Structure-aware contrastive hashing for unsupervised cross-modal retrieval.

Cross-modal hashing has attracted a lot of attention and achieved remarkable success in large-scale cross-media similarity retrieval applications because of its superior computational efficiency and low storage overhead. However, constructing similarity relationship among samples in cross-modal unsupervised hashing is challenging because of the lack of manual annotation. Most existing unsupervised methods directly use the representations extracted from the backbone of their respective modality to construct instance similarity matrices, leading to inaccurate similarity matrices and resulting in suboptimal hash codes. To address this issue, a novel unsupervised hashing model, named Structure-aware Contrastive Hashing for Unsupervised Cross-modal Retrieval (SACH), is proposed in this paper. Specifically, we concurrently employ both high-dimensional representations and discriminative representations learned by the network to construct a more informative semantic correlative matrix across modalities. Moreover, we design a multimodal structure-aware alignment network to minimize heterogeneous gap in the high-order semantic space of each modality, effectively reducing disparities within heterogeneous data sources and enhancing the consistency of semantic information across modalities. Extensive experimental results on two widely utilized datasets demonstrate the superiority of our proposed SACH method in cross-modal retrieval tasks over existing state-of-the-art methods.

Anchor Graph Network for Incomplete Multiview Clustering.

Incomplete multiview clustering (IMVC) has received extensive attention in recent years. However, existing works still have several shortcomings: 1) some works ignore the correlation of sample pairs in the global structural distribution; 2) many methods are computational expensive, thus cannot be applicable to the large-scale incomplete data clustering tasks; and 3) some methods ignore the refinement of the bipartite graph structure. To address the above issues, we propose a novel anchor graph network for IMVC, which includes a generative model and a similarity metric network. Concretely, the method uses a generative model to construct bipartite graphs, which can mine latent global structure distributions of sample pairs. Later, we use graph convolution network (GCN) with the constructed bipartite graphs to learn the structural embeddings. Notably, the introduction of bipartite graphs can greatly reduce the computational complexity and thus enable our model to handle large-scale data. Unlike previous works based on bipartite graph, our method employs bipartite graphs to guide the learning process in GCNs. In addition, an innovative adaptive learning strategy that can construct robust bipartite graphs is incorporated into our method. Extensive experiments demonstrate that our method achieves the comparable or superior performance compared with the state-of-the-art methods.

Incomplete multi-view clustering network via nonlinear manifold embedding and probability-induced loss.

Incomplete multi-view clustering, which included missing data in different views, is more challenging than multi-view clustering. For the purpose of eliminating the negative influence of incomplete data, researchers have proposed a series of solutions. However, the present incomplete multi-view clustering methods still confront three major issues: (1) The interference of redundant features hinders these methods to learn the most discriminative features. (2) The importance role of local structure is not considered during clustering. (3) These methods fail to utilize data distribution information to guide models update to decrease the effects of outliers and noise. To address above issues, a novel deep clustering network which exerted on incomplete multi-view data was proposed in this paper. We combine multi-view autoencoders with nonlinear manifold embedding method UMAP to extract latent consistent features of incomplete multi-view data. In the clustering method, we introduce Gaussian Mixture Model (GMM) to fit the complex distribution of data and deal with the interference of outliers. In addition, we reasonably utilize the probability distribution information generated by GMM, using probability-induced loss function to integrate feature learning and clustering as a joint framework. In experiments conducted on multiple benchmark datasets, our method captures incomplete multi-view data features effectively and perform excellent.

Low-Rank Graph Completion-Based Incomplete Multiview Clustering.

In order to reduce the negative effect of missing data on clustering, incomplete multiview clustering (IMVC) has become an important research content in machine learning. At present, graph-based methods are widely used in IMVC, but these methods still have some defects. First, some of the methods overlook potential relationships across views. Second, most of the methods depend on local structure information and ignore the global structure information. Third, most of the methods cannot use both global structure information and potential information across views to adaptively recover the incomplete relationship structure. To address the above issues, we propose a unified optimization framework to learn reasonable affinity relationships, called low-rank graph completion-based IMVC (LRGR_IMVC). 1) Our method introduces adaptive graph embedding to effectively explore the potential relationship among views; 2) we append a low-rank constraint to adequately exploit the global structure information among views; and 3) this method unites related information within views, potential information across views, and global structure information to adaptively recover the incomplete graph structure and obtain complete affinity relationships. Experimental results on several commonly used datasets show that the proposed method achieves better clustering performance significantly than some of the most advanced methods.

Western medication plus Traditional Chinese Medicine preparations in patients with chronic heart failure: a prospective, single-blind, randomized, controlled, and multicenter clinical trial.

OBJECTIVE: To assess the efficacy and safety in patients with chronic heart failure (CHF) of Western medication plus Traditional Chinese Medicine (TCM) preparations. METHODS: This prospective, single-blind, randomized, controlled, and multicenter clinical trial began on September 17, 2008, and was completed on June 25, 2011. A total of 340 inpatients, aged 40-79 years, with exacerbating CHF from 10 hospitals were enrolled and randomly allocated within 24 h of admission. The trial included three intervention periods. During hospitalization, the control group received western medication for CHF and the treatment group received Danhong injection with Shenfu injection or Shenmai injection. After discharge, all patients were treated with Qiliqiangxin capsules and Buyiqiangxin tablets or a placebo for 6 months. After the 6-month intervention, both groups received only continuous western medication. The primary endpoint was all-cause mortality. The efficacy assessments were as follows: B-type natriuretic peptide (BNP), Lee's HF score, the 6-minute walking test (6MWT), left ventricular ejection fraction (LVEF), and the Minnesota Living with Heart Failure Questionnaire (MLHFQ). The safety assessments were as follows: blood and urine routine examination, hepatic and renal function, electrolytes in blood and adverse events. RESULTS: Compared with the control group, the treatment group showed a 30.99% reduction in all-cause mortality and an improved survival rate. The treatment group showed greater improvement in 6MWT (P = 0.02) than the control group on discharge, after 12-month follow-up, there was a time-group interaction for MLHFQ (P = 0.03). Incidence rate of adverse events and other relevant safety indexes were not statistically significant between the two groups. CONCLUSION: Western medication plus TCM treatment can increase 6-minute walking distance (improve exercise tolerance) and quality of life with heart failure patients.

Globostelletins A-I, cytotoxic isomalabaricane derivatives from the marine sponge Rhabdastrella globostellata.

Nine new isomalabaricane-derived natural products, globostelletins A-I (1-9), were isolated from the marine sponge Rhabdastrella globostellata, together with jaspolides F (10), rhabdastrellic acid-A (11), (-)-stellettin E (12), stellettins C (13) and D (14). The structures of these compounds were determined on the basis of extensive spectroscopic analyses and by comparison with the reported data in the literature. The inhibitory activities of compounds 1-12 against human tumor cell lines were evaluated, and their structure-activity relationships were discussed. In addition, rhabdastrellic acid-A (11) showed potent inhibition against HL-60 cells, and it induced the apoptosis of HL-60 cells in M/G2 phase. The mechanism of 11 targeting the ubiquitin-proteasome system, including the regulation of ChT-L and T-L target proteins is discussed.

Study on the evaluation of the clinical effects of traditional chinese medicine in heart failure by complex intervention: protocol of SECETCM-HF.

BACKGROUND: Experts in Traditional Chinese Medicine (TCM) have studied the TCM subject of the pathogenesis of heart failure (HF) for several decades. As a result, the general idea is ben deficiency and biao excess. However, the clinical evaluation system which combined the TCM and western medicine in HF has not been developed yet. The objective is to establish the evaluation index system for the integration of TCM and western medicine. The evaluation indexes which include TCM items will specify the research design and methods. METHODS: Nine medical centers in different cities in China will participate in the trial. A population of 340 patients with HF will be enrolled through a central randomized system for different test groups. Group A will be treated with only western medicine, while group B with western and Chinese medicine together. The study will last for 12 months from the date of enrollment. The cardiovascular death will be the primary outcome. DISCUSSION: By putting the protocol into practice, the clinical effects of TCM for HF will be identified scientifically, objectively as well as rationally. The proper index system which built in the study will be helpful for the clinical effect expression of HF by integrated medicine in future. TRIAL REGISTRATION: ChiCTR-TRC-00000059.