To compare the clinic outcomes of endoscopic stenting and laparoscopic gastrojejunostomy (LGJ) for patients with malignant gastric outlet obstruction (GOO). We retrospectively reviewed 63 patients ...with malignant GOO that underwent endoscopic stenting Stent Group (SG), n = 29 or LGJ Laparoscopic Group (LG), n = 34. Then, we evaluated the medical effects, postoperative hospital stay, and hospitalization expenses in both groups. Compared to LG, SG has a shorter operation time SG: (41.1 ± 9.3) minutes vs LG: (137.4 ± 21.7) minutes, P = 0.000, less intra-operative blood loss (23.7 ± 9.0) mL vs (121.1 ± 24.3) mL, P = 0.000, relatively lower hospitalization expenses (2272.7 ± 413.9) $ vs (5182.4 ± 517.3) $, P = 0.000. Besides, the median intake time was significantly shorter in the SG than that in the LG (0.9 ± 0.3) days vs (4.1 ± 0.6) days, P = 0.000. However, there were no significant differences between SG with LG in surgical success rate (100 vs 100%, P = 1.000), length of hospital stay (6.1 ± 3.3) days vs (10.9 ± 4.7) days, P = 0.422, recurrent obstructive rate (37.9 vs 26.5%, P = 0.949) and median survivals (141.4 ± 81.4) days vs (122.7 ± 88.8) days, P = 0.879. Endoscopic stenting and LGJ are both relatively safe and effective treatments for patients with malignant GOO. But we suggest that endoscopic stenting should be considered first in patients with malignant GOO because it has many advantages over LGJ.
Incomplete multiview clustering (IMVC) has attracted extensive attention in the field of machine learning due to its excellent performance in handling incomplete multiview data (IMVD). However, ...existing IMVC methods often use spectral clustering or k-means on the obtained affinity matrices to obtain the final clustering results, that is, the affinity matrices learning process and clustering process are separated. In this paper, we propose a new ingenious IMVC method, one-step incomplete multiview clustering with low-rank tensor graph learning (OIMVC/LTGL), to solve the problem. This method combines graph learning, low-rank tensor constraint, common representation learning and clustering into a unified optimization framework. First, we utilize the adjacency relationship between views to complete the similarity graph matrices. Second, we stack all similarity graph matrices into a third-order tensor. To mine higher-order correlations among different views, we impose a low-rank tensor constraint, the tensor nuclear norm (TNN), into the constructed tensor. Third, to explore consistent information between views, we introduce a common representation learning term to learn the optimal consensus representation. Last, we introduce spectral rotation to the consensus representation matrices to directly obtain the clustering labels. These steps promote each other for better clustering performance. We solve the optimization problem by using the augmented Lagrange multiplier (ALM) method. Experiments based on five well-known datasets show the superiority of our method.
Background
Although ileal faecal diversion is commonly used in clinical settings, complications accompany it. Elucidating the intestinal changes caused by ileal faecal diversion will help resolve ...postoperative complications and elucidate the pathogenic mechanisms of associated intestinal disorders, such as Crohn's disease (CD). Therefore, our study aimed to provide new insights into the effects of ileal faecal diversion on the intestine and the potential mechanisms.
Methods
Single‐cell RNA sequencing was performed on proximal functional and paired distal defunctioned intestinal mucosae from three patients with ileal faecal diversion. We also performed in vitro cellular and animal experiments, tissue staining and analysed public datasets to validate our findings.
Results
We found that the epithelium in the defunctioned intestine tended to be immature, with defective mechanical and mucous barriers. However, the innate immune barrier in the defunctioned intestine was enhanced. Focusing on the changes in goblet cells, we demonstrated that mechanical stimulation promotes the differentiation and maturation of goblet cells through the TRPA1‐ERK pathway, indicating that the absence of mechanical stimulation may be the main cause of defects in the goblet cells of the defunctioned intestine. Furthermore, we found obvious fibrosis with a pro‐fibrotic microenvironment in the defunctioned intestine and identified that monocytes may be important targets for faecal diversion to alleviate CD.
Conclusions
This study revealed the different transcription landscapes of various cell subsets and the potential underlying mechanisms within the defunctioned intestine, when compared to the functional intestine, based on the background of ileal faecal diversion. These findings provide novel insights for understanding the physiological and pathological roles of the faecal stream in the intestine.
Single‐cell RNA sequencing revealed the different transcription landscapes of the defunctioned intestine compared to the functional intestine based on the background of ileal faecal diversion.
There is new intestinal homeostasis and enhanced fibrosis in the defunctioned intestine.
Monocytes may be important targets for faecal diversion to alleviate Crohn's disease.
In recent years, researchers have proposed many graph-based multi-view clustering (GMC) algorithms to solve the multi-view clustering (MVC) problem. However, there are still some limitations in the ...existing GMC algorithm. In these algorithms, a graph is usually constructed to represent the relationship between the samples in a view; however, it cannot represent the relationship very well since it is often preconstructed. In addition, these algorithms ignore the robustness problem of each graph and high-level information between different graphs. Then, in the paper, we propose a novel MVC method, i.e., robust and optimal neighborhood graph learning for MVC (RONGL/MVC). Specifically, we first build an initial graph for each view. However, these initial graphs cannot represent the relationship between the samples in each view well, so we look for the optimal graph with k connected components in the neighborhood of each initial graph, where k is the number of clusters. Then, to improve the robustness of RONGL/MVC, we reconstruct the optimal graph with the self-representation matrix. Furthermore, we stack all the self-representation matrices into a tensor and impose the tensor low-rank constraint, which can maximize consistent features and explore the high-order relationship between optimal graphs. In addition, we provide an optimization strategy by utilizing the Augmented Lagrange Multiplier (ALM) method. Experimental results on several datasets indicate that RONGL/MVC outperforms state-of-the-art methods.
Metastasis is one of the most common manifestations of malignancy.As the treatment options and life expectancy of the cancer patients are improving,the numbers of different metastatic ...lesions,including those uncommon metastatic sites are expected to increase.Uveal metastasis from carcinoma,which is the most common type of ocular malignancy in adults,is found in an increasing number of patients,mostly with breast and lung cancer.
Graph-based incomplete multi-view clustering (IMVC) methods have drawn considerable attention due to their good performance in exploring the nonlinear structure of data. However, they still have the ...following shortcomings. First, graph construction and eigen decomposition of the Laplacian matrix included in the IMVC methods generally have high computational complexity. Second, most methods do not consider the impact of missing views and neglect the potential relationships between different views. Third, few algorithms consider both intra-view and inter-view information for clustering. Therefore, we innovatively propose a scalable incomplete multi-view clustering via the tensor Schatten p-norm and tensorized bipartite graph (SIMVC/TSTBG) method, which combines tensorized bipartite graph, graph completion, and tensor low-rank constraint into a joint framework. Concretely, we first construct bipartite graphs based on the selected m anchor points and the n data points, reducing the size of the graph from n×n to n×m(m<<n), which considerably reduces the computational complexity. Second, we adaptively complete the missing bipartite graph, which reduces the effect of missing view information on the clustering results. Third, to explore connections between missing views and mine high-order information between views, we splice the bipartite graphs into a tensor and impose a tensor low-rank constraint, i.e., the tensor Schatten p-norm, on it. At the same time, we also design an efficient algorithm to solve SIMVC/TSTBG. To our knowledge, we are the first successful practice to integrate the tensor technique with the scalable IMVC method. Compared with other IMVC methods, the results on seven datasets fully show the high efficiency and effectiveness of SIMVC/TSTBG.
Recently, with the assumption that samples can be reconstructed by themselves, subspace clustering (SC) methods have achieved great success. Generally, SC methods contain some parameters to be tuned, ...and different affinity matrices can obtain with different parameter values. In this paper, for the first time, we study a method for fusing these different affinity matrices to promote clustering performance and provide the corresponding solution from a multi-view clustering (MVC) perspective. That is, we argue that the different affinity matrices are consistent and complementary, which is similar to the fundamental assumption of MVC methods. Based on this observation, in this paper, we use least squares regression (LSR), which is a typical SC method, as an example since it can be efficiently optimized and has shown good clustering performance and we propose a novel robust least squares regression method from an MVC perspective (RLSR/MVCP). Specifically, we first utilize LSR with different parameter values to obtain different affinity matrices. Then, to fully explore the information contained in these different affinity matrices and to remove noise, we further fuse these affinity matrices into a tensor, which is constrained by the tensor low-rank constraint, i.e., the tensor nuclear norm (TNN). The two steps are combined into a framework that is solved by the augmented Lagrange multiplier (ALM) method. The experimental results on several datasets indicate that RLSR/MVCP has very encouraging clustering performance and is superior to state-of-the-art SC methods.
Graph regularized non-negative matrix factorization (GNMF), which is an important extension of NMF, shows good clustering performance on some datasets. However, GNMF only uses one graph to simulate ...the manifold structure of the data, which may be not accurate enough. Then, some scholars proposed multi-graph regularized NMF(MGNMF). MGNMF first combines multiple graphs into one graph through a linear combination method, and then obtains a low-dimensional representation of the data. However, the representation ability of MGNMF is limited and cannot fully make use of the multi-graph information since MGNMF first combines multiple graphs into one graph and then obtains only one low-dimensional representation of the data, which makes clustering performance unsatisfactory enough. Therefore, we propose an innovative method, i.e., a late fusion scheme for multi-graph regularized NMF(LFS/MGNMF). Different from the existing algorithms, LFS/MGNMF does not directly combine multiple graphs into a graph, but first obtains their own low-dimensional representation matrices, and then uses self-expressiveness property of data to obtain the self-representation matrices of all the low-dimensional representations and removes noise simultaneously. In addition, by using the tensor low-rank constraint, i.e., tensor nuclear norm constraint, LFS/MGNMF can explore higher-order information among different self-representation matrices. Finally, LFS/MGNMF fuses the self-representation matrices for clustering. Therefore, we believe the proposed method is a late fusion scheme and can make full use of the multi-graph information. As far as we know, LFS/MGNMF is the first late fusion method for multi-graph regularized methods. The Augmented Lagrange Multiplier method is exploited to solve LFS/MGNMF, and the experimental results on four datasets are very promising and fully demonstrate the superiority of LFS/MGNMF.
Multi-view subspace clustering aims to leverage the consensus and complementary information embedded within the data and effectively fuse the multiple views. However, existing methods have ...limitations in fully exploring the complementary information. Tensor-based methods tend to overlook the underlying structures, while latent representation-based methods struggle to capture high-order correlations, both of which are important components of complementary information. In this paper, we introduce a novel method called complete multi-view subspace clustering (CMVSC) using visible and latent views. The latent view with the underlying structures is obtained by the latent representation, along with visible views (i.e., original features) constitute the complete multi-view data. Then, the low-rank tensor constraint is imposed on the complete views to comprehensively explore the consensus and complementary information. Besides, to effectively combine the advantages of visible and latent views, we devise an auto-weighted strategy that can automatically assign the ideal weights to each view. Finally, an efficient alternating iterative algorithm using the augmented Lagrange multiplier method is designed to solve the CMVSC model. Experimental results show that our model outperforms the state-of-the-art counterparts while achieving almost perfect clustering performance on several benchmark datasets.
Incomplete multi-view clustering (IMVC) methods have attracted extensive attention in the field of clustering due to their superior performance in addressing incomplete multi-view data. However, ...existing IMVC methods often address balanced incomplete multi-view data, i.e., the missing rate of each view is the same, which does not match reality. In real life, the missing rate of each view in incomplete multi-view data is often different; these are referred to as unbalanced incomplete multi-view data. However, few articles consider the processing of unbalanced incomplete multi-view data. Therefore, we propose an innovative method, unbalanced incomplete multi-view clustering based on low-rank tensor graph learning (UIMVC/LTGL), to handle unbalanced incomplete multi-view data. Specifically, we first use the adjacency relationship between views to adaptively complete similarity graph matrices. To explore the consistency and high-order correlation among views, we further introduce a consensus representation learning term and low-rank tensor constraint into UIMVC/LTGL. In practical applications, each view's contribution to clustering should be different, especially for UIMVC problems. Therefore, we also apply the adaptive weight strategy to each view, which makes reasonable use of the information of each view. The abovementioned steps are integrated into a unified framework to obtain the optimal clustering effect. The augmented Lagrange multiplier (ALM) method is employed to solve the optimization problem. The experimental results on seven well-known datasets fully demonstrate the superiority of the proposed method.
