Session-Enhanced Graph Neural Network Recommendation Model (SE-GNNRM)

Session-based recommendation aims to predict anonymous user actions. Many existing session recommendation models do not fully consider the impact of similar sessions on recommendation performance. Graph neural networks can better capture the conversion relationship of items within a session, but some intra-session conversion relationships are not conducive to recommendation, which requires model learning more representative session embeddings. To solve these problems, an improved session-enhanced graph neural network recommendation model, namely SE-GNNRM, is proposed in this paper. In our model, the complex transitions relationship of items and more representative item features are captured through graph neural network and self-attention mechanism in the encoding stage. Then, the attention mechanism is employed to combine short-term and long-term preferences to construct a global session graph and capture similar session information by using a graph attention network fused with similarity. In order to prove the effectiveness of the constructed SE-GNNRM model, three public data sets are selected here. The experiment results show that the SE-GNNRM outperforms the existing baseline models and is an effective model for session-based recommendation

A Novel Image Recognition Method Based on DenseNet and DPRN

Image recognition is one of the important branches of computer vision, which has important theoretical and practical significance. For the insufficient use of features, the single type of convolution kernel and the incomplete network optimization problems in densely connected networks (DenseNet), a novel image recognition method based on DenseNet and deep pyramidal residual networks (DPRN) is proposed in this paper. In the proposed method, a new residual unit based on DPRN is designed, and the idea of a pyramid residual unit is introduced, which makes the input greater than the output. Then, a module based on dilated convolution is designed for parallel feature extraction. Finally, the designed module is fused with DenseNet in order to construct the image recognition model. This model not only overcomes some of the existing problems in DenseNet, but also has the same general applicability as DensenNet. The CIFAR10 and CIFAR100 are selected to prove the effectiveness of the proposed method. The experiment results show that the proposed method can effectively reuse features and has obtained accuracy rates of 83.98 and 51.19%, respectively. It is an effective method for dealing with images in different fields

Recommendation Algorithm for Multi-Task Learning with Directed Graph Convolutional Networks

As an important branch of machine learning, recommendation algorithms have attracted the attention of many experts and scholars. The current recommendation algorithms all more or less have problems such as cold start and single recommended items. In order to overcome these problems and improve the accuracy of personalized recommendation algorithms, this paper proposes a recommendation for multi-task learning based on directed graph convolutional network (referred to as MTL-DGCNR) and applies it to recommended areas for e-commerce. First, the user’s micro-behavior is constructed and converted into directed graph structure data for model embedding. It can fully consider the embedding of first-order proximity nodes and second-order proximity nodes, which can effectively enhance the transformation ability of features. Secondly, this model adopts the multi-task learning method, and uses knowledge graph embedding to effectively deal with the one-to-many or many-to-many relationship between users and commodities. Finally, it is verified by experiments that MTL-DGCNR has a higher interpretability and accuracy in the field of e-commerce recommendation than other recommendation models. The ranking evaluation experiments, various training methods comparison experiments, and controlling parameter experiments are designed from multiple perspectives to verify the rationality of MTL-DGCNR

Recommendation Algorithm for Multi-Task Learning with Directed Graph Convolutional Networks

As an important branch of machine learning, recommendation algorithms have attracted the attention of many experts and scholars. The current recommendation algorithms all more or less have problems such as cold start and single recommended items. In order to overcome these problems and improve the accuracy of personalized recommendation algorithms, this paper proposes a recommendation for multi-task learning based on directed graph convolutional network (referred to as MTL-DGCNR) and applies it to recommended areas for e-commerce. First, the user’s micro-behavior is constructed and converted into directed graph structure data for model embedding. It can fully consider the embedding of first-order proximity nodes and second-order proximity nodes, which can effectively enhance the transformation ability of features. Secondly, this model adopts the multi-task learning method, and uses knowledge graph embedding to effectively deal with the one-to-many or many-to-many relationship between users and commodities. Finally, it is verified by experiments that MTL-DGCNR has a higher interpretability and accuracy in the field of e-commerce recommendation than other recommendation models. The ranking evaluation experiments, various training methods comparison experiments, and controlling parameter experiments are designed from multiple perspectives to verify the rationality of MTL-DGCNR

Metabolite Profiling Analysis of the Tongmai Sini Decoction in Rats after Oral Administration through UHPLC-Q-Exactive-MS/MS

Tongmai Sini decoction (TSD), the classical prescriptions of traditional Chinese medicine, consisting of three commonly used herbal medicines, has been widely applied for the treatment of myocardial infarction and heart failure. However, the absorbed components and their metabolism in vivo of TSD still remain unknown. In this study, a reliable and effective method using ultra-performance liquid chromatography coupled with hybrid quadrupole-Orbitrap mass spectrometry (UHPLC-Q-Exactive-MS/MS) was employed to identify prototype components and metabolites in vivo (rat plasma and urine). Combined with mass defect filtering (MDF), dynamic background subtraction (DBS), and neutral loss filtering (NLF) data-mining tools, a total of thirty-two major compounds were selected and investigated for their metabolism in vivo. As a result, a total of 82 prototype compounds were identified or tentatively characterized in vivo, including 41 alkaloids, 35 phenolic compounds, 6 saponins. Meanwhile, A total of 65 metabolites (40 alkaloids and 25 phenolic compounds) were tentatively identified. The metabolic reactions were mainly hydrogenation, demethylation, hydroxylation, hydration, methylation, deoxylation, and sulfation. These findings will be beneficial for an in-depth understanding of the pharmacological mechanism and pharmacodynamic substance basis of TSD

A Novel Image Recognition Method Based on DenseNet and DPRN

Image recognition is one of the important branches of computer vision, which has important theoretical and practical significance. For the insufficient use of features, the single type of convolution kernel and the incomplete network optimization problems in densely connected networks (DenseNet), a novel image recognition method based on DenseNet and deep pyramidal residual networks (DPRN) is proposed in this paper. In the proposed method, a new residual unit based on DPRN is designed, and the idea of a pyramid residual unit is introduced, which makes the input greater than the output. Then, a module based on dilated convolution is designed for parallel feature extraction. Finally, the designed module is fused with DenseNet in order to construct the image recognition model. This model not only overcomes some of the existing problems in DenseNet, but also has the same general applicability as DensenNet. The CIFAR10 and CIFAR100 are selected to prove the effectiveness of the proposed method. The experiment results show that the proposed method can effectively reuse features and has obtained accuracy rates of 83.98 and 51.19%, respectively. It is an effective method for dealing with images in different fields

Microstructure and Ultrastructure of the Oviducal Gland of Sepioteuthis lessoniana

Cephalopods, which have a rapid growth rate and short life cycle, are regarded as an important marine fishing resource. Sepioteuthis lessoniana is among the most widely distributed species in the Loliginidae family in the Indo-Western Pacific Ocean. Its meat is delicious and nutritious. It is widespread in the East China Sea, South China Sea, and other marine areas in China, and it is considered an important local marine resource. In this study, the ultrastructure of the oviducal gland of S. lessoniana was investigated for the first time via anatomical dissection, tissue sectioning, and electron microscope projection. The external morphology and internal structure of the gland were clearly described, as well as the role of the oviduct gland in the reproductive activities of cephalopods. In this experiment, all the samples were collected from the open ocean, with the samples of wild S. lessoniana coming from marine areas in Fujian Province. The body surfaces of the samples were healthy and undamaged, with an average mantle length of (17.5±6.4) cm and an average body weight of (392.0±76.0) g. The samples were dissected using standard anatomical methods, and the glandular characteristics were recorded. The oviducal gland was dissected with a scalpel for tissue sectioning and preserved for electron microscope projection and observation. The experimental results showed that S. lessoniana had a single oviducal gland, which was located on the right side of its abdominal cavity. The sexually mature gland was milky white overall, with brownish-yellow pigmentation near the inner shell. The oviducal gland was enveloped in a transparent membrane, and regular gaps were visible. The gland consisted of three parts: the proximal oviducal gland, the distal oviducal gland, and the transparent valve. The proximal oviducal gland was infundibular and connected with the hyaline oviduct. The distal oviducal gland was cylindrically tapering, and a single mature egg was observed inside. The transparent valve was at the end of the gland, also known as the valve. The microstructure of the oviducal gland was observed via a microscope. The gland was composed of a glandular wall, lamellar, and muscle tissue. The glandular wall tissue was composed of adventitia, loose connective tissue, and a small amount of muscle tissue, blood vessels, and ducts that were scattered in the connective tissue. The transparent valve was composed of columnar epithelial cells and muscle tissue. A small number of water-droplet goblet cells were dispersed between the epithelial cells, while a large number of cilia were generated on the exterior. The lamellar was attached to the glandular wall tissue and distributed in layers within the gland; it was mainly composed of ciliated columnar epithelium and support cells. When the oviducal gland was at various development stages, the cell types of the lamellar and the size of the intercellular space within the leaflet were different. When the gland was immature, the lobe had a regular shape, with more connective tissue in the center and a single layer of columnar epithelial cells on each side. When the gland was about to mature, the connective tissue increased, the number of columnar epithelial cells decreased, and a large number of mucous acinus were simultaneously generated. After the glands matured and spawned, the amount of connective tissue decreased, the columnar epithelial cells disappeared, the mucous acinus ruptured, and the secretory leaflets were filled with secretory substances. The individual oviduct gland secretory cells of S. lessoniana were large and contained many closely arranged mucus granules, which were round or oval in shape. The cytoplasm contained many organelles, such as the endoplasmic reticulum, mitochondria, and Golgi apparatus. In addition, secretory cells were continuously distributed outside the cell with cilia and numerous secretory granules. The nucleus of the muscle cells in the hyaline valve was irregular, and a substantial number of myofilaments, collagen fibers, and capillaries could be seen around it. The oviducal gland is an important gland in the reproductive system of female cephalopods. The morphological characteristics of oviducal glands, such as the number, shape, size, and presence or absence of pigmentation, are some of the distinguishing characteristics between different cephalopod species. The tissue structure and cell types of the cephalopod oviducal glands changed with the growth of individuals. The changes in oviducal glands in S. lessoniana were similar to those in Loligo forbesi. The secretory lobes successively generated an increasing number of mucous acini and secretory substances as cell types shifted. These secreted substances had multiple functions, such as attracting sperm during the sperm-egg hatching process, expanding the chorionic membrane, forming the vitelline space, and regulating the osmotic pressure between the fertilized egg and the ambient seawater. However, the oviducal glands of the orders Cuttlefish and Liliformes differ from those of the order Occarpus, whose oviducal glands had the function of storing sperm. It is concluded that the oviducal gland primarily plays a secretory function in the reproductive activities of cephalopods and that its secretory material forms the second layer of the egg membrane of fertilized eggs, ensuring the normal hatching of fertilized eggs

Impacts of a bacterial algicide on metabolic pathways in Chlorella vulgaris

Chlorella is a dominant species during harmful algal blooms (HABs) worldwide, which bring about great environmental problems and are also a serious threat to drinking water safety. Application of bacterial algicides is a promising way to control HABs. However, the identified bacterial algicides against Chlorella and the understanding of their effects on algal metabolism are very limited. Here, we isolated a novel bacterium Microbacterium paraoxydans strain M1 that has significant algicidal activities against Chlorella vulgaris (algicidal rate 64.38 %, at 120 h). Atrazine-desethyl (AD) was then identified from strain M1 as an effective bacterial algicide, with inhibition or algae-lysing concentration values (EC50) of 1.64 μg/mL and 1.38 μg/mL, at 72 h and 120 h, respectively. LAD (2 μg/mL AD) or HAD (20 μg/mL AD) causes morphology alteration and ultrastructure damage, chlorophyll a reduction, gene expression regulation (for example, psbA, 0.05 fold at 24 h, 2.97 fold at 72 h, and 0.23 fold of the control in HAD), oxidative stress, lipid oxidation (MDA, 2.09 and 3.08 fold of the control in LAD and HAD, respectively, at 120 h) and DNA damage (average percentage of tail DNA 6.23 % at 120 h in HAD, slight damage: 5∼20 %) in the algal cells. The impacts of AD on algal metabolites and metabolic pathways, as well as the algal response to the adverse effects were investigated. The results revealed that amino acids, amines, glycosides and urea decreased significantly compared to the control after 24 h exposure to AD (p < 0.05). The main up-regulated metabolic pathways implied metabonomic resistance and defense against osmotic pressure, oxidative stress, photosynthesis inhibition or partial cellular structure damage, such as phenylalanine metabolism, arginine biosynthesis. The down-regulated glycine, serine and threonine metabolism is a major lead in the algicidal mechanism according to the value of pathway impact. The down-regulated glycine, and serine are responsible for the downregulation of glyoxylate and dicarboxylate metabolism, aminoacyl-tRNA biosynthesis, glutathione metabolism, and sulfur metabolism, which strengthen the algae-lysing effect. It is the first time to highlight the pivotal role of glycine, serine and threonine metabolism in algicidal activities, which provided a new perspective for understanding the mechanism of bacterial algicides exerting on algal cells at the metabolic level

Imperatorin Suppresses Anaphylactic Reaction and IgE-Mediated Allergic Responses by Inhibiting Multiple Steps of FceRI Signaling in Mast Cells: IMP Alleviates Allergic Responses in PCA

This study is to investigate the effects of imperatorin (IMP) on allergic responses mediated by mast cells, both in vitro and in vivo. Passive cutaneous anaphylaxis (PCA) model was established. Histological detection was performed to assess the ear histology. ELISA and Western blot analysis were used to detect the levels of corresponding cytokines and signalling pathway proteins. IMP decreased the leakage of Evans blue and the ear thickness in the PCA models, in a dose-dependent manner, and alleviated the degranulation of mast cells. Moreover, IMP reduced the expression of TNF-α, IL-4, IL-1β, IL-8, and IL-13. Furthermore, IMP inhibited the phosphorylation levels of Syk, Lyn, PLC-γ1, and Gab2, as well as the downstream MAPK, PI3K/AKT, and NF-κB signaling pathways. In addition, IMP inhibited the mast cell-mediated allergic responses through the Nrf2/HO-1 pathway. IMP attenuates the allergic responses through inhibiting the degranulation and decreasing the expression levels of proinflammatory cytokines in the mast cells, involving the PI3K/Akt, MAPK, NF-κB, and Nrf2/HO-1 pathways