MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs.

BACKGROUND: Accurately identifying drug-target affinity (DTA) plays a pivotal role in drug screening, design, and repurposing in pharmaceutical industry. It not only reduces the time, labor, and economic costs associated with biological experiments but also expedites drug development process. However, achieving the desired level of computational accuracy for DTA identification methods remains a significant challenge. RESULTS: We proposed a novel multi-view-based graph deep model known as MvGraphDTA for DTA prediction. MvGraphDTA employed a graph convolutional network (GCN) to extract the structural features from original graphs of drugs and targets, respectively. It went a step further by constructing line graphs with edges as vertices based on original graphs of drugs and targets. GCN was also used to extract the relationship features within their line graphs. To enhance the complementarity between the extracted features from original graphs and line graphs, MvGraphDTA fused the extracted multi-view features of drugs and targets, respectively. Finally, these fused features were concatenated and passed through a fully connected (FC) network to predict DTA. CONCLUSIONS: During the experiments, we performed data augmentation on all the training sets used. Experimental results showed that MvGraphDTA outperformed the competitive state-of-the-art methods on benchmark datasets for DTA prediction. Additionally, we evaluated the universality and generalization performance of MvGraphDTA on additional datasets. Experimental outcomes revealed that MvGraphDTA exhibited good universality and generalization capability, making it a reliable tool for drug-target interaction prediction.

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning.

BACKGROUND: Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online platforms based on deep learning for drug-target interaction, affinity, and binding sites identification, there is currently no integrated online platforms for all three aspects. RESULTS: Our solution, the novel integrated online platform Drug-Online, has been developed to facilitate drug screening, target identification, and understanding the functions of target in a progressive manner of "interaction-affinity-binding sites". Drug-Online platform consists of three parts: the first part uses the drug-target interaction identification method MGraphDTA, based on graph neural networks (GNN) and convolutional neural networks (CNN), to identify whether there is a drug-target interaction. If an interaction is identified, the second part employs the drug-target affinity identification method MMDTA, also based on GNN and CNN, to calculate the strength of drug-target interaction, i.e., affinity. Finally, the third part identifies drug-target binding sites, i.e., pockets. The method pt-lm-gnn used in this part is also based on GNN. CONCLUSIONS: Drug-Online is a reliable online platform that integrates drug-target interaction, affinity, and binding sites identification. It is freely available via the Internet at http://39.106.7.26:8000/Drug-Online/ .

AMPK, a hub for the microenvironmental regulation of bone homeostasis and diseases.

AMP-activated protein kinase (AMPK), a crucial regulatory kinase, monitors energy levels, conserving ATP and boosting synthesis in low-nutrition, low-energy states. Its sensitivity links microenvironmental changes to cellular responses. As the primary support structure and endocrine organ, the maintenance, and repair of bones are closely associated with the microenvironment. While a series of studies have explored the effects of specific microenvironments on bone, there is lack of angles to comprehensively evaluate the interactions between microenvironment and bone cells, especially for bone marrow mesenchymal stem cells (BMMSCs) which mediate the differentiation of osteogenic lineage. It is noteworthy that accumulating evidence has indicated that AMPK may serve as a hub between BMMSCs and microenvironment factors, thus providing a new perspective for us to understand the biology and pathophysiology of stem cells and bone. In this review, we emphasize AMPK's pivotal role in bone microenvironment modulation via ATP, inflammation, reactive oxygen species (ROS), calcium, and glucose, particularly in BMMSCs. We further explore the use of AMPK-activating drugs in the context of osteoarthritis and osteoporosis. Moreover, building upon the foundation of AMPK, we elucidate a viewpoint that facilitates a comprehensive understanding of the dynamic relationship between the microenvironment and bone homeostasis, offering valuable insights for prospective investigations into stem cell biology and the treatment of bone diseases.

GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs.

Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.

Integrating network pharmacology and experimental validation reveals therapeutic effects of D-mannose on NAFLD through mTOR suppression.

Non-alcoholic fatty liver disease (NAFLD), a chronic liver condition and metabolic disorder, has emerged as a significant health issue worldwide. D-mannose, a natural monosaccharide widely existing in plants and animals, has demonstrated metabolic regulatory properties. However, the effect and mechanism by which D-mannose may counteract NAFLD have not been studied. In this study, network pharmacology followed by molecular docking analysis was utilized to identify potential targets of mannose against NAFLD, and the leptin receptor-deficient, genetically obese db/db mice was employed as an animal model of NAFLD to validate the regulation of D-mannose on core targets. As a result, 67 targets of mannose are predicted associated with NAFLD, which are surprisingly centered on the mechanistic target of rapamycin (mTOR). Further analyses suggest that mTOR signaling is functionally enriched in potential targets of mannose treating NAFLD, and that mannose putatively binds to mTOR as a core mechanism. Expectedly, repeated oral gavage of supraphysiological D-mannose ameliorates liver steatosis of db/db mice, which is based on suppression of hepatic mTOR signaling. Moreover, daily D-mannose administration reduced hepatic expression of lipogenic regulatory genes in counteracting NAFLD. Together, these findings reveal D-mannose as an effective and potential NAFLD therapeutic through mTOR suppression, which holds translational promise.

GNN-DDAS: Drug discovery for identifying anti-schistosome small molecules based on graph neural network.

Schistosomiasis is a tropical disease that poses a significant risk to hundreds of millions of people, yet often goes unnoticed. While praziquantel, a widely used anti-schistosome drug, has a low cost and a high cure rate, it has several drawbacks. These include ineffectiveness against schistosome larvae, reduced efficacy in young children, and emerging drug resistance. Discovering new and active anti-schistosome small molecules is therefore critical, but this process presents the challenge of low accuracy in computer-aided methods. To address this issue, we proposed GNN-DDAS, a novel deep learning framework based on graph neural networks (GNN), designed for drug discovery to identify active anti-schistosome (DDAS) small molecules. Initially, a multi-layer perceptron was used to derive sequence features from various representations of small molecule SMILES. Next, GNN was employed to extract structural features from molecular graphs. Finally, the extracted sequence and structural features were then concatenated and fed into a fully connected network to predict active anti-schistosome small molecules. Experimental results showed that GNN-DDAS exhibited superior performance compared to the benchmark methods on both benchmark and real-world application datasets. Additionally, the use of GNNExplainer model allowed us to analyze the key substructure features of small molecules, providing insight into the effectiveness of GNN-DDAS. Overall, GNN-DDAS provided a promising solution for discovering new and active anti-schistosome small molecules.

Amorphous Vanadium Oxides with Dual ion Storage Mechanism for High-Performance Aqueous Zinc ion Batteries.

Owing to the extremely limited structural deformation caused by the introduction of guest ions that their rigid structure can sustain, crystalline materials typically fail owing to structural collapse when utilized as electrode materials. Amorphous materials, conversely, are more resistant to volume expansion during dynamic ion transport and can introduce a lot of defects as active sites. Here, The amorphous polyaniline-coated/intercalated V2O5·nH2O (PVOH) nanowires are prepared by in situ chemical oxidation combined with self-assembly strategy, which exhibited impressive electrochemical properties because of its short-range ordered crystal structure, oxygen vacancy/defect-rich, improved electronic channels, and ionic channels. Through in situ techniques, the energy storage mechanism of its Zn2+/H+ co-storage is investigated and elucidated. Additionally, this work provides new insights and perspectives for the investigation and application of amorphous cathodes for aqueous zinc ion batteries.

One-Step Pyrolysis Construction of Bimetallic Atom-Cluster Sites for Boosting Bifunctional Catalytic Activity in Zn-Air Batteries.

Due to their unique advantages, single atoms and clusters of transition metals are expected to achieve a breakthrough in catalytic activity, but large-scale production of active materials remains a challenge. In this work, a simple solvent-free one-step annealing method is developed and applied to construct diatomic and cluster active sites in activated carbon by utilizing the strong anchoring ability of phenanthroline to metal ions, which can be scaled for mass productions. Benefiting from the synergy between the different metals, the obtained sub-nano-bimetallic atom-cluster catalysts (FeNiAC -NC) exhibit high oxygen reduction reactions (ORR) activity (E1/2 = 0.936 V vs. RHE) and a small ORR/oxygen evolution reaction (OER) potential gap of only 0.594 V. An in-house pouch Zn-air battery is assembled using an FeNiAC -NC catalyst, which demonstrates a stability of 1000 h, outperforming previous reports. The existence of clusters and their effects on catalytic activity is analyzed by density functional theory calculations to reveal the chemistry of nano-bimetallic atom-cluster catalysts.

In Situ Fast Construction of Ni3S4/FeS Catalysts on 3D Foam Structure Achieving Stable Large-Current-Density Water Oxidation.

By increasing the content of Ni3+, the catalytic activity of nickel-based catalysts for the oxygen evolution reaction (OER), which is still problematic with current synthesis routes, can be increased. Herein, a Ni3+-rich of Ni3S4/FeS on FeNi Foam (Ni3S4/FeS@FNF) via anodic electrodeposition to direct obtain high valence metal ions for OER catalyst is presented. XPS showed that the introduction of Fe not only further increased the Ni3+ concentration in Ni3S4/FeS to 95.02%, but also inhibited the dissolution of NiOOH by up to seven times. Furthermore, the OER kinetics is enhanced by the combination of the inner Ni3S4/FeS heterostructures and the electrochemically induced surface layers of oxides/hydroxides. Ni3S4/FeS@FNF shows the most excellent OER activity with a low Tafel slope of 11.2 mV dec-1 and overpotentials of 196 and 445 mV at current densities of 10 and 1400 mA cm-2, respectively. Furthermore, the Ni3S4/FeS@FNF catalyst can be operated stably at 1500 mA cm-2 for 200 h without significant performance degradation. In conclusion, this work has significantly increased the high activity Ni3+ content in nickel-based OER electrocatalysts through an anodic electrodeposition strategy. The preparation process is time-saving and mature, which is expected to be applied in large-scale industrialization.

Con-AAE: contrastive cycle adversarial autoencoders for single-cell multi-omics alignment and integration.

MOTIVATION: We have entered the multi-omics era and can measure cells from different aspects. Hence, we can get a more comprehensive view by integrating or matching data from different spaces corresponding to the same object. However, it is particularly challenging in the single-cell multi-omics scenario because such data are very sparse with extremely high dimensions. Though some techniques can be used to measure scATAC-seq and scRNA-seq simultaneously, the data are usually highly noisy due to the limitations of the experimental environment. RESULTS: To promote single-cell multi-omics research, we overcome the above challenges, proposing a novel framework, contrastive cycle adversarial autoencoders, which can align and integrate single-cell RNA-seq data and single-cell ATAC-seq data. Con-AAE can efficiently map the above data with high sparsity and noise from different spaces to a coordinated subspace, where alignment and integration tasks can be easier. We demonstrate its advantages on several datasets. AVAILABILITY AND IMPLEMENTATION: Zenodo link: https://zenodo.org/badge/latestdoi/368779433. github: https://github.com/kakarotcq/Con-AAE.

Actin-bundling protein fimbrin serves as a new auxin biosynthesis orchestrator in Arabidopsis root tips.

Plants delicately regulate endogenous auxin levels through the coordination of transport, biosynthesis, and inactivation, which is crucial for growth and development. While it is well-established that the actin cytoskeleton can regulate auxin levels by affecting polar transport, its potential role in auxin biosynthesis has remained largely unexplored. Using LC-MS/MS-based methods combined with fluorescent auxin marker detection, we observed a significant increase in root auxin levels upon deletion of the actin bundling proteins AtFIM4 and AtFIM5. Fluorescent observation, immunoblotting analysis, and biochemical approaches revealed that AtFIM4 and AtFIM5 affect the protein abundance of the key auxin synthesis enzyme YUC8 in roots. AtFIM4 and AtFIM5 regulate the auxin synthesis enzyme YUC8 at the protein level, with its degradation mediated by the 26S proteasome. This regulation modulates auxin synthesis and endogenous auxin levels in roots, consequently impacting root development. Based on these findings, we propose a molecular pathway centered on the 'actin cytoskeleton-26S proteasome-YUC8-auxin' axis that controls auxin levels. Our findings shed light on a new pathway through which plants regulate auxin synthesis. Moreover, this study illuminates a newfound role of the actin cytoskeleton in regulating plant growth and development, particularly through its involvement in maintaining protein homeostasis via the 26S proteasome.

Pseudouridine synthase 1 promotes hepatocellular carcinoma through mRNA pseudouridylation to enhance the translation of oncogenic mRNAs.

BACKGROUND AND AIMS: Pseudouridine is a prevalent RNA modification and is highly present in the serum and urine of patients with HCC. However, the role of pseudouridylation and its modifiers in HCC remains unknown. We investigated the function and underlying mechanism of pseudouridine synthase 1 (PUS1) in HCC. APPROACH AND RESULTS: By analyzing the TCGA data set, PUS1 was found to be significantly upregulated in human HCC specimens and positively correlated with tumor grade and poor prognosis of HCC. Knockdown of PUS1 inhibited cell proliferation and the growth of tumors in a subcutaneous xenograft mouse model. Accordingly, increased cell proliferation and tumor growth were observed in PUS1-overexpressing cells. Furthermore, overexpression of PUS1 significantly accelerates tumor formation in a mouse HCC model established by hydrodynamic tail vein injection, while knockout of PUS1 decreases it. Additionally, PUS1 catalytic activity is required for HCC tumorigenesis. Mechanistically, we profiled the mRNA targets of PUS1 by utilizing surveying targets by apolipoprotein B mRNA-editing enzyme 1 (APOBEC1)-mediated profiling and found that PUS1 incorporated pseudouridine into mRNAs of a set of oncogenes, thereby endowing them with greater translation capacity. CONCLUSIONS: Our study highlights the critical role of PUS1 and pseudouridylation in HCC development, and provides new insight that PUS1 enhances the protein levels of a set of oncogenes, including insulin receptor substrate 1 (IRS1) and c-MYC, by means of pseudouridylation-mediated mRNA translation.

Gas-Solid Reaction Synthesis and Electrocatalytic Oxygen Reduction of Pt-Skin L10-PtFe/C.

Compared to Pt/C, the atomic ordered Pt-based intermetallic compounds can deliver higher efficiency and reliable stability, and they are considered one of the ideal cathode catalysts for the next generation of fuel cells. This work proposed a simple ferrocene atmosphere annealing method to improve commercial Pt/C and convert Pt to L10-PtFe. After further acid etching treatment, the obtained carbon-supported Pt-skin L10-PtFe (Pt-skin L10-PtFe/C) with superfine particle size (∼3.3 nm) not only was highly dispersed on the carbon but possesses a thin Pt skin, like the armor of L10-PtFe. As excepted, the ORR activity of Pt-skin L10-PtFe/C (0.375 A mg-1; 0.921 mA cm-2) is far better than that of commercial Pt/C (0.121 A mg-1; 0.260 mA cm-2), and its stability is also greatly improved. Our proposed gas-solid reaction is straightforward and has great potential in producing Pt-based intermetallic catalysts on a large scale.

Hepatocyte-derived tissue extracellular vesicles safeguard liver regeneration and support regenerative therapy.

Tissue-derived extracellular vesicles (EVs) are emerging as pivotal players to maintain organ homeostasis, which show promise as a next-generation candidate for medical use with extensive source. However, the detailed function and therapeutic potential of tissue EVs remain insufficiently studied. Here, through bulk and single-cell RNA sequencing analyses combined with ultrastructural tissue examinations, we first reveal that in situ liver tissue EVs (LT-EVs) contribute to the intricate liver regenerative process after partial hepatectomy (PHx), and that hepatocytes are the primary source of tissue EVs in the regenerating liver. Nanoscale and proteomic profiling further identify that the hepatocyte-specific tissue EVs (Hep-EVs) are strengthened to release with carrying proliferative messages after PHx. Moreover, targeted inhibition of Hep-EV release via AAV-shRab27a in vivo confirms that Hep-EVs are required to orchestrate liver regeneration. Mechanistically, Hep-EVs from the regenerating liver reciprocally stimulate hepatocyte proliferation by promoting cell cycle progression through Cyclin-dependent kinase 1 (Cdk1) activity. Notably, supplementing with Hep-EVs from the regenerating liver demonstrates translational potential and ameliorates insufficient liver regeneration. This study provides a functional and mechanistic framework showing that the release of regenerative Hep-EVs governs rapid liver regeneration, thereby enriching our understanding of physiological and endogenous tissue EVs in organ regeneration and therapy.

Why more successful? An analysis of participants' self-monitoring data in an online weight loss intervention.

BACKGROUND: Self-monitoring is crucial for behavioral weight loss. However, few studies have examined the role of self-monitoring using mixed methods, which may hinder our understanding of its impact. METHODS: This study examined self-monitoring data from 61 Chinese adults who participated in a 5-week online group intervention for weight loss. Participants reported their baseline Body Mass Index (BMI), weight loss motivation, and engaged in both daily quantitative self-monitoring (e.g., caloric intake, mood, sedentary behavior, etc.) and qualitative self-monitoring (e.g., daily log that summarizes the progress of weight loss). The timeliness of participants' daily self-monitoring data filling was assessed using a scoring rule. One-way repeated measurement ANOVA was employed to analyze the dynamics of each self-monitoring indicator. Correlation and regression analyses were used to reveal the relationship between baseline data, self-monitoring indicators, and weight change. Content analysis was utilized to analyze participants' qualitative self-monitoring data. Participants were categorized into three groups based on their weight loss outcomes, and a chi-square test was used to compare the frequency distribution between these groups. RESULTS: After the intervention, participants achieved an average weight loss of 2.52 kg (SD = 1.36) and 3.99% (SD = 1.96%) of their initial weight. Daily caloric intake, weight loss satisfaction, frequency of daily log, and the speed of weight loss showed a downward trend, but daily sedentary time gradually increased. Moreover, regression analysis showed that baseline BMI, weight loss motivation, and timeliness of daily filling predicted final weight loss. Qualitative self-monitoring data analysis revealed four categories and nineteen subcategories. A significant difference in the frequency of qualitative data was observed, with the excellent group reporting a greater number of daily logs than expected in all categories and most subcategories, and the moderate and poor groups reporting less than expected in all categories and most subcategories. CONCLUSION: The self-monitoring data in short-term online group intervention exhibited fluctuations. Participants with higher baseline BMI, higher levels of weight loss motivation, and timely self-monitoring achieved more weight loss. Participants who achieved greater weight loss reported a higher quantity of qualitative self-monitoring data. Practitioners should focus on enhancing dieters' weight loss motivation and promote adherence to self-monitoring practices.

Molecular strategies of the pygmy grasshopper Eucriotettix oculatus adapting to long-term heavy metal pollution.

To study the heavy metal accumulation and its impact on insect exterior and chromosome morphology, and reveal the molecular mechanism of insects adapting to long-term heavy metal compound pollution habitats, this study, in the Diaojiang river basin, which has been polluted by heavy metals(HMs) for nearly a thousand years, two Eucriotettix oculatus populations was collected from mining and non-mining areas. It was found that the contents of 7 heavy metals (As, Cd, Pb, Zn, Cu, Sn, Sb) in E. oculatus of the mining area were higher than that in the non-mining 1-11 times. The analysis of morphology shows that the external morphology, the hind wing type and the chromosomal morphology of E. oculatus are significant differences between the two populations. Based on the heavy metal accumulation，morphological change, and stable population density, it is inferred that the mining area population has been affected by heavy metals and has adapted to the environment of heavy metals pollution. Then, by analyzing the transcriptome of the two populations, it was found that the digestion, immunity, excretion, endocrine, nerve, circulation, reproductive and other systems and lysosomes, endoplasmic reticulum and other cell structure-related gene expression were suppressed. This shows that the functions of the above-mentioned related systems of E. oculatus are inhibited by heavy metal stress. However, it has also been found that through the significant up-regulation of genes related to the above system, such as ATP2B, pepsin A, ubiquitin, AQP1, ACOX, ATPeV0A, SEC61A, CANX, ALDH7A1, DLD, aceE, Hsp40, and catalase, etc., and the down-regulation of MAPK signalling pathway genes, can enhanced nutrient absorption, improve energy metabolism, repair damaged cells and degrade abnormal proteins, maintain the stability of cells and systems, and resist heavy metal damage so that E. oculatus can adapt to the environment of heavy metal pollution for a long time.

Fecal calprotectin is a novel biomarker to predict the clinical outcomes of patients with ruptured intracranial aneurysm.

BACKGROUND: Intracranial aneurysm (IA) is a common cerebrovascular disease and the leading cause of spontaneous subarachnoid hemorrhage. Recent evidence suggests that gut microbiota is involved in the pathophysiological process of IA through the gut-brain axis. However, the role of gut inflammation in the development of IA has yet to be clarified. Our study aimed to investigate whether fecal calprotectin (FC) level, a sensitive marker of gut inflammation, is correlated with the development of IA and the prognosis of patients with ruptured IA (RIA). METHODS: 182 patients were collected from January 2022 to January 2023, including 151 patients with IA and 31 healthy individuals. 151 IA patients included 109 patients with unruptured IA (UIA) and 42 patients with RIA. The FC level was measured by enzyme-linked immunosorbent assay. Other detailed information was obtained from an electronic medical record system. RESULTS: Compared with healthy controls, the FC levels in patients with IA were increased (P < 0.0001). Patients with RIA had significantly higher FC levels than UIA patients (P < 0.0001). Moreover, the FC level in RIA patients with unfavorable outcomes was higher than in RIA patients with favorable outcomes. Logistic regression analysis showed that the elevated FC level was an independent risk factor for a 3-month poor prognosis in patients with RIA (OR=1.005, 95% CI = 1.000 -1.009, P = 0.044). CONCLUSION: Fecal calprotectin level is significantly elevated in IA patients, especially those with RIA. FC is a novel biomarker of 3-month poor outcomes in RIA patients.

Recent Advances on Selenium Nutrition and Keshan Disease.

Keshan disease (KD) is a type of endemic cardiomyopathy with an unknown cause. It is primarily found in areas in China with low selenium levels, from northeast to southwest. The nutritional biogeochemical etiology hypothesis suggests that selenium deficiency is a major factor in KD development. Selenium is important in removing free radicals and protecting cells and tissues from peroxide-induced damage. Thus, low environmental selenium may affect the selenium level within the human body, and selenium level differences are commonly observed between healthy people in KD and nonKD areas. From the 1970s to the 1990s, China successfully reduced KD incidence in endemic KD areas through a selenium supplementation program. After years of implementing prevention and control measures, the selenium level of the population in the KD areas has gradually increased, and the prevalence of KD in China has remained low and stable in recent years. Currently, the pathogenesis of KD remains vague, and the effect of selenium supplementation on the prognosis of KD still needs further study. This paper comprehensively reviews selenium deficiency and its connection to KD. Thus, this study aims to offer novel ideas and directions to effectively prevent and treat KD in light of the current situation.

Facile Fabrication of Co-Doped Porous Carbon from Coal Hydrogasification Semi-Coke for Efficient Microwave Absorption.

A Co-doped porous carbon was successfully fabricated by a facile carbonizing procedure using coal hydrogasification semi-coke (SC) as the carbon and cobalt nitrate as the magnetic precursors, respectively. The mass ratio of the precursors was changed to regulate the microwave absorption (MA) capabilities. The favorable MA capabilities are a result of a synergistic interaction be-tween the dielectric loss from the carbon framework, the magnetic loss from nano-sized Co particles, and multiple scattering from the residual pores. At a thickness of 4.0 mm, the Co/C composite showed the lowest reflection loss of -33.45 dB when the initial mass ratio of cobalt nitrate and SC was 1:1. The effective absorbing bandwidth (EAB) could achieve 3.5 GHz at 2 mm thickness. This work not only opens up a new avenue for the facile fabrication of dielectric and magnetic loss combinations and their structural design, but it also creates a new route for the high value-added exploitation of SC.

Response Properties of Electrorheological Composite Hydrophilic Elastomers Based on Different Morphologies of Magnesium-Doped Strontium Titanate.

As smart materials, electrorheological elastomers (EREs) formed by pre-treating active electrorheological particles are attracting more and more attention. In this work, four Mg-doped strontium titanate (Mg-STO) particles with spherical, dendritic, flake-like, and pinecone-like morphologies were obtained via hydrothermal and low-temperature co-precipitation. XRD, SEM, Raman, and FT-IR were used to characterize these products. The results showed that Mg-STOs are about 1.5-2.0 µm in size, and their phase structures are dominated by cubic crystals. These Mg-STOs were dispersed in a hydrogel composite elastic medium. Then, Mg-STO/glycerol/gelatin electrorheological composite hydrophilic elastomers were obtained with or without an electric field. The electric field response properties of Mg-doped strontium titanate composite elastomers were investigated. We concluded that dendritic Mg-STO composite elastomers are high-performance EREs, and the maximum value of their energy storage was 8.70 MPa. The significant electrorheological performance of these products is helpful for their applications in vibration control, force transducers, smart structures, dampers, and other fields.