Search Results (146)

Cucumber green mottle mosaic virus (CGMMV) represents a serious threat in the production of watermelon. Small RNAs facilitate a mechanism known as RNA interference (RNAi), which regulates gene expression. RNAi technology employs foreign double-stranded RNAs (dsRNAs) to target and reduce the expression levels of specific genes in plants by interfering with their mRNAs. In this study, watermelon plants were treated with dsRNAs of CGMMV MET, IR, and HEL fragments that had been generated in E. coli HT115. We investigated variations in several factors, including viral accumulation, virus-derived small interfering RNAs (vsiRNAs), and symptom severity. MET-dsRNA, IR-dsRNA and HEL-dsRNA dramatically decreased the symptoms of CGMMV in plants in the growth chamber test. Plants treated with viral-derived dsRNA showed a considerable decrease in both virus titers and vsiRNA levels. We also explored the mobility of spray-on dsRNA-derived long dsRNA and discovered that it could be identified in both inoculated leaves and the systemic leaves. IR-dsRNA outperformed MET-dsRNA and HEL-dsRNA in dsRNA therapy. Illumina sequencing of small RNAs from watermelon plants treated with IR-dsRNA and those that were not treated showed that the decreased accumulation of vsiRNAs was consistent with interference with CGMMV infection in systemic leaves. dsRNA-treated plants showed a higher level of 24-nt viral siRNA and lower level of 22-nt viral siRNA accumulation, while 22-nt viral siRNA predominated in untreated plants, indicating that dsRNA treatment improved DCL3 activity. In conclusion, our research provides deeper insights into the mechanism of antiviral RNA interference and confirms the effectiveness of applying dsRNA locally to enhance plant antiviral activity. Full article

Investigating the relationship between coating structure and thermal stress is crucial for improving the service performance of double-ceramic-layer (DCL) thermal barrier coatings (TBCs). This study systematically examines a DCL TBC comprising a Gd₂O₃-Yb₂O₃-Y₂O₃ co-doped ZrO₂ (GYYZ) top layer and Y₂O₃-stabilized ZrO₂ (YSZ) intermediate layer. Using combined finite element analysis and experimental validation, the influence of top-layer structural parameters (porosity, pore size, thickness) on thermal stress distribution under thermal shock conditions and resultant coating performance was investigated. Results indicate that coating interfaces, particularly GYYZ/YSZ and YSZ/bond coat (BC) interfaces, exhibit high sensitivity to top-layer structural parameters. Optimal GYYZ top-layer parameters were identified as: 10–15 vol.% porosity, 10–20 μm pore diameter, and ~0.15 mm thickness. Reducing the top-layer porosity from 20 vol.% to 15 vol.% increased microhardness by 12.8% and extended thermal cycling life by 87.5%. The coating failure mode shifted from the YSZ/BC interface to the GYYZ/YSZ interface, aligning with simulated thermal stress distributions. Full article

In this paper, we derive integer pseudo-parametric solutions to two sets of Diophantine equations. Moreover, we describe the so-called Double Crossed Ladder (DCL) and show how these results can be used to calculate an infinite number of integer solutions of its sides. In addition, we describe the fact that these results can be used to derive some corresponding sets of integer sides of more complex geometric structures. Full article

Gastroesophageal reflux disease (GERD) is associated with symptoms such as heartburn, resulting from gastric content reflux. Alginate-based raft-forming gel formulations represent a non-pharmacological strategy for GERD management by forming a floating gel barrier in the stomach. This study evaluated three commercial anti-reflux oral gel systems under simulated fed-state gastric conditions, using in vitro magnetic resonance relaxometry techniques. Magnetic resonance imaging (MRI) was performed in 0.01 M hydrochloric acid (HCl) to visualize gel raft formation, spatial structure, and spatial distribution of effective T₂ relaxation time. Nuclear magnetic resonance (NMR) relaxometry in 0.01 M deuterium chloride (DCl) measured T₁ and T₂ relaxation times of the protons that were initially included in the preparation to assess its molecular mobility within the gel matrix. Two formulations formed floating, coherent gels, whereas the remaining one exhibited only polymer swelling without flotation. In one case, relaxometry data revealed a solid-like component that can be detected, indicating enhanced mechanical stability. The performance of each formulation was influenced by interactions among alginate, bicarbonates, and calcium ions, which determined gel consistency and flotation behavior. MRI and NMR relaxometry in vitro provide valuable non-invasive insights into the structural and functional behavior of alginate-based gel formulations. This approach supports the rational design of advanced gel-based therapies for GERD by linking molecular composition with in situ performance. Full article

The pursuit of universal, symmetric semantic representations within large language models (LLMs) faces a fundamental challenge: the inherent asymmetry of natural languages. Different languages exhibit vast disparities in syntactic structures, lexical choices, and cultural nuances, making the creation of a truly shared, symmetric embedding space a non-trivial task. This paper aims to address this critical problem by introducing a novel framework to forge robust and symmetric multilingual sentence embeddings. Our approach, named DACL (Dynamic Asymmetric Contrastive Learning), is anchored in two powerful asymmetric learning paradigms: Contrastive Learning and Dynamic Curriculum Learning (DCL). We extend Contrastive Learning to the multilingual context, where it asymmetrically treats semantically equivalent sentences from different languages (positive pairs) and sentences with distinct meanings (negative pairs) to enforce semantic symmetry in the target embedding space. To further refine this process, we incorporate Dynamic Curriculum Learning, which introduces a second layer of asymmetry by dynamically scheduling training instances from easy to hard. This dual-asymmetric strategy enables the model to progressively master complex cross-lingual relationships, starting with more obvious semantic equivalences and advancing to subtler ones. Our comprehensive experiments on benchmark cross-lingual tasks, including sentence retrieval and cross-lingual classification (XNLI, PAWS-X, MLDoc, MARC), demonstrate that DACL significantly outperforms a wide range of established baselines. The results validate our dual-asymmetric framework as a highly effective approach for forging robust multilingual embeddings, particularly excelling in tasks involving complex linguistic asymmetries. Ultimately, this work contributes a novel dual-asymmetric learning framework that effectively leverages linguistic asymmetry to achieve robust semantic symmetry across languages. It offers valuable insights for developing more capable, fair, and interpretable multilingual LLMs, emphasizing that deliberately leveraging asymmetry in the learning process is a highly effective strategy. Full article

Accurate real-time detection of hawthorn by vision systems is a fundamental prerequisite for automated harvesting. This study addresses the challenges in hawthorn orchards—including target overlap, leaf occlusion, and environmental variations—which lead to compromised detection accuracy, high computational resource demands, and poor real-time performance in existing methods. To overcome these limitations, we propose YOLO-DCL (group shuffling convolution and coordinate attention integrated with a lightweight head based on YOLOv8n), a novel lightweight hawthorn detection model. The backbone network employs dynamic group shuffling convolution (DGCST) for efficient and effective feature extraction. Within the neck network, coordinate attention (CA) is integrated into the feature pyramid network (FPN), forming an enhanced multi-scale feature pyramid network (HSPFN); this integration further optimizes the C2f structure. The detection head is designed utilizing shared convolution and batch normalization to streamline computation. Additionally, the PIoUv2 (powerful intersection over union version 2) loss function is introduced to significantly reduce model complexity. Experimental validation demonstrates that YOLO-DCL achieves a precision of 91.6%, recall of 90.1%, and mean average precision (mAP) of 95.6%, while simultaneously reducing the model size to 2.46 MB with only 1.2 million parameters and 4.8 GFLOPs computational cost. To rigorously assess real-world applicability, we developed and deployed a detection system based on the PySide6 framework on an NVIDIA Jetson Xavier NX edge device. Field testing validated the model’s robustness, high accuracy, and real-time performance, confirming its suitability for integration into harvesting robots operating in practical orchard environments. Full article

The treatment of type 1 diabetes through pancreatic islet transplantation faces significant limitations, including donor organ shortages and poor islet survival due to post-transplantation loss of extracellular matrix support and inadequate vascularization. Developing biocompatible scaffolds that mimic the native islet microenvironment could substantially improve transplantation outcomes. This study aimed to create and evaluate decellularized (DCL) matrices from porcine organs as potential platforms for islet transplantation. Porcine lung and pancreatic tissues were decellularized using four different protocols combining detergents (Triton X-100, SDS and SDC) with optimized incubation times. The resulting matrices were characterized through DNA quantification and histological staining (H&E and Van Gieson). Islet viability was assessed in vitro using Live/Dead staining after 3 and 7 days of culture on the matrices. In vivo biocompatibility was evaluated by implanting matrices into rat omentum or peritoneum, with histological analysis at 1-, 4-, and 8 weeks post-transplantation. Protocols 3 (for lung tissue) and 4 (for pancreas tissue) demonstrated optimal decellularization efficiency with residual DNA levels below 8%, while preserving the collagen and elastin networks. In vitro, islets cultured on decellularized lung matrix had maintained 95% viability by day 7, significantly higher than the controls (60%) and pancreatic matrix (83%). The omentum showed superior performance as an implantation site, exhibiting minimal inflammation and fibrosis compared to the peritoneum sites throughout the 8-week study period. These findings establish DCL as a promising scaffold for islet transplantation due to its superior preservation of ECM components and excellent support of islet viability. This work provides a significant step toward developing effective tissue-engineered therapies for diabetes treatment. Full article

Jet fuel from direct coal liquefaction (DCL) is an important alternative kerosene and represents a high-performance fuel for specific applications in civil applications. The study on its chemical positions and combustion properties is critical for the development of surrogate models and related combustion reaction mechanisms, which is valuable for promoting its usage in aeroengines. However, research on DCL-derived jet fuel is rather scarce. Herein, this work reports a systematic study on a DCL-derived jet fuel and its blends with traditional RP-3 jet fuel in two different ratios. Specifically, major physicochemical properties related to the aviation fuel airworthiness certification process are measured. Advanced two-dimensional gas chromatography (GC × GC) analysis is used to analyze the detailed chemical compositions on the DCL derived jet fuel and its blend with RP-3, which is then employed for surrogate model development. Moreover, ignition delay times (IDTs) are measured by using a heated shock-tube (ST) facility for the blended fuels over a wide range of conditions. Combustion reaction mechanisms based on the surrogate models are developed to predict the experimental measured IDTs. Finally, sensitivity analysis and rate-of-production analysis are carried out to identify the key chemical kinetics controlling the ignition characteristics. This work extends the understanding of the physicochemical properties and ignition characteristics of alternative jet fuels and should be valuable for the practical usage of DCL derived jet fuels. Full article

RNA silencing regulates diverse cellular processes in plants. Argonaute (AGO), Dicer-like (DCL), and RNA-dependent RNA polymerase (RDR) proteins are core components of RNA interference (RNAi). Despite their functional significance, the systematic identification and characterization of these families have remained largely unexplored in Siraitia grosvenorii. Using HMMER and Pfam analyses, we identified six SgAGO, four SgDCL, and six SgRDR genes. Phylogenetic analysis classified SgAGOs, SgDCLs, and SgRDRs into five, four, and four clades, respectively, all of which clustered closely with homologs from other Cucurbitaceae species, demonstrating lineage-specific evolutionary conservation. Promoter cis-element analysis revealed the significant enrichment of hormonal (methyl jasmonate, abscisic acid) and stress-responsive (light, hypoxia) elements, indicating their roles in environmental adaptation. Tissue-specific expression profiling showed that most SgAGO, SgDCL, and SgRDR genes were highly expressed in flowers and mid-stage fruits (35 days after pollination), while SgAGO10.1 exhibited stem-specific expression. By contrast, SgRDR1.2 displayed no tissue specificity. Notably, sex-biased expression patterns in dioecious flowers suggested the RNAi-mediated regulation of gametophyte development and their potential roles in reproductive and secondary metabolic processes. This study lays the foundation for further exploration of RNAi machinery’s role in coordinating mogroside biosynthesis and stress resilience in S. grosvenorii while providing potential targets for genetic improvement. Full article

Dendrobium is a famous edible and medicinal plants, and polysaccharides are their main bioactive components. Polysaccharides from five species, namely, DO (Dendrobium officinale Kimura et Migo), DH (Dendrobium huoshanense C. Z. Tang et S. J. Cheng), DNL (Dendrobium nobile Lindl.), DFH (Dendrobium fimbriatum Hook.), and DCL (Dendrobium chrysanthum Lindl.), were compared based on molecular weight (Mw), monosaccharide composition, and glycosidic bond types. The results showed that Dendrobium polysaccharides (DPs) contain relatively simple compositional monosaccharides and mainly consist of mannose (Man) and glucose (Glc), along with small amounts of arabinose (Ara), xylose (Xyl), and galactose (Gal). The Am/Ag (the ratio of Man to Glc) values in DO, DH, and DNL polysaccharides were 3.23, 3.81, and 3.88, while those in DFH and DCL were 0.45 and 0.81. DPs are mainly composed of →4)Manp(1→ and →4)Glcp(1→, but their molar ratios were different. →4)Manp(1→ and →4)Glcp(1→ ratios were 2.85, 2.92, 1.50, 1.45, and 1.05 in DO, DH, DNL, DFH, and DCL, respectively. Hierarchical cluster analysis (HCA) showed that there were significant differences in structural information, especially in glycosidic bond types and proportions. DH, DO, and DCL were clustered into different groups based on glycosidic bond types and proportions, respectively. Moreover, the five species of Dendrobium could significantly inhibit NO production and apoptosis induced by LPS in RAW 264.7, especially DH. The results of a correlation analysis of structure and anti-inflammatory activity showed that polysaccharides with a high →4)Manp(1→/→4)Glcp(1→ ratio and a molecular weight distribution between 3.343 × 10⁵ Da and 13.540 × 10⁵ Da had better anti-inflammatory activity. The results indicated that the quality evaluation of Dendrobium in clinical applications should investigate molecular weight and the composition of the glycoside bond types and proportions to ensure the consistency of curative effects. Full article

Distributed Collaborative Learning (DCL) addresses critical challenges in privacy-aware machine learning by enabling indirect knowledge transfer across nodes with heterogeneous feature distributions. Unlike conventional federated learning approaches, DCL assumes non-IID data and prediction task distributions that span beyond local training data, requiring selective collaboration to achieve generalization. In this work, we propose a novel collaborative transfer learning (CTL) framework that utilizes representative datasets and adaptive distillation weights to facilitate efficient and privacy-preserving collaboration. By leveraging Energy Coefficients to quantify node similarity, CTL dynamically selects optimal collaborators and refines local models through knowledge distillation on shared representative datasets. Simulations demonstrate the efficacy of CTL in improving prediction accuracy across diverse tasks while balancing trade-offs between local and global performance. Furthermore, we explore the impact of data spread and dispersion on collaboration, highlighting the importance of tailored node alignment. This framework provides a scalable foundation for cross-domain generalization in distributed machine learning. Full article

Potyviruses, a major group of plant viruses, utilize HC-Pro, a multifunctional protein, to suppress RNA silencing, a crucial plant defense mechanism. While HC-Pro’s role in RNA silencing suppression has been studied in several potyviruses, the specific mechanisms and interactions of HC-Pro from bean yellow mosaic virus (BYMV), a potyvirus with a broad host range, remain poorly understood. To address this knowledge gap, this study aimed to investigate the role of P1 and HC-Pro from BYMV in enhancing gene expression and suppressing RNA silencing in Nicotiana benthamiana. The findings revealed that BYMV HC-Pro significantly enhanced reporter transgene expression, likely through the suppression of RNA silencing pathways. This effect was further amplified by the presence of the P1 protein, another viral component. Analysis of HC-Pro mutants revealed that the conserved FRNK box within HC-Pro is crucial for its suppression activity and its ability to enhance gene expression. Furthermore, HC-Pro significantly downregulated the expression of key RNA silencing-related genes, including DCL2, DCL4, RDR6, AGO1-1, AGO1-2, and AGO2. These findings demonstrate that the BYMV P1::HC-Pro complex serves as a potent suppressor of RNA silencing and a promising tool for enhancing gene expression in plants. The results have significant implications for developing novel strategies in plant biotechnology, particularly for the production of high-value recombinant proteins. Full article

Recent initiatives have discouraged the treatment of asymptomatic bacteriuria in specific patient populations due to its lack of clinical benefit, no improvement in morbidity or mortality, and its contribution to antibiotic overuse. This study aimed to evaluate whether an intervention at order entry, combined with DCLS laboratory consultation for urine cultures and urinalyses, could reduce unnecessary lab tests and inappropriate antibiotic use, thereby improving patient outcomes. Our research design was a quasi-experimental study with a retrospective and prospective chart review on non-pregnant adult patients 18 years of age and older from July 2021 to September 2022. Data collected for both reviews included patient demographics, provider demographics, patient signs and symptoms, laboratory test results, test order type, test order utilization and antibiotic prescriptions. Our study included 6372 patients, with 3408 in the retrospective review and 2964 in the prospective review. Before the intervention, 60% (n = 2053) of test orders were inappropriate, which decreased to 20% (n = 591) post-intervention. In asymptomatic patients, reflexed urine cultures decreased from 51% to 13% post-intervention. Lastly, in asymptomatic patients, antibiotic therapy at discharge dropped from 54% to 25% after the intervention. Post-intervention ordering practices improved, decreasing the number of inappropriate orders across all patient and provider types. Overall, this initiative showed a significant reduction in the treatment of asymptomatic bacteriuria, which has been linked to the overuse of antibiotic therapy. Full article

Background and Objectives: Mixed ductolobular carcinomas (mDLCs) are tumors that contain both ductal and lobular components. The clinicopathological characteristics and impacts on survival of the two components, which have distinct biological behaviors, are still not clearly understood. This study aimed to compare the clinicopathological characteristics, recurrence/metastasis patterns, and survival outcomes of mDLC and invasive lobular carcinoma (ILC), as well as to investigate the prognostic significance of both histopathologies. Materials and Methods: The outcomes of 132 patients who were followed and treated between 2010 and 2021 were analyzed. Patients were examined in two groups, ILC and mDLC. Chi-square tests were performed to compare the baseline clinicopathological characteristics and treatments. Survival rates were subsequently analyzed using the Kaplan–Meier method and compared using the Cox proportional hazards model. Results: In this study, 80 (61%) patients had ILC histopathology, while 52 (39%) had mDLC histopathology. Differences between the groups were observed in median age (p = 0.038), N stage (p = 0.046), estrogen receptor (ER) status (p = 0.005), lymphovascular invasion (p = 0.007), median tumor diameter (p = 0.050), and frequency of distant metastasis (p = 0.029). The treatments, relapse patterns, and metastasis patterns were similar (p > 0.05). No differences in overall survival (OS) and disease-free survival (DFS) were observed. In the multivariate analysis, mDLC histopathology was identified as a poor prognostic factor (HR: 2.95, CI 95%: 1.10–7.88, p = 0.030). Histopathology (ILC vs. mDCL) was not identified as a prognostic factor in the Cox regression analysis for DFS. Conclusion: Although mDLC has poor clinicopathological features (younger age, more advanced N stage, more ER negativity, more lymphovascular invasion, and more frequency of metastases) and appears more aggressive than ILC, these changes do not affect survival in this study. However, mDLC histopathology seems to be associated with poor prognosis for OS. Full article

A comprehensive understanding of the compositions and physicochemical properties of coal-based liquids is conducive to the rapid development of multipurpose, high-performance, and high-value functional chemicals. However, because of their complex compositions, coal-based liquids generate two-dimensional gas chromatography (GC × GC) chromatograms that are very complex and very time consuming to analyze. Therefore, the development of a method for accurately and rapidly analyzing chromatograms is crucial for understanding the chemical compositions and structures of coal-based liquids, such as direct coal liquefaction (DCL) oils and coal tar. In this study, DCL oils were distilled and qualitatively analyzed using GC × GC chromatograms. A deep-learning (DL) model was used to identify spectral features in GC × GC chromatograms and predominantly categorize the corresponding DCL oils as aliphatic alkanes, cycloalkanes, mono-, bi-, tri-, and tetracyclic aromatics. Regional labels associated with areas in the GC × GC chromatograms were fed into the mask-region-based convolutional neural network’s (Mask R-CNN’s) algorithm. The Mask R-CNN accurately and rapidly segmented the GC × GC chromatograms into regions representing different compounds, thereby automatically qualitatively classifying the compounds according to their spots in the chromatograms. Results show that the Mask R-CNN model’s accuracy, precision, recall, F1 value, and Intersection over Union (IoU) value were 93.71%, 96.99%, 96.27%, 0.95, and 0.93, respectively. DL is effective for visually comparing GC × GC chromatograms to analyze the compositions of chemical mixtures, accelerating GC × GC chromatogram interpretation and compound characterization and facilitating comparisons of the chemical compositions of multiple coal-based liquids produced in the coal and petroleum industry. Applying DL to analyze chromatograms improves analysis efficiency and provides a new method for analyzing GC × GC chromatograms, which is important for fast and accurate analysis. Full article