One-step Multi-view Clustering with Diverse Representation

Multi-view clustering has attracted broad attention due to its capacity to utilize consistent and complementary information among views. Although tremendous progress has been made recently, most existing methods undergo high complexity, preventing them from being applied to large-scale tasks. Multi-view clustering via matrix factorization is a representative to address this issue. However, most of them map the data matrices into a fixed dimension, which limits the expressiveness of the model. Moreover, a range of methods suffer from a two-step process, i.e., multimodal learning and the subsequent $k$-means, inevitably causing a sub-optimal clustering result. In light of this, we propose a one-step multi-view clustering with diverse representation method, which incorporates multi-view learning and $k$-means into a unified framework. Specifically, we first project original data matrices into various latent spaces to attain comprehensive information and auto-weight them in a self-supervised manner. Then we directly use the information matrices under diverse dimensions to obtain consensus discrete clustering labels. The unified work of representation learning and clustering boosts the quality of the final results. Furthermore, we develop an efficient optimization algorithm to solve the resultant problem with proven convergence. Comprehensive experiments on various datasets demonstrate the promising clustering performance of our proposed method

Multiple Latent Space Mapping for Compressed Dark Image Enhancement

Dark image enhancement aims at converting dark images to normal-light images. Existing dark image enhancement methods take uncompressed dark images as inputs and achieve great performance. However, in practice, dark images are often compressed before storage or transmission over the Internet. Current methods get poor performance when processing compressed dark images. Artifacts hidden in the dark regions are amplified by current methods, which results in uncomfortable visual effects for observers. Based on this observation, this study aims at enhancing compressed dark images while avoiding compression artifacts amplification. Since texture details intertwine with compression artifacts in compressed dark images, detail enhancement and blocking artifacts suppression contradict each other in image space. Therefore, we handle the task in latent space. To this end, we propose a novel latent mapping network based on variational auto-encoder (VAE). Firstly, different from previous VAE-based methods with single-resolution features only, we exploit multiple latent spaces with multi-resolution features, to reduce the detail blur and improve image fidelity. Specifically, we train two multi-level VAEs to project compressed dark images and normal-light images into their latent spaces respectively. Secondly, we leverage a latent mapping network to transform features from compressed dark space to normal-light space. Specifically, since the degradation models of darkness and compression are different from each other, the latent mapping process is divided mapping into enlightening branch and deblocking branch. Comprehensive experiments demonstrate that the proposed method achieves state-of-the-art performance in compressed dark image enhancement

Diketopiperazine alkaloids from a mangrove rhizosphere soil derived fungus Aspergillus effuses H1-1

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Early Gastric Cancer: Current Advances of Endoscopic Diagnosis and Treatment

Endoscopy is a major method for early gastric cancer screening because of its high detection rate, but its diagnostic accuracy depends heavily on the availability of endoscopic instruments. Many novel endoscopic techniques have been shown to increase the diagnostic yield of early gastric cancer. With the improved detection rate of EGC, the endoscopic treatment has become widespread due to advances in the instruments available and endoscopist’s experience. The aim of this review is to summarize frequently-used endoscopic diagnosis and treatment in early gastric cancer (EGC)

Key Lab on Wideband Wireless Communications and Sensor Network Technology of Ministry of Education

A fairness-aware resource allocation scheme in a cooperative orthogonal frequency division multiple (OFDM) network is proposed based on jointly optimizing the subcarrier pairing, power allocation, and channel-user assignment. Compared with traditional OFDM relaying networks, the source is permitted to retransfer the same data transmitted by it in the first time slot, further improving the system capacity performance. The problem which maximizes the energy efficiency (EE) of the system with total power constraint and minimal spectral efficiency constraint is formulated into a mixed-integer nonlinear programming (MINLP) problem which has an intractable complexity in general. The optimization model is simplified into a typical fractional programming problem which is testified to be quasiconcave. Thus we can adopt Dinkelbach method to deal with MINLP problem proposed to achieve the optimal solution. The simulation results show that the joint resource allocation method proposed can achieve an optimal EE performance under the minimum system service rate requirement with a good global convergence

H11-induced immunoprotection is predominantly linked to N-glycan moieties during Haemonchus contortus infection

Nematodes are one of the largest groups of animals on the planet. Many of them are major pathogens of humans, animals and plants, and cause destructive diseases and socioeconomic losses worldwide. Despite their adverse impacts on human health and agriculture, nematodes can be challenging to control, because anthelmintic treatments do not prevent re-infection, and excessive treatment has led to widespread drug resistance in nematode populations. Indeed, many nematode species of livestock animals have become resistant to almost all classes of anthelmintics used. Most efforts to develop commercial anti-nematode vaccines (native or recombinant) for use in animals and humans have not succeeded, although one effective (dead) vaccine (Barbervax) has been developed to protect animals against one of the most pathogenic parasites of livestock animals – Haemonchus contortus (the barber’s pole worm). This vaccine contains native molecules, called H11 and H-Gal-GP, derived from the intestine of this blood-feeding worm. In its native form, H11 alone consistently induces high levels (75-95%) of immunoprotection in animals against disease (haemonchosis), but recombinant forms thereof do not. Here, to test the hypothesis that post-translational modification (glycosylation) of H11 plays a crucial role in achieving such high immunoprotection, we explored the N-glycoproteome and N-glycome of H11 using the high-resolution mass spectrometry and assessed the roles of N-glycosylation in protective immunity against H. contortus. Our results showed conclusively that N-glycan moieties on H11 are the dominant immunogens, which induce high IgG serum antibody levels in immunised animals, and that anti-H11 IgG antibodies can confer specific, passive immunity in naïve animals. This work provides the first detailed account of the relevance and role of protein glycosylation in protective immunity against a parasitic nematode, with important implications for the design of vaccines against metazoan parasites.Peer Reviewe

Chloctanspirones A and B, novel chlorinated polyketides with an unprecedented skeleton, from marine sediment derived fungus Penicillium terrestre

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Heart failure causally affects the brain cortical structure: a Mendelian randomization study

BackgroundThe effects of heart failure (HF) on cortical brain structure remain unclear. Therefore, the present study aimed to investigate the causal effects of heart failure on cortical structures in the brain using Mendelian randomization (MR) analysis.MethodsWe conducted a two-sample MR analysis utilizing genetically-predicted HF trait, left ventricular ejection fraction (LVEF), and N-terminal prohormone brain natriuretic peptide (NT-proBNP) levels to examine their effects on the cortical surface area (SA) and thickness (TH) across 34 cortical brain regions. Genome-wide association study summary data were extracted from studies by Rasooly (1,266,315 participants) for HF trait, Schmidt (36,548 participants) for LVEF, the SCALLOP consortium (21,758 participants) for NT-proBNP, and the ENIGMA Consortium (51,665 participants) for cortical SA and TH. A series of MR analyses were employed to exclude heterogeneity and pleiotropy, ensuring the stability of the results. Given the exploratory nature of the study, p-values between 1.22E−04 and 0.05 were considered suggestive of association, and p-values below 1.22E−04 were defined as statistically significant.ResultsIn this study, we found no significant association between HF and cortical TH or SA (all p > 1.22E−04). We found that the HF trait and elevated NT-proBNP levels were not associated with cortical SA, but were suggested to decrease cortical TH in the pars orbitalis, lateral orbitofrontal cortex, temporal pole, lingual gyrus, precuneus, and supramarginal gyrus. Reduced LVEF was primarily suggested to decrease cortical SA in the isthmus cingulate gyrus, frontal pole, postcentral gyrus, cuneus, and rostral middle frontal gyrus, as well as TH in the postcentral gyrus. However, it was suggested to causally increase in the SA of the posterior cingulate gyrus and medial orbitofrontal cortex and the TH of the entorhinal cortex and superior temporal gyrus.ConclusionWe found 15 brain regions potentially affected by HF, which may lead to impairments in cognition, emotion, perception, memory, language, sensory processing, vision, and executive control in HF patients