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Structural insights into chemoresistance mutants of BCL-2 and their targeting by stapled BAD BH3 helices
Enhanced feature representation for real time UAV image object detection using contextual information and adaptive fusion
Biochemical characterisation and in vitro mitigation of Xanthomonas citri pv. punicae, causing bacterial blight in pomegranate, using antibiotics and copper compounds
The pericardium forms as a distinct structure during heart formation
Metabolomic biomarkers discovery across chronic gastritis to gastric cancer progression
Abstract Gastric cancer (GC) is a severe malignancy characterized by late diagnosis, poor prognosis, and low survival rates. Its progression is often linked to chronic non-atrophic gastritis (CNAG) and chronic atrophic gastritis (CAG), which show atypical symptoms. Identifying biomarkers for CNAG, CAG, and GC progression is crucial for earlier diagnosis and prevention. This study conducted non-targeted metabolomics on 81 clinical samples (17 controls; 23, 23, and 18 from CNAG, CAG, and GC patients, respectively) using ultra-high-performance Liquid chromatography and high-resolution mass spectrometry. A total of 763 metabolites were identified, of which eight metabolic pathways were in dysregulation at different disease stages. Disease progression showed pronounced disruptions in amino acid, Lipid, and microbial metabolism. Targeted metabolomics identified 56 metabolites, with significant differences in O-(4,8-dimethylnonanoyl) carnitine and dehydroepiandrosterone sulfate (DHEAS). DHEAS and L-threonic acid (L-TA) were validated as biomarkers, with detection methods developed and applied to confirm their clinical significance. These findings enhance understanding of CNAG, CAG, and GC progression and provide validated biomarkers for potential clinical application in GC diagnosis and treatment.
Deep learning model for diagnosing lupus erythematosus in cardiac patients using ECG and audio spectrograms
Abstract Individuals with both Lupus Erythematosus and pre-existing heart conditions are more likely to develop severe symptoms, emphasizing the complex and not fully understood interaction between the disease and cardiovascular health. A universal diagnostic model based on fixed rules has proven ineffective, as demonstrated in the experimental section of this study. To address this challenge, we propose an efficient and novel approach. Our model consists of two complementary subsystems. The first leverages Residual Network (ResNet) to capture complex patterns within ECG datasets, capitalizing on its ability to identify complex patterns in sequential data. The captured features are subsequently processed through Long Short-Term Memory (LSTM) networks. The second subsystem takes an alternative approach, we introduce a novel pipeline that converts ECG images into audio, enabling Mel-spectrogram generation and deep analysis via a fine-tuned Audio Spectrogram Transformer (AST). This audio-based representation reveals richer temporal and spectral features, leading to more accurate and interpretable classification than traditional methods. Experimental findings indicate that our hybrid approach achieves exceptional performance, with accuracy, sensitivity, specificity, and AUC scores of 99%, 99.2%, 96.8%, and 97%, respectively. Furthermore, we validate our model’s effectiveness through an explainable deep learning framework using a heatmap algorithm. The results suggest that Lupus Erythematosus may contribute to ventricular hypertrophy, as indicated by the model’s emphasis on the QRS region in ECG images from the test dataset.
Genome-resolved biogeography of Phaeocystales, cosmopolitan bloom-forming algae
Abstract Phaeocystales, comprising the genus Phaeocystis and an uncharacterized sister lineage, are nanoplanktonic haptophytes widespread in the global ocean. Several species form mucilaginous colonies and influence key biogeochemical cycles, yet their underlying diversity and ecological strategies remain underexplored. Here, we present new genomic data from 13 strains, including three high-quality reference genomes (N50 > 30 kbp), and integrate previous metagenome-assembled genomes to resolve a robust phylogeny. Divergence timing of P. antarctica aligns with Miocene cooling and Southern Ocean isolation. Genomic traits reveal metabolic flexibility, including mixotrophic nitrogen acquisition in temperate waters and gene expansions linked to polar nutrient adaptation. Concordantly, transcriptomic comparisons between temperate and polar Phaeocystis suggest Southern Ocean populations experience iron and B 12 limitation. We also identify signatures of horizontal gene transfer and endogenous giant virus/virophage insertions. Together, these findings highlight Phaeocystales as an ecologically versatile and geographically widespread lineage shaped by evolutionary innovation and adaptation to contrasting environmental stressors.
Effects of continuous cover management on bird communities in a beech dominated forest region of Slovenia
Abstract Forest management can have a negative impact on the populations of some forest species. One solution has been to use management that is better at mimicking natural processes. However, the effectiveness of such systems for maintaining some taxa needs further research. We evaluated forest structure in European beech (Fagus sylvatica L.) dominated stands managed with uneven-aged, continuous cover silviculture, compared to an old-growth stand, and characterized their bird communities. The managed stands represented different developmental stages of the forest, including a mature stand (before regeneration cutting), a gap-cut stand (a mature stand in the process of partial cutting), and a young stand. The old-growth stand had many features distinguishing it from the managed stands. In the managed stands, tree diameter reached a maximum of 67 cm in the gap-cut stand, and the highest mean stand volume was 459 m3 ha− 1 in the mature stand. In the old-growth forest stand, the volume was 807 m3 ha− 1 and there were 24 trees ha− 1 larger than 67 cm in DBH. These large stems alone had a volume of more than 300 m3 ha− 1. Additionally, the managed stands had a deadwood volume in the range of 19–25 m3 ha− 1, mainly consisting of stumps and small diameter fragments, compared to 318 m3 ha− 1 in the old-growth forest. The number of bird species was linked to the number of niches available, resulting from the presence of large-diameter trees, a heterogeneous diameter at breast height structure, and varying densities in the lower forest layers, which was reflected in the density of breeding pairs, from 104 per 10 ha in the old-growth forest to 22–87 pairs per 10 ha in the managed stands. In the breeding season the Shannon index for the old-growth forest was 4.29 versus 2.81–3.94, in the young and gap-cut stand, respectively. Half of the species found in the old-growth forest nested in cavities. Rare, more specialized species occurred more often in the old-growth forest. The presence of a dynamic mosaic of forest development stages turned out to be important for the diversity of bird species. However, the presence of large old trees and deadwood are likely key factors for maintaining bird diversity. Thus, management practices should be modified to better focus on the protection of microhabitats supporting the species associated with them.
Detailed microCT imaging protocol for ex vivo rat stomachs with comparative analysis
Dynamic shaping of multi-touch stimuli by programmable acoustic metamaterial
Early career setback and future achievement in professional sports
MambaOVD: a Mamba-based open-vocabulary object detection method
Reversible single crystal photochemistry and spin state switching in a metal-cyanide complex
Abstract Manipulating the physical properties of solid matter using only photons is a major challenge in materials science. However, achieving such control over a chemical reaction in the solid state is even more challenging. Here we demonstrate the reversible photochemistry occurring in a single crystal of a simple cyanide complex, K 4 [Mo III (CN) 7 ]·2H 2 O. Upon exposure to visible light at different wavelengths, a reversible breaking and reformation of dative bonds is triggered, resulting in a photoswitching of the Mo III coordination geometry between 6- and 7-coordinate. This transformation, in turn, induces a spin state change. The observed solid-state photochemical reactivity is robust, quantitative and occurs at a record-high temperature. It paves the way for the development of new photo-switchable high-temperature magnets and nanomagnets.
Limb muscle mass and phase angle asymmetry in 8-year-old children
Integrated multi omics and machine learning reveal mitochondrial immunometabolic networks in sepsis associated encephalopathy
Magnetic imaging under high pressure with a spin-based quantum sensor integrated in a van der Waals heterostructure
Three-dimensional reconstruction of lung tumors from computed tomography scans using adversarial and transductive learning
Abstract Lung cancer is a critical health issue, and early detection is crucial for enhancing patient outcomes. This study presents a novel framework for generating three-dimensional (3D) representations of lung tumors from computed tomography (CT) scans, addressing three key challenges in the analysis process. Firstly, we address the precise segmentation of lung tissues, which is complicated by a high proportion of non-lung pixels that skew the classifier. Our method uses a customized generative adversarial network (GAN) enhanced with an off-policy proximal policy optimization (PPO) strategy. This strategy enhances segmentation performance by addressing inherent classifier biases and implementing a reward system to more accurately identify minority samples. Secondly, the framework enhances tumor detection in the segmented areas by employing a specialized GAN trained with an adversarial loss, which helps the generator create tumor regions that match real ones in both shape and internal features, even when contrast is low or boundaries are unclear. Thirdly, after tumor detection, the EfficientNet model extracts essential features for 3D reconstruction. The features are then enhanced by a spatial attention-based transductive long short-term memory (TLSTM) network for better performance. The TLSTM network enhances performance by assigning greater weight to samples near the test point within a transductive learning framework. Tested on the Lung Image Database Consortium Image Collection (LIDC-IDRI) dataset, our methodology achieved Hausdorff distance (HD) and Euclidean distance (ED) metrics of 0.648 and 0.985, respectively, indicating superior performance compared to existing methods. Our research introduces a clinical tool that significantly boosts the capabilities of radiologists in diagnosing and planning treatment for lung cancer. Code is publicly available at https://github.com/ZhisenHe/3D-representation/.
Integrative rock physics and computer vision analysis of elastic properties and pore aspect ratios in Brazilian pre-salt carbonates
Spatiotemporal dynamics of moiré excitons in van der Waals heterostructures
Abstract Heterostructures of transition metal dichalcogenides (TMDs) offer unique opportunities in optoelectronics due to their strong light-matter interaction and the formation of dipolar interlayer excitons. Introducing a twist angle or lattice mismatch between layers creates a periodic moiré potential that significantly reshapes the energy landscape and introduces a high-dimensional complexity absent in aligned bilayers. Recent experimental advances have enabled direct observation and control of interlayer excitons in such moiré-patterned systems, yet a microscopic theoretical framework capturing both their thermalization and spatiotemporal dynamics remains lacking. Here, we address this challenge by developing a predictive, material-specific many-body model that tracks exciton dynamics across time, space, and momentum, fully accounting for the moiré potential and the complex non-parabolic exciton band structure. Surprisingly, we reveal that flat bands, which typically trap excitons, can significantly enhance exciton propagation. This counterintuitive behavior emerges from the interplay between the flat-band structure giving rise to a bottleneck effect for exciton relaxation and thermal occupation dynamics creating hot excitons. Our work not only reveals the microscopic mechanisms behind the enhanced propagation but also enables the control of exciton transport via twist-angle engineering. These insights lay the foundation for next-generation moiré-based optoelectronic and quantum technologies.