A Cross-Client Coordinator in Federated Learning Framework for Conquering Heterogeneity.

Federated learning, as a privacy-preserving learning paradigm, restricts the access to data of each local client, for protecting the privacy of the parties. However, in the case of heterogeneous data settings, the different data distributions among clients usually lead to the divergence of learning targets, which is an essential challenge for federated learning. In this article, we propose a federated learning framework with a unified coding space, called FedUCS, for learning cross-client uniform coding rules to solve the problem of divergent targets among multiple clients due to heterogeneous data. A cross-client coordinator co-trained by multiple clients is used as a criterion of the coding space to supervise all clients coding to a uniform space, which is the significant contribution of this article. Furthermore, in order to appropriately retain historical information and avoid forgetting previous knowledge, a partial memory mechanism is applied. Moreover, in order to further enhance the distinguishability of the unified encoding space, supervised contrastive learning is used to avoid the intersection of the encoding spaces belonging to different categories. A series of experiments are performed to verify the effectiveness of the proposed method in a federated learning setting with heterogeneous data.

Data-free knowledge distillation via generator-free data generation for Non-IID federated learning.

Data heterogeneity (Non-IID) on Federated Learning (FL) is currently a widely publicized problem, which leads to local model drift and performance degradation. Because of the advantage of knowledge distillation, it has been explored in some recent work to refine global models. However, these approaches rely on a proxy dataset or a data generator. First, in many FL scenarios, proxy dataset do not necessarily exist on the server. Second, the quality of data generated by the generator is unstable and the generator depends on the computing resources of the server. In this work, we propose a novel data-Free knowledge distillation approach via generator-Free Data Generation for Non-IID FL, dubbed as FedF2DG. Specifically, FedF2DG requires only local models to generate pseudo datasets for each client, and can generate hard samples by adding an additional regularization term that exploit disagreements between local model and global model. Meanwhile, FedF2DG enables flexible utilization of computational resources by generating pseudo dataset locally or on the server. And to address the label distribution shift in Non-IID FL, we propose a Data Generation Principle that can adaptively control the label distribution and number of pseudo dataset based on client current state, and this allows for the extraction of more client knowledge. Then knowledge distillation is performed to transfer the knowledge in local models to the global model. Extensive experiments demonstrate that our proposed method significantly outperforms the state-of-the-art FL methods and can serve as plugin for existing Federated Learning methds such as FedAvg, FedProx, etc, and improve their performance.

Migrate demographic group for fair Graph Neural Networks.

Graph Neural networks (GNNs) have been applied in many scenarios due to the superior performance of graph learning. However, fairness is always ignored when designing GNNs. As a consequence, biased information in training data can easily affect vanilla GNNs, causing biased results toward particular demographic groups (divided by sensitive attributes, such as race and age). There have been efforts to address the fairness issue. However, existing fair techniques generally divide the demographic groups by raw sensitive attributes and assume that are fixed. The biased information correlated with raw sensitive attributes will run through the training process regardless of the implemented fair techniques. It is urgent to resolve this problem for training fair GNNs. To tackle this problem, we propose a brand new framework, FairMigration, which is able to migrate the demographic groups dynamically, instead of keeping that fixed with raw sensitive attributes. FairMigration is composed of two training stages. In the first stage, the GNNs are initially optimized by personalized self-supervised learning, and the demographic groups are adjusted dynamically. In the second stage, the new demographic groups are frozen and supervised learning is carried out under the constraints of new demographic groups and adversarial training. Extensive experiments reveal that FairMigration achieves a high trade-off between model performance and fairness.

Enhanced Scalable Graph Neural Network via Knowledge Distillation.

Graph neural networks (GNNs) have achieved state-of-the-art performance in various graph representation learning scenarios. However, when applied to graph data in real world, GNNs have encountered scalability issues. Existing GNNs often have high computational load in both training and inference stages, making them incapable of meeting the performance needs of large-scale scenarios with a large number of nodes. Although several studies on scalable GNNs have developed, they either merely improve GNNs with limited scalability or come at the expense of reduced effectiveness. Inspired by knowledge distillation's (KDs) achievement in preserving performances while balancing scalability in computer vision and natural language processing, we propose an enhanced scalable GNN via KD (KD-SGNN) to improve the scalability and effectiveness of GNNs. On the one hand, KD-SGNN adopts the idea of decoupled GNNs, which decouples feature transformation and feature propagation in GNNs and leverages preprocessing techniques to improve the scalability of GNNs. On the other hand, KD-SGNN proposes two KD mechanisms (i.e., soft-target (ST) distillation and shallow imitation (SI) distillation) to improve the expressiveness. The scalability and effectiveness of KD-SGNN are evaluated on multiple real datasets. Besides, the effectiveness of the proposed KD mechanisms is also verified through comprehensive analyses.

Phylogenetic and expression analysis of the angiopoietin-like gene family and their role in lipid metabolism in pigs.

OBJECTIVE: The objective of this study was to investigate the phylogenetic and expression analysis of the angiopoietin-like (ANGPTL) gene family and their role in lipid metabolism in pigs. METHODS: In this study, the amino acid sequence analysis, phylogenetic analysis, and chromosome adjacent gene analysis were performed to identify the ANGPTL gene family in pigs. According to the body weight data from 60 Jinhua pigs, different tissues of 6 pigs with average body weight were used to determine the expression profile of ANGPTL1-8. The ileum, subcutaneous fat, and liver of 8 pigs with distinct fatness were selected to analyze the gene expression of ANGPTL3, ANGPTL4, and ANGPTL8. RESULTS: The sequence length of ANGPTLs in pigs was between 1,186 and 1,991 bp, and the pig ANGPTL family members shared common features with human homologous genes, including the high similarity of the amino acid sequence and chromosome flanking genes. Amino acid sequence analysis showed that ANGPTL1-7 had a highly conserved domain except for ANGPTL8. Phylogenetic analysis showed that each ANGPTL homologous gene shared a common origin. Quantitative reverse-transcription polymerase chain reaction analysis showed that ANGPTL family members had different expression patterns in different tissues. ANGPTL3 and ANGPTL8 were mainly expressed in the liver, while ANGPTL4 was expressed in many other tissues, such as the intestine and subcutaneous fat. The expression levels of ANGPTL3 in the liver and ANGPTL4 in the liver, intestine and subcutaneous fat of Jinhua pigs with low propensity for adipogenesis were significantly higher than those of high propensity for adipogenesis. CONCLUSION: These results increase our knowledge about the biological role of the ANGPTL family in this important economic species, it will also help to better understand the role of ANGPTL3, ANGPTL4, and ANGPTL8 in lipid metabolism of pigs, and provide innovative ideas for developing strategies to improve meat quality of pigs.

Supplementary Feeding of Grazing Inner Mongolian Cashmere Goats during Pregnancy-Based on "Nutrient Requirements of Cashmere Goats".

This study aimed to conduct precise supplementation for pregnant cashmere goats under grazing based on the feeding standard. Eight Inner Mongolian pregnant cashmere goats of near-average body weight were selected at early gestation (44.41 ± 4.03 kg) and late gestation (46.54 ± 4.02 kg) to measure their nutrient intake. Then, two pregnant cashmere goat flocks, No. 10 (control group, on-farm supplement) and No. 11 (supplemented group, supplement based on standard), with the same goat herd structure and grassland type, were chosen to conduct the supplemental feeding experiment. The results showed that pregnant cashmere goats lacked daily the intake of dry matter, digestive energy, crude protein and most essential mineral elements under grazing. After supplemental feeding, the supplementation based on the feeding standard increased the cashmere length and cashmere length growth volume and decreased the cashmere fineness, with no statistical significance. The goat cashmere yield, goat weight after shearing, single and twin-birth kid weight and kids' mature secondary hair follicle density were significantly higher in the supplemented group (p < 0.05). In conclusion, supplementation in accordance with "Nutrient Requirements of Cashmere Goats" can enhance pregnant cashmere goats' fiber production, growth performance, fertility and kids' secondary hair follicles development, which is of great importance for the healthy and precise nutrition and management of cashmere goats.

The pure spin current and fully spin-polarized current induced by the photogalvanic effect and spin-Seebeck effect in halogen-decorated phosphorene.

The control of spin transport is a fundamental but crucial task in spintronics and realization of high spin polarization transport and pure spin currents is particularly desired. By combining the non-equilibrium Green's function with first principles calculations, it is shown that halogen adsorption can transform a black phosphorene monolayer from a nonmagnetic semiconductor to a magnetic semiconductor with two almost symmetric spin-split states near the Fermi level, which provides two isolated transport channels. Further investigations demonstrate that a device based on halogen-decorated phosphorene can behave multifunctionally, where a pure spin photocurrent and a fully spin-polarized photocurrent can be effectively controlled by tuning the photon energy or polarization angle of the incident light. In addition, pure spin current can also be induced by a temperature gradient, resulting in a perfect spin Seebeck effect. This work demonstrates that the halogen-decorated phosphorene systems have potential applications of high integration density and low energy dissipation in two-dimensional spintronic devices.

Accelerated Partially Shared Dictionary Learning With Differentiable Scale-Invariant Sparsity for Multi-View Clustering.

Multiview dictionary learning (DL) is attracting attention in multiview clustering due to the efficient feature learning ability. However, most existing multiview DL algorithms are facing problems in fully utilizing consistent and complementary information simultaneously in the multiview data and learning the most precise representation for multiview clustering because of gaps between views. This article proposes an efficient multiview DL algorithm for multiview clustering, which uses the partially shared DL model with a flexible ratio of shared sparse coefficients to excavate both consistency and complementarity in the multiview data. In particular, a differentiable scale-invariant function is used as the sparsity regularizer, which considers the absolute sparsity of coefficients as the l0 norm regularizer but is continuous and differentiable almost everywhere. The corresponding optimization problem is solved by the proximal splitting method with extrapolation technology; moreover, the proximal operator of the differentiable scale-invariant regularizer can be derived. The synthetic experiment results demonstrate that the proposed algorithm can recover the synthetic dictionary well with reasonable convergence time costs. Multiview clustering experiments include six real-world multiview datasets, and the performances show that the proposed algorithm is not sensitive to the regularizer parameter as the other algorithms. Furthermore, an appropriate coefficient sharing ratio can help to exploit consistent information while keeping complementary information from multiview data and thus enhance performances in multiview clustering. In addition, the convergence performances show that the proposed algorithm can obtain the best performances in multiview clustering among compared algorithms and can converge faster than compared multiview algorithms mostly.

Double Sparse Deep Reinforcement Learning via Multilayer Sparse Coding and Nonconvex Regularized Pruning.

Deep reinforcement learning (DRL), which highly depends on the data representation, has shown its potential in many practical decision-making problems. However, the process of acquiring representations in DRL is easily affected by interference from models, and moreover leaves unnecessary parameters, leading to control performance reduction. In this article, we propose a double sparse DRL via multilayer sparse coding and nonconvex regularized pruning. To alleviate interference in DRL, we propose a multilayer sparse-coding-structural network to obtain deep sparse representation for control in reinforcement learning. Furthermore, we employ a nonconvex log regularizer to promote strong sparsity, efficiently removing the unnecessary weights with a regularizer-based pruning scheme. Hence, a double sparse DRL algorithm is developed, which can not only learn deep sparse representation to reduce the interference but also remove redundant weights while keeping the robust performance. The experimental results in five benchmark environments of the deep q network (DQN) architecture demonstrate that the proposed method with deep sparse representations from the multilayer sparse-coding structure can outperform existing sparse-coding-based DRL in control, for example, completing Mountain Car with 140.81 steps, achieving near 10% reward increase from the single-layer sparse-coding DRL algorithm, and obtaining 286.08 scores in Catcher, which are over two times the rewards of the other algorithms. Moreover, the proposed algorithm can reduce over 80% parameters while keeping performance improvements from deep sparse representations.

SHNE: Semantics and Homophily Preserving Network Embedding.

Graph convolutional networks (GCNs) have achieved great success in many applications and have caught significant attention in both academic and industrial domains. However, repeatedly employing graph convolutional layers would render the node embeddings indistinguishable. For the sake of avoiding oversmoothing, most GCN-based models are restricted in a shallow architecture. Therefore, the expressive power of these models is insufficient since they ignore information beyond local neighborhoods. Furthermore, existing methods either do not consider the semantics from high-order local structures or neglect the node homophily (i.e., node similarity), which severely limits the performance of the model. In this article, we take above problems into consideration and propose a novel Semantics and Homophily preserving Network Embedding (SHNE) model. In particular, SHNE leverages higher order connectivity patterns to capture structural semantics. To exploit node homophily, SHNE utilizes both structural and feature similarity to discover potential correlated neighbors for each node from the whole graph; thus, distant but informative nodes can also contribute to the model. Moreover, with the proposed dual-attention mechanisms, SHNE learns comprehensive embeddings with additional information from various semantic spaces. Furthermore, we also design a semantic regularizer to improve the quality of the combined representation. Extensive experiments demonstrate that SHNE outperforms state-of-the-art methods on benchmark datasets.

A Spatiotemporal Deep Learning Approach for Unsupervised Anomaly Detection in Cloud Systems.

Anomaly detection is a critical task for maintaining the performance of a cloud system. Using data-driven methods to address this issue is the mainstream in recent years. However, due to the lack of labeled data for training in practice, it is necessary to enable an anomaly detection model trained on contaminated data in an unsupervised way. Besides, with the increasing complexity of cloud systems, effectively organizing data collected from a wide range of components of a system and modeling spatiotemporal dependence among them become a challenge. In this article, we propose TopoMAD, a stochastic seq2seq model which can robustly model spatial and temporal dependence among contaminated data. We include system topological information to organize metrics from different components and apply sliding windows over metrics collected continuously to capture the temporal dependence. We extract spatial features with the help of graph neural networks and temporal features with long short-term memory networks. Moreover, we develop our model based on variational auto-encoder, enabling it to work well robustly even when trained on contaminated data. Our approach is validated on the run-time performance data collected from two representative cloud systems, namely, a big data batch processing system and a microservice-based transaction processing system. The experimental results show that TopoMAD outperforms some state-of-the-art methods on these two data sets.

Robust Corrupted Data Recovery and Clustering via Generalized Transformed Tensor Low-Rank Representation.

Tensor analysis has received widespread attention in high-dimensional data learning. Unfortunately, the tensor data are often accompanied by arbitrary signal corruptions, including missing entries and sparse noise. How to recover the characteristics of the corrupted tensor data and make it compatible with the downstream clustering task remains a challenging problem. In this article, we study a generalized transformed tensor low-rank representation (TTLRR) model for simultaneously recovering and clustering the corrupted tensor data. The core idea is to find the latent low-rank tensor structure from the corrupted measurements using the transformed tensor singular value decomposition (SVD). Theoretically, we prove that TTLRR can recover the clean tensor data with a high probability guarantee under mild conditions. Furthermore, by using the transform adaptively learning from the data itself, the proposed TTLRR model can approximately represent and exploit the intrinsic subspace and seek out the cluster structure of the tensor data precisely. An effective algorithm is designed to solve the proposed model under the alternating direction method of multipliers (ADMMs) algorithm framework. The effectiveness and superiority of the proposed method against the compared methods are showcased over different tasks, including video/face data recovery and face/object/scene data clustering.

Effect of ß-1,3/1,6-glucan on gut microbiota of yellow-feathered broilers.

ß-1,3/1,6-glucan as a prebiotic improves immune performance in animals. These functions are closely related to the effect of ß-1,3/1,6-glucan on gut microbiota structure. However, the effect of ß-1,3/1,6-glucan on the gut microbiota structure of broilers is unclear. The aim of this study was to confirm the effects of ß-1,3/1,6-glucan on the cecal microflora structure of yellow-feathered broilers. This study monitored the antimicrobial resistance (AMR) level of Escherichia coli in feces of yellow-feathered broilers by standard broth dilution method and mastered the AMR level of chickens selected. The effects of ß-1,3/1,6-glucan on gut microbiota were investigated by 16S rRNA sequencing. The results showed that the number of isolated multidrug-resistant E. coli strains accounted for 98.41%. At 14, 21, and 28 days of age, supplemented of 0.2%, 0.1%, and 0.1% ß-1,3/1,6-glucan in yellow-feathered broiler diets significantly altered gut microbial composition, and beneficial bacteria Alistipes, Bacteroides and Faecalibacterium were significantly increased. These findings provide guidance and recommendations for ß-1,3/1,6-glucan as a broiler feed additive to improve the growth of broilers.

Liver Transcriptome and Gut Microbiome Analysis Reveals the Effects of High Fructose Corn Syrup in Mice.

High fructose corn syrup (HFCS) is a viscous mixture of glucose and fructose that is used primarily as a food additive. This article explored the effect of HFCS on lipid metabolism-expressed genes and the mouse gut microbiome. In total, ten 3-week-old male C57BL/6J mice were randomly divided into two groups, including the control group, given purified water (Group C) and 30% HFCS in water (Group H) for 16 weeks. Liver and colonic content were collected for transcriptome sequencing and 16S rRNA gene sequencing, respectively. HFCS significantly increased body weight, epididymal, perirenal fat weight in mice (p < 0.05), and the proportion of lipid droplets in liver tissue. The expression of the ELOVL fatty acid elongase 3 (Elovl3) gene was reduced, while Stearoyl-Coenzyme A desaturase 1 (Scd1), peroxisome proliferator activated receptor gamma (Pparg), fatty acid desaturase 2 (Fads2), acyl-CoA thioesterase 2 (Acot2), acyl-CoA thioesterase 2 (Acot3), acyl-CoA thioesterase 4 (Acot4), and fatty acid binding protein 2 (Fabp2) was increased in Group H. Compared with Group C, the abundance of Firmicutes was decreased in Group H, while the abundance of Bacteroidetes was increased, and the ratio of Firmicutes/Bacteroidetes was obviously decreased. At the genus level, the relative abundance of Bifidobacterium, Lactobacillus, Faecalibaculum, Erysipelatoclostridium, and Parasutterella was increased in Group H, whereas that of Staphylococcus, Peptococcus, Parabacteroides, Donghicola, and Turicibacter was reduced in Group H. Pparg, Acot2, Acot3, and Scd1 were positively correlated with Erysipelatoclostridium and negatively correlated with Parabacteroides, Staphylococcus, and Turicibacter. Bifidobacterium was negatively correlated with Elovl3. Overall, HFCS affects body lipid metabolism by affecting the expression of lipid metabolism genes in the liver through the gut microbiome.

Effect of Dietary Supplementation with Mannose Oligosaccharides on the Body Condition, Lactation Performance and Their Offspring of Heat-Stressed Sows.

The aim of this study was to determine the effects of dietary supplementation with mannose oligosaccharide (MOS) on the condition of the body and the reproductive and lactation performances of sows. Eighty pregnant sows were randomly assigned to four groups with a 2 × 2 factorial design: with or without MOS (1 g/kg) and with or without heat stress (HS) challenge. The temperature in the HS groups (HS and HM group) was controlled at 31.56 ± 1.22 °C, while the temperature in the active cooling (AC) groups (AC and AM group) was controlled at 23.49 ± 0.72 °C. The weight loss of sows in the AC group was significantly lower than that of sows in the HS group (p < 0.01). The weight and backfat thickness loss of sows supplemented with MOS displayed a downward trend. The average birth weight of the litter significantly increased in the HM group (basic diet + MOS) compared with the HS group (p < 0.05). The milk protein of sows significantly decreased under the HS condition at 2 and 12 h after delivery (p < 0.05). However, the milk immunoglobin G (IgG) of sows in the HS group increased significantly compared with that of sows in the HM group (p < 0.05) at 12 and 24 h after delivery. The levels of serum urea nitrogen (UREA) and glucose (GLU) decreased significantly under the HS condition (p < 0.05), while the level of interleukin-6 (IL-6) increased significantly under the HS condition (p < 0.05). Dietary supplementation with MOS also significantly reduced TNF-α under the AC conditions (p < 0.05). In conclusion, HS significantly affected the body condition, lactation performances and their offspring of sows. However, dietary supplementation with 1 g/kg MOS did not result in statistically significant changes.

Low Dose of Sucralose Alter Gut Microbiome in Mice.

Sucralose is a non-nutritive artificial sweetener (NNS) used in foods or beverages to control blood glucose levels and body weight gain. The consumption of NNS has increased in recent years over the world, and many researches have indicated long-term sucralose administration altered the gut microbiome composition of mice. These studies all focus on the US Food and Drug Administration (FDA) defined acceptable daily intake (ADI), approximately 5 mg/kg BW/day for human. In our study, mice were given with T1-4 (0.0003, 0.003, 0.03, and 0.3 mg/mL) of sucralose, respectively, Control group mice were given normal water. In particular, 0.3 mg/mL of sucralose was equal to the ADI (5 mg/kg BW/day). After 16 weeks, all mice were weighted and sacrificed, the liver of each mouse was isolated and weighed, segments of jejunum, ileum and colon were collected for H&E-stained. The contents of jejunum, ileum, cecum and colon were collected for 16S rRNA gene sequencing. The results showed sucralose administration affects the intestinal barrier function evidenced by distinct lymphocyte aggregation in ileum and colon while not change the mice body weight. The 16S rRNA gene sequencing of the mice gut microbiome suggested sucralose administration significantly changed the composition of gut microbiota, especially in T1 and T4 group. For example, a reduction of probiotics abundance (Lachnoclostridium and Lachnospiraceae) was found in cecum of T4 group mice compared with Control group. On the other hand, Allobaculum, which was reported positively correlated with diabetes, was increased in the T1 and T4 group. In addition, the potential pathogens, including Tenacibaculum, Ruegeria, Staphylococcus were also increased in jejunum, ileum and colon by sucralose administration in T1 and T4 group. These new findings indicate that low dose of sucralose (T1) alter gut microbiome in mice, and these adverse health effects are equal to ADI level (T4). Overall, our study provides guidance and suggestions for the use of sucralose in foods and beverages.

Comparative Study of the Nutritional Value and Degradation Characteristics of Amaranth Hay in the Rumen of Goats at Different Growth Stages.

The objective of this study was to investigate the rumen degradation characteristics of grain amaranth hay (Amaranthus hypochondriacus) at four different growth stages. The aim of this study was to evaluate the nutritional value of grain amaranth hay at different growth stages by chemical composition, in vivo digestibility, and in situ degradability. Three Boer goats with permanent ruminal fistulas were selected in this study. Amaranthus hay at four different growth stages (squaring stage (SS), initial bloom stage (IS), full-bloom stage (FS) and mature stage (MS)) was crushed and placed into nylon bags. Each sample was set up with three replicates, and two parallel samples were set up in fistulas at each time point. The rumen degradation rates of dry matter (DM), crude protein (CP), neutral detergent fibre (NDF) and acid detergent fibre (ADF) were determined at 0, 6, 12, 24, 36, 48 and 72 h. The results were as follows: (1) The concentration of CP in SS was the highest and was significantly higher than that in other stages (p < 0.05), whereas the contents of NDF and ADF gradually increased with the extension of the growing period and reached a maximum in MS; (2)The degradation of CP in the rumen at 72 h of SS and IS was more than 80%. Compared with other stages, the effective degradability of CP was highest in SS (p < 0.05) and reached 87.05% at 72 h, and the degradation rate was the lowest in MS; and (3) The effective degradability of NDF in IS was the highest (p < 0.05) and reached 69.326% at 72 h. The effective degradability of ADF in MS was the highest (p < 0.05) and reached 65.728% at 72 h. The effective degradability of DM and CP in SS was the highest. In conclusion, among the four stages, IS was superior in chemical composition and rumen degradability characteristics.

Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey.

Federated learning (FL) is a promising decentralized deep learning technology, which allows users to update models cooperatively without sharing their data. FL is reshaping existing industry paradigms for mathematical modeling and analysis, enabling an increasing number of industries to build privacy-preserving, secure distributed machine learning models. However, the inherent characteristics of FL have led to problems such as privacy protection, communication cost, systems heterogeneity, and unreliability model upload in actual operation. Interestingly, the integration with Blockchain technology provides an opportunity to further improve the FL security and performance, besides increasing its scope of applications. Therefore, we denote this integration of Blockchain and FL as the Blockchain-based federated learning (BCFL) framework. This paper introduces an in-depth survey of BCFL and discusses the insights of such a new paradigm. In particular, we first briefly introduce the FL technology and discuss the challenges faced by such technology. Then, we summarize the Blockchain ecosystem. Next, we highlight the structural design and platform of BCFL. Furthermore, we present the attempts ins improving FL performance with Blockchain and several combined applications of incentive mechanisms in FL. Finally, we summarize the industrial application scenarios of BCFL.

Allobaculum Involves in the Modulation of Intestinal ANGPTLT4 Expression in Mice Treated by High-Fat Diet.

Increasing studies have shown that obesity is the primary cause of cardiovascular diseases, non-alcoholic fatty liver diseases, type 2 diabetes, and a variety of cancers. The dysfunction of gut microbiota was proved to result in obesity. Recent research indicated ANGPTL4 was a key regulator in lipid metabolism and a circulating medium for gut microbiota and fat deposition. The present study was conducted to investigate the alteration of gut microbiota and ANGPTL4 expression in the gastrointestinal tract of mice treated by the high-fat diet. Ten C57BL/6J mice were randomly allocated to two groups and fed with a high-fat diet (HFD) containing 60% fat or a normal-fat diet (Control) containing 10% fat. The segments of ileum and colon were collected for the determination of ANGPTL4 expression by RT-qPCR and immunohistochemical analysis while the ileal and colonic contents were collected for 16S rRNA gene sequencing. The results showed HFD significantly increased mice body weight, epididymal fat weight, perirenal fat weight, liver weight, and the lipid content in the liver (P < 0.05). The relative expression of ANGPTL4 and the ANGPTL4-positive cells in the ileum and colon of mice was significantly increased by HFD treatment. Furthermore, 16S rRNA gene sequencing of the ileal and colonic microbiota suggested that HFD treatment changed the composition of the gut microbiota. The ratio of Firmicutes to Bacteroidetes and the abundance of Allobaculum was significantly higher in the HFD group than in the Control group while the abundance of Adlercreutzia, Bifidobacterium, Prevotellaceae UCG-001, and Ruminococcus was significantly decreased. Interestingly, the abundance of Allobaculum was positively correlated with the expression of ANGPTL4. These findings provide a theoretical foundation for the development of strategies to control the obesity and related diseases by the regulation of ANGPTL4 and gut microbiota.

Long-range dependence, multi-fractality and volume-return causality of Ether market.

In spite of the increasing popularity of Ethereum, market analysis of the corresponding cryptocurrencies Ether is relatively unexplored until now. This paper is devoted to filling in the research gap of Ether market analysis, the purpose being to provide useful insights on Ether investment. In particular, we first employ the detrended fluctuation analysis and the asymmetric multifractal detrended fluctuation analysis to investigate the properties of long-range dependence, multifractality, and its asymmetry. After that, we study the causality between returns and volume of Ether to find how the activity of investors influences returns based on a nonparametric causality-in-quantiles test. Besides, by making a comparison with the Bitcoin market, we further uncover some unique properties of the Ether market.