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Customizable front end design using improved StyleGAN with detail control
Flora and vegetation of fallow lands invaded by the black Cherry Padus serotina (Ehrh.) Borkh. in Lower Silesia (SW Poland)
Development and validation of a risk prediction model for poor sleep quality among senior high school students in China
Pilose antler extract promotes angiogenesis and vascular maturation to accelerate wound healing
Effect of traumatic brain injury on the trough concentration of linezolid in patients with hospital-acquired pneumonia
Abstract To investigate the effect of traumatic brain injury (TBI) on linezolid trough concentration (C min ) and Gram-positive bacteria eradication in patients with hospital-acquired pneumonia (HAP). HAP patients who treated with linezolid were collected and divided into TBI and non-TBI groups. Differences in the linezolid C min , Gram-positive bacterial eradication, clinical treatment success, and adverse reactions were analyzed between the two groups. A total of 177 patients were enrolled in the study, including 57 with TBI and 120 without TBI. After 1:1 propensity score matching (PSM), 46 patients with well-balanced baseline levels were enrolled. Linezolid C min and Gram-positive bacterial eradication rate were lower in TBI group than in non-TBI group [2.06 (0.89, 6.89) mg/L, 6.70 (3.09, 13.48) mg/L, P < 0.001; 69.0% vs. 90.9%, P = 0.029]. Linezolid-induced thrombocytopenia (LIT) and severe LIT occurred in fewer patients in TBI group than in non-TBI group (4.3% vs. 28.3%, P = 0.005; 2.2% vs. 17.4%, P = 0.035). The linezolid C min for the predicted eradication of Gram-positive bacterial was 1.62 mg/L. Further studies have found that the use of mannitol, glycerin fructose and high fluid intake and output may be responsible for the low C min of linezolid in TBI group. Patients in TBI group had lower linezolid C min and Gram-positive bacterial eradication rates compared with those in non-TBI group. A lower C min level may predict a decrease in Gram-positive bacterial eradication rates, and patients with concomitant TBI may need to explore higher linezolid doses.
The effects of screen habits on attentional skills and prosocial behaviors in 6-to 36-month-old toddlers
Geotechnical improvement of dune sand embankments using rubber crumb and brick powder: RSM vs. ANN-GA hybrid optimization
Adaptive deep clustering integrating DINOv2 embeddings, graph attention, and bio-inspired optimization
Abstract This paper presents a unified and adaptively integrated framework for unsupervised image clustering that establishes a novel synergistic interaction between self-supervised representation learning, graph-based embedding refinement, and bio-inspired optimization. Rather than employing DINOv2, GAT, and the Bat Algorithm as isolated components, the proposed DINOv2–GAT–BAT pipeline introduces a closed-loop adaptive mechanism in which semantic embeddings, attention-guided structural information, and cluster-shaping optimization dynamically influence one another. The framework first extracts high-level visual features using pretrained DINOv2 Vision Transformers, then refines relational structures through a multi-head Graph Attention Network (GAT), and finally employs a bat-inspired metaheuristic that jointly estimates the optimal number of clusters and adaptively tunes structural and hyperparameter configurations. This tightly coupled interaction results in a new form of adaptive deep clustering not present in existing transformer- or GNN-based systems. To improve interpretability, two composite internal indices— $$\hbox {SEHI}^{*}$$ and $$\hbox {UCI}_{\text {ext}}$$ —are introduced, jointly capturing separability, entropy, compactness, stability, and outlier sensitivity. These indices exhibit strong correlations with external evaluation metrics, enabling reliable and meaningful assessment in fully unsupervised scenarios. Extensive experiments on CIFAR-10, Oxford-IIIT Pet, and STL-10 demonstrate the effectiveness and generalization capability of the proposed framework. On CIFAR-10, it achieves NMI = 0.938, ARI = 0.932, and a Composite Score = 0.894, surpassing several state-of-the-art baselines. Overall, this work (1) introduces a novel adaptive integration mechanism linking transformers, graph attention, and metaheuristic optimization, (2) proposes interpretable composite metrics for unsupervised evaluation, and (3) achieves state-of-the-art clustering performance across diverse benchmarks.
Synergistic augmentation of BDS PPP-B2b: integrating LEO constellations and wide-lane ambiguity resolution for instantaneous convergence
Development of a solar-integrated energy management system for grid-to-vehicle and vehicle-to-grid power exchange
Spatiotemporal trends and machine learning-based prediction of temperature variability during the T. Aman rice-growing season in Bangladesh
Rapid virus-free production of recombinant yellow fever virus envelope protein and its in-depth biophysical analysis
Abstract The aim of this study was to develop a rapid and easy-to-adapt protocol for recombinant production of Orthoflavivirus proteins. We introduced the new-generation pOpiE2 vector for virus-free transient transfection of Trichopluisa ni High Five insect cells to produce the envelope protein ectodomain of Yellow Fever Virus Asibi and 17D strains. We achieved high yields in the range of 6–7 mg/L within five working days. In-depth biophysical analysis demonstrated highest protein purity, homogeneity, stability, and affinities to specific antibodies in the low nanomolar range. A combination of peptide mass fingerprint and intact mass spectrometry characterized the copurified precursor chaperone peptide pr and discovered a new posttranslational processing site for the recombinantly produced protein.
Design of a hybrid learning model for establishing consistency in smart grid environment
Dynamic effects of outpatient pooling scheme on socioeconomic inequality in healthcare utilization in China from 2011 to 2020
Boosting pre-trained model with silica nanoparticles cellular toxicity prediction
AI-driven antimicrobial peptide characterization unveils novel motifs for drug design
Abstract Antibiotics have been developed to effectively target and eliminate bacteria, but the rise in antimicrobial resistance (AR) complicates the treatment of certain infections. To address this issue, researchers have explored antimicrobial peptides (AMPs) that disrupt bacterial membranes. A promising method for this exploration is motif-based analysis, which identifies hidden patterns in AMPs to better understand their mechanism of action. While existing methods rely on expert knowledge, incorporating topic models can enhance analysis by revealing the contextual relationships between sequence elements. This is complemented by a data analytics tool designed to analyze AMP motifs and their biochemical properties. Such integration allows for the extraction of valuable motifs and the development of a robust data analytics module for predicting membrane activity. Additionally, we evaluated the biological relevance of motifs by extracting biochemical features, making structural predictions via Evolutionary Scale Modeling (ESM). Our results indicate that topic model-derived motifs are strongly associated with antimicrobial activity and demonstrate lower minimum inhibitory concentration values and capture contextual information more effectively than traditional frequency-based motifs. We also performed a comparative analysis between the two approaches regarding motif evolution, sequence-level attributes, and entropy measures, ultimately contributing to ongoing efforts to combat AR.
First molecular confirmation of Lasiodiplodia theobromae causing grapevine trunk disease in southern Egypt
Abstract Grapevine trunk diseases constitute a significant phytopathological concern in Egyptian viticulture, with ongoing debates regarding their origin and transmission dynamics. These complexities are attributed to the heterogeneous manifestation of symptoms and the involvement of multiple wood-associated pathogens, both suspected and confirmed. This study investigates the mycological aspects of grapevine trunk diseases, focusing on Lasiodiplodia theobromae as a causal agent. The pathogen was associated with vascular cankers, dark brown trunk discoloration, pycnidia formation on necrotic tissues, and grapevine dieback. Identification of L. theobromae was achieved through morphological characteristics and molecular analysis targeting the β-tubulin gene and Internal Transcribed Spacer (ITS) region. Pathogenicity tests were conducted by inoculating detached canes, leaves, petioles, and entire branches with mycelial plugs of L. theobromae . The resulting symptoms closely resembled those observed in naturally infected grapevines in the field. The pathogen was then re-isolated and identified, confirming Koch’s postulates. A disease index (DI) ranging from 60 to 100% provided strong evidence of the high pathogenic potential of L. theobromae under experimental conditions.