Synapses of hippocampal neurons play important roles in learning and memory processes and are involved in aberrant hippocampal function in temporal lobe epilepsy. Major neuronal types in the hippocampus as well as their input and output synapses are well known, but it has remained an open question to what extent conventional electron microscopy (EM) has provided us with the real appearance of synaptic fine structure under in vivo conditions. There is reason to assume that conventional aldehyde fixation and dehydration lead to protein denaturation and tissue shrinkage, likely associated with the occurrence of artifacts. However, realistic fine-structural data of synapses are required for our understanding of the transmission process and for its simulation. Here, we used high-pressure freezing and cryosubstitution of hippocampal tissue that was not subjected to aldehyde fixation and dehydration in ethanol to monitor the fine structure of an identified synapse in the hippocampal CA3 region, that is, the synapse between granule cell axons, the mossy fibers, and the proximal dendrites of CA3 pyramidal neurons. Our results showed that high-pressure freezing nicely preserved ultrastructural detail of this particular synapse and allowed us to study rapid structural changes associated with synaptic plasticity.
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Angew Chem Int Ed Engl
January 2025
Institute of Materials Research and Engineering, Sensor and Flexible Electronics, 2 Fusionopolis Way, 138634, SINGAPORE.
Radical covalent organic frameworks (RCOFs) have demonstrated significant potential in redox catalysis and energy conversion applications. However, the synthesis of stable RCOFs with well-defined neutral carbon radical centers is challenging due to the inherent radical instability, limited synthetic methods and characterization difficulties. Building upon the understanding of stable carbon radicals and structural modulations for preparing crystalline COFs, herein we report the synthesis of a crystalline carbon-centered RCOF through a facile post-oxidation process.
View Article and Find Full Text PDFACS Appl Mater Interfaces
January 2025
State Key Laboratory of Fine Chemicals, Research and Development Center of Membrane Science and Technology, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China.
The electrocatalytic nitrogen reduction reaction (eNRR) is an attractive strategy for the green and distributed production of ammonia (NH); however, it suffers from weak N adsorption and a high energy barrier of hydrogenation. Atomically dispersed metal dual-site catalysts with an optimized electronic structure and exceptional catalytic activity are expected to be competent for knotty hydrogenation reactions including the eNRR. Inspired by the bimetallic FeMo cofactor in biological nitrogenase, herein, an atomically dispersed FeMo dual site anchored in nitrogen-doped carbon is proposed to induce a favorable electronic structure and binding energy.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
The Internet of Vehicles (IoV), a key component of smart transportation systems, leverages 5G communication for low-latency data transmission, facilitating real-time interactions between vehicles, roadside units (RSUs), and sensor networks. However, the open nature of 5G communication channels exposes IoV systems to significant security threats, such as eavesdropping, replay attacks, and message tampering. To address these challenges, this paper proposes the Efficient Cluster-based Mutual Authentication and Key Update Protocol (ECAUP) designed to secure IoV systems within 5G-enabled sensor networks.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Faculty of Science and Environmental Studies, Department of Computer Science, Lakehead University, Thunder Bay, ON P7B 5E1, Canada.
In recent years, significant progress has been achieved in understanding and processing tabular data. However, existing approaches often rely on task-specific features and model architectures, posing challenges in accurately extracting table structures amidst diverse layouts, styles, and noise contamination. This study introduces a comprehensive deep learning methodology that is tailored for the precise identification and extraction of rows and columns from document images that contain tables.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Computer Science Department, Instituto Nacional de Astrofísica Óptica y Electrónica, Luis Enrrique Erro No. 1, Sta. María Tonantzintla, Puebla 72840, Mexico.
Accurate synthetic image generation is crucial for addressing data scarcity challenges in medical image classification tasks, particularly in sensor-derived medical imaging. In this work, we propose a novel method using a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and nearest-neighbor interpolation to generate high-quality synthetic images for diabetic retinopathy classification. Our approach enhances training datasets by generating realistic retinal images that retain critical pathological features.
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