Investigating the mechanism of Fuling-Banxia-Dafupi in the treatment of diabetic kidney disease using network pharmacology and molecular docking.

Go deeply into the molecular mechanism of Fuling-Banxia-Dafupi in the treatment of diabetic kidney disease (DKD) by network pharmacology and molecular docking. Fuling-Banxia-Dafupi is a pair of traditional Chinese medicine for diabetic kidney disease, which can slow down the development of diabetic kidney disease. Screening active components and targets of Fuling-Banxia-Dafupi using the TCMSP database. The Uniprot database was also used to identify effective drug targets. DKD-related Targets were retrieved from the Gene Cards database, and the overlap between these targets and Fuling-Banxia-Dafupi was obtained. GO and KEGG pathway concentration analyses were showed using Metascape, and the results were presented by the microcredit platform. A total of 616 active ingredients and targets were confrimed and intersected with 3,951 diabetic neuropathy-related targets, resulting in 306 common targets. Baicalein and cerevisterol are the core components of Fuling-Banxia-Dafupi, and the key targets are TP53, SRC, and STAT 3. PI3K-Akt signalling pathway is an important pathway. The molecular docking indicated that its main active components and target proteins have good binding activity.

MSDCNN: A multiscale dilated convolution neural network for fine-grained 3D shape classification.

Multi-view deep neural networks have shown excellent performance on 3D shape classification tasks. However, global features aggregated from multiple views data often lack content information and spatial relationship, which leads to difficult identification the small variance among subcategories in the same category. To solve this problem, in this paper, a novel multiscale dilated convolution neural network termed as MSDCNN is proposed for multi-view fine-grained 3D shape classification. Firstly, a sequence of views are rendered from 12-viewpoints around the input 3D shape by the sequential view capturing module. Then, the first 22 convolution layers of ResNeXt50 is employed to extract the semantic features of each view, and a global mixed feature map is obtained through the element-wise maximum operation of the 12 output feature maps. Furthermore, attention dilated module (ADM), which combines four concatenated attention dilated block (ADB), is designed to extract larger receptive field features from global mixed feature map to enhance context information among the views. Specifically, each ADB is consisted by an attention mechanism module and a dilated convolution with different dilation rates. In addition, prediction module with label smoothing is proposed to classify features, which contains 3 × 3 convolution and adaptive average pooling. The performance of our method is validated experimentally on the ModelNet10, ModelNet40 and FG3D datasets. Experimental results demonstrate the effectiveness and superiority of the proposed MSDCNN framework for 3D shape fine-grained classification.

Counting manatee aggregations using deep neural networks and Anisotropic Gaussian Kernel.

Manatees are aquatic mammals with voracious appetites. They rely on sea grass as the main food source, and often spend up to eight hours a day grazing. They move slow and frequently stay in groups (i.e. aggregations) in shallow water to search for food, making them vulnerable to environment change and other risks. Accurate counting manatee aggregations within a region is not only biologically meaningful in observing their habit, but also crucial for designing safety rules for boaters, divers, etc., as well as scheduling nursing, intervention, and other plans. In this paper, we propose a deep learning based crowd counting approach to automatically count number of manatees within a region, by using low quality images as input. Because manatees have unique shape and they often stay in shallow water in groups, water surface reflection, occlusion, camouflage etc. making it difficult to accurately count manatee numbers. To address the challenges, we propose to use Anisotropic Gaussian Kernel (AGK), with tunable rotation and variances, to ensure that density functions can maximally capture shapes of individual manatees in different aggregations. After that, we apply AGK kernel to different types of deep neural networks primarily designed for crowd counting, including VGG, SANet, Congested Scene Recognition network (CSRNet), MARUNet etc. to learn manatee densities and calculate number of manatees in the scene. By using generic low quality images extracted from surveillance videos, our experiment results and comparison show that AGK kernel based manatee counting achieves minimum Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The proposed method works particularly well for counting manatee aggregations in environments with complex background.