Gut microbes and their metabolites are actively involved in the development and regulation of host immunity, which can influence disease susceptibility. Herein, we review the most recent research advancements in the gut microbiota–immune axis. We discuss in detail how the gut microbiota is a tipping point for neonatal immune development as indicated by newly uncovered phenomenon, such as maternal imprinting, in utero intestinal metabolome, and weaning reaction. We describe how the gut microbiota shapes both innate and adaptive immunity with emphasis on the metabolites short-chain fatty acids and secondary bile acids. We also comprehensively delineate how disruption in the microbiota–immune axis results in immune-mediated diseases, such as gastrointestinal infections, inflammatory bowel diseases, cardiometabolic disorders (e.g., cardiovascular diseases, diabetes, and hypertension), autoimmunity (e.g., rheumatoid arthritis), hypersensitivity (e.g., asthma and allergies), psychological disorders (e.g., anxiety), and cancer (e.g., colorectal and hepatic). We further encompass the role of fecal microbiota transplantation, probiotics, prebiotics, and dietary polyphenols in reshaping the gut microbiota and their therapeutic potential. Continuing, we examine how the gut microbiota modulates immune therapies, including immune checkpoint inhibitors, JAK inhibitors, and anti-TNF therapies. We lastly mention the current challenges in metagenomics, germ-free models, and microbiota recapitulation to a achieve fundamental understanding for how gut microbiota regulates immunity. Altogether, this review proposes improving immunotherapy efficacy from the perspective of microbiome-targeted interventions.

/pdf/crosstalk-between-gut-microbiota-and-host-immunity-impact-on-28czsdqd.pdf

Crosstalk between Gut Microbiota and Host Immunity: Impact on Inflammation and Immunotherapy

A major mechanism for immunomodulation: Dietary fibres and acid metabolites.

“Dietary fiber” (DF) refers to a type of carbohydrate that cannot be digested fully. DF is not an essential nutrient, but it plays an important part in enhancing digestive capacity and maintaining intestinal health. Therefore, DF supplementation in the daily diet is highly recommended. Inulin is a soluble DF, and commonly added to foods. Recently, several studies have found that dietary supplementation of inulin can improve metabolic function and regulate intestinal immunity. Inulin is fermented in the colon by the gut microbiota and a series of metabolites is generated. Among these metabolites, short-chain fatty acids provide energy to intestinal epithelial cells and participate in regulating the differentiation of immune cells. Inulin and its intestinal metabolites contribute to host immunity. This review summarizes the effect of inulin and its metabolites on intestinal immunity, and the underlying mechanisms of inulin in preventing diseases such as type 2 diabetes mellitus, inflammatory bowel disease, chronic kidney disease, and certain cancer types. 

Immunomodulatory effects of inulin and its intestinal metabolites

The changing metabolic landscape of bile acids - keys to metabolism and immune regulation.

<h2>Summary</h2> Nutrition profoundly shapes immunity and inflammation across the lifespan of mammals, from pre- and post-natal periods to later life. Emerging insights into diet-microbiota interactions indicate that nutrition has a dominant influence on the composition—and metabolic output—of the intestinal microbiota, which in turn has major consequences for host immunity and inflammation. Here, we discuss recent findings that support the concept that dietary effects on microbiota-derived metabolites potently alter immune responses in health and disease. We discuss how specific dietary components and metabolites can be either pro-inflammatory or anti-inflammatory in a context- and tissue-dependent manner during infection, chronic inflammation, and cancer. Together, these studies emphasize the influence of diet-microbiota crosstalk on immune regulation that will have a significant impact on precision nutrition approaches and therapeutic interventions for managing inflammation, infection, and cancer immunotherapy. 

Nutritional regulation of microbiota-derived metabolites: Implications for immunity and inflammation.

Inulin fibre promotes microbiota-derived bile acids and type 2 inflammation

The poor quality of underwater images has become a widely-known cause affecting the performance of the underwater development projects, including mineral exploitation, driving photography, and navigation for autonomous underwater vehicles. In recent years, deep learning-based techniques have achieved remarkable successes in image restoration and enhancement tasks. However, the limited availability of paired training data (underwater images and their corresponding clear images) and the requirement for vivid colour correction remain challenging for underwater image enhancement, as almost all learning-based methods require paired data for training. In this study, instead of creating the time-consuming paired data, we explore the unsupervised training strategy. Specifically, we introduce a universal cross-domain GAN-based framework to generate high-quality images to address the dependence on paired training data. To ensure the vivid colourfulness, the colour loss is designed to constrain the training process. Also, a feature fusion module (FFM) is proposed to increase the capacity of the whole model as well as the dual discriminator channel adopted in the architecture. Extensive quantitative and perceptual experiments show that our approach overcomes the limitation of paired data and obtains superior performance over the state-of-the-art on several underwater benchmarks in terms of both accuracy and model deployment. • Proposed an unsupervised learning scheme for underwater image enhancement without requiring paired training data. • Proposed to apply cross-domain transfer to obtain similar distribution close to the images captured in air. • Designed a novel color loss to constrain the model training process, which can effectively recover colorful underwater images and remove the color cast caused by the scatting. 

Cross-domain learning for underwater image enhancement

Cross-domain sentiment classification transfers the knowledge from the source domain to the target domain lacking supervised information for sentiment classification. Existing cross-domain sentiment classification methods establish connections by extracting domain-invariant features manually. However, these methods have poor adaptability to bridge connections across different domains and ignore important sentiment information. Hence, we propose a Topic Lite Bidirectional Encoder Representations from Transformers (T-LBERT) model with domain adaption to improve the adaptability of cross-domain sentiment classification. It combines the learning content of the source domain and the topic information of the target domain to improve the domain adaptability of the model. Due to the unbalanced distribution of information in the combined data, we apply a two-layer attention adaptive mechanism for classification. A shallow attention layer is applied to weigh the important features of the combined data. Inspired by active learning, we propose a deep domain adaption layer, which actively adjusts model parameters to balance the difference and representativeness between domains. Experimental results on Amazon review datasets demonstrate that the T-LBERT model considerably outperforms other state-of-the-art methods. T-LBERT shows stable classification performance on multiple metrics.

/pdf/t-lbert-with-domain-adaptation-for-cross-domain-sentiment-2k876t8j.pdf

T-LBERT with Domain Adaptation for Cross-Domain Sentiment Classification

By transferring knowledge from the source domain labeled data to the target domain, cross-domain sentiment analysis can predict sentiment polarity in the lacking of labeled data in the target domain. However, most existing cross-domain sentiment analysis methods establish the relationship of domains by extracting domain-invariant (pivot) features. These methods ignore domain-specific (non-pivot) features or introduce a lot of noisy features to make domain adaptability weak. Hence, we propose a cross-domain reinforcement learning framework for sentiment analysis. We extract pivot and non-pivot features separately to fully mine sentiment information. To avoid the Hughes phenomena caused by the feature redundancy, the proposed framework applies a multi-level policy to select appropriate features extracted from the data. And a sentiment predictor is applied to calculate delayed reward for policy improvement and predict the sentiment polarity. The decision-making capacity of reinforcement learning can effectively tackle the problem of noisy feature data and improve domain adaptability. Extensive experiments on the Amazon review datasets demonstrate that the proposed model for cross-domain sentiment analysis outperforms state-of-the-art methods. 

Cross-Domain Reinforcement Learning for Sentiment Analysis

Traditional deep learning methods such as convolutional neural networks (CNN) have a high requirement for the number of labeled samples. In some cases, the cost of obtaining labeled samples is too high to obtain enough samples. To solve this problem, few-shot learning (FSL) is used. Currently, typical FSL methods work well on coarse-grained image data, but not as well on fine-grained image classification work, as they cannot properly assess the in-class similarity and inter-class difference of fine-grained images. In this work, an FSL framework based on graph neural network (GNN) is proposed for fine-grained image classification. Particularly, we use the information transmission of GNN to represent subtle differences between different images. Moreover, feature extraction is optimized by the method of meta-learning to improve the classification. The experiments on three datasets (CIFAR-100, CUB, and DOGS) have shown that the proposed method yields better performances. This indicates that the proposed method is a feasible solution for fine-grained image classification with FSL.

/pdf/few-shot-fine-grained-image-classification-via-gnn-3fhxsq9n.pdf

Few-Shot Fine-Grained Image Classification via GNN

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