Search Results (1,605)

Atractylodes macrocephala Koidz. (A. macrocephala), a medicinal plant extensively used in traditional Chinese medicine, is greatly susceptible to root rot under continuous monoculture, leading to serious yield and quality losses. To develop a sustainable control strategy, we isolated the endophytic bacterium Bacillus velezensis (B. velezensis) Amzn015 from healthy A. macrocephala plants and assessed its biocontrol efficacy and underlying mechanisms. In vitro assays showed that Amzn015 significantly inhibited Fusarium oxysporum and other phytopathogenic fungi by disrupting hyphal morphology and reducing spore viability. Pot experiments confirmed its effectiveness in reducing disease incidence and promoting plant growth. Mechanistically, Amzn015 induced reactive oxygen species accumulation and upregulated key defense responsive genes involved in salicylic acid, jasmonic acid/ethylene, and phenylpropanoid signaling pathways. The findings imply that Amzn015 synchronously activates systemic acquired resistance and induced systemic resistance in A. macrocephala. This dual activation contributes to enhanced immunity and plant vigor under pathogen challenge. Our findings offer fresh perspectives on the biocontrol potential of endophytic B. velezensis Amzn015 and support its application as an eco-friendly agent for managing root rot in medicinal crops. Full article

This study presents the first systematic investigation of ancient Egyptian votive animal mummy wrappings, based on the analysis of an extensive dataset encompassing specimens from various museum collections and archaeologicalcontexts. The research addresses the long-standing neglect and fragmented understanding of the wrapping chaîne opératoire and aims to establish a consistent terminology, as the different stages of the wrapping sequence, bundle shapes, and decorative patterns have often been described vaguely. Through an interdisciplinary methodology that integrates photogrammetry, colorant identification, textile analysis, and experimental archeology, the study explores the complexity of wrapping practices across their different stages. This approach offers new insights into the structural logic, raw material selection, and design conventions behind this production. The analysis reveals that the bundles exhibit standardized shapes and decorative patterns grounded in well-established visual criteria and manufacturing sequences. These findings demonstrate that the wrappings reflect a codified visual language and a high level of technical knowledge, deeply rooted in Egyptian tradition. The study also emphasizes its economic implications: the wrapping significantly enhanced the perceived value of the offering, becoming the primary element influencing both its material and symbolic worth. Ultimately, this work provides an interpretative framework for understanding wrapping as an essential medium of ritual sacralization for votive animal mummies, allowing the individual prayer to be effectively conveyed to the intended deity. Consequently, this research marks a significant step forward in advancing the technical, aesthetic, and ritual insight of wrapping practices, which preserve a wealth of still-overlooked information. Full article

This study investigates the optimization of electric discharge machining (EDM) parameters for gunmetal using copper electrodes in two different dielectric environments, which are conventional EDM oil and EDM oil infused with Al₂O₃ nanoparticles. A Taguchi L27 orthogonal array design was used to evaluate the effects of current, voltage, and pulse-on time on Material Removal Rate (MRR), Electrode Wear Rate (EWR), and surface roughness (Ra, Rq, and Rz). Analysis of Variance (ANOVA) was used to statistically evaluate the influence of each parameter on machining performance. In addition, machine learning models including Linear Regression, Ridge Regression, Support Vector Regression, Random Forest, Gradient Boosting, and Neural Networks were implemented to predict performance outcomes. The originality of this research is not only rooted in the introduction of new models; rather, it is also found in the comparative analysis of various machine learning methodologies applied to the performance of electrical discharge machining (EDM) utilizing Al₂O₃-enhanced dielectrics. This investigation focuses specifically on gunmetal, a material that has not been extensively studied within this framework. The nanoparticle-enhanced dielectric demonstrated improved machining performance, achieving approximately 15% higher MRR, 20% lower EWR, and 10% improved surface finish compared to conventional EDM oil. Neural Networks consistently outperformed other models in predictive accuracy. Results indicate that the use of nanoparticle-infused dielectrics in EDM, coupled with data-driven optimization techniques, enhances productivity, tool life, and surface quality. Full article

This paper investigates the application of image segmentation techniques in endodontics, focusing on improving diagnostic accuracy and achieving faster segmentation by delineating specific dental regions such as teeth and root canals. Deep learning architectures, notably 3D U-Net and GANs, have advanced the image segmentation process for dental structures, supporting more precise dental procedures. However, challenges like the demand for extensive labeled datasets and ensuring model generalizability remain. Two semi-automatic segmentation workflows, Grow From Seeds (GFS) and Watershed (WS), were developed to provide quicker acquisition of ground truth training data for deep learning models using 3D Slicer software version 5.8.1. These workflows were evaluated against a manual segmentation benchmark and a recent dental segmentation automated tool on three separate datasets. The evaluations were performed by the overall shapes of a maxillary central incisor and a maxillary second molar and by the region of the root canal of both teeth. Results from Kruskal–Wallis and Nemenyi tests indicated that the semi-automated workflows, more often than not, were not statistically different from the manual benchmark based on dice coefficient similarity, while the automated method consistently provided significantly different 3D models from their manual counterparts. The study also explores the benefits of labor reduction and time savings achieved by the semi-automated methods. Full article

Generally speaking, the conception of God serves as the theoretical focal point and central concern of Kant’s philosophy of religion. Its content is multidimensional, covering many aspects, such as proof of God’s existence, the image of God, and God’s status and functions. The purpose of this paper is to examine the evolution of the concept of God in Kant’s philosophy of religion in three different philosophical periods—the pre-critical period, the period of the critical philosophy and the post-critical period—to analyze the evolution of the internal contradictions in Kant’s philosophy of religion and the course of its systematic construction, and, on this basis, to reveal the three pivotal systemic transformations achieved by Kant’s philosophy of religion—the deconstruction of traditional theology, the reconstruction of rational theology and the construction of moral religion. Finally, this paper elucidates four internal roots which drive these pivotal transformations: (1) methodological foundation: the development of critical philosophy; (2) systematic goal: the establishment of scientific metaphysics; (3) axiological orientation: the secularization of theology into anthropological theology; and (4) practical culmination: the extension of pure moral philosophy. Full article

The temporal dominance of sensations (TDS) and temporal liking (TL) methods offer complementary insights into the evolution of sensory and hedonic responses during food consumption. This study investigates the feasibility of predicting TL curves for food pairings from their TDS profiles using reservoir computing, a type of recurrent neural network. Participants evaluated eight samples—two crackers (plain, sesame), two spreads (peanut butter, strawberry jam), and their four binary combinations—performing both TDS and TL evaluations. This process yielded paired time-series data of TDS and TL curves. We trained various reservoir models under different conditions, including varying reservoir sizes (64, 128, 192, or 256 neurons) and the inclusion of auxiliary input dimensions, such as flags indicating the types of foods tasted. Our results show that models with minimal auxiliary inputs achieved the lowest root mean squared errors (RMSEs), with the best performance being an RMSE of 0.44 points on a 9-point liking scale between the observed and predicted TL curves. The ability to predict TL curves for food pairings holds some promise for reducing the need for extensive sensory evaluation, especially when a large number of food combinations are targeted. Full article

CFRP is extensively utilized in the manufacturing of aerospace equipment owing to its distinctive properties, and hole-making processing continues to be the predominant processing method for this material. However, due to the anisotropy of CFRP, in its processing process, processing damage appears easily, such as stratification, fiber tearing, burrs, etc. These damages will seriously affect the performance of CFRP components in the service process. This work employs acoustic emission (AE) and infrared thermography (IT) techniques to analyze the characteristics of AE signals and temperature signals generated during the CFRP drilling process. Fast Fourier transform (FFT) and short-time Fourier transform (STFT) are used to process the collected AE signals. And in combination with the actual damage morphology, the material removal behavior during the drilling process and the AE signal characteristics corresponding to processing defects are studied. The results show that the time-frequency graph and root mean square (RMS) curve of the AE signal can accurately distinguish the different stages of the drilling process. Through the analysis of the frequency domain characteristics of the AE signal, the specific frequency range of the damage mode of the CFRP composite material during drilling is determined. This paper aims to demonstrate the feasibility of real-time monitoring of the drilling process. By analyzing the relationship between the RMS values of acoustic emission signals and hole surface topography under different drilling parameters, it provides a new approach for the research on online monitoring of CFRP drilling damage and improvement of CFRP machining quality. Full article

This study introduces a non-destructive computer vision method for estimating postharvest quality parameters of oranges, including maturity index, soluble solid content (expressed in degrees Brix), and firmness. A novel image-based descriptor, termed Citrus Color Index—Gray Level Co-occurrence Matrix Texture Features (CCI–GLCM-TF), was developed by integrating the Citrus Color Index (CCI) with texture features derived from the Gray Level Co-occurrence Matrix (GLCM). By combining contrast, correlation, energy, and homogeneity across multiscale regions of interest and applying geometric calibration to correct image acquisition distortions, the descriptor effectively captures both chromatic and structural information from RGB images. These features served as input to an Adaptive Neuro-Fuzzy Inference System (ANFIS), selected for its ability to model nonlinear relationships and gradual transitions in citrus ripening. The proposed ANFIS models achieved R-squared values greater than or equal to 0.81 and root mean square error values less than or equal to 1.1 across all quality parameters, confirming their predictive robustness. Notably, representative models (ANFIS 2, 4, 6, and 8) demonstrated superior performance, supporting the extension of this approach to full-surface exploration of citrus fruits. The results outperform methods relying solely on color features, underscoring the importance of combining spectral and textural descriptors. This work highlights the potential of the CCI–GLCM-TF descriptor, in conjunction with ANFIS, for accurate, real-time, and non-invasive assessment of citrus quality, with practical implications for automated classification, postharvest process optimization, and cost reduction in the citrus industry. Full article

Mangroves represent a promising yet underexplored source of natural products. Sonneratia caseolaris (mangrove apple) is a widely distributed species with a long history of use in traditional medicine, and it is receiving increasing recognition for its bioactive secondary metabolites. Research has expanded in recent decades, but findings remain dispersed across diverse sources, complicating interpretation of its chemistry and pharmacological potential. This review consolidates four decades of investigations, documenting 141 identified compounds from studies largely restricted to India, Bangladesh, Indonesia, and China and focusing on leaves, fruits, bark, stems, and twigs, with roots notably unexplored. The phytochemical profile is dominated by phenolic acids, flavonoids, and tannins, alongside terpenoids, steroids, fatty acids, fatty alcohols, aldehydes, hydrocarbons, and polysaccharides. The most extensively studied activities are antioxidant and antimicrobial, with extracts consistently exhibiting strong free-radical scavenging capacity and broad-spectrum antibacterial and antifungal effects, including efficacy against drug-resistant strains. Additional reports describe central nervous system depressant, antidiarrheal, metabolic, anti-inflammatory, analgesic, antipyretic, and anti-allergic activities. In contrast, anticancer investigations remain scarce, despite promising outcomes reported for related mangrove taxa. By consolidating and critically evaluating the existing evidence, this review highlights the pharmacological potential of S. caseolaris and identifies key knowledge gaps to guide future marine drug discovery. Full article

Plant cell wall-derived oligosaccharides, such as xylo-oligosaccharides (XOS), serve as key signaling molecules regulating plant growth and immunity. The bioactivity of XOS is closely tied to their degree of polymerization (DP), yet the molecular mechanisms underlying DP-specific effects remain poorly understood. Here, we investigated the transcriptional and phenotypic responses of lettuce (Lactuca sativa) to foliar application of four high-purity XOS variants: xylobiose (XOSY, DP2), xylotriose (XOSB, DP3), xylotetraose (XOSD, DP4), and xylopentose (XOSW, DP5). Phenotypic analyses revealed that high-DP XOS (XOSD and XOSW) significantly enhanced aboveground biomass and root system development, with XOSD showing the most pronounced effects, including a 31.74% increase in leaf area and a 20.71% increase in aboveground biomass. Transcriptomic profiling identified extensive transcriptional reprogramming across treatments, with XOSD eliciting the highest number of differentially expressed genes (DEGs). Functional enrichment analyses indicated that XOSD and XOSW upregulated genes involved in plant hormone signaling, starch and sucrose metabolism, and cell wall biosynthesis, while downregulating photosynthesis-related genes. Notably, MapMan and KEGG pathway analyses revealed that XOSD significantly activated biotic stress-related pathways, including MAPK signaling, β-1,3-glucanase activity, and PR protein pathways. In contrast, XOSY treatment primarily upregulated genes linked to basal immunity, highlighting distinct mechanisms employed by low- and high-DP XOS. These findings demonstrate that XOS with varying DP differentially modulate growth- and immunity-related processes in lettuce. High-DP XOS, particularly XOSD, not only promote plant biomass accumulation but also enhance immune responses, highlighting their potential as biostimulants for sustainable agriculture. This study provides a molecular framework for understanding the DP-specific bioactivity of XOS and their dual role in optimizing plant growth and defense. Full article

Single Event Transients (SETs) in clock-distribution networks are a major source of soft errors in synchronous systems. We present a practical framework that assesses SET risk early in the design cycle, before layout and parasitics, using a Vulnerability Function (VF) derived from Verilog fault injection. This framework guides targeted Engineering Change Orders (ECOs), such as clock-net remapping, re-routing, and the selective insertion of SET filters, within a reproducible open-source flow (Yosys, OpenROAD, OpenSTA). A new analytical Soft Error Rate (SER) model for clock trees is also proposed, which decomposes contributions from the root, intermediate levels, and leaves, and is calibrated by SPICE-measured propagation probabilities, area, and particle flux. When coupled with throughput, this model yields a frequency-aware system-level Bit Error Rate (${BER}_{sys}$). The methodology was validated on a First-In First-Out (FIFO) memory, demonstrating a significant vulnerability reduction of approximately 3.35× in READ mode and 2.67× in WRITE mode. Frequency sweeps show monotonic decreases in both clock-tree vulnerability and ${BER}_{sys}$ at higher clock frequencies, a trend attributed to temporal masking and throughput effects. Cross-node SPICE characterization between 65 nm and 28 nm reveals a technology-dependent effect: for the same injected charge, the 28 nm process produces a shorter root-level pulse, which lowers the propagation probability relative to 65 nm and shifts the optimal clock-tree partition. These findings underscore the framework’s key innovations: a technology-independent, early-stage VF for ranking critical clock nets; a clock-tree SER model calibrated by measured propagation probabilities; an ECO loop that converts VF insights into concrete hardening actions; and a fully reproducible open-source implementation. The paper’s scope is architectural and pre-layout, with extensions to broader circuit classes and a full electrical analysis outlined for future work. Full article

Rootstock choice offers a powerful lever for tailoring economically important trees to adverse environments. Camellia oleifera Abel., a premier oil-producing species cultivated widely on red-soil hills, suffers large yield losses under chronic phosphorus deficiency. We grafted a single elite scion (CL4) onto three contrasting rootstocks (CL4, CL3, CL53) and monitored growth and root transcriptomes for 1.5 years under adequate (1 mM) or limiting (0 mM) P supply. Under low-P stress, the rootstock identity reshaped the root architecture: CL4/CL3 produced the longest, most extensive network, increasing the total root length by 49.7%, the surface area by 52.9%, and the volume by 42.6% relative to the control, whereas leaf morphology responded solely to P supply, not to the graft combination. CL4/CL3 also accumulated up to more than 17.5% of root biomass and 28.25% of whole-plant biomass than any other combination. Physiologically, CL4/CL3 acted as an aggressive P miner, accumulating 67.8% more P in its roots than the self-grafted control under P limitation, while CL4/CL4 maximized the internal P use efficiency, showing a 44.74% higher root P use efficiency than CL4/CL53—two contrasting yet effective strategies for coping with low-P stress. Transcriptome profiling uncovered 1733 DEGs in the CL4/CL3 and 2585 in the CL4/CL4 roots, with 150 and 255 uniquely co-expressed genes, respectively. CL4/CL3 up-regulated organic-acid and phenylpropanoid pathways; CL4/CL4 activated defense and phosphate transport networks. qRT-PCR of six genes confirmed that CL4/CL3 mounted a stronger low-P response via MAPK, hormonal, and lipid–metabolic signaling. These results provide a mechanistic framework for rootstock-mediated P efficiency and establish a foundation for the molecular breeding of C. oleifera under nutrient-limited conditions. Full article

The PLATZ gene family influences plant growth, development, and responses to both biotic and abiotic stresses. Flax (Linum usitatissimum L.), an important oilseed and fiber crop, has not been extensively studied for its PLATZ genes. In this study, 27 LuPLATZ genes were identified in the recently assembled T2T (Telomere-to-Telomere) flax genome through bioinformatics analyses. Phylogenetic analysis grouped these genes into five subfamilies. Examination of gene structure and motifs showed conserved exon–intron arrangements and similar motif compositions within the same clade. Promoter analysis revealed that most cis-elements are associated with plant hormone responses (such as MeJA and ABA) and abiotic stresses, including anaerobic induction, drought, and low temperature. Duplication analysis identified 33 segmental duplication events, and miRNA target prediction indicated that lus-miR167 is the primary regulator of LuPLATZ genes. Expression profiling based on RNA-seq data showed high expression levels of most LuPLATZ genes in leaves and roots, and qRT-PCR confirmed their stress-responsive expression under cold, drought, and salt conditions, with LuPLATZ14 and LuPLATZ21 significantly upregulated in all treatments. Furthermore, overexpression of these two genes enhanced drought tolerance in yeast transformants. Full article

In traditional in situ leaching (ISL) uranium mining, the injection volume depends on technicians’ on-site experience. Therefore, applying artificial intelligence technologies such as machine learning to analyze the relationship between injection volume and leaching rate in ISL uranium mining, thereby reducing human factor interference, holds significant guiding importance for production process control. This study proposes a novel uranium leaching rate prediction method based on a CNN–LSTM–LightGBM fusion model integrated with an attention mechanism. Ablation experiments demonstrate that the proposed fusion model outperforms its component models across three key metrics: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE). Furthermore, comparative experiments reveal that this fusion model achieves superior performance on MAE, MAPE, and RMSE metrics compared to six extensively utilized machine learning methods, including Multi-Layer Perceptron, Support Vector Regression, and K-Nearest Neighbors. Specifically, the model achieves an MAE of 0.085%, an MAPE of 0.833%, and an RMSE of 0.201%. This attention-enhanced fusion model provides technical support for production control in ISL uranium mining and offers valuable references for informatization and intelligentization research in uranium mining operations. Full article

Three previously undescribed clerodane diterpenoids, including two cis-clerodanes, solidagolactone IX (1) and solidagoic acid K (2), and one trans-clerodane, solidagodiol (3), along with two known cis-clerodane diterpenoids, (−)-(5R,8R,9R,10S)-15,16-epoxy-ent-neo-cleroda-3,13,14-trien-18-ol (4) and solidagoic acid J (5), were isolated and comprehensively characterized from the ethanolic and ethyl acetate root extract of Solidago gigantea Ait. (giant goldenrod). Compound 4 has previously been reported from the roots of this species, whereas compound 5 was identified from the leaves of S. gigantea but not from the roots. The bioassay-guided isolation involved thin-layer chromatography–direct bioautography (TLC–DB) with a Bacillus subtilis antibacterial assay, preparative flash column chromatography, and TLC–mass spectrometry (MS). The chemical structures of the isolated compounds (1–5) were elucidated through extensive in-depth spectroscopic and spectrometric analyses, including one- and two-dimensional nuclear magnetic resonance (NMR) spectroscopy, high-resolution tandem mass spectrometry (HRMS/MS), and attenuated total reflectance Fourier-transform infrared (ATR–FTIR) spectroscopy. Their antimicrobial activities were evaluated using in vitro microdilution assays against B. subtilis and different plant pathogens. Compound 3 was the most active against the tested Gram-positive strains, exerting particularly potent effects against Clavibacter michiganensis with a minimal inhibitory concentration (MIC) value of 5.1 µM as well as B. subtilis and Curtobacterium flaccumfaciens pv. flaccumfaciens (MIC 21 µM for both). Compound 4 also strongly inhibited the growth of C. michiganensis (MIC 6.3 µM). Compounds 2, 4, and 5 displayed moderate to weak activity against B. subtilis and C. flaccumfaciens pv. flaccumfaciens with MIC values ranging from 100 to 402 µM. Rhodococcus fascians bacteria were moderately inhibited by compounds 3 (MIC 41 µM) and 4 (MIC 201 µM). Bactericidal activity was observed for compound 3 against C. michiganensis with a minimal bactericidal concentration (MBC) value of 83 µM. Compounds 2 and 3 demonstrated weak antifungal activity against Fusarium graminearum. Our findings underscore the value of bioassay-guided approaches in discovering previously undescribed bioactive compounds. Full article