Search Results (450)

This study details the design, synthesis, and evaluation of a novel series of fourteen 2-((1H-1,2,4-triazol-5-yl)thio)-N-benzylidene-N-arylacetohydrazide hybrid compounds. The primary objective was to investigate their potential as antimicrobial agents and assess their cytotoxicity. A systematic approach combining in silico screening and experimental validation was employed. The initial in silico analysis, using SwissADME, identified compounds with favorable drug-like properties. Subsequently, all fourteen compounds were synthesized and characterized using various spectroscopic methods. Their antibacterial efficacy was evaluated in vitro against Gram-negative (Klebsiella aerogenes) and Gram-positive (Enterococcus sp.) bacteria through growth kinetics and colony-forming unit (CFU) assays. Cytotoxicity was assessed using MTT assays on HEK (human embryonic kidney) cell lines. The compound, 2-((1H-1,2,4-triazol-3-yl)thio)-N′-(2-fluorobenzylidene)-N-phenylacetohydrazide emerged as the most promising candidate, demonstrating broad-spectrum antibacterial activity. These findings highlight the potential of 2-((1H-1,2,4-triazol-5-yl)thio)-N-benzylidene-N-arylacetohydrazide hybrids as a scaffold for developing new antimicrobial agents. Furthermore, this study suggests possible environmental applications for these compounds in antimicrobial resistance (AMR) management. Full article

Nymphaea ‘Blue Bird’, a tropical water lily prized for its ornamental appeal, has been less explored as a source of bioactive flavonoids. This study developed an efficient extraction and purification protocol for flavonoids from this plant and compared their distribution and bioactivities across different tissues. Supercritical CO₂ fluid extraction (SFE) proved optimal, yielding 2.56% under conditions of 24.3 MPa, 39 °C, 91 min, and a CO₂ flow rate of 16 L/min. Subsequent purification with HPD500 macroporous resin enhanced flavonoid purity from 3.05% to 11.46%. Among the tissues analyzed, petals contained the highest levels of total flavonoids (6.43 mg/g) and total phenolics (45.71 mg/g), and exhibited the most potent antioxidant (as shown by the lowest EC₅₀ values for ABTS⁺ and DPPH scavenging) and broad-spectrum antibacterial activities (indicated by the lowest MIC and MBC against Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, and Candida albicans). Antibacterial efficacy was generally superior against Gram-positive bacteria. Widely targeted metabolomics identified 560 metabolites, predominantly flavonols and flavonoids. Principal component and cluster analyses revealed tissue-specific metabolite profiles. KEGG enrichment analysis underscored the significance of the flavonoid biosynthetic pathway, and key differential metabolites—such as luteolin, myricetin, taxifolin, and quercetin—were strongly correlated with the observed bioactivities. These results highlight N. ‘Blue Bird’ petals as a promising source of natural antioxidants and antimicrobials, providing a scientific basis for their future functional applications. Full article

Vancomycin remains a cornerstone for severe Gram-positive infections in the ICU, yet creatinine elevation—a sensitive marker of early renal stress—occurs frequently and complicates therapy. We developed a machine learning model to predict vancomycin-associated creatinine elevation using routinely available clinical data, enabling preemptive risk stratification. In this retrospective MIMIC-IV cohort study ($n=10,288$ ICU adults aged 18–80 receiving vancomycin), the primary outcome was creatinine elevation per KDIGO criteria (≥0.3 mg/dL within 48 h or ≥50% within 7 d). A two-stage feature selection (SelectKBest + Random Forest) identified 15 predictors from 30 candidates. Six algorithms were compared via 5-fold cross-validation. CatBoost was selected for final modeling; interpretability was assessed using SHAP values and Accumulated Local Effects (ALE) plots. Creatinine elevation occurred in 2903 patients (28.2%). CatBoost achieved AUROC 0.818 (95% CI: 0.801–0.834), sensitivity 0.800, specificity 0.681, and NPV 0.900. Top predictors were serum phosphate, total bilirubin, magnesium, Charlson Comorbidity Index, and APSIII score. SHAP analysis confirmed hyperphosphatemia as the strongest driver; ALE plots revealed non-linear, clinically plausible thresholds (e.g., phosphate >4.5 mg/dL sharply increased risk). This interpretable model accurately predicts vancomycin-associated creatinine elevation using standard ICU monitoring data. With high negative predictive value, it supports early exclusion of low-risk patients and targeted interventions (e.g., intensified TDM, nephrotoxin avoidance) in high-risk cases—facilitating precision antimicrobial stewardship in critical care. Full article

Online reviews on platforms such as Google Maps strongly influence consumer decisions. However, aggregated ratings mask nuanced opinions about specific aspects such as food, drinks, service, lounge, and price. This study presents a multi-aspect sentiment analysis framework for Arabic café reviews. Specifically, we combine machine learning (Linear SVC, Naïve Bayes, Logistic Regression, Decision Tree, Random Forest) and a Convolutional Neural Network (CNN) to perform aspect identification and sentiment classification. A rigorous preprocessing and feature-engineering with TF-IDF and n-gram was implemented and statistically validated through bootstrap confidence intervals and Friedman–Nemenyi significance tests. Experimental results demonstrate that Linear SVC with optimized TF-IDF tri-grams achieved a macro-F1 of 0.89 for aspect identification and 0.71 for sentiment classification. Meanwhile, the CNN model yielded a comparable F1 of 0.89 for aspect identification and a higher 0.76 for sentiment classification. The findings highlight that effective feature representation and model selection can substantially improve Arabic opinion mining. The proposed framework provides a reliable foundation for analyzing Arabic user feedback on location-based platforms and supports more interpretable and data-driven business insights. These insights are essential to enhance personalized recommendations and business intelligence in the hospitality sector. Full article

As technology advances, developers continually create innovative solutions to enhance smartphone security. However, the rapid spread of Android malware poses significant threats to devices and sensitive data. The Android Operating System (OS)’s open-source nature and Software Development Kit (SDK) availability mainly contribute to this alarming growth. Conventional malware detection methods, such as signature-based, static, and dynamic analysis, face challenges in detecting obfuscated techniques, including encryption, packing, and compression, in malware. Although developers have created several visualization techniques for malware detection using deep learning (DL), they often fail to accurately identify the critical malicious features of malware. This research introduces MalVis, a unified visualization framework that integrates entropy and N-gram analysis to emphasize meaningful structural and anomalous operational patterns within the malware bytecode. By addressing significant limitations of existing visualization methods, such as insufficient feature representation, limited interpretability, small dataset sizes, and restricted data access, MalVis delivers enhanced detection capabilities, particularly for obfuscated and previously unseen (zero-day) malware. The framework leverages the MalVis dataset introduced in this work, a publicly available large-scale dataset comprising more than 1.3 million visual representations in nine malware classes and one benign class. A comprehensive comparative evaluation was performed against existing state-of-the-art visualization techniques using leading convolutional neural network (CNN) architectures, MobileNet-V2, DenseNet201, ResNet50, VGG16, and Inception-V3. To further boost classification performance and mitigate overfitting, the outputs of these models were combined using eight distinct ensemble strategies. To address the issue of imbalanced class distribution in the multiclass dataset, we employed an undersampling technique to ensure balanced learning across all types of malware. MalVis achieved superior results, with 95% accuracy, 90% F1-score, 92% precision, 89% recall, 87% Matthews Correlation Coefficient (MCC), and 98% Receiver Operating Characteristic Area Under Curve (ROC-AUC). These findings highlight the effectiveness of MalVis in providing interpretable and accurate representation features for malware detection and classification, making it valuable for research and real-world security applications. Full article

Plant cell wall polysaccharides (PCWPs) serve as an abundant but recalcitrant carbon source for many microbes living in the gut of humans and animals. An adhesion to PCWPs is common in gut bacteria and can even be observed in the lactobacilli, which are supposed to promote the growth competence of these non-PCWP degraders because of the facilitated acquisition of newly released oligosaccharides. Nevertheless, the binding of molecules of lactobacilli to PCWPs and the underlying mechanisms remain largely unknown. By analyzing the transcriptome of Lactobacillus brevis grown in xylan supplemented with a xylanase, a gene was identified to encode a putative S-layer PCWP-binding protein (Lb1145). Lb1145 was predicted to have four domains, among which domains 1 and 2 were responsible for binding PCWPs. The binding was nonspecific, since structurally distinct PCWPs, e.g., cellulose, xylan, mannan, and chitin, and even lignin, were all bound by Lb1145. Both of the two N-terminal domains have a high pI, and we demonstrated that a non-enzymatic glycosylation-like process plays an important role in binding. Compared with another L. brevis surface protein, i.e., the WxL protein Lb630, Lb1145 displayed a binding preference for the phloem sieve tube in the wheat stem section. Moreover, Lb1145 could bind ten strains within the Lactobacillus, Enterococcus, Pediococcus, and Bacillus genera among the seventeen selected gut bacterial species. An analysis of the reported S-layer proteins from the Gram-positive bacteria (lactobacilli and bifidobacteria) and outer membrane proteins from the Gram-negative (Bacteroides fragilis and Prevotella intermedia) indicated that bacterial cell surface proteins with high pI values are not rare. The high pI-based and non-enzymatic glycosylation-like process-mediated binding represents a new paradigm and may be popular in gut bacterial surface proteins binding to PCWPs, with important physiological implications in growth competition in the gut microbiota. Full article

Energy efficiency in buildings is a vital subject within sustainable construction and climate change mitigation, yet comprehensive bibliometric analyses mapping the complete evolution of this domain remain limited. This study provides a comprehensive four-decade analysis (1981–2025) of building energy efficiency research using data from the Web of Science database, employing VOSviewer (1.6.20), Bibliometrix (4.3.0), and custom Python (3.12.3) scripts with automated terminology normalization through TF-IDF vectorization (n-grams 2–3) and cosine similarity algorithms (threshold = 0.75). Two critical methodological innovations distinguish this investigation: first, Pruned Exact Linear Time changepoint detection statistically validated 2011 as the field’s statistically validated transition point (Mann–Whitney U test, p < 0.000001, effect size = 2.48), replacing arbitrary decade-based periodization; second, computational keyword harmonization enabled precise thematic evolution mapping across inconsistent terminology. The analysis reveals marked increase in research post-2011, with median annual output increasing from 15 articles (1981–2011) to 840.5 articles (2012–2024), and China emerging as the preeminent research center with 2978 publications. Thematic evolution analysis demonstrates fundamental transformation from seven specialized research themes (i.e., behavior, heat-transfer, simulation, impact, performance, consumption, optimization) in the foundational period to dramatic consolidation into two dominant themes (i.e., performance and simulation) in the contemporary period, reflecting maturation from fragmented, component-focused investigations toward holistic, integrated frameworks. International collaboration network analysis identifies four distinct geographic clusters with China, United States, United Kingdom, and Italy serving as central hubs. These findings provide actionable intelligence for researchers, policymakers, and industry stakeholders, while the computationally enhanced framework offers a replicable methodology for bibliometric analysis in other rapidly evolving interdisciplinary domains. Full article

N-tetradecanoyl-L-homoserine lactone (C₁₄-HSL) is a long-chain signaling molecule belonging to acyl-homoserine lactones (AHLs), which is widely present in the quorum sensing (QS) system of Gram-negative bacteria. In this study, the effects of C₁₄-HSL on chalcopyrite bioleaching mediated by Acidithiobacillus ferrooxidans (A. ferrooxidans) were investigated. After cultivating A. ferrooxidans with different energy substrates and exploring the potential mechanisms of signal molecule production, chalcopyrite was selected as the energy substrate for further study. Molecular docking analysis revealed that the high binding affinity between AHL and the receptor protein AfeR in A. ferrooxidans was beneficial for the activation of transcription by the AfeR-AHL complex, promoting their biological impact. The variations in the physicochemical parameters of pH, redox potential, and copper ions revealed that after adding C₁₄-HSL, the leaching rate of chalcopyrite increased (1.15 times during the initial 12 days). Further analysis of the mechanism of extracellular polymers formation indicated that the presence of C₁₄-HSL could promote the formation of biofilms and the adhesion of bacteria, facilitating mineral leaching rate of A. ferrooxidans. This research provides a theoretical basis for regulating the biological leaching process of chalcopyrite and metal recovery using signaling molecules, which could also be used to control environmental damage caused by acid mine/rock drainage. Full article

Among many metal ions in biological systems, iron plays a fundamental role. Transferrins are iron-binding glycoproteins responsible for transporting Fe³⁺ in vertebrate blood. Neisseria meningitidis, a Gram-negative pathogen causing meningitis, relies on iron for survival and acquires it from human transferrin (hTf) using two surface proteins, TbpA and TbpB. These proteins interact with hTf to form a ternary TbpA–TbpB–hTf complex, enabling iron capture from the host. The absence of an experimental crystal structure for this complex has hindered computational studies, a detailed understanding of Fe³⁺ dissociation, and designing efficient therapeutics. This study presents the first computational model of the ternary complex, its validation, and molecular dynamics simulations. Structural analyses revealed key electrostatic interactions regulating Fe³⁺ coordination and essential contact regions between proteins. The role of Lys359 from TbpA was investigated via QM/MM calculations by evaluating Fe³⁺ binding energies of isolated hTf, the ternary complex, and Lys359Ala, Lys359Arg, Lys359Asp mutant models. Results revealed that the proton transfer from Lys359 leads to disruption of Tyr517–Fe³⁺ coordination, facilitating iron transfer to the bacterial system. Natural bond orbital analysis confirmed this mechanism. The findings provide new molecular insight into N. meningitidis iron acquisition and identify Lys359 as a potential target for covalent inhibitor design, guiding the development of novel therapeutics against meningococcal infection. Full article

With continuous increases in nitrogen (N) deposition in the future, its impacts on terrestrial ecosystems are attracting growing concern. Arbuscular mycorrhiza (AM) fungi play a crucial role in shaping both soil microbial and plant communities. AM fungi play a crucial role in shaping the soil microbial and plant communities, yet their patterns of influence under increased N deposition scenarios remain unclear, particularly in desert ecosystems. Therefore, we conducted a field experiment simulating increased N deposition and AM fungal suppression to assess the effects of increased N deposition and AM fungi on soil microbial communities, employing phospholipid fatty acid (PLFA) biomarker technology in the Gurbantunggut Desert of Xinjiang. We found that increased N deposition promoted soil microbial biomass, including AM fungi, fungi, Actinomycetes (Act), Gram-positive bacteria (G⁺), Gram-negative bacteria (G⁻), and Dark Septate Endophyte (DSE). AM fungal suppression significantly increased the content of soil Act and G⁺. There were clearly and significantly interactive effects of increased N deposition and AM fungi on soil microbial contents. Both increased N deposition and AM fungi caused significant changes in soil microbial community structure. Random forest analysis revealed that soil nitrate N (NO₃⁻-N), Soil Organic Carbon (SOC), and pH were main factors influencing soil microorganisms; soil AM fungi, G⁺, and Act significantly affected plant Shannon diversity; soil G⁻, Act, and fungi posed significant effects on plant community biomass. Finally, the structure equation model results indicated that soil fungi, especially AM fungi, were the main soil microorganisms altering the plant community diversity and biomass under increased N deposition. This study reveals the crucial role of AM fungi in regulating soil microbial responses to increased N deposition, providing experimental evidence for understanding how N deposition affects plant communities through soil microorganisms. Full article

Rhizosphere microbiota, serving as pivotal drivers of multifunctionality in grassland ecosystems, are jointly shaped by the dual influences of biotic and abiotic factors. Among biotic components, plant functional types selectively modulate microbial communities through root exudate specificity, while soil fauna (e.g., nematodes and earthworms) drive microbial interaction networks via biophysical disturbances and trophic cascades. However, excessive nematode grazing suppresses the hyphal extension of arbuscular mycorrhizal fungi (AMF). Moderate grazing facilitates the proliferation of ammonia-oxidizing bacteria through fecal input, whereas intensive grazing induces topsoil compaction, leading to a dramatic 40–60% reduction in lipopolysaccharide content in Gram-negative bacteria. Long-term chemical fertilization significantly decreases the fungal-to-bacterial ratio, while organic amendments enhance microbial carbon use efficiency by activating extracellular enzymatic activities. Regarding abiotic factors, the stoichiometric characteristics of soil carbon, nitrogen, and phosphorus directly regulate microbial metabolic strategies. Hydrological dynamics influence microbial respiratory pathways through oxygen partial pressure shifts—drought stress inhibits mycelial network development. Future research should focus on predicting the emissions of gases such as N₂O (ozone monomer) and optimizing nitrogen fertilizer management to significantly reduce greenhouse gas emissions at the source. The soil organic carbon storage in grassland ecosystems is extremely large. Effective prediction and management can make these soils become important carbon “sinks”, offsetting the carbon dioxide in the atmosphere. At the same time, transcriptomics and metabolic flux analysis should be combined with multi-omics technologies and in situ labeling methods to provide theoretical basis and technical support for developing mechanism-based and predictable grassland restoration and adaptive management strategies from both macroscopic and microscopic perspectives. Full article

Rapid urbanization has intensified the shortage of school places in many developing countries, prompting the rise of compact, high-floor area ratio (FAR) school models. However, research on high-FAR school design strategies remains limited. This study systematically analyzes 67 high-FAR schools in Shenzhen, China. Using design descriptions as the sample, the analysis applied the N-gram model and identified five major design strategies: responses to regulations, functional integration of classroom spaces, functional integration of public spaces, climate adaptation and sustainability, and alleviation of psychological stress. Correlation analysis revealed that factors including FAR, total floor area, design year of the schools, regional GDP and investment in the education sector significantly influence preferences for different design strategies. Further, K-means clustering categorized four types based on strategy adoption and FAR: the comprehensive strategy type; the user-centered innovation type; the spatial integration type; the psychological well-being type. The results emphasize the need for adaptable design strategies that reflect local development stages. These findings contribute to a data-informed foundation for improving spatial efficiency in rapidly urbanizing settings, offering policy and design guidance for rapid developing cities. Full article

Automated Machine Learning (AutoML) has become an important area of modern artificial intelligence, enabling computers to automate the selection, training, and tuning of machine learning models and offering exciting opportunities for enhanced decision-making across various sectors. As the global adoption of machine learning technologies grows, it has been observed that also the importance of understanding the development and proliferation of AutoML research continues to grow, as highlighted by the increased number of scientific papers published each year. The present paper explores the scientific literature associated with AutoML with the aim of highlighting emerging trends, key topics, and collaborative networks that have contributed to the rise of this field. Using data from the Institute for Scientific Information (ISI) Web of Science database, we analyzed 920 papers dedicated to AutoML research, extracted based on specific keywords. A key finding is the significant annual growth rate of 87.76%, which underscores the increasing interest of the academic community in AutoML. Furthermore, we employed n-gram analysis and reviewed the most cited papers in the database, providing a comprehensive bibliometric overview of the current state of AutoML research. Additionally, topic discovery has been conducted through the use of Latent Dirichlet Allocation (LDA) and BERTopic, showcasing the interest of the researchers in this area. The analysis is completed by a review of the most cited papers, as well as discussions of the papers in the research areas associated with this AutoML. These findings offer valuable insights into the evolution of AutoML and highlight the key challenges and opportunities addressed by the academic community in this rapidly growing field. Full article

Polymyxin antibiotics are often the last line of defense against multidrug-resistant Gram-negative pathogens. A key resistance mechanism involves the addition of 4-amino-4-deoxy-L-arabinose (L-Ara4N) to lipid A, mediated by the bifunctional enzyme ArnA. However, the evolutionary rationale and structural basis for ArnA’s domain fusion, hexameric assembly, and catalytic coordination remain mechanistically unresolved. Here, we integrate evolutionary genomics, high-resolution cryo-electron microscopy (cryo-EM), and computational protein design to provide a comprehensive mechanistic analysis of ArnA. Our evolutionary analysis reveals that the dehydrogenase (DH) and formyltransferase (TF) domains evolved independently and were selectively fused in Gammaproteobacteria, suggesting an adaptive advantage. A 2.89 Å cryo-EM structure of apo-ArnA resolves the flexible interdomain linker and reveals a DH-driven hexameric architecture essential for enzymatic activity. 3D variability analysis captures intrinsic conformational dynamics, indicating a molecular switch that may coordinate sequential catalysis and substrate channeling. Structure-based peptide inhibitors targeting the hexamerization and predicted ArnA–ArnB interaction interfaces were computationally designed, offering a novel strategy for disrupting L-Ara4N biosynthesis. These findings illuminate a previously uncharacterized structural mechanism of antimicrobial resistance and lay the groundwork for therapeutic intervention. Full article

The rise of e-commerce platforms has completely revolutionized the way in which consumers interact with the market. In our digital world, due to the evolution of technology, people can purchase with ease the desired products, regardless of time and place, directly from their personal devices. This has led to a considerable improvement in users’ experiences, saving both time and money and avoiding stores’ congestions. At the same time, the emerging technologies, such as machine learning, artificial intelligence, augmented reality, and blockchain, registered a substantial contribution to optimizing e-commerce platforms by enhancing the efficiency of the processes, better understanding users’ needs, and offering personalized solutions. Therefore, the present bibliometric investigation aims to provide a comprehensive overview of the research domain-electronic commerce exploration using emerging technologies. Based on a dataset collected from the Web of Science database, the study reveals key details of the field, research characteristics, main themes, and current trends. Within the analysis, the R-tool—Biblioshiny 4.2.1—has been used for the creation of tables, graphs, and visual representations. The high importance of the domain, together with the significant interest within academics in publishing papers around this area, is validated by the value obtained for the annual growth rate, more specifically 44.65%, as well as by the cross-validation analyses performed in VOSviewer 1.6.20 and CiteSpace 6.3.R1, along with topic analysis performed through Latent Dirichlet Allocation and BERTopic. The results of this research represent precious information for the scientific community, authorities, and even companies that are oriented to e-commerce platforms, since crucial details about the market trends, domain’s impact, and key contributions are exposed. Full article