A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective

Multi-view clustering (MVC) is a popular technique for improving clustering performance using various data sources. However, existing methods primarily focus on acquiring consistent information while often neglecting the issue of redundancy across multiple views. This study presents a new approach called Sufficient Multi-View Clustering (SUMVC) that examines the multi-view clustering framework from an information-theoretic standpoint. Our proposed method consists of two parts. Firstly, we develop a simple and reliable multi-view clustering method SCMVC (simple consistent multi-view clustering) that employs variational analysis to generate consistent information. Secondly, we propose a sufficient representation lower bound to enhance consistent information and minimise unnecessary information among views. The proposed SUMVC method offers a promising solution to the problem of multi-view clustering and provides a new perspective for analyzing multi-view data. To verify the effectiveness of our model, we conducted a theoretical analysis based on the Bayes Error Rate, and experiments on multiple multi-view datasets demonstrate the superior performance of SUMVC

Deep Clustering: A Comprehensive Survey

Cluster analysis plays an indispensable role in machine learning and data mining. Learning a good data representation is crucial for clustering algorithms. Recently, deep clustering, which can learn clustering-friendly representations using deep neural networks, has been broadly applied in a wide range of clustering tasks. Existing surveys for deep clustering mainly focus on the single-view fields and the network architectures, ignoring the complex application scenarios of clustering. To address this issue, in this paper we provide a comprehensive survey for deep clustering in views of data sources. With different data sources and initial conditions, we systematically distinguish the clustering methods in terms of methodology, prior knowledge, and architecture. Concretely, deep clustering methods are introduced according to four categories, i.e., traditional single-view deep clustering, semi-supervised deep clustering, deep multi-view clustering, and deep transfer clustering. Finally, we discuss the open challenges and potential future opportunities in different fields of deep clustering

Degradation of Toxic Organic Contaminants by Graphene Cathode in an Electro‐Fenton System

A novel composite electrode was constructed by pressing graphene and CuO, using a cathode in an electro‐Fenton (EF) system. Cyclic voltammetry, charge/discharge curve and electrochemical impedance spectroscopy (EIS) were used to characterize the composite electrode. The degradation of a toxic organic contaminant, Terramycin, by EF system was studied in an undivided electrolysis cell. The possible degradation products of Terramycin were studied by a Fourier transform‐infrared spectrum, and the findings showed that the structure of Terramycin was damaged. The variations of hydrogen peroxide and the relative content of hydroxyl radical (.OH) during the degradation process were traced by enzyme catalysis method and fluorescence spectrometry. The results showed that the electro‐catalytic degradation of Terramycin occurred by an ·OH radical mechanism. More importantly, this as‐prepared cathode was very stable and could be reused without any catalytic activity decrease, suggesting its potential application in the wastewater treatment

Synergistic Treatment of Tumor by Targeted Biotherapy and Chemotherapy via Site-Specific Anchoring of Aptamers on DNA Nanotubes

Background: Aptamers have been widely used as targeted therapeutic agents due to its relatively small physical size, flexible structure, high specificity, and selectivity. Aptamers functionalized nanomaterials, not only enhance the targeting of nanomaterials, but can also improve the stability of the aptamers. We developed aptamer C2NP (Apt) conjugated straight DNA nanotubes (S-DNT-Apt) and twisted DNA nanotubes (T-DNT-Apt) as nanocarriers for doxorubicin (DOX). Methods: The twisted DNA nanotubes (T-DNT) and straight DNA nanotubes (S-DNT) were assembled with a scaffold and hundreds of staples. Apt was site-specifically anchored on DNA nanotubes with either different spatial distribution (3 or 6 nm) or varied stoichiometry (15Apt or 30Apt). The developed nanocarriers were characterized with agarose gel electrophoresis and transmission electron microscopy. The drug loading and release in vitro were evaluated by measuring the fluorescence intensity of DOX using a microplate reader. The stability of DNT in cell culture medium plus 10% of FBS was evaluated by agarose gel electrophoresis. The cytotoxicity of DNA nanostructures against K299 cells was tested with a standard CCK8 method. Cellular uptake, cell apoptosis, cell cycle and reactive oxygen species level were investigated by flow cytometry. The expression of p53 was examined by Western Blot. Results: T-DNT-30Apt-6 exhibited the highest cytotoxicity when the concentration of Apt was 120 nM. After intercalation of DOX, the cytotoxicity of DOX@T-DNT-30Apt-6 was further enhanced due to the combination of chemotherapy of DOX and biotherapy of Apt. The enhanced cytotoxicity of DOX@T-DNT-30Apt-6 can be explained by the increase in the cellular uptake, cell apoptosis and intracellular ROS levels. Additionally, the interaction between Apt and its receptor CD30 could upregulate the expression of p53. Conclusion: These results demonstrate that both stoichiometry and spatial arrangement of Apt on T-DNT-Apt influence the anticancer activity. The developed twisted DNA nanotubes may be a solution for the synergistic treatment of cancer

Federated Deep Multi-View Clustering with Global Self-Supervision

Federated multi-view clustering has the potential to learn a global clustering model from data distributed across multiple devices. In this setting, label information is unknown and data privacy must be preserved, leading to two major challenges. First, views on different clients often have feature heterogeneity, and mining their complementary cluster information is not trivial. Second, the storage and usage of data from multiple clients in a distributed environment can lead to incompleteness of multi-view data. To address these challenges, we propose a novel federated deep multi-view clustering method that can mine complementary cluster structures from multiple clients, while dealing with data incompleteness and privacy concerns. Specifically, in the server environment, we propose sample alignment and data extension techniques to explore the complementary cluster structures of multiple views. The server then distributes global prototypes and global pseudo-labels to each client as global self-supervised information. In the client environment, multiple clients use the global self-supervised information and deep autoencoders to learn view-specific cluster assignments and embedded features, which are then uploaded to the server for refining the global self-supervised information. Finally, the results of our extensive experiments demonstrate that our proposed method exhibits superior performance in addressing the challenges of incomplete multi-view data in distributed environments

Deep Learning and Medical Imaging for COVID-19 Diagnosis: A Comprehensive Survey

COVID-19 (Coronavirus disease 2019) has been quickly spreading since its outbreak, impacting financial markets and healthcare systems globally. Countries all around the world have adopted a number of extraordinary steps to restrict the spreading virus, where early COVID-19 diagnosis is essential. Medical images such as X-ray images and Computed Tomography scans are becoming one of the main diagnostic tools to combat COVID-19 with the aid of deep learning-based systems. In this survey, we investigate the main contributions of deep learning applications using medical images in fighting against COVID-19 from the aspects of image classification, lesion localization, and severity quantification, and review different deep learning architectures and some image preprocessing techniques for achieving a preciser diagnosis. We also provide a summary of the X-ray and CT image datasets used in various studies for COVID-19 detection. The key difficulties and potential applications of deep learning in fighting against COVID-19 are finally discussed. This work summarizes the latest methods of deep learning using medical images to diagnose COVID-19, highlighting the challenges and inspiring more studies to keep utilizing the advantages of deep learning to combat COVID-19

Growth inhibition of mouse embryonic stem (ES) cells on the feeders from domestic animals

Mouse embryonic stem cells (mESCs) can be propagated in vitro on the feeders of mouse embryonic fibroblasts. In this study, we found growth inhibition of mESCs cultured on embryonic fibroblast feeders derived from different livestock animals. Under the same condition, mESCs derived from mouse embryonic fibroblast feeders were seen on the mass-like colonies and round or oval images, and more significant growth in the total number of colonies (p<0.05) and viable cells in the colonies (p<0.01) than that from goat embryonic fibroblast feeders, and viable cells in the colonies (p<0.05) than that from porcine embryonic fibroblast feeders. The feeders from bovine embryonic fibroblasts also reduced viable cells in the colonies, but were not significantly different in the total number of colonies and viable cells in the colonies with mouse embryonic fibroblast feeders. mESCs on the different embryonic fibroblast feeders were expressed as stem cell-specific markers Oct 4 and stage-specific embryonic antigen 1 (SSEA 1). Here, our results indicate that the feeders from goat, porcine and bovine embryonic fibroblasts inhibit the proliferation of mESCs.Key words: Domestic animals, feeders, mouse embryonic stem cells (mESCs), growth