Optimizing tissue clearing and imaging methods for human brain tissue

ObjectivesTo identify optimum sample conditions for human brains, we compared the clearing efficiency, antibody staining efficiency, and artifacts between fresh and cadaver samples.MethodsFresh and cadaver samples were cleared using X-CLARITY™. Clearing efficiency and artifact levels were calculated using ImageJ, and antibody staining efficiency was evaluated after confocal microscopy imaging. Three staining methods were compared: 4-day staining (4DS), 11-day staining (11DS), and 4-day staining with a commercial kit (4DS-C). The optimum staining method was then selected by evaluating staining time, depth, method complexity, contamination, and cost.ResultsFresh samples outperformed cadaver samples in terms of the time and quality of clearing, artifacts, and 4′,6-diamidino-2-phenylindole (DAPI) staining efficiency, but had a glial fibrillary acidic protein (GFAP) staining efficiency that was similar to that of cadaver samples. The penetration depth and DAPI staining improved in fresh samples as the incubation period lengthened. 4DS-C was the best method, with the deepest penetration. Human brain images containing blood vessels, cell nuclei, and astrocytes were visualized three-dimensionally. The chemical dye staining depth reached 800 µm and immunostaining depth exceeded 200 µm in 4 days.ConclusionsWe present optimized sample preparation and staining protocols for the visualization of three-dimensional macrostructure in the human brain.     

Automatic Concrete Damage Recognition Using Multi-Level Attention Convolutional Neural Network

There has been an increase in the deterioration of buildings and infrastructure in dense urban regions, and several defects in the structures are being exposed. To ensure the effective diagnosis of building conditions, vision-based automatic damage recognition techniques have been developed. However, conventional image processing techniques have some limitations in real-world situations owing to their manual feature extraction approach. To overcome these limitations, a convolutional neural network-based image recognition technique was adopted in this study, and a convolution-based concrete multi-damage recognition neural network (CMDnet) was developed. The image datasets consisted of 1981 types of concrete surface damages, including surface cracks, rebar exposure and delamination, as well as intact. Furthermore, it was experimentally demonstrated that the proposed model could accurately classify the damage types. The results obtained in this study reveal that the proposed model can recognize the different damage types from digital images of the surfaces of concrete structures. The trained CMDnet demonstrated a damage-detection accuracy of 98.9%. Moreover, the proposed model could be applied in automatic damage detection networks to achieve superior performance with regard to concrete surface damage detection and recognition, as well as accelerating efficient damage identification during the diagnosis of deteriorating structures used in civil engineering applications.     

Trabecular Microfracture Precedes Cortical Shell Failure in the Rat Caudal Vertebra Under Cyclic Overloading

Microscopic tissue damage has been observed in otherwise healthy cancellous bone in humans and is believed to contribute to bone fragility and increased fracture risk. Animal models to study microscopic tissue damage and repair in cancellous bone would be useful, but it is currently not clear how loads applied to a whole animal bone are related to the amount and type of resulting microdamage in cancellous bone. In the current study we determine the relationship between applied cyclic compressive overloading and the resulting amount of microdamage in isolated rat tail vertebrae, a bone that has been used previously for in vivo loading experiments. Rat caudal vertebrae (C7–C9, n = 22) were potted in bone cement and subjected to cyclic compressive loading from 0 to 260 N. Loading was terminated in the secondary and tertiary phases of the creep-fatigue curve using custom data-monitoring software. In cancellous bone, trabecular microfracture was the primary form of microdamage observed with few microcracks. Trabecular microfracture prevalence increased with the amount of cyclic loading and occurred in nine out of 10 specimens loaded into the tertiary phase. Only small amounts of microdamage were observed in the cortical shell of the vertebrae, demonstrating that, under axial cyclic loading, damage occurs primarily in regions of cancellous bone before overt fracture of the bone (macroscopic cracks in the cortical shell). These experiments in isolated rat tail vertebrae suggest that it may be possible to use an animal model to study the generation and repair of microscopic tissue damage in cancellous bone.