Dual Information Enhanced Multiview Attributed Graph Clustering.

Multiview attributed graph clustering is an important approach to partition multiview data based on the attribute characteristics and adjacent matrices from different views. Some attempts have been made in using graph neural network (GNN), which have achieved promising clustering performance. Despite this, few of them pay attention to the inherent specific information embedded in multiple views. Meanwhile, they are incapable of recovering the latent high-level representation from the low-level ones, greatly limiting the downstream clustering performance. To fill these gaps, a novel dual information enhanced multiview attributed graph clustering (DIAGC) method is proposed in this article. Specifically, the proposed method introduces the specific information reconstruction (SIR) module to disentangle the explorations of the consensus and specific information from multiple views, which enables graph convolutional network (GCN) to capture the more essential low-level representations. Besides, the contrastive learning (CL) module maximizes the agreement between the latent high-level representation and low-level ones and enables the high-level representation to satisfy the desired clustering structure with the help of the self-supervised clustering (SC) module. Extensive experiments on several real-world benchmarks demonstrate the effectiveness of the proposed DIAGC method compared with the state-of-the-art baselines.

Contrastive Multiview Attribute Graph Clustering With Adaptive Encoders.

Multiview attribute graph clustering aims to cluster nodes into disjoint categories by taking advantage of the multiview topological structures and the node attribute values. However, the existing works fail to explicitly discover the inherent relationships in multiview topological graph matrices while considering different properties between the graphs. Besides, they cannot well handle the sparse structure of some graphs in the learning procedure of graph embeddings. Therefore, in this article, we propose a novel contrastive multiview attribute graph clustering (CMAGC) with adaptive encoders method. Within this framework, the adaptive encoders concerning different properties of distinct topological graphs are chosen to integrate multiview attribute graph information by checking whether there exists high-order neighbor information or not. Meanwhile, the number of layers of the GCN encoders is selected according to the prior knowledge related to the characteristics of different topological graphs. In particular, the feature-level and cluster-level contrastive learning are conducted on the multiview soft assignment representations, where the union of the first-order neighbors from the corresponding graph pairs is regarded as the positive pairs for data augmentation and the sparse neighbor information problem in some graphs can be well dealt with. To the best of our knowledge, it is the first time to explicitly deal with the inherent relationships from the interview and intraview perspectives. Extensive experiments are conducted on several datasets to verify the superiority of the proposed CMAGC method compared with the state-of-the-art methods.

Motif-Based Contrastive Learning for Community Detection.

Community detection has become a prominent task in complex network analysis. However, most of the existing methods for community detection only focus on the lower order structure at the level of individual nodes and edges and ignore the higher order connectivity patterns that characterize the fundamental building blocks within the network. In recent years, researchers have shown interest in motifs and their role in network analysis. However, most of the existing higher order approaches are based on shallow methods, failing to capture the intricate nonlinear relationships between nodes. In order to better fuse higher order and lower order structural information, a novel deep learning framework called motif-based contrastive learning for community detection (MotifCC) is proposed. First, a higher order network is constructed based on motifs. Subnetworks are then obtained by removing isolated nodes, addressing the fragmentation issue in the higher order network. Next, the concept of contrastive learning is applied to effectively fuse various kinds of information from nodes, edges, and higher order and lower order structures. This aims to maximize the similarity of corresponding node information, while distinguishing different nodes and different communities. Finally, based on the community structure of subnetworks, the community labels of all nodes are obtained by using the idea of label propagation. Extensive experiments on real-world datasets validate the effectiveness of MotifCC.

Incomplete Data Meets Uncoupled Case: A Challenging Task of Multiview Clustering.

Incomplete multiview clustering (IMC) methods have achieved remarkable progress by exploring the complementary information and consensus representation of incomplete multiview data. However, to our best knowledge, none of the existing methods attempts to handle the uncoupled and incomplete data simultaneously, which affects their generalization ability in real-world scenarios. For uncoupled incomplete data, the unclear and partial cross-view correlation introduces the difficulty to explore the complementary information between views, which results in the unpromising clustering performance for the existing multiview clustering methods. Besides, the presence of hyperparameters limits their applications. To fill these gaps, a novel uncoupled IMC (UIMC) method is proposed in this article. Specifically, UIMC develops a joint framework for feature inferring and recoupling. The high-order correlations of all views are explored by performing a tensor singular value decomposition (t-SVD)-based tensor nuclear norm (TNN) on recoupled and inferred self-representation matrices. Moreover, all hyperparameters of the UIMC method are updated in an exploratory manner. Extensive experiments on six widely used real-world datasets have confirmed the superiority of the proposed method in handling the uncoupled incomplete multiview data compared with the state-of-the-art methods.

A Tensor Approach for Uncoupled Multiview Clustering.

Multiview clustering plays an important part in unsupervised learning. Although the existing methods have shown promising clustering performances, most of them assume that the data is completely coupled between different views, which is unfortunately not always ensured in real-world applications. The clustering performance of these methods drops dramatically when handling the uncoupled data. The main reason is that: 1) cross-view correlation of uncoupled data is unclear, which limits the existing multiview clustering methods to explore the complementary information between views and 2) features from different views are uncoupled with each other, which may mislead the multiview clustering methods to partition data into wrong clusters. To address these limitations, we propose a tensor approach for uncoupled multiview clustering (T-UMC) in this article. Instead of pairwise correlation, T-UMC chooses a most reliable view by view-specific silhouette coefficient (VSSC) at first, and then couples the self-representation matrix of each view with it by pairwise cross-view coupling learning. After that, by integrating recoupled self-representation matrices into a third-order tensor, the high-order correlations of all views are explored with tensor singular value decomposition (t-SVD)-based tensor nuclear norm (TNN). And the view-specific local structures of each individual view are also preserved with the local structure learning scheme with manifold learning. Besides, the physical meaning of view-specific coupling matrix is also discussed in this article. Extensive experiments on six commonly used benchmark datasets have demonstrated the superiority of the proposed method compared with the state-of-the-art multiview clustering methods.

Physiological effects and subjective tolerability of prone positioning in COVID-19 and healthy hypoxic challenge.

BACKGROUND: Prone positioning has a beneficial role in coronavirus disease 2019 (COVID-19) patients receiving ventilation but lacks evidence in awake non-ventilated patients, with most studies being retrospective, lacking control populations and information on subjective tolerability. METHODS: We conducted a prospective, single-centre study of prone positioning in awake non-ventilated patients with COVID-19 and non-COVID-19 pneumonia. The primary outcome was change in peripheral oxygenation in prone versus supine position. Secondary outcomes assessed effects on end-tidal CO2, respiratory rate, heart rate and subjective symptoms. We also recruited healthy volunteers to undergo proning during hypoxic challenge. RESULTS: 238 hospitalised patients with pneumonia were screened; 55 were eligible with 25 COVID-19 patients and three non-COVID-19 patients agreeing to undergo proning - the latter insufficient for further analysis. 10 healthy control volunteers underwent hypoxic challenge. Patients with COVID-19 had a median age of 64 years (interquartile range 53-75). Proning led to an increase in oxygen saturation measured by pulse oximetry (SpO2) compared to supine position (difference +1.62%; p=0.003) and occurred within 10 min of proning. There were no effects on end-tidal CO2, respiratory rate or heart rate. There was an increase in subjective discomfort (p=0.003), with no difference in breathlessness. Among healthy controls undergoing hypoxic challenge, proning did not lead to a change in SpO2 or subjective symptom scores. CONCLUSION: Identification of suitable patients with COVID-19 requiring oxygen supplementation from general ward environments for awake proning is challenging. Prone positioning leads to a small increase in SpO2 within 10 min of proning though is associated with increased discomfort.

Daunomycin-loaded superparamagnetic iron oxide nanoparticles: Preparation, magnetic targeting, cell cytotoxicity, and protein delivery research.

The clinical use of daunomycin is restricted by dose-dependent toxicity and low specificity against cancer cells. In the present study, modified superparamagnetic iron oxide nanoparticles were employed to load daunomycin and the drug-loaded nanospheres exhibited satisfactory size and smart pH-responsive release. The cellular uptake efficiency, targeted cell accumulation, and cell cytotoxicity experimental results proved that the superparamagnetic iron oxide nanoparticle-loading process brings high drug targeting without decreasing the cytotoxicity of daunomycin. Moreover, a new concern for the evaluation of nanophase drug delivery's effects was considered, with monitoring the interactions between human serum albumin and the drug-loaded nanospheres. Results from the multispectroscopic techniques and molecular modeling calculation elucidate that the drug delivery has detectable deleterious effects on the frame conformation of protein, which may affect its physiological function.

Synthesis, G-quadruplexes DNA binding, and photocytotoxicity of novel cationic expanded porphyrins.

Intensive reports allowed the conclusion that molecules with extended aromatic surfaces always do good jobs in the DNA interactions. Inspired by the previous successful researches, herein, we designed a series of cationic porphyrins with expanded planar substituents, and evaluated their binding behaviors to G-quadruplex DNA using the combination of surface-enhanced raman, circular dichroism, absorption spectroscopy and fluorescence resonance energy transfer melting assays. Asymmetrical tetracationic porphyrin with one phenyl-4-N-methyl-4-pyridyl group and three N-methyl-4-pyridyl groups exhibit the best G4-DNA binding affinities among all the designed compounds, suggesting that the bulk of the substituents should be matched to the width of the grooves they putatively lie in. Theoretical calculations applying the density functional theory have been carried out and explain the binding properties of these porphyrins reasonably. Meanwhile, these porphyrins were proved to be potential photochemotherapeutic agents since they have photocytotoxic activities against both myeloma cell (Ag8.653) and gliomas cell (U251) lines.

A simulation study on nanoscale holes generated by gold nanoparticles on negative lipid bilayers.

Understanding the interactions of gold nanoparticles (AuNPs) with cellular compartments, especially cell membranes, is of fundamental importance in obtaining their control in biomedical applications. An effort is made in this paper to investigate the interactions of 2.2 nm core AuNPs with negative model bilayer membranes by coarse-grained (CG) molecular dynamics (MD) simulation. The CG model of lipid bilayer was taken from Marrink et al. ( J. Phys. Chem. B 2004, 108, 750-760 ), whereas the CG AuNPs model was developed on the basis of both atomistic MD simulations and experimental data. It was found that AuNPs functionalized with cationic ligands penetrated into the negative bilayer membranes and generated significant disruptions on bilayers. The lipids surrounding the nanoparticle were highly disordered and the bulk surface of the bilayer exhibits some defective areas. Most importantly, it is observed that a nanoscale hole can be formed and expanded spontaneously on the peripheral regions of the 20 × 20 nm bilayer. The expansion of the hole is on the time scale of hundreds of nanosceonds. The fully expanded hole had a radius of ∼5.5 nm and could transport water molecules at a rate of up to ∼1100 molecule/ns. However holes could not be formed on a larger bilayer (28 × 28 nm). The factors that can eliminate hole formation on the bilayer also include the decrease of cationic lignads on the AuNP, the reduction of negative lipids in the bilayer, the release of bilayer surface tension, the lowering of temperature, and the addition of a high concentration of salt. The results suggest that a hole can only be formed on living cell membranes under extreme conditions.