[Preparation of Iron-improved Blue Algae Biochar and Its Co-adsorption Mechanism for Phosphorus in Surface Water].

To alleviate the problems of eutrophication and blue algae accumulation in water, biochar was prepared from blue algae dehydrated using polymerized ferrous sulfate(PFS) to absorb phosphate in water, and the biochar was activated using steam to adjust the pore structure. The preparation conditions of blue algae biochar were optimized using the response surface method. The optimal results were as follows:the dosage of PFS was 458 mg·L-1, the carbonization temperature was 433℃, and the mass ratio of biochar precursor to steam was 1:11. Biochar without PFS(F0H11-433) and biochar with PFS(F458H11-433) were characterized using X-ray diffraction(XRD), Fourier-transform infrared spectroscopy(FTIR), zeta potential, and Raman spectra(Raman) were used to study whether blue algae biochar and PFS had a synergic effect on phosphate removal. The results showed that:compared with F0H11-433, iron oxide appeared on the surface, the zero point of charge(pHpzc) increased from 4.41 to 6.19, and the disorder and defect degree of biochar was increased in F458H11-433. The pseudo-second-order model and Langmuir model were suitable for describing the adsorption process of F458H11-433, and the saturated adsorption capacity was 31.97 mg·g-1. F458H11-433 had excellent phosphorus removal efficiency in actual lake water, and the residual phosphate content of effluent was less than 0.025 mg·L-1. In the presence of several common anions, it still showed excellent selective adsorption. After five cycles, the phosphate removal of F458H11-433 still reached 75.78%, indicating that F458H11-433 had the characteristic of being renewable. Combined with the material characterization results before and after adsorption, the phosphorus removal mechanism of F458H11-433 mainly involved electrostatic attraction and ligand exchange.

Effects of Fu brick tea polysaccharides on gut microbiota and fecal metabolites of HFD/STZ-induced type 2 diabetes rats.

The prevalence of type 2 diabetes mellitus (T2DM) has dramatically increased globally, and the antidiabetic effects and underlying mechanisms of the polysaccharides extracted from Fu brick tea (FBTP) were investigated in high-fat diet (HFD)/streptozotocin (STZ)-induced T2DM rats. Administration of FBTP at 200 and 400 mg per kg bw significantly relieved dyslipidemia (i.e. TC, TG, LDL-C and HDL-C), insulin resistance (IR) and pancreas oxidative stress (i.e. CAT and GSH-Px) in T2DM rats. Mechanistically, FBTP rescued the HFD/STZ-induced alterations in the abundance of Bacteroidota, Actinobacteriota, Proteobacteria and Firmicutes. At the genus level, FBTP notably increased the abundance of Ruminococcus, Lactobacillus and Lachnospiraece_NK4A136_group, but reduced the population of Prevotella and Faecalibaculum in T2DM rats. FBTP also significantly elevated colonic short-chain fatty acid (SCFAs) levels. Moreover, apparent changes in amino acid absorption and metabolism were observed upon FBTP intervention. These findings suggested that FBTP might alleviate T2DM by reshaping the gut microbiota and regulating intestinal metabolites.

Multi-modal feature selection with self-expression topological manifold for end-stage renal disease associated with mild cognitive impairment.

Effectively selecting discriminative brain regions in multi-modal neuroimages is one of the effective means to reveal the neuropathological mechanism of end-stage renal disease associated with mild cognitive impairment (ESRDaMCI). Existing multi-modal feature selection methods usually depend on the Euclidean distance to measure the similarity between data, which tends to ignore the implied data manifold. A self-expression topological manifold based multi-modal feature selection method (SETMFS) is proposed to address this issue employing self-expression topological manifold. First, a dynamic brain functional network is established using functional magnetic resonance imaging (fMRI), after which the betweenness centrality is extracted. The feature matrix of fMRI is constructed based on this centrality measure. Second, the feature matrix of arterial spin labeling (ASL) is constructed by extracting the cerebral blood flow (CBF). Then, the topological relationship matrices are constructed by calculating the topological relationship between each data point in the two feature matrices to measure the intrinsic similarity between the features, respectively. Subsequently, the graph regularization is utilized to embed the self-expression model into topological manifold learning to identify the linear self-expression of the features. Finally, the selected well-represented feature vectors are fed into a multicore support vector machine (MKSVM) for classification. The experimental results show that the classification performance of SETMFS is significantly superior to several state-of-the-art feature selection methods, especially its classification accuracy reaches 86.10%, which is at least 4.34% higher than other comparable methods. This method fully considers the topological correlation between the multi-modal features and provides a reference for ESRDaMCI auxiliary diagnosis.

Characterizing Topological Properties of Brain Functional Networks Using Multi-Threshold Derivative for End-Stage Renal Disease with Mild Cognitive Impairment.

Patients with end-stage renal disease (ESRD) experience changes in both the structure and function of their brain networks. In the past, cognitive impairment was often classified based on connectivity features, which only reflected the characteristics of the binary brain network or weighted brain network. It exhibited limited interpretability and stability. This study aims to quantitatively characterize the topological properties of brain functional networks (BFNs) using multi-threshold derivative (MTD), and to establish a new classification framework for end-stage renal disease with mild cognitive impairment (ESRDaMCI). The dynamic BFNs (DBFNs) were constructed and binarized with multiple thresholds, and then their topological properties were extracted from each binary brain network. These properties were then quantified by calculating their derivative curves and expressing them as multi-threshold derivative (MTD) features. The classification results of MTD features were compared with several commonly used DBFN features, and the effectiveness of MTD features in the classification of ESRDaMCI was evaluated based on the classification performance test. The results indicated that the linear fusion of MTD features improved classification performance and outperformed individual MTD features. Its accuracy, sensitivity, and specificity were 85.98 ± 2.92%, 86.10 ± 4.11%, and 81.54 ± 4.27%, respectively. Finally, the feature weights of MTD were analyzed, and MTD-cc had the highest weight percentage of 28.32% in the fused features. The MTD features effectively supplemented traditional feature quantification by addressing the issue of indistinct classification differentiation. It improved the quantification of topological properties and provided more detailed features for diagnosing cognitive disorders.

Multimodal Classification Framework Based on Hypergraph Latent Relation for End-Stage Renal Disease Associated with Mild Cognitive Impairment.

Combined arterial spin labeling (ASL) and functional magnetic resonance imaging (fMRI) can reveal more comprehensive properties of the spatiotemporal and quantitative properties of brain networks. Imaging markers of end-stage renal disease associated with mild cognitive impairment (ESRDaMCI) will be sought from these properties. The current multimodal classification methods often neglect to collect high-order relationships of brain regions and remove noise from the feature matrix. A multimodal classification framework is proposed to address this issue using hypergraph latent relation (HLR). A brain functional network with hypergraph structural information is constructed by fMRI data. The feature matrix is obtained through graph theory (GT). The cerebral blood flow (CBF) from ASL is selected as the second modal feature matrix. Then, the adaptive similarity matrix is constructed by learning the latent relation between feature matrices. Latent relation adaptive similarity learning (LRAS) is introduced to multi-task feature learning to construct a multimodal feature selection method based on latent relation (LRMFS). The experimental results show that the best classification accuracy (ACC) reaches 88.67%, at least 2.84% better than the state-of-the-art methods. The proposed framework preserves more valuable information between brain regions and reduces noise among feature matrixes. It provides an essential reference value for ESRDaMCI recognition.

Caloric Restriction, Friend or Foe: Effects on Metabolic Status in Association with the Intestinal Microbiome and Metabolome.

Daily calorie restriction (CR) has shown benefits on weight loss and alleviation of metabolic disorders. We investigated the effects of three CR regimens, i.e., 20% (CR-20), 40% (CR-40), and 60% (CR-60) less than the average daily calorie intake, respectively, on the metabolic parameters, gut microbiome composition, and its related metabolites in healthy mice. Compared with mice fed ad libitum (AL), CR dose-dependently reduced the body weight, and weights of liver and epididymal adipose tissues, and enhanced the insulin sensitivity, glucose tolerance, and lipid homeostasis. Moreover, expression levels of intestinal tight junction proteins (i.e., ZO-1, claudin, and occludin) were significantly promoted by CR than those of AL mice, demonstrating the CR-induced improvement of the intestinal barrier integrity. CR contributed to the enrichment of beneficial microbiota (e.g., Lactobacillus, Bacteroides, and Akkermansia) and increased propionic acid levels. Notably, CR-60 deleteriously caused liver injury, and enhanced hepatic inflammatory cytokines (i.e., IL-1, IL-6, and TNF-α) and lipopolysaccharides, which were accompanied by high levels of trimethylamine (TMA) and trimethylamine oxide (TMAO) in relation to CR-60-altered gut microbiota structure and fecal metabolome. Additionally, we found differential impacts of CR-20, -40, or -60 on amino acid absorption and metabolism. Our findings support the health-promoting benefits of 60-80% daily calorie intake on the metabolic status by regulating the gut microbiota in healthy mice. However, excessive CR caused liver injury and gut microbiota-dependent elevation of TMAO. The differential effects of CR regimens on the intestinal microbiome and fecal metabolome provide novel insights into the dietary pattern-gut microbiome interactions linked with host metabolism.