error are reduced by 13.06% and 4.49% compared with ResNet-18. The experimental results indicate that the proposed framework demonstrates competitive predictive performance and generalization capability across different time horizons and weather conditions compared with benchmark frameworks. Full article

Myopia patients wear rigid gas-permeable contact lenses during the day to achieve normal vision, but they might feel uncomfortable, since they are made of hard materials that can cause inappropriate friction and adhesion. These forces affect the biological tissues of the cornea and eyelid. In this study, an in vitro rigid gas-permeable contact lens friction testing method was established to mimic the friction between the eyelid and the rigid contact lens. The lens was rubbed against a gelatin membrane to investigate the tribological properties of artificial tear, saline, and two kinds of care solutions using a dedicated experimental setup. The viscosity, pH value, and surface tension of each lubricant was also analyzed. The friction coefficient of the artificial tear solution was the highest: 0.18 for its static friction and 0.09 for its dynamic friction. In contrast, polysaccharide-containing care solution demonstrated the lowest friction coefficient. The viscosity of artificial tear solutions ranged from 0.97 ± 00 to 1.15 ± 0.16 mPa·s, when the shear rate was increased from 19.2 to 192 1/s, while it ranged from 2.26 ± 1.12 to 2.91 ± 0.00 for polysaccharide-containing care solution. Although the physical–chemical properties of various lubricants could not explain the distinct tribological outcomes, the in vitro tribological testing method for rigid gas-permeable lenses was successfully established in this study. Full article

ReRAM-based DNN accelerators have emerged as a promising solution to mitigate the von Neumann bottleneck. While prior research has introduced tools for simulating the hardware behavior of ReRAM’s non-linear characteristics, there remains a notable gap in high-level design automation tools capable of efficiently deploying DNN models onto ReRAM-based accelerators with simultaneous optimization of execution time and memory usage. In this paper, we propose a neural network compiler built on the open-source TVM framework to address this challenge. The compiler incorporates both layer fusion and model partitioning techniques to enhance data storage efficiency. The core contribution of our work is an algorithm that determines the optimal mapping strategy by jointly considering layer fusion and model partitioning under hardware resource constraints. Experimental evaluations demonstrate that the proposed compiler adapts effectively to varying hardware resource limitations, enabling efficient storage optimization and supporting early-stage design space exploration. Full article

Background: The microbiota–gut–brain axis plays a key role in regulating mental health, including anxiety, stress, and sleep quality. Vitamins B1 and B2 may influence these outcomes by modulating gut microbiota. This study aimed to examine the relationships among mental health indicators, gut microbiota, and levels of vitamins B1 and B2. Methods: This study conducted a cross-sectional analysis to explore associations between mental health status, gut microbiota composition and function, and circulating vitamin B1/B2 levels. Ten representative microbes were selected for analysis. Mediation models were used to assess whether gut microbiota mediate the effects of vitamins on mental health. Results: Vitamin B1 and B2 levels were significantly associated with stress, sleep quality, and sleepiness (p < 0.05). The abundance of specific gut microbiota also showed significant inter-correlations (p < 0.05). Specific gut microbiota abundances are correlated with host anxiety, stress, sleep, and sleepiness levels (p < 0.05). We did not observe significant differences in the abundance of specific gut microbiota associated with different vitamin B1 and B2 nutritional levels in the host (p > 0.05). Gut microbial diversity and composition varied notably between different vitamin level groups and anxiety, stress, sleep quality, and sleepiness groups. Although both vitamin B2 and Bacteroides had significant direct effects on sleep quality (p < 0.05), no mediating effect of Bacteroides was observed (p > 0.05). Conclusions: These findings suggest potential associations between vitamins B1 and B2 and mental health, as well as between gut microbiota and host psychological outcomes; no significant mediating effect of the microbiota was observed. These exploratory results offer preliminary insights for future research on microbiota-targeted interventions and precision nutrition strategies. Full article

Background: Anxiety, stress, and sleep disturbances significantly affect overall health. Research suggests that vitamins B1 and B2 may play a role in mood regulation and neuroprotection. This study aimed to investigate the effects of vitamin B1 and B2 supplementation in alleviating anxiety and stress and improving sleep quality. Methods: This study was a parallel randomized, double-blind, placebo-controlled clinical trial. Participants (n = 43) were randomized to receive one of the following two interventions: 100 mg of vitamin B1 and 100 mg of vitamin B2 or placebo. Intervention outcomes were assessed at baseline and week four, including SAS (Self-Rating Anxiety Scale), PSS (Perceived Stress Scale), PSQI (Sleep Quality Index), ESS (Sleepiness Scale), and measurement of urinary vitamin B1 and B2 levels. Results: After four weeks, urinary vitamin B1 levels increased from 158 ± 108.9 ng to 1333.1 ± 1204.5 ng (p < 0.01), and urinary vitamin B2 levels increased from 308.0 ± 198.3 ng to 6123.2 ± 4847.2 ng in the supplement group (p < 0.01). The PSS scores decreased significantly in the supplement group from 21.5 ± 4.1 to 15.5 ± 4.5 (p < 0.05), while the placebo group showed a change from 20.3 ± 4.3 to 19.8 ± 5.5. Vitamins B1 and B2 did not have a significant effect on anxiety improvement (p > 0.05). The PSQI scores decreased in the supplement group from 8.0 ± 3.12 to 6.3 ± 2.0 (p < 0.05), while the placebo group worsened from 5.7 ± 2.7 to 7.4 ± 2.9. Meanwhile, the ESS scores in the supplement group decreased from 13.0 ± 3.4 to 9.1 ± 3.9 (p < 0.05), demonstrating a significant improvement compared to the placebo group. Conclusions: The clinical trial findings demonstrated that while vitamin B1 and B2 supplements helped reduce stress, enhance sleep, and reduce sleepiness, they had no discernible impact on reducing anxiety. Future studies should focus on the long-term effects of vitamin B1 and B2 supplements, exploring the combined effects of combined vitamin B1 and B2 medications for the treatment of stress and sleep disorders. Full article

This study investigates damage characteristics of red sandstone under dynamic loads to clarify the effects of construction disturbances and blasting on the stability of surrounding rock during mountain tunnel construction in water-rich strata. Dynamic impact experiments at various loads were conducted using the Split Hopkinson Pressure Bar (SHPB) instrument, complemented by simulations of the fracturing process in saturated sandstone using finite element software. This analysis systematically examines the post-fracture granularity mass fraction, stress-strain curves, peak stress-average strain rate relationship, and fracture patterns. The dynamic response mechanism of red sandstone during the process of tunnel blasting construction was thoroughly investigated. Experimental results reveal that the peak stress and failure strain exhibit strain rate dependency, increasing from 45.65 MPa to 115.34 MPa and 0.95% to 5.23%, respectively, as strain rate elevates from 35.53 s⁻¹ to 118.71 s⁻¹. The failure process of red sandstone is divided into four stages: crack closure, nearly elastic phase, rapid crack development, and rapid unloading. Dynamic peak stress and average strain rate in sandstone demonstrate an approximately linear relationship, with the correlation coefficient being 0.962. Under different impact loads, fractures in specimens typically expand from the edges to the center and evolve from internal squeezing fractures to external development. Peak stress, degree of specimen breakage, and energy dissipation during fracturing are significantly influenced by the strain rate. The numerical simulations confirmed experimental findings while elucidating the failure mechanism in surrounding rocks under varying strain rates. This work pioneers a multiscale analysis framework bridging numerical simulation with a blasting construction site, addressing the critical gap in time-dependent deformation during tunnel excavation. Full article

Terrestrial ecosystem vegetation are vulnerable to the joint impacts of human activities and climate change, particularly in ecologically fragile areas such as the Tibetan Plateau. Identifying vegetation cover changes and distinguishing their driving factors are crucial for ecological conservation in this region. This study utilized MODIS normalized difference vegetation index (NDVI) data from 2000 to 2019, combined with trend analysis (univariate linear regression and the Mann–Kendall test), partial correlation analysis, and residual analysis methods, to investigate the spatial and temporal dynamics of vegetation cover and its responses to climate change and human activities in the Yarlung Tsangpo River, Lhasa River, and Nianchu River Basin (YLN Basin) on the Tibetan Plateau. The results revealed significant differences in vegetation dynamics both in summer and the growing season: the average summer NDVI showed a significant decreasing trend during the study period, whereas the growing season NDVI exhibited no significant overall temporal trend, which highlighted the necessity of assessing vegetation dynamics seasonally to accurately capture their interannual complexity. Partial correlation analysis indicated that precipitation was the key limiting climatic factor for vegetation growth in this region, with its positive influence covering over 90% of the land area during summer and over 60% during the growing season. The residual analysis further indicated the dual and spatially heterogeneous roles of human activities: on the one hand, positive impacts, primarily from vegetation restoration projects, promoted NDVI increases in some areas; on the other hand, negative impacts, such as continuous grazing pressure, population growth, and associated land use changes, inhibited vegetation development in other areas. This study quantitatively assessed the combined effects of climate variability and complex human activities on the vegetation NDVI in the YLN Basin, emphasizing that the development of adaptive management and effective vegetation restoration strategies must fully consider seasonal differences, the key climatic limiting factor (water availability), and the spatial heterogeneity of human impacts to promote sustainable development in this ecologically fragile region. Full article