Biochemical and Ultrastructural Changes in the Hepatopancreas of Bellamya aeruginosa (Gastropoda) Fed with Toxic Cyanobacteria

This study was conducted to investigate ultrastructural alterations and biochemical responses in the hepatopancreas of the freshwater snail Bellamya aeruginosa after exposure to two treatments: toxic cyanobacterium (Microcystis aeruginosa) and toxic cyanobacterial cells mixed with a non-toxic green alga (Scendesmus quadricauda) for a period of 15 days of intoxication, followed by a 15-day detoxification period. The toxic algal suspension induced a very pronounced increase of the activities of acid phosphatases, alkaline phosphatases and glutathione S-transferases (ACP, ALP and GST) in the liver at the later stage of intoxication. During the depuration, enzymatic activity tended to return to the levels close to those in the control. The activity of GST displayed the most pronounced response among different algal suspensions. Severe cytoplasmic vacuolization, condensation and deformation of nucleus, dilation and myeloid-like in mitochondria, disruption of rough endoplasmic reticulum, proliferation of lysosome, telolysosomes and apoptotic body were observed in the tissues. All cellular organelles began recovery after the snails were transferred to the S. quadricauda. The occurrence of a large amount of activated lysosomes and heterolysosomes and augment in activity of detoxification enzyme GST might be an adaptive mechanism to eliminate or lessen cell damage caused by hepatotoxicity to B. aeruginosa

Metric-based Few-shot Classification in Remote Sensing Image

Target recognition based on deep learning relies on a large quantity of samples, but in some specific remote sensing scenes, the samples are very rare. Currently, few-shot learning can obtain high-performance target classification models using only a few samples, but most researches are based on the natural scene. Therefore, this paper proposes a metric-based few-shot classification technology in remote sensing. First, we constructed a dataset (RSD-FSC) for few-shot classification in remote sensing, which contained 21 classes typical target sample slices of remote sensing images. Second, based on metric learning, a k-nearest neighbor classification network is proposed, to find multiple training samples similar to the testing target, and then the similarity between the testing target and multiple similar samples is calculated to classify the testing target. Finally, the 5-way 1-shot, 5-way 5-shot and 5-way 10-shot experiments are conducted to improve the generalization of the model on few-shot classification tasks. The experimental results show that for the newly emerged classes few-shot samples, when the number of training samples is 1, 5 and 10, the average accuracy of target recognition can reach 59.134%, 82.553% and 87.796%, respectively. It demonstrates that our proposed method can resolve fewshot classification in remote sensing image and perform better than other few-shot classification methods

Observation and Analysis of Leather Structure Based on Nano-CT

Content: The composition, working principle and the image acquisition procedure of nano-CT were introduced. A dried piece of blue stock of chrome-tanned cattle hide was chosen for this work and a sequence of 2356 images was obtained. 3D visible digital models (5mm*3.5mm*3.5mm) of leather fiber bundle braided network (Figure 1) and the interspace between fiber bundles (Figure 2) were reconstructed. The inner structure and composition of leather were shown accurately and intuitively in the form of 2D sectional images and 3D image. Based on the 3D model, the diameter, volume, surface area and other parameters of the fiber bundles, the pore structure and inclusions were measured and calculated. Take-Away: 1. 3D visible digital model of leather fiber bundle braided network was reconstructed. 2. The inner structure and composition of leather were shown accurately and intuitively in the form of 2D sectional images and 3D image

Study on the Difference of Collagen Fibre Structure Caused by Epoxy Resin Embedding

Content: The research on Collagen that possesses unique fibre structure are reported frequently. In this paper, the cross image of leather fibre of dried wet blue cowhide embedded with and without epoxy resin were investigated with micro computed topography(MCT). The images obtained by MCT of leather fibre are original status without any damage, while the embedded leather can emerge distortion because the fibre was fixed during the solidifying and immersing of the resin. In this research, 2357 images of leather fibre were investigated on wet blue leather(original fibre) and the same piece of leather embedded by epoxy resin(embedded fibre). The area ratio of the sections from the original fibre and the embedded fibre was examined for each image. The statistic results showed that the mode of area ratio of the original fibre section to the entire fibre section is 75%, and the mode of area ratio of the embedded fibre section to the embedded fibre entire section is only 48%. The mode of the area ratio of the original fibre is obviously higher than the mode of the area ratio of the embedded fibre, that is diverse with the anticipation of fibre swelling caused by resin. The reason might be the expansion of interval space among the fibre filled with epoxy resin, otherwise the conglutination of fibre caused by the evaporation of solvent(acetone used in embedding) in the course of the resion solidifying. Likewise, it can be the adhesion of the tiny fibre with the larger fibre that will diminish the area caculated. The factors will be studied further on embedding to achieve a method with minimum deformation on cross image of fibre. Take-Away: The area ratio of cross section on embedded fibre shrunk comparing with the original fibre, that is out of anticipation of the probable swelling created by resin