The causal relationship between gut microbiota and lymphoma: a two-sample Mendelian randomization study.

Background: Previous studies have indicated a potential link between the gut microbiota and lymphoma. However, the exact causal interplay between the two remains an area of ambiguity.

Methods: We performed a two-sample Mendelian randomization (MR) analysis to elucidate the causal relationship between gut microbiota and five types of lymphoma.

Risk factors for invasive fungal infections after haematopoietic stem cell transplantation: a systematic review and meta-analysis.

Background: Invasive fungal infections (IFIs) are common infectious complications after haematopoietic stem cell transplantation (HSCT), seriously threatening the survival of patients.

Objectives: This systematic review aimed to investigate risk factors associated with IFIs following HSCT.

Methods: Two authors independently conducted the selection of studies and extraction of data.

Graph Clustering with High-Order Contrastive Learning.

Graph clustering is a fundamental and challenging task in unsupervised learning. It has achieved great progress due to contrastive learning. However, we find that there are two problems that need to be addressed: (1) The augmentations in most graph contrastive clustering methods are manual, which can result in semantic drift.

Auxiliary Graph for Attribute Graph Clustering.

Attribute graph clustering algorithms that include topological structural information into node characteristics for building robust representations have proven to have promising efficacy in a variety of applications. However, the presented topological structure emphasizes local links between linked nodes but fails to convey relationships between nodes that are not directly linked, limiting the potential for future clustering performance improvement. To solve this issue, we offer the Auxiliary Graph for Attribute Graph Clustering technique (AGAGC).

Multiview Subspace Clustering via Co-Training Robust Data Representation.

Taking the assumption that data samples are able to be reconstructed with the dictionary formed by themselves, recent multiview subspace clustering (MSC) algorithms aim to find a consensus reconstruction matrix via exploring complementary information across multiple views. Most of them directly operate on the original data observations without preprocessing, while others operate on the corresponding kernel matrices. However, they both ignore that the collected features may be designed arbitrarily and hard guaranteed to be independent and nonoverlapping.

Adaptive Weighted Graph Fusion Incomplete Multi-View Subspace Clustering.

With the enormous amount of multi-source data produced by various sensors and feature extraction approaches, multi-view clustering (MVC) has attracted developing research attention and is widely exploited in data analysis. Most of the existing multi-view clustering methods hold on the assumption that all of the views are complete. However, in many real scenarios, multi-view data are often incomplete for many reasons, e.

An Efficient and Robust Unsupervised Anomaly Detection Method Using Ensemble Random Projection in Surveillance Videos.

Video anomaly detection is widely applied in modern society, which is achieved by sensors such as surveillance cameras. This paper learns anomalies by exploiting videos under the fully unsupervised setting. To avoid massive computation caused by back-prorogation in existing methods, we propose a novel efficient three-stage unsupervised anomaly detection method.

Incomplete Multiview Clustering via Late Fusion.

In real-world applications of multiview clustering, some views may be incomplete due to noise, sensor failure, etc. Most existing studies in the field of incomplete multiview clustering have focused on early fusion strategies, for example, learning subspace from multiple views. However, these studies overlook the fact that clustering results with the visible instances in each view could be reliable under the random missing assumption; accordingly, it seems that learning a final clustering decision via late fusion of the clustering results from incomplete views would be more natural.

A Depth-Based Weighted Point Cloud Registration for Indoor Scene.

Point cloud registration plays a key role in three-dimensional scene reconstruction, and determines the effect of reconstruction. The iterative closest point algorithm is widely used for point cloud registration. To improve the accuracy of point cloud registration and the convergence speed of registration error, point pairs with smaller Euclidean distances are used as the points to be registered, and the depth measurement error model and weight function are analyzed.

Cross-Coupling Reactions of Alkenyl Halides with 4-Benzyl-1,4- Dihydropyridines Associated with E to Z Isomerization under Nickel and Photoredox Catalysis.

Cross-coupling reactions of alkenyl halides with 4-alkyl-1,4-dihydropyridines as alkylation reagents have been achieved by combination of nickel and photoredox catalysts. Alkenyl halides bearing alkyl and aryl substituents are available. Particularly, in the use of aryl-substituted alkenyl halides, cross-coupling reactions are associated with E to Z isomerization of alkenes.

Separation and determination of nitroaniline isomers by capillary zone electrophoresis with amperometric detection.

In this paper, capillary zone electrophoresis with amperometric detection (CZE-AD) was firstly applied to the simultaneous separation and determination of nitroaniline positional isomers. The three analytes could be perfectly analyzed by using the buffer of extreme pH. The effects of several important factors were investigated to find optimum conditions.

The separation and determination of nitrophenol isomers by high-performance capillary zone electrophoresis.

o-Nitrophenol, m-nitrophenol, and p-nitrophenol could well be separated by capillary zone electrophoresis (CZE) by only adjusting the run buffer with methanol. Efficiency up to 10(5) theoretical plates per meter was achieved. The effects of several important factors were investigated to find optimum conditions.