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A novel planctomycetotal isolate from subsurface percolates belongs to the novel species Anatilimnocola aquadivae sp. nov. in the family Pirellulaceae

Abstract The family Pirellulaceae (phylum Planctomycetota ) is known for its environmental versatility, with members isolated from marine habitats, algal surfaces, soil and lakes; yet, no member has been isolated from terrestrial subsurface habitats. Here, we describe the planctomycetal strain NA78 T that was discovered in percolates from fractured limestone in ca. 0.6 m depth at the Hainich Critical Zone Exploratory (CZE) in central Germany. Cells of the isolated strain are pear-shaped, measuring approximately 1.1 × 1.8 μm, and divide by asymmetrical cell division (“polar budding”). Liquid cultures have a whitish color and cells of the strain form aggregates. Colonies are rigid, round and of whitish to beige color. Strain NA78 T grows under oxic conditions and thrives at temperatures between 18 and 24 °C, with an optimum at 18 °C. The strain tolerates pH values from 6.0 to 9.0, with optimal growth at pH 7.5, and matches the pH range of the bedrock percolate. Its genome has a size of 7.97 Mbp and a DNA G + C content of 58.2%. From combined results of phylogenetic analyses and phenotypic and genomic characterization, we conclude that strain NA78 T belongs to a novel species of the genus Anatilimnocola . We thus introduce the name Anatilimnocola aquadivae sp. nov., represented by NA78 T (= CECT 30429 T  = STH00992 T ; the STH number refers to the Jena Microbial Resource collection JMRC) as the type strain.

Advanced hybrid transformer CNN framework for improved skin lesion classification and segmentation

A parallel UNet integrating KAN and mamba for medical image segmentation

Spatiotemporal evolution and spatial differentiation of carbon emission intensity in the Chinese transport sector

Preferential path attachment model for quantum key distribution networks

Abstract This paper presents a model for path-based growing network with preferential attachment motivated by the deployment of quantum key distribution networks. The model is based on a network constructed from path segments of $$\langle \hbox {n}\rangle$$ nodes on average to mimic real-world quantum key distribution network architectures. Using continuum formalism and the rate equation method, we derive degree exponent, exact degree distributions and demonstrate properties similar to random networks. The theoretical framework incorporates preferential attachment with variable crossover rates and strategic shortcuts, the satellite links. The approach is validated through extensive simulations implemented in Python. Key findings reveal that network robustness, measured by critical fraction for giant component loss, increases with crossover rate and number of satellite links but decreases with segment length. Average distance scales logarithmically with network size, directly impacting secret key consumption during relaying processes in quantum key distribution networks. While preferential attachment enhances connectivity, the model network does not achieve ultra-small world properties of scale-free networks that would minimize key consumption, providing insights for designing cost-effective quantum communication infrastructures.

Lipodystrophy and associated factors among patients with diabetes receiving insulin therapy: a multicenter study in Ethiopia

Bio-inspired synthesis of silver selenide (Ag₂Se) binary chalcogenide nanoparticles mediated by Punica granatum L. peel extract and a comprehensive evaluation of their biological activities

Modulation of feedback-related negativity by objective and subjective response correctness

QRNN-GRU framework for automatic argument and annotation extraction in medical drug reviews

APR-246 drives ROS-dependent ferroptosis and apoptosis and enhances anti–PD-1 efficacy in bladder cancer

Non-Hermitian quantum state discrimination and information flow

Relying on AI at work reduces self-efficacy, ownership, and meaning while active collaboration mitigates the effects

Optimization of hole quality in drilling of direct hot-pressed Al/SiC composites using Taguchi method

Abstract In this study, aluminium (Al) matrix composites reinforced with 0, 5, and 10 vol% silicon carbide (SiC) particles were fabricated by direct hot-pressing under 35 MPa pressure at 600 °C for a holding time of 5 min. The morphological characteristics of the initial Al and SiC powders were examined using scanning electron microscopy. The fabricated composites were characterized through density measurements, microstructural analysis, X-ray diffraction, and microhardness testing. Microstructural observations confirmed a homogeneous distribution of SiC particles within the Al matrix. Although the relative density decreased with increasing SiC content, microhardness increased due to restricted dislocation motion induced by the hard ceramic reinforcement. The cutting speed, feed rate, point angle, and reinforcement ratio were selected as control factors in the drilling process of Al/SiC composites. In contrast, thrust force, surface roughness, deviation from diameter, and deviation from circularity were selected as performance indicators. The Taguchi method was used to determine the optimal experimental conditions for hole performance indicators. The effects of drilling parameters on the drill bit, hole quality, and chip morphology were investigated. The contribution ratios of the control factors on the responses were determined using analysis of variance. The feed rate was found to be the most effective control factor on hole quality. Regression analysis was applied to establish a mathematical relationship between the control factors and the responses. The R 2 values obtained from the regression equations were found to be quite high. Finally, confirmation experiments conducted with the determined optimal parameter sets have proven the validity of the models by yielding statistically significant results within a 95% confidence interval.

Hybrid CNN–transformer model with BM3D and YOLOv8 for early detection of lung cancer in low-dose CT scans

Abstract Lung cancer remains the primary cause of cancer-related deaths throughout the world. The main reason behind this is late diagnosis and the restrictions in the manual interpretation of imaging data. In these days Low-Dose Computed Tomography (LDCT) has been widely adopted for early screening. LDCT contains Low Dose x-rays as compared to the normal CT scan. But the existence of noise and subtle nodular patterns often impairs diagnostic accuracy. In this study, authors proposed a novel hybrid deep learning model which uses BM3D for pre-processing and YOLOv8 for segmentation. Further this model integrates Convolutional Neural Networks (CNNs) with Transformer Encoders to enhance the early detection of lung cancer using LDCT scan images. The model powers the spatial feature extraction with the help of CNNs and the contextual reasoning capability of Transformers to achieve superior classification performance. In this work, during the training of model BM3D filtering (advanced image preprocessing technique) are applied to reduce noise and enhance structural details. Further YOLOv8 is used for segmentation. The proposed hybrid model achieved 93.8% sensitivity, 95.1% accuracy, 94.4% F1-Score, 96.2% Specificity, 0.92 Dice Metric and 0.97 AUC for classification. Experimental results demonstrate that the proposed model outperforms existing models in terms of accuracy, precision, recall, AUC, and Dice coefficient. These findings suggest that the hybrid model holds strong potential as a robust tool for early lung cancer screening and clinical decision support.

Ecosystem health in the Yellow River Estuary based on the DPSIR model: a case study in China

SMC ensures efficient chromosome replication and oriC positioning during Streptomyces spore germination

Abstract Bacterial chromosomes are organized by condensins, such as Structural Maintenance of Chromosomes (SMC) proteins. In Streptomyces , a genus of sporulating bacteria, SMC proteins align chromosomal arms and promote efficient compaction of chromosomal DNA during spore formation. We hypothesized that disrupting nucleoid architecture by deleting the smc gene would affect the positioning of the origin of replication ( oriC ) or the process of chromosome replication during spore germination. To test this hypothesis, we conducted marker frequency analyses and microscopy studies to observe the positioning of labelled oriC and replisomes in both wild type and Δ smc backgrounds. Additionally, we investigated the positioning of three chromosomal loci in early vegetative cells. Our results indicate that the deletion of smc impairs chromosome replication and hinders germ tube development. Furthermore, detailed analysis of chromosome organization revealed that, in the absence of SMC, the oriC region becomes mispositioned within the nucleoid. These findings underscore the important role of SMC in maintaining nucleoid architecture during the early growth stages of Streptomyces .

Association between β-blocker use and outcomes in patients with heart failure and chronic obstructive pulmonary disease: a retrospective cohort study

Adaptive reinforcement learning for lithography optimization: a scalable AI-driven solution for next-generation semiconductor manufacturing

Abstract Semiconductor lithography, a pivotal process in integrated circuit (IC) fabrication, accounts for approximately 30% of production costs and faces significant challenges as feature sizes shrink to sub-nanometer scales. Optical diffraction and process-induced distortions complicate precise patterning, necessitating advanced techniques beyond traditional Optical Proximity Correction (OPC). Inverse Lithography Technology (ILT) offers a mathematically robust approach to enhance pattern fidelity, yet its high computational complexity limits scalability. We propose Adaptive Reinforcement Learning for Lithography Optimization (ARLO), a U-Net-based framework integrating self-attention mechanisms and reinforcement learning (RL) to iteratively optimize photomasks using real-time lithographic simulations. Evaluated on the LithoBench benchmark, ARLO achieves a 37.8% reduction in $$L_2$$ Loss and a 74.0% reduction in Process Variation Band (PVB) compared to GAN-OPC, alongside 14.7% and 9.1% $$L_2$$ Loss reductions and 51.3% and 37.1% PVB reductions versus Deep LithoNet (DLN) and RL-ILT, respectively. Despite a higher shot count (181.4% increase vs. GAN-OPC, 59.0% vs. DLN-1, 29.4% vs. RL-ILT), ARLO maintains a competitive runtime of 0.035 seconds per patch. These results position ARLO as a scalable, efficient solution for next-generation semiconductor manufacturing.

Modeling and optimization of performance and emissions in a gasoline-isopropanol SI engine: multi-model prediction and a PID-based search algorithm

Association of ALDH2 rs671 Polymorphism with chronic kidney disease incidence in a population-based Korean cohort

Abstract The Aldehyde dehydrogenase 2 ( ALDH2 ) rs671 polymorphism, a common variant that impairs aldehyde detoxification, has been linked to cardiovascular disease, but its role in chronic kidney disease (CKD) remains unclear. This study examined the association between the ALDH2 rs671 polymorphism and incident CKD in a population-based cohort, and whether alcohol consumption modifies this relationship. We analyzed 5,369 Korean adults aged 40–69 years without CKD at baseline from the community-based Korean Genome and Epidemiology Stud y , followed biennially for up to 18 years. The main exposures were ALDH2 genotype (GG vs. GA/AA) and categorized alcohol consumption (none, low, moderate, high). Incident CKD was defined as an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m² or new-onset proteinuria (≥ 1 + on dipstick). Cox proportional hazards models estimated adjusted hazard ratios (HRs). During a mean 11.7-year follow-up, 1,396 participants (26.0%) developed CKD. CKD risk did not differ significantly between genotypes, and alcohol intake was not associated with CKD incidence. These associations were consistent across genotype or sex. Overall, ALDH2 rs671 and alcohol intake showed limited relevance to CKD onset, suggesting that ALDH2 -related biological effects may be more pertinent to disease progression rather than initiation in the general population.