Revealing the atomistic architecture of supramolecular complexes is a fundamental step toward a deeper understanding of cellular functioning. To date, this formidable task is facilitated by an emerging array of integrative modeling approaches that combine experimental data from different sources. One major challenge these methods have to face is the treatment of the dynamic rearrangements of the individual subunits upon assembly. While this flexibility can be sampled at different levels, integrating native dynamic determinants with available experimental inputs can provide an effective way to reveal the molecular recognition mechanisms at the basis of supramolecular assembly.
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http://dx.doi.org/10.1016/j.sbi.2015.02.018 | DOI Listing |
Physiol Plant
January 2025
Laboratory of Biochemistry, Institut Químic de Sarrià, Universitat Ramon Llull, Barcelona, Spain.
Photosynthetic microalgae are promising green cell factories for the sustainable production of high-value chemicals and biopharmaceuticals. The chloroplast organelle is being developed as a chassis for synthetic biology as it contains its own genome (the plastome) and some interesting advantages, such as high recombinant protein titers and a diverse and dynamic metabolism. However, chloroplast engineering is currently hampered by the lack of standardized cloning tools and Design-Build-Test-Learn workflows to ease genomic and metabolic engineering.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
College of Engineering, Department of Computer Engineering, Koç University, Rumelifeneri Yolu, 34450, Sarıyer, Istanbul, Turkey.
This study explores a transfer learning approach with vision transformers (ViTs) and convolutional neural networks (CNNs) for classifying retinal diseases, specifically diabetic retinopathy, glaucoma, and cataracts, from ophthalmoscopy images. Using a balanced subset of 4217 images and ophthalmology-specific pretrained ViT backbones, this method demonstrates significant improvements in classification accuracy, offering potential for broader applications in medical imaging. Glaucoma, diabetic retinopathy, and cataracts are common eye diseases that can cause vision loss if not treated.
View Article and Find Full Text PDFCardiovasc Eng Technol
January 2025
Institute for Medical Engineering and Science, Massachusetts Institute of Technology, MA, Cambridge, USA.
Purpose: Atrial fibrillation (AF) is the most common chronic cardiac arrhythmia that increases the risk of stroke, primarily due to thrombus formation in the left atrial appendage (LAA). Left atrial appendage occlusion (LAAO) devices offer an alternative to oral anticoagulation for stroke prevention. However, the complex and variable anatomy of the LAA presents significant challenges to device design and deployment.
View Article and Find Full Text PDFMicrob Ecol
January 2025
State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China.
The ecological niche separation of microbial interactions in forest ecosystems is critical to maintaining ecological balance and biodiversity and has yet to be comprehensively explored in microbial ecology. This study investigated the impacts of soil properties on microbial interactions and carbon metabolism potential in forest soils across 67 sites in China. Using redundancy analysis and random forest models, we identified soil pH and dissolved organic matter (DOM) aromaticity as the primary drivers of microbial interactions, representing abiotic conditions and resource niches, respectively.
View Article and Find Full Text PDFEur J Nucl Med Mol Imaging
January 2025
The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Purpose: The study explores the role of multimodal imaging techniques, such as [F]F-PSMA-1007 PET/CT and multiparametric MRI (mpMRI), in predicting the ISUP (International Society of Urological Pathology) grading of prostate cancer. The goal is to enhance diagnostic accuracy and improve clinical decision-making by integrating these advanced imaging modalities with clinical variables. In particular, the study investigates the application of few-shot learning to address the challenge of limited data in prostate cancer imaging, which is often a common issue in medical research.
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