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Yang G, Zhang P, Li L, et al. Diabetes Metab Syndr Obes. 2023;16:1515–1523 The authors have advised there is an error in the affiliation on page 1515. The affiliation should read “Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, Henan, People’s Republic of China”.

Guanyu Yang,&ast; Pin Zhang,&ast; Liumei Li, Jingjing Wang, Pengfei Jiao, Jie Wang, Qinjun Chu Department of Anesthesiology and Perioperative Medicine, Zhengzhou Central Hospital, Zhengzhou, Henan, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Qinjun Chu, Tongbai North Road 16, Zhongyuan District, Zhengzhou, Henan, People’s Republic of China, Tel/Fax +86 13643711142, Email mzk123456@zzu.edu.cnPurpose: This study aims to compare the conventional lung protective ventilation strategy (LPVS) with driving pressure-guided ventilation in obese patients undergoing laparoscopic sleeve gastrectomy (LSG).Methods: Forty-five patients undergoing elective LSG under general anesthesia were randomly assigned to the conventional LPVS group (group L) or the driving pressure-guided ventilation group (group D) using random numbers generated by Excel. The primary outcome was the driving pressure of both groups 90 min after pneumoperitoneum.Results: After 30 min of pneumoperitoneum, 90 min of pneumoperitoneum, 10 min of closing the pneumoperitoneum, and restoring the supine position, the driving pressure of group L and group D were 20.0 ± 2.9 cm H2O vs 16.6 ± 3.0 cm H2O (P < 0.001), 20.7 ± 3.2 cm H2O vs 17.3 ± 2.8 cm H2O (P < 0.001), and 16.3 ± 3.1 cm H2O vs 13.3 ± 2.5 cm H2O (P = 0.001), respectively; the respiratory compliance of groups L and D were 23.4 ± 3.7 mL/cm H2O vs 27.6 ± 5.1 mL/cm H2O (P = 0.003), 22.7 ± 3.8 mL/cm H2O vs 26.4 ± 3.5 mL/cm H2O (P = 0.005), and 29.6 ± 6.8 mL/cm H2O vs 34.7 ± 5.3 mL/cm H2O (P = 0.007), respectively. The intraoperative PEEP in groups L and group D was 5 (5– 5) cm H2O vs 10 (9– 11) cm H2O (P < 0.001).Conclusion: An individualized peep-based driving pressure-guided ventilation strategy can reduce intraoperative driving pressure and increase respiratory compliance in obese patients undergoing LSG.Keywords: lung protective ventilation strategy, driving pressure-guid

Gaofei Ren,1 Pengfei Jiao,2 Yushan Yan,1 Xiaojun Ma,1 Guijun Qin1 1Department of Endocrinology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People’s Republic of China; 2Department of Respiratory Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People’s Republic of ChinaCorrespondence: Xiaojun Ma; Guijun Qin, Department of Endocrinology, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe East Road, Zhengzhou, 450052, People’s Republic of China, Tel +86-0371-66295052, Email fccmaxj1@zzu.edu.cn; hyqingj@zzu.edu.cnBackground: Inflammation and oxidative stress contribute to the development of diabetic nephropathy (DN). Baicalin (BA) shows renal protection against DN through its anti-inflammatory and anti-oxidant properties. However, the molecular mechanism by which BA exerts the therapeutic effects on DN remains to be investigated.Methods: The db/db mice and high glucose (HG)-induced HK-2 cells were used as the in vivo and in vitro model of DN, respectively. The effects of BA were assessed by detecting the related blood and urine biochemical parameters, kidney histopathology, inflammatory cytokine production, oxidative stress indicators, and apoptosis. Cell viability and apoptosis were detected by CCK-8 assay and TUNEL assay, respectively. Related protein levels were measured by an immunoblotting method.Results: In db/db model mice, BA reduced serum glucose concentration, decreased blood lipid levels, ameliorated kidney functions, and decreased histopathological changes in kidney tissues. BA also alleviated oxidative stress and inflammation in db/db mice. In addition, BA blocked the activation of sphingosine kinases type 1/sphingosine 1-phosphate (SphK1/S1P)/NF-κB pathway in db/db mice. In HK-2 cells, BA hindered HG-induced apoptosis, oxidative stress and inflammation, while overexpression of SphK1 or S1P could reverse these effects. BA alleviated HG-induced apoptosis, oxidative stress and inflamma

Fuel cell air filter can absorb harmful impurities and gases to fuel cell and is an important component to ensure the service life of fuel cell. The previous researches mainly focus on the activated carbon and filter paper materials in the laboratory. The object of this paper is the fuel cell air filter product assembly being used by the main engine plant. According to the tests of air filters with different carbon contents, it was found that the adsorption efficiency of current products for NO2 was better than that of C7H8, SO2 and C4H10. Based on the adsorption mechanism of activated carbon for different gases, the adsorption difference was explained. The testes also found that there was no direct relationship between the change of carbon content of products containing activated carbon and the fractional filtration efficiency. The main reason is that the gap between the activated carbon particles is large, which cannot effectively intercept dust particles. The filtering of small size particles still depends on the melt-blown layer material in the air filter element and its electrostatic electret process. It was also found that there was a positive correlation between inlet air resistance and carbon content, and a negative correlation between dust holding capacity and carbon content. This test results can help the main engine factory understand the performance and improvement direction of products.

Medical image segmentation annotations exhibit variations among experts due to the ambiguous boundaries of segmented objects and backgrounds in medical images. Although using multiple annotations for each image in the fully-supervised has been extensively studied for training deep models, obtaining a large amount of multi-annotated data is challenging due to the substantial time and manpower costs required for segmentation annotations, resulting in most images lacking any annotations. To address this, we propose Multi-annotated Semi-supervised Ensemble Networks (MSE-Nets) for learning segmentation from limited multi-annotated and abundant unannotated data. Specifically, we introduce the Network Pairwise Consistency Enhancement (NPCE) module and Multi-Network Pseudo Supervised (MNPS) module to enhance MSE-Nets for the segmentation task by considering two major factors: (1) to optimize the utilization of all accessible multi-annotated data, the NPCE separates (dis)agreement annotations of multi-annotated data at the pixel level and handles agreement and disagreement annotations in different ways, (2) to mitigate the introduction of imprecise pseudo-labels, the MNPS extends the training data by leveraging consistent pseudo-labels from unannotated data. Finally, we improve confidence calibration by averaging the predictions of base networks. Experiments on the ISIC dataset show that we reduced the demand for multi-annotated data by 97.75\% and narrowed the gap with the best fully-supervised baseline to just a Jaccard index of 4\%. Furthermore, compared to other semi-supervised methods that rely only on a single annotation or a combined fusion approach, the comprehensive experimental results on ISIC and RIGA datasets demonstrate the superior performance of our proposed method in medical image segmentation with ambiguous boundaries.

Temporal graph representation learning aims to generate low-dimensional dynamic node embeddings to capture temporal information as well as structural and property information. Current representation learning methods for temporal networks often focus on capturing fine-grained information, which may lead to the model capturing random noise instead of essential semantic information. While graph contrastive learning has shown promise in dealing with noise, it only applies to static graphs or snapshots and may not be suitable for handling time-dependent noise. To alleviate the above challenge, we propose a novel Temporal Graph representation learning with Adaptive augmentation Contrastive (TGAC) model. The adaptive augmentation on the temporal graph is made by combining prior knowledge with temporal information, and the contrastive objective function is constructed by defining the augmented inter-view contrast and intra-view contrast. To complement TGAC, we propose three adaptive augmentation strategies that modify topological features to reduce noise from the network. Our extensive experiments on various real networks demonstrate that the proposed model outperforms other temporal graph representation learning methods.

Capturing structural similarity has been a hot topic in the field of network embedding recently due to its great help in understanding the node functions and behaviors. However, existing works have paid very much attention to learning structures on homogeneous networks while the related study on heterogeneous networks is still a void. In this paper, we try to take the first step for representation learning on heterostructures, which is very challenging due to their highly diverse combinations of node types and underlying structures. To effectively distinguish diverse heterostructures, we firstly propose a theoretically guaranteed technique called heterogeneous anonymous walk (HAW) and its variant coarse HAW (CHAW). Then, we devise the heterogeneous anonymous walk embedding (HAWE) and its variant coarse HAWE in a data-driven manner to circumvent using an extremely large number of possible walks and train embeddings by predicting occurring walks in the neighborhood of each node. Finally, we design and apply extensive and illustrative experiments on synthetic and real-world networks to build a benchmark on heterostructure learning and evaluate the effectiveness of our methods. The results demonstrate our methods achieve outstanding performance compared with both homogeneous and heterogeneous classic methods, and can be applied on large-scale networks., Comment: 13 pages, 6 figures, 5 tables

Graph Convolutional Network (GCN) has shown remarkable potential of exploring graph representation. However, the GCN aggregating mechanism fails to generalize to networks with heterophily where most nodes have neighbors from different classes, which commonly exists in real-world networks. In order to make the propagation and aggregation mechanism of GCN suitable for both homophily and heterophily (or even their mixture), we introduce block modeling into the framework of GCN so that it can realize "block-guided classified aggregation", and automatically learn the corresponding aggregation rules for neighbors of different classes. By incorporating block modeling into the aggregation process, GCN is able to aggregate information from homophilic and heterophilic neighbors discriminately according to their homophily degree. We compared our algorithm with state-of-art methods which deal with the heterophily problem. Empirical results demonstrate the superiority of our new approach over existing methods in heterophilic datasets while maintaining a competitive performance in homophilic datasets., Comment: Accepted by Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22)

With the rapid development of 5th Generation Mobile Communication Technology (5G), the diverse forms of collaboration and extensive data in academic social networks constructed by 5G papers make the management and analysis of academic social networks increasingly challenging. Despite the particular success achieved by representation learning in analyzing academic and social networks, most present presentation learning models focus on maintaining the first-order and second-order similarity of nodes. They rarely possess similar structural characteristics of spatial independence in the network. This paper proposes a Low-order Network representation Learning Model (LNLM) based on Non-negative Matrix Factorization (NMF) to solve these problems. The model uses the random walk method to extract low-order features of nodes and map multiple components to a low-dimensional space, effectively maintaining the internal correlation between members. This paper verifies the performance of this model, conducts comparative experiments on four test datasets and four real network datasets through downstream tasks such as multi-label classification, clustering, and link prediction. Comparing eight mainstream network representation learning models shows that the proposed model can significantly improve the detection efficiency and learning methods and effectively extract local and low-order features of the network., Comment: 38 pages, 9 figures

Recently, Network Embedding (NE) has become one of the most attractive research topics in machine learning and data mining. NE approaches have achieved promising performance in various of graph mining tasks including link prediction and node clustering and classification. A wide variety of NE methods focus on the proximity of networks. They learn community-oriented embedding for each node, where the corresponding representations are similar if two nodes are closer to each other in the network. Meanwhile, there is another type of structural similarity, i.e., role-based similarity, which is usually complementary and completely different from the proximity. In order to preserve the role-based structural similarity, the problem of role-oriented NE is raised. However, compared to community-oriented NE problem, there are only a few role-oriented embedding approaches proposed recently. Although less explored, considering the importance of roles in analyzing networks and many applications that role-oriented NE can shed light on, it is necessary and timely to provide a comprehensive overview of existing role-oriented NE methods. In this review, we first clarify the differences between community-oriented and role-oriented network embedding. Afterwards, we propose a general framework for understanding role-oriented NE and a two-level categorization to better classify existing methods. Then, we select some representative methods according to the proposed categorization and briefly introduce them by discussing their motivation, development and differences. Moreover, we conduct comprehensive experiments to empirically evaluate these methods on a variety of role-related tasks including node classification and clustering (role discovery), top-k similarity search and visualization using some widely used synthetic and real-world datasets..., Comment: 20 pages,9 figures, 5 tables

Community detection, a fundamental task for network analysis, aims to partition a network into multiple sub-structures to help reveal their latent functions. Community detection has been extensively studied in and broadly applied to many real-world network problems. Classical approaches to community detection typically utilize probabilistic graphical models and adopt a variety of prior knowledge to infer community structures. As the problems that network methods try to solve and the network data to be analyzed become increasingly more sophisticated, new approaches have also been proposed and developed, particularly those that utilize deep learning and convert networked data into low dimensional representation. Despite all the recent advancement, there is still a lack of insightful understanding of the theoretical and methodological underpinning of community detection, which will be critically important for future development of the area of network analysis. In this paper, we develop and present a unified architecture of network community-finding methods to characterize the state-of-the-art of the field of community detection. Specifically, we provide a comprehensive review of the existing community detection methods and introduce a new taxonomy that divides the existing methods into two categories, namely probabilistic graphical model and deep learning. We then discuss in detail the main idea behind each method in the two categories. Furthermore, to promote future development of community detection, we release several benchmark datasets from several problem domains and highlight their applications to various network analysis tasks. We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.

Existing review-based recommendation methods usually use the same model to learn the representations of all users/items from reviews posted by users towards items. However, different users have different preference and different items have different characteristics. Thus, the same word or similar reviews may have different informativeness for different users and items. In this paper we propose a neural recommendation approach with personalized attention to learn personalized representations of users and items from reviews. We use a review encoder to learn representations of reviews from words, and a user/item encoder to learn representations of users or items from reviews. We propose a personalized attention model, and apply it to both review and user/item encoders to select different important words and reviews for different users/items. Experiments on five datasets validate our approach can effectively improve the performance of neural recommendation., Comment: 4 pages, 4 figures