Search Results (191)

Myo-inositol (MI) is recognized as a potential stress regulator capable of alleviating abiotic stress. The objective of this study is to analyze the role of MI in the salt stress response of Daucus carota L. and its potential mechanisms. “Hongxin Qicun” carrot seedlings were subjected to five treatments: control; salt stress (50 mM NaCl); and salt stress combined with 50, 100, or 200 μM of MI. Through an integrated approach combining physiological assays, non-invasive micro-test technology (NMT), and gene expression profiling, we found that salt stress severely inhibited seedling growth, disrupted K⁺/Na⁺ homeostasis, and triggered excessive H₂O₂ accumulation. Exogenous MI application mitigated these salt-induced damages, with 100 μM MI exerting the optimal effect. MI enhanced Na⁺ efflux and reduced K⁺ efflux in carrot roots under salt stress. Inhibitor experiments indicated that MI-promoted Na⁺ efflux relies on active transport via the plasma membrane (PM) Na⁺/H⁺ antiporter system, and qRT-PCR analysis showed that this response was accompanied by the upregulation of DcSOS1. Furthermore, MI contributes to K⁺ homeostasis by synergistically modulating PM H⁺-ATPase and high-affinity potassium transporters. The established proton gradient helps reduce salt-induced K⁺ loss through depolarization-activated potassium channels and non-selective cation channels. MI treatment decreased electrolyte leakage, malondialdehyde content, and H₂O₂ accumulation by enhancing the activities of the plant antioxidant defense system. Meanwhile, MI upregulated the expression of myo-inositol oxygenase (DcMIOXs) genes, which may contribute to osmotic balance maintenance and facilitate ROS scavenging. In conclusion, exogenous MI alleviates salt-induced physiological disorders in Daucus carota L. by coordinately regulating K⁺/Na⁺ and ROS homeostasis, with 100 μM identified as the optimal concentration for this effect. Full article

The tensile capacity of a connection is predicted through the use of established models, among which the bond behavior between CFRP layers and concrete is always considered. In structures reinforced with CFRP, the prediction of the bond force between concrete and CFRP is essential, as the connection must be designed to withstand the required tensile capacity. An underestimation can lead to inefficient design, while an overestimation risks premature debonding failure, potentially compromising structural safety and serviceability. In recent applications, the bond force between concrete and CFRP has been increased through the use of the Externally Bonded Reinforcement on Groove (EBROG) method. However, due to the structural complexity introduced by the grooved interface, accurate prediction of its bond strength remains challenging, and conventional analytical models may not fully capture the underlying nonlinear interactions. In this technique, CFRP layers are placed into grooves to enhance the interaction among the adhesive, concrete, and CFRP. However, due to the structural complexity of this connection, accurate prediction of its bond force is challenging and requires the application of artificial intelligence methods. This study develops a machine learning (ML) framework to predict the bond strength of the EBROG technique. Four ML models, Support Vector Machine (SVM), Gaussian Process Regression (GPR), Decision Tree, and XGBoost, were implemented, and their hyperparameters were optimized via Bayesian optimization. The models were evaluated using multiple statistical metrics, with the XGBoost algorithm demonstrating superior predictive performance, achieving an R² of 0.987 and an RMSE of 0.522 kN. This represents an improvement of approximately 5.6% in R² and a reduction of over 53% in RMSE compared to the existing analytical model. SHAP analysis provided interpretable, data-driven insights, revealing that fracture energy is the predominant factor governing bond strength and elucidating nonlinear interactions between key design parameters. This ML-fracture mechanics framework not only offers superior prediction but also advances the mechanistic understanding of the EBROG bond behavior. Full article

Neural machine translation (NMT) performance is strongly influenced by tokenization strategies, particularly for morphologically rich languages such as Arabic. Despite the importance of tokenization, there is a lack of controlled, reproducible studies examining its impact under low-resource conditions, which limits our understanding of how different methods affect translation quality and training dynamics. This paper presents a controlled experimental study analyzing the impact of different tokenization methods on English → Arabic (EN → AR) translation using a Tiny Transformer model under low-resource conditions. The study aims to provide a systematic and reproducible comparison that isolates the effect of tokenization choices under fixed modeling and training constraints. Experiments are conducted with identical architecture, training steps, decoding procedure, and evaluation pipeline to ensure reproducibility. Translation quality is assessed using multiple metrics including BLEU, ChrF++, TER, and BERTScore, revealing substantial divergences and demonstrating empirically, in the context of low-resource Arabic NMT, that BLEU alone is insufficient for reliable evaluation. While the limitations of BLEU are known in general, our results provide new evidence showing that, under low-resource conditions and across different tokenization strategies, reliance on BLEU can lead to misleading conclusions about translation quality. Training dynamics are analyzed using TensorBoard, linking tokenization strategies to differences in convergence, saturation, and stability. For validation, a small-scale English → German (EN → DE) experiment confirms that the Tiny Transformer setup reproduces expected behavior. The contribution of this work lies in establishing controlled empirical evidence and practical insights, rather than absolute performance gains, for low-resource Arabic NMT. Our results provide controlled evidence that tokenization choice critically affects both translation quality and optimization dynamics, offering practical guidance for low-resource Arabic NMT research. Overall, byte-pair encoding (BPE) achieves the strongest balance across surface-level and semantic metrics under controlled low-resource conditions (BLEU: 8.57, ChrF++: 18.56, TER: 97.38, BERTScore-F1: 0.785). Character-level tokenization yields higher semantic similarity than subword-based methods, as reflected by BERTScore, but remains weaker in structural fidelity and surface-form accuracy, while SentencePiece exhibits intermediate behavior, favoring semantic adequacy over exact n-gram matching. These results confirm that tokenization choice critically influences both evaluation outcomes and optimization behavior, and that BLEU alone is insufficient for assessing Arabic translation quality. Full article

Nuclear medicine is a medical specialty combining parenteral radioactive drug handling and complex clinical workflows, making systematic process-related incident (PRI) analysis essential to support healthcare quality improvement. This study reports a four-year single-center retrospective analysis of PRIs in a nuclear medicine department and describes the development and implementation of a scenario-based radiopharmacy training program for nuclear medicine technologists (NMTs) derived from these findings. PRIs were extracted from the institutional reporting system and categorized according to a structured classification. Training scenarios were designed from recurrent radiopharmacy-related PRIs, and their impact was evaluated using a knowledge questionnaire administered pre and post training. A total of 223 PRIs were analyzed, of which 38.6% (n = 86) were related to the radiopharmaceutical circuit. Among these, 28.3% occurred exclusively within the radiopharmacy cleanroom. Administration (19%), dispensing (15%), delivery and reception (15%), and preparation and quality control (15%) of radiopharmaceuticals were the most frequently involved stages. No PRI exceeded a moderate criticality level. Eight NMTs participated in the training program, consisting of an analysis of videos depicting the developed scenarios. The mean knowledge score increased significantly from 7.51/10 before training to 8.46/10 four weeks after training (p = 0.02), with marked improvements in hygiene- and radioactivity-related topics. These results support the use of retrospective PRI analysis as an operational basis for specific, scenario-based training to strengthen safety practices in radiopharmacy settings. Full article

Backgrounds/Objectives: Adolescent soccer players are exposed to elevated injury risk due to rapid musculoskeletal development and high physical demands. Neuromuscular training (NMT) and speed–agility–quickness (SAQ) training are widely used to enhance performance and reduce injury risk in youth athletes. While both approaches are effective, comparative evidence regarding their modality-specific performance adaptations remains limited. Furthermore, few studies have discussed how such performance data may inform evidence-based or data-driven training selection in youth sports contexts. Methods: Thirty-six male youth soccer players with at least three years of playing experience, affiliated with Team A in Gyeonggi-do and Team B in Chungcheongbuk-do, participated in the study (NMTG, n = 21; SAQG, n = 15). Participants completed either an NMT or SAQ training program for eight weeks. To objectively assess exercise performance, pre- and post-tests were conducted measuring dynamic balance, vertical jump, zigzag run, and carioca. Results: Findings revealed a significant main effect of time for lower limb power (p < 0.05), but no significant group × time interaction, indicating that both NMTG and SAQG improved significantly over the 8-week period. Conversely, significant interaction effects were found for agility (p < 0.001), with SAQG demonstrating superior enhancements compared to NMTG. Dynamic balance showed no significant time effect or interaction. Conclusions: While NMTG and SAQG are equally effective for enhancing lower limb power, SAQG provides modality-specific advantages for agility in youth soccer players. These results emphasize time-dependent adaptations for power and the distinct benefits of SAQG for multi-directional speed. These adaptation profiles offer a data-driven framework for optimizing training selection in youth athletes. Full article

Investigating the spatio-temporal differentiation patterns and driving factors of ecosystem services (ESs) supply and demand is of great significance for early warning of ecosystem imbalance risks and identifying regional natural resource supply–demand conflicts. This study takes the typical coal-grain overlapping area (CGOA) in Eastern China as the research object, dividing it into mining townships (MT) and non-mining townships (NMT) for comparative analysis. By integrating the InVEST model, ESs supply–demand ratio (ESDR) index, four-quadrant model, and the XGBoost-SHAP algorithm, the study systematically reveals the spatiotemporal differentiation characteristics and driving mechanisms of ESs supply and demand from 2000 to 2020. The results indicated that: (1) grain production (GP) service maintained a continuous supply–demand surplus, with the ESDR of NMT areas surpassing that of MT areas in 2020. The ESDR of water yield (WY) service was significantly influenced by interannual fluctuations in supply, showing deficits in multiple years. The decline in carbon sequestration (CS) service and sharp increase in carbon emissions led to a continuous decrease in the ESDR of CS service, with MT areas facing a higher risk of carbon deficit. (2) The spatial heterogeneity of ESs supply and demand was significant, with GP and CS services exhibiting a typical urban-rural dual spatial structure, and the overall region was dominated by the Type II ESs supply–demand matching (ESDM) pattern. The ESDR of WY service generally decreases from Southeast to Northwest across the region. with the Type IV ESDM pattern dominating in most years. (3) Human activities are the core driving force shaping the supply–demand patterns of ESs. Among these, land use intensity exhibits a nonlinear effect, high population density demonstrates an inhibitory effect, and MT areas are more significantly affected by coal mining subsidence. Natural environmental factors primarily drive WY service. The research findings can provide a scientific reference for the coordinated allocation of regional natural resources and the sustainable development of the human–land system. Full article

Glycyrrhiza uralensis is a highly valued medicinal species worldwide. However, a paradox arises in its cultivation in that high nitrogen fertilization boosts yield at the expense of root quality, a problem linked to nitrogen’s regulation of tricarboxylic acid (TCA) cycle-driven respiration. It remains unclear how different nitrogen forms coordinate respiratory and primary metabolism. We examined the regulatory mechanisms of nitrate (NO₃⁻) versus ammonium (NH₄⁺) on these processes in cultivated G. uralensis by supplying seedlings with varying concentrations of K¹⁵NO₃ or (¹⁵NH₄)₂SO₄ in a modified Hoagland solution (HNS). Using non-invasive micro-test technology (NMT) and ¹⁵N tracing, we found that G. uralensis employs distinct nitrogen acquisition strategies: sustaining uptake at optimal NH₄⁺ and low-to-moderate NO₃⁻, while declining uptake under high NO₃⁻. These strategies drove form-specific differences in the activity of key nitrogen assimilation enzymes, nitrate reductase and nitrite reductase (NR/NiR), as well as glutamine synthetase and glutamate synthase (GS/GOGAT), and subsequent glutamate and glutamine accumulation. Ammonium nutrition enhanced primary ammonia assimilation and gamma-aminobutyric acid (GABA) metabolism, leading to greater glutamate and endogenous GABA levels. In contrast, nitrate nutrition preferentially stimulated the TCA cycle, resulting in higher accumulation of α-ketoglutarate (KGA) and succinate. The concomitant increase in GABA catabolism supported this nitrogen-responsive respiratory metabolism, acting as a compensatory mechanism to maintain KGA homeostasis. Our findings inform nitrogen form strategies for G. uralensis cultivation. Full article

Cold resistance is an important characteristic of sustainable development in the grape industry. The intraspecific recurrent selection in the Vitis vinifera (V. vinifera) method uses high-quality varieties as breeding materials and the substitution and accumulation of minor resistance genes, breeding high-quality grapes with cold resistance. This study was conducted to identify and genetically analyse the cold resistance of a V. vinifera hybrid population (Ecolly × Dunkelfelder), screen for highly resistant and sensitive plant samples, and use high-throughput sequencing to perform transcriptome sequencing and related differential gene expression analysis on each sample. The results revealed that the cold resistance of the hybrid offspring population was characterised by continuous quantitative trait inheritance, with 38 differentially expressed genes (7 upregulated genes and 31 downregulated genes) between the high resistance and high-sensitivity types. Analysis of genes related to various pathways, related to cold resistance, revealed that CYP76F10, Dxs, GERD, NMT, GDE1, glgC, and DHQ-SDH, as well as transcription factor MYB, HB, and MADS family genes, are key candidate genes for V. vinifera cold resistance research. Real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) was used to investigate the expression characteristics of the six genes that were differentially expressed genes, the results of which were essentially consistent with the results of RNA-seq. Specifically, NMT may enhance cold resistance by enhancing membrane lipid stability. The synergistic expression pattern of CYP76F14 and Dxs suggests its key role in terpene synthesis. By exploring potential genes related to micro effects, a theoretical foundation for further exploration of new high-quality cold-resistant grape varieties has been provided. Full article

Botrytis cinerea, a necrotrophic fungal pathogen responsible for gray mold, poses a severe threat to over 1400 plant species, causing significant pre- and postharvest losses worldwide. RNA interference (RNAi)-based strategies, particularly spray-induced gene silencing (SIGS), have emerged as environmentally friendly alternatives to chemical fungicides. However, the application of naked double-stranded RNA (dsRNA) suffers from poor stability and low cellular uptake. In this study, we engineered a self-assembling triangular RNA nanoparticle, termed Bc-triangle, targeting four virulence genes of B. cinerea—BcDCL1, BcPPI10, BcNMT1 and BcBAC. The nanostructure was designed using RNA origami principles and produced in Escherichia coli. Functional assays demonstrated that Bc-triangle significantly inhibited conidial germination and mycelial growth in vitro, and markedly reduced disease severity in plants. Compared with linear dsRNA, Bc-triangle showed superior persistence and efficacy, with lesion area reduction sustained up to 10 days post-spraying. qRT-PCR analysis revealed substantial downregulation of the target genes, especially BcNMT1, indicating enhanced RNAi activation. These findings establish RNA nanotechnology as a powerful platform for transgene-free, programmable, and sustainable control of fungal pathogens in crop production. Full article

The recent rise in living standards has been accompanied by increased awareness and emphasis on oral health. Non-invasive assessment of gingival microcirculation and accurate evaluation of oxygen supply to oral tissues are critical for the early diagnosis of oral diseases. These factors also play a pivotal role in optimizing treatment planning and improving outcomes in dental implantology. In this study, we report the development and implementation of a novel pulse oximetry device based on reflective photoplethysmography technology, designed for non-invasive, real-time monitoring of gingival health through the measurement of oxygen saturation levels. A detailed description of the technology, including key aspects of sensor probe design, is provided, with particular emphasis on the calibration process and performance evaluation of the prototype. Furthermore, we present and discuss the first proof-of-concept gingival oxygen saturation measurements obtained in a clinical setting during oral rehabilitation consultations. Full article

The spatiotemporal regulatory mechanism underlying silicon (Si)-mediated cadmium (Cd) detoxification in rice (Oryza sativa L.) was investigated using non-invasive micro-test technology (NMT), combined with physiological and biochemical analyses. The results revealed the following: (1) Si significantly inhibited Cd²⁺ influx into rice roots, with the most pronounced reductions in ion flux observed under moderate Cd stress (Cd50, 50 μmol·L⁻¹), reaching 35.57% at 7 days and 42.30% at 14 days. Cd accumulation in roots decreased by 34.03%, more substantially than the 28.27% reduction observed in leaves. (2) Si application enhanced photosynthetic performance, as evidenced by a 14.21% increase in net photosynthetic rate (Pn), a 32.14% increase in stomatal conductance (Gs), and a marked restoration of Rubisco activity. (3) Si mitigated oxidative damage, with malondialdehyde (MDA) and hydrogen peroxide (H₂O₂) levels reduced by 11.29–21.88%, through the upregulation of antioxidant enzyme activities (SOD, APX, CAT increased by 15.34–38.33%) and glutathione metabolism (GST activity and GSH content increased by 60.78% and 51.35%, respectively). (4) The mitigation effects of Si were found to be spatiotemporally specific, with stronger responses under Cd50 than Cd100 (100 μmol·L⁻¹), at 7 days (d) compared to 14 d, and in roots relative to leaves. Our study reveals a coordinated mechanism by which Si modulates Cd uptake, enhances photosynthetic capacity, and strengthens antioxidant defenses to alleviate Cd toxicity in rice. These findings provide a scientific basis for the application of Si in mitigating heavy metal stress in agricultural systems. Full article

Humor translation remains a significant challenge due to its reliance on wordplay, cultural context, and nuance. This study compares a Neural Machine Translation (NMT) system (hereafter referred to as MT) with a Large Language Model (GPT-based translation using three different prompts) for translating jokes from English to Thai. Results show that GPT-based models significantly outperform MT in humor retention, with the explanation-enhanced prompt (GPT-Ex) achieving the highest joke preservation rate (62.94%) compared to 50.12% in MT. Additionally, humor loss was more frequent in MT, while GPT-based models, particularly GPT-Ex, better retained jokes. A McNemar test confirmed significant differences in annotation distributions across models. Beyond evaluation, we propose using GPT-based models with optimized prompt engineering to enhance humor translation. Our refined prompts improved joke retention by guiding the model’s understanding of humor and cultural nuances. Full article

The COVID-19 pandemic caused the world to pause temporarily on an almost planetary scale. The creation and diffusion of knowledge about environmental planning and public health are now almost taken for granted. However, such processes were rather different in pre-pandemic times. It took a substantial dose of labor and resources to generate the information needed to produce useful and usable knowledge, and especially to make it available to others in a timely and effective way. As automobility has come to occupy center stage in the lives of an increasing number of suburbanized dwellers, it has taken multiple energy and public health crises, bold leadership, and the real threat of climate change to create the conditions needed to bolster sustainable Non-Motorized Transportation (NMT) as a complement to cleaner and more convenient mass transit options in cities. How does knowledge about sustainable NMT get created? How are sustainable NMT innovations diffused? How can technological and societal transitions to more sustainable realities be nurtured and augmented? This article utilizes a longitudinal and integrated knowledge creation and diffusion model with a Participatory Planning Process to analyze the adoption of measures aimed at reducing the negative consequences of too much automobility and encouraging higher levels of walking, cycling, and mass transportation. The research methods comprised autoethnographic, qualitative, and policy evaluation techniques. The study makes use of the means and ends matrix to discuss cases from five distinct realms: personal, academic, institutional, volunteering NGO, and private sector. The key findings and lessons learned promote scenarios of managed degrowth and sustainable urban transitions. Full article

Machine learning in natural language processing (NLP) analyzes datasets to make future predictions, but developing accurate models requires large, high-quality, and balanced datasets. However, collecting such datasets, especially for low-resource languages, is time-consuming and costly. As a solution, data augmentation can be used to increase the dataset size by generating synthetic samples from existing data. This study examines the effect of translation-based data augmentation on sentiment analysis using small datasets in three diverse languages: French, German, and Japanese. We use two neural machine translation (NMT) services—Google Translate and DeepL—to generate augmented datasets through intermediate language translation. Sentiment analysis models based on Support Vector Machine (SVM) are trained on both original and augmented datasets and evaluated using accuracy, precision, recall, and F1 score. Our results demonstrate that translation augmentation significantly enhances model performance in both French and Japanese. For example, using Google Translate, model accuracy improved from 62.50% to 83.55% in Japanese (+21.05%) and from 87.66% to 90.26% in French (+2.6%). In contrast, the German dataset showed a minor improvement or decline, depending on the translator used. Google-based augmentation generally outperformed DeepL, which yielded smaller or negative gains. To evaluate cross-lingual generalization, models trained on one language were tested on datasets in the other two. Notably, a model trained on augmented German data improved its accuracy on French test data from 81.17% to 85.71% and on Japanese test data from 71.71% to 79.61%. Similarly, a model trained on augmented Japanese data improved accuracy on German test data by up to 3.4%. These findings highlight that translation-based augmentation can enhance sentiment classification and cross-language adaptability, particularly in low-resource and multilingual NLP settings. Full article

Neglected tropical diseases (NTDs) constitute a group of infectious diseases that severely affect the health of impoverished populations, and the health, economies, and health systems of affected countries. Leishmaniasis and human African trypanosomiasis (HAT) are particularly notable, and malaria, despite not being neglected, is part of the “big three” (HIV, tuberculosis, and malaria) with high incidence, increasing the probability of infection by NTDs. Therefore, efforts are ongoing in the search for new drugs targeting the enzyme N-myristoyltransferase (NMT), a potential drug target that has been explored. Thus, we provide a review here that highlights the epidemiological data for these diseases and the importance of discovering new drugs against these agents. Here, the importance of NMT and its inhibitors is clear, with this study highlighting thiochromene, pyrazole, thienopyridine, oxadiazole, benzothiophene, and quinoline scaffolds, identified by computational methods followed by biological assays to validate the findings; for example, this study shows the action of the aminoacylpyrrolidine derivative 13 against Leishmania donovani NMT (IC₅₀ of 1.6 nM) and the pyrazole analog 23 against Plasmodium vivax NMT (IC₅₀ of 9.48 nM), providing several insights that can be used in drug design in further work. Furthermore, the selectivity and improvement in activity are related to interactions with the residues Val⁸¹, Phe⁹⁰, Tyr²¹⁷, Tyr³²⁶, Tyr³⁴⁵, and Met⁴²⁰ for leishmaniasis (LmNMT); Tyr²¹¹, Leu⁴¹⁰, and Ser³¹⁹ for malaria (PvNMT); and Lys²⁵ and Lys³⁸⁹ for HAT (TbNMT). We hope our work provides valuable insights that research groups worldwide can use to search for innovative drugs to combat these diseases. Full article