Information Theory-Guided Heuristic Progressive Multi-View Coding

Multi-view representation learning aims to capture comprehensive information from multiple views of a shared context. Recent works intuitively apply contrastive learning to different views in a pairwise manner, which is still scalable: view-specific noise is not filtered in learning view-shared representations; the fake negative pairs, where the negative terms are actually within the same class as the positive, and the real negative pairs are coequally treated; evenly measuring the similarities between terms might interfere with optimization. Importantly, few works study the theoretical framework of generalized self-supervised multi-view learning, especially for more than two views. To this end, we rethink the existing multi-view learning paradigm from the perspective of information theory and then propose a novel information theoretical framework for generalized multi-view learning. Guided by it, we build a multi-view coding method with a three-tier progressive architecture, namely Information theory-guided hierarchical Progressive Multi-view Coding (IPMC). In the distribution-tier, IPMC aligns the distribution between views to reduce view-specific noise. In the set-tier, IPMC constructs self-adjusted contrasting pools, which are adaptively modified by a view filter. Lastly, in the instance-tier, we adopt a designed unified loss to learn representations and reduce the gradient interference. Theoretically and empirically, we demonstrate the superiority of IPMC over state-of-the-art methods.Comment: This paper is accepted by the jourcal of Neural Networks (Elsevier) by 2023. A revised manuscript of arXiv:2109.0234

Peg Precipitation Coupled with Chromatography is a New and Sufficient Method for the Purification of Botulinum Neurotoxin Type B

Clostridium botulinum neurotoxins are used to treat a variety of neuro-muscular disorders, as well as in cosmetology. The increased demand requires efficient methods for the production and purification of these toxins. In this study, a new purification process was developed for purifying type B neurotoxin. The kinetics of C.botulinum strain growth and neurotoxin production were determined for maximum yield of toxin. The neurotoxin was purified by polyethylene glycol (PEG) precipitation and chromatography. Based on design of full factorial experiment, 20% (w/v) PEG-6000, 4°C, pH 5.0 and 0.3 M NaCl were optimal conditions to obtain a high recovery rate of 87% for the type B neurotoxin complex, as indicated by a purification factor of 61.5 fold. Furthermore, residual bacterial cells, impurity proteins and some nucleic acids were removed by PEG precipitation. The following purification of neurotoxin was accomplished by two chromatography techniques using Sephacryl™ S-100 and phenyl HP columns. The neurotoxin was recovered with an overall yield of 21.5% and the purification factor increased to 216.7 fold. In addition, a mouse bioassay determined the purified neurotoxin complex possessed a specific toxicity (LD50) of 4.095 ng/kg

Research on Some Phenomenon of E-Government Service Capacity Distribution in Mainland China Based on Multi-channel Perspective

In the context of the government\u27s increasing emphasis on e-government services, this is an urgent need for empirical research of large sample and multi-channels. Therefore, based on the government website, WeChat, Micro-blog, app, by using the existing mature evaluation index system, this paper analyzes e-government service capacity of the city above prefecture- level and provincial. Then, this paper selects the administrative level, economic level, regional balance as the differentiation attribute. It is found that both administrative level and economic level are positively correlated with government service capacity in all the channels. The channel capacity distribution varies related to attribute of administrative and economic, government type of city and province, but it is not restricted by level and region. It provides direction and intensity management to balance and promote channel service capacity for China government

Two stage Robust Nash Bargaining based Benefit Sharing between Electric and HCNG Distribution Networks Bridged with SOFC

Hydrogen-enriched compressed natural gas (HCNG) networks have potentized sustainability and efficiency of integrated electricity and natural gas systems. However, paucity of benefit sharing risks the IENGS's development in multiple entities and bottlenecks its efficacy. To fill the gap, a robust Nash bargaining-based benefit sharing mechanism for HCNG-enabled IENGS is proposed

Unbiased Image Synthesis via Manifold-Driven Sampling in Diffusion Models

Diffusion models are a potent class of generative models capable of producing high-quality images. However, they can face challenges related to data bias, favoring specific modes of data, especially when the training data does not accurately represent the true data distribution and exhibits skewed or imbalanced patterns. For instance, the CelebA dataset contains more female images than male images, leading to biased generation results and impacting downstream applications. To address this issue, we propose a novel method that leverages manifold guidance to mitigate data bias in diffusion models. Our key idea is to estimate the manifold of the training data using an unsupervised approach, and then use it to guide the sampling process of diffusion models. This encourages the generated images to be uniformly distributed on the data manifold without altering the model architecture or necessitating labels or retraining. Theoretical analysis and empirical evidence demonstrate the effectiveness of our method in improving the quality and unbiasedness of image generation compared to standard diffusion models

Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective

Benefiting from the injection of human prior knowledge, graphs, as derived discrete data, are semantically dense so that models can efficiently learn the semantic information from such data. Accordingly, graph neural networks (GNNs) indeed achieve impressive success in various fields. Revisiting the GNN learning paradigms, we discover that the relationship between human expertise and the knowledge modeled by GNNs still confuses researchers. To this end, we introduce motivating experiments and derive an empirical observation that the human expertise is gradually learned by the GNNs in general domains. By further observing the ramifications of introducing expertise logic into graph representation learning, we conclude that leading the GNNs to learn human expertise can improve the model performance. By exploring the intrinsic mechanism behind such observations, we elaborate the Structural Causal Model for the graph representation learning paradigm. Following the theoretical guidance, we innovatively introduce the auxiliary causal logic learning paradigm to improve the model to learn the expertise logic causally related to the graph representation learning task. In practice, the counterfactual technique is further performed to tackle the insufficient training issue during optimization. Plentiful experiments on the crafted and real-world domains support the consistent effectiveness of the proposed method

Photoactivatable senolysis with single-cell resolution delays aging

Strategies that can selectively eliminate senescent cells (SnCs), namely senolytics, have been shown to promote healthy lifespan. However, it is challenging to achieve precise, broad-spectrum and tractable senolysis. Here, we integrate multiple technologies that combine the enzyme substrate of senescence-associated β-galactosidase (SA-β-gal) with fluorescence tag for the precise tracking of SnCs, construction of a bioorthogonal receptor triggered by SA-β-gal to target and anchor SnCs with single-cell resolution and incorporation of a selenium atom to generate singlet oxygen and achieve precise senolysis through controllable photodynamic therapy (PDT). We generate KSL0608-Se, a photosensitive senolytic prodrug, which is selectively activated by SA-β-gal. In naturally-aged mice, KSL0608-Se-mediated PDT prevented upregulation of age-related SnCs markers and senescence-associated secretory phenotype factors. This treatment also countered age-induced losses in liver and renal function and inhibited the age-associated physical dysfunction in mice. We therefore provide a strategy to monitor and selectively eliminate SnCs to regulate aging

A population-based study on prevalence and risk factors of gastroesophageal reflux disease in the Tibet Autonomous Region, China

Objective To investigate the prevalence and risk factors of gastroesophageal reflux disease (GERD) in the Tibet Autonomous Region, China. Methods In this cross-sectional study, a stratified random sampling method was used for collecting samples in the Tibet Autonomous Region. A total of 10,000 individuals were selected from October 2016 to June 2017. A previously-published, validated questionnaire including six items related to the symptoms of GERD was used for evaluating GERD. In addition, basic demographic data, lifestyle, dietary habits, medical history and family history of GERD were investigated to identify risk factors of GERD. Results A total of 5,680 completed questionnaires were collected and analyzed. The prevalence of GERD in this area was 10.8%. Age (30–40 years vs. under 18 years, odds ratio (OR): 3.025; 40–50 years vs. under 18 years, OR: 4.484), education level (high school vs. primary, OR: 0.698; university vs. primary, OR: 2.804), ethnic group (Han vs. Tibetan, OR: 0.230; others vs. Tibetan, OR: 0.304), altitude of residence (4.0–4.5 km vs. 2.5–3.0 km, OR: 2.469), length of residence (<5 years vs. ≥5 years, OR: 2.218), Tibetan sweet tea (yes vs. no, OR: 2.158), Tibetan barley wine (yes vs. no, OR: 1.271), Tibetan dried meat (yes vs. no, OR: 1.278) and staying up late (yes vs. no, OR: 1.223) were significantly (all P < 0.05) and independently associated with GERD. Conclusions The prevalence of GERD is high in the Tibet Autonomous Region, China. Geographic conditions, ethnic group and lifestyle are risk factors for GERD