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Retraction: Optimizing chemotherapeutic targets in non-small cell lung cancer with transfer learning for precision medicine
First evaluation framework based on the one welfare concept in extensive broiler rearing systems
Luminescent Orthochromite Microcrystals: Synthesis, Magnetic-Exchange Splittings, and Simultaneous Pair Excitation in Yb <sup>3+</sup> -Doped YCrO <sub>3</sub> and YbCrO <sub>3</sub>
Chitinase-like proteins de-N-glycosylating CD36 modify cholesterol metabolism in atherosclerotic macrophages
AI-assisted optimization design of seismic performance parameters for timber structures
Timber multi-story buildings offer environmental benefits, lightweight construction, and seismic resilience, but Artificial Intelligence (AI) based integrated frameworks for optimizing seismic parameters, including inter-story drift and roof displacement, remain limited. The Gradient Boosted Random Forest Machine with Scalable Cheetah Optimizer (GBRF-SCO) is proposed for improving prediction accuracy while facilitating optimal design decisions. The dataset consists of 4,000 timber building samples obtained from a publicly available Kaggle repository (Timber Seismic Performance Dataset). Data pre-processing employs normalization and outlier detection using Robust Scaling and Isolation Forest, ensuring high-quality inputs. For exploratory analysis, t-Distributed Stochastic Neighbor Embedding (t-SNE) is applied to visualize high-dimensional feature relationships and identify structural parameter patterns relevant to seismic performance. The proposed framework uses GBRF to predict seismic response metrics, with the SCO tweaking hyperparameters to optimize model performance. It also enables the optimization of seismic performance characteristics, guiding engineers in selecting structural designs that minimize drift and enhance robustness. Multiple Linear Regression (MLR) was employed to examine the influence of key structural and seismic elements on roof displacement, providing insights into the overall seismic performance of wood buildings. Comparative evaluation shows superior performance over conventional regression and ensemble methods, demonstrating a higher accuracy of 0.949, which corresponds to the classification of roof displacement levels (low, medium, high) under seismic loading conditions and seismic intensities using Python 3.10. By providing a strong and clever method for designing sustainable and earthquake-resilient buildings, the suggested GBRF-SCO framework successfully improves the seismic performance optimization of timber structures.
Nationwide survey of Japanese Physicians on Kampo medicine use in frailty care
Influence of Phosphate Activation Chemistry on the Selection of the Primordial Genetic Alphabet
Molecular basis of target RNA cleavage by Cas13
Farm scale on the transformation of agricultural mechanization services:Evidence from China
Understanding the dynamics of agricultural mechanization services is essential for fostering the sustainable development of grain production in countries where small-scale farming predominates. This study analyzes data from 584 villages and 586 service providers across Jiangsu, Sichuan, and Jilin provinces in China to investigate the factors driving changes in agricultural mechanization services and their potential impacts. The findings indicate that the scale of land parcels significantly and positively influences the evolution of agricultural mechanization in China in recent years. Specifically, an increase in plot size leads to substantial reductions in costs and improvements in the efficiency of local services, thereby creating a viable alternative to cross-regional services. Therefore, while fully leveraging the comparative advantages of cross-regional services, we should accelerate the establishment of local service systems in key areas that primarily rely on non-cross-regional services. By optimizing the organizational structure of agricultural machinery services and innovating large-scale land management models, we can comprehensively enhance agricultural production and operational efficiency.
Prospective clinical evaluation of cell-free DNA next generation sequencing in patients with suspected metastatic lung cancer
Selective Deoxygenative Electroreduction of Amides
CB1 receptor signaling at the cingulate-striatal circuit is anxiogenic
Correction: Symmetry, presumptions, and the judges design
Awareness motivations and barriers related to rhinoplasty among adults in Palestine, Syria, Lebanon, Jordan and Iraq
Cobalt-Catalyzed Carbonylative Conversion of Unactivated Alkyl Chlorides
Daratumumab in high-risk MGUS and low-risk smoldering myeloma: results of the Phase II D-PRISM study
Microbial diversity, functional genomics and antibiotic resistance in integrated chicken and fish farming systems of Bangladesh
The integrated chicken and fish farming system in Bangladesh is widely practiced for its resource efficiency, yet its microbial structure, functional potential, and associated antimicrobial resistance risks remain poorly understood. This study investigated microbial communities, metabolic functions, and antimicrobial resistance profiles across multiple components of integrated farming systems, including chicken gut, chicken droppings, feed, fish intestine, and pond sediment. Microbial profiling was performed using 16S ribosomal ribonucleic acid (rRNA) gene sequencing, functional metagenomic prediction, and culture-based isolation, complemented by antimicrobial susceptibility testing. A total of 2,838 operational taxonomic units were identified, with bacteria constituting the vast majority of detected microorganisms. Microbial community composition was strongly shaped by sample type, reflecting distinct ecological niches within the farming system. Chicken gut samples were dominated by Firmicutes, feed samples by Cyanobacteria, and sediment samples exhibited the highest microbial diversity, including taxa involved in biogeochemical cycling. Functional analysis revealed that pathways related to amino acid and carbohydrate metabolism were most abundant across all samples, while sediment and feed were enriched in pathways associated with xenobiotic degradation, suggesting a role in environmental detoxification. Culture-based methods isolated clinically relevant bacteria, including Escherichia coli and Proteus mirabilis , although metagenomic analysis indicated that these organisms represented only a minor fraction of the overall microbial community. Antimicrobial susceptibility testing demonstrated notable resistance, particularly to tetracyclines and fluoroquinolones. Metagenomic analysis further identified multiple antimicrobial resistance genes, with several showing strong associations with specific bacterial genera. This study provides the first comprehensive characterization of microbial diversity, functional capacity, and antimicrobial resistance within integrated chicken and fish farming systems in Bangladesh, highlighting potential environmental reservoirs of resistance and underscoring the need for improved management strategies to enhance sustainability and reduce public health risks.