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It is well established that changes in the underlying architecture of the cell's microtubule (MT) network can affect organelle organization within the cytoplasm, but it remains unclear whether the spatial arrangement of organelles reciprocally influences the MT network. Here we use a combination of cell-free extracts and hydrogel microenclosures to characterize the relationship between membranes and MTs during MT aster centration. We found that initially disperse ER membranes are collected by the aster and compacted near its nucleating center, all while the whole ensemble moves toward the geometric center of its confining enclosure. Once there, aster MTs adopt a bull's-eye pattern with a high-density annular ring of MTs surrounding the compacted membrane core of lower MT density. Formation of this pattern was inhibited when dynein-dependent transport was perturbed or when membranes were depleted from the extracts. Asters in membrane-depleted extracts were able to move away from the most proximal wall but failed to center in cylindrical enclosures with diameters greater than or equal to 150 µm. Taken as whole, our data suggest that the dynein-dependent transport of membranes buttresses MTs near the aster center and that this plays an important role in modulating aster architecture and position.
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http://dx.doi.org/10.1091/mbc.E22-03-0074 | DOI Listing |
Biomater Adv
September 2025
Key Laboratory of Artificial Intelligence & Micro Nano Sensors, Shanxi Province, College of Integrated Circuits, Taiyuan University of Technology, Taiyuan, China; Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education, Taiyuan University of Technology, Taiyuan, C
This study addresses critical technical challenges in fabricating functional pigmented skin models via 3D bioprinting through the synergistic integration of droplet-based deposition and precision motion control. A hybrid bioprinting strategy was developed to create multilayer biomimetic architectures: the dermal layer was fabricated through extrusion of gelatin methacryloyl-polyacrylamide (GelMA-PAM) composites, while the epidermal layer incorporated precisely patterned melanocyte-laden GelMA-PAM arrays deposited via microvalve technology, subsequently solidified and populated with keratinocytes. To enhance printing reliability, a fractional-order proportional-integral control system optimized through particle swarm optimization (PSO-FOPI) was implemented, significantly improving motor speed regulation and positioning accuracy.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Robotics, Hanyang University, Ansan, Republic of Korea.
Convolutional Neural Networks (CNNs) stand as indispensable tools in deep learning, capable of autonomously extracting crucial features from diverse data types. However, the intricacies of CNN architectures can present challenges such as overfitting and underfitting, necessitating thoughtful strategies to optimize their performance. In this work, these issues have been resolved by introducing L1 regularization in the basic architecture of CNN when it is applied for image classification.
View Article and Find Full Text PDFOrg Biomol Chem
September 2025
Laboratoire d'Innovation Moléculaire et Applications (LIMA), Univ. de Strasbourg, Univ. de Haute-Alsace, CNRS (UMR 7042), Equipe de Synthèse Organique et Molécules Bioactives (SYBIO), ECPM, 25 Rue Becquerel, 67000 Strasbourg, France.
,-glycosides--glycosides characterized by two carbon substituents at the pseudo-anomeric position-constitute a structurally distinctive class of glycomimetics with growing relevance in natural products and drug discovery. These motifs appear in diverse bioactive compounds such as maitotoxin, nogalamycins, zaragozic acids and remdesivir, displaying antimicrobial, anti-inflammatory, and anticancer properties. The unique architectures of ,-glycosides expand the glycochemical space and hold promise for therapeutic development.
View Article and Find Full Text PDFChromogranin A (CgA), a neuroendocrine pro-hormone, undergoes proteolytic cleavage to yield bioactive peptides, notably catestatin (CST) and pancreastatin (PST), which exert opposing effects on metabolic and inflammatory processes. Using CgA and CST knockout (KO) mice, this study investigated their roles in pancreatic endocrine function, morphology, neurotransmitter dynamics, and systemic glucose homeostasis. CST deficiency induced insulin resistance, altered islet architecture, and heightened catecholamine levels, whereas CgA-KO mice lacking both CST and PST exhibited improved insulin sensitivity due to absence of PST.
View Article and Find Full Text PDFFoundation models have recently emerged as powerful feature extractors in computational pathology, yet they typically omit mechanisms for leveraging the global spatial structure of tissues and the local contextual relationships among diagnostically relevant regions - key elements for understanding the tumor microenvironment. Multiple instance learning (MIL) remains an essential next step following foundation model, designing a framework to aggregate patch-level features into slide-level predictions. We present EAGLE-Net, a structure-preserving, attention-guided MIL architecture designed to augment prediction and interpretability.
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