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The progressive decline of the coal industry necessitates the development of effective treatment solutions for acid mine drainage (AMD), which is characterized by high acidity and elevated concentrations of heavy metals. This study proposes an innovative approach leveraging sulfate-reducing bacteria (SRB) acclimated to contaminated anaerobic environments. The research focused on elucidating the physiological characteristics and optimal growth conditions of SRB, particularly in relation to the pH level and temperature. The experimental findings reveal that the SRB exhibited a sulfate removal rate of 88.86% at an optimal temperature of 30 °C. Additionally, SRB gel particles were formulated using sodium alginate (SA) and carboxymethyl cellulose (CMC), and their performance was assessed under specific conditions (pH = 6, C/S = 1.5, T = 30 °C, CMC = 4.5%, BSNa = 0.4 mol/L, and cross-linking time = 9 h). Under these conditions, the SRB gel particles demonstrated an enhanced sulfate removal efficiency of 91.6%. Thermal analysis via differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) provided further insights into the stability and properties of the SRB gel spheres. The findings underscore the potential of SRB-based bioremediation as a sustainable and efficient method for AMD treatment, offering a novel and environmentally friendly solution to mitigating the adverse effects of environmental contamination.
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Biodegradación Ambiental , Minería , Concentración de Iones de Hidrógeno , Alginatos/química , Sulfatos/química , Bacterias/metabolismo , Temperatura , Geles/química , Carboximetilcelulosa de Sodio/química , Metales Pesados/química , Metales Pesados/aislamiento & purificación , Contaminantes Químicos del Agua/química , Contaminantes Químicos del Agua/aislamiento & purificaciónRESUMEN
Based on the characteristics of commodity polymers in large quantities and low costs, modification of existing commodity polymers emerges as the most effective approach for exploring novel materials. Nevertheless, conventional modification methods typically involve high-energy processes (e.g., high temperature, high-energy radiation), which may lead to irreversible detrimental effects on the polymers, contradicting the desired performance enhancement through modification. In this work, we propose a carbene-mediated postpolymerization modification (PPM) strategy utilizing diazo compounds. Under photochemical or thermal activation conditions, insertion of the C-H bond can be achieved without compromising the performance of polymers. These diazo compounds can be easily synthesized in just two steps and applied to all C-H-containing polymers. This practical and effective modification strategy offers new opportunities and possibilities for enhancing the value and expanding the applications of polymers.
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BACKGROUND: Better utilization of rape straw can provide alternative strategies for sustainable ruminant and food production. The research reported here investigated changes in the carbohydrate composition of rape straw as a result of mixed ensiling with whole-crop corn or inoculated with nitrate, and the consequent effects on ruminal fermentation through in vitro batch culture. The three treatments included: rape straw and corn silage (RSTC), and ensiling treatment of rape straw with whole-crop corn (RSIC) or with calcium nitrate inoculation (RSICN). RESULTS: Ensiling treatment of rape straw and whole-crop corn or plus nitrate enriched lactic acid bacteria and lactate. The treatments broke the fiber surface connections of rape straw, leading to higher neutral detergent soluble (NDS) content and lower fiber content. Ensiling treatments led to greater (P < 0.05) dry matter degradation (DMD), molar proportions of propionate and butyrate, relative abundance of the phylum Bacteroidetes and genus Prevotella, and lower (P < 0.05) methane production in terms of g kg-1 DMD, molar proportions of acetate, and lower acetate to propionate ratio than the RSTC treatment. The RSICN treatment led to the lowest (P < 0.05) hydrogen concentration and methane production among the three treatments. CONCLUSION: Ensiling treatments of rape straw and whole-crop corn destroy the micro-structure of rape straw, promote substrate degradation by enriching the phylum Bacteroidetes and the genus Prevotella, and decrease methane production by favoring propionate and butyrate production. Nitrate inoculation in the ensiling treatment of rape straw and whole-crop corn further decreases methane production without influencing substrate degradation by providing an additional hydrogen sink. © 2023 Society of Chemical Industry.
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Nitratos , Propionatos , Animales , Propionatos/metabolismo , Fermentación , Nitratos/metabolismo , Rumen/metabolismo , Carbohidratos , Ensilaje/análisis , Butiratos/metabolismo , Acetatos , Metano/metabolismo , Hidrógeno/metabolismo , Zea mays/química , Digestión , DietaRESUMEN
Fuzzing has become an important method for finding vulnerabilities in software. For fuzzing programs expecting structural inputs, syntactic- and semantic-aware fuzzing approaches have been particularly proposed. However, they still cannot fuzz in-memory data stores sufficiently, since some code paths are only executed when the required data are available. In this article, we propose a data-aware fuzzing method, DAFuzz, which is designed by considering the data used during fuzzing. Specifically, to ensure different data-sensitive code paths are exercised, DAFuzz first loads different kinds of data into the stores before feeding fuzzing inputs. Then, when generating inputs, DAFuzz ensures the generated inputs are not only syntactically and semantically valid but also use the data correctly. We implement a prototype of DAFuzz based on Superion and use it to fuzz Redis and Memcached. Experiments show that DAFuzz covers 13~95% more edges than AFL, Superion, AFL++, and AFLNet, and discovers vulnerabilities over 2.7× faster. In total, we discovered four new vulnerabilities in Redis and Memcached. All the vulnerabilities were reported to developers and have been acknowledged and fixed.
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Graph clustering, which learns the node representations for effective cluster assignments, is a fundamental yet challenging task in data analysis and has received considerable attention accompanied by graph neural networks (GNNs) in recent years. However, most existing methods overlook the inherent relational information among the nonindependent and nonidentically distributed nodes in a graph. Due to the lack of exploration of relational attributes, the semantic information of the graph-structured data fails to be fully exploited which leads to poor clustering performance. In this article, we propose a novel self-supervised deep graph clustering method named relational redundancy-free graph clustering (R 2 FGC) to tackle the problem. It extracts the attribute-and structure-level relational information from both global and local views based on an autoencoder (AE) and a graph AE (GAE). To obtain effective representations of the semantic information, we preserve the consistent relationship among augmented nodes, whereas the redundant relationship is further reduced for learning discriminative embeddings. In addition, a simple yet valid strategy is used to alleviate the oversmoothing issue. Extensive experiments are performed on widely used benchmark datasets to validate the superiority of our R 2 FGC over state-of-the-art baselines. Our codes are available at https://github.com/yisiyu95/R2FGC.
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Grain starch has a faster rate of rumen fermentation than straw fiber and causes a rapid increase in ruminal molecular hydrogen (H2) partial pressure, which may promote other H2 sinks to compete H2 away from methanogenesis. The study was designed to investigate the effects of increasing ratios of grain starch to straw fiber on hydrogen allocation and methanogenesis through in vitro ruminal batch incubation. Corn grain and corn straw were employed as starch and fiber source respectively. Seven treatments were the ratios of corn grain to corn straw (RGS) being 0:6, 1:5, 2:4, 3:3, 4:2, 5:1, and 6:0. Elevating RGS increased dry matter (DM) degradation and decreased methane (CH4) and hydrogen gas (gH2) production relative to DM degraded. Elevating RGS increased volatile fatty acid (VFA) concentration, propionate molar percentage and microbial protein (MCP) concentration, decreased acetate molar percentage, acetate to propionate ratio and estimated net metabolic hydrogen ([H]) production relative to DM degraded. Elevating RGS decreased the molar percentage of [H] utilized for CH4 and gH2 production. In summary, increasing ratios of grain starch to straw fiber altered rumen fermentation pathway from acetate to propionate production, reduced the efficiency of [H] production with the enhancement of MCP synthesis, and led to a reduction in the efficiency of CH4 and gH2 production.
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Hidrógeno , Propionatos , Animales , Fermentación , Propionatos/metabolismo , Hidrógeno/metabolismo , Dieta , Almidón/metabolismo , Técnicas de Cultivo Celular por Lotes , Acetatos/metabolismo , Rumen/metabolismoRESUMEN
Brittle Culm 15 (BC15) gene encodes a membrane-associated chitinase-like protein that participates in cellulose synthesis, and BC15 gene mutation affects cell wall composition in plant, such as cellulose or hemicellulose. The present study was designed to investigate the changes of carbohydrates composition in bc15 mutant straw, and the resulting consequence on rumen fermentation, methanogenesis, and microbial populations (qPCR) during in vitro ruminal fermentation process. Two substrates, bc15 mutant and wild-type (WT) rice straws, were selected for in vitro rumen batch culture. The first experiment was designed to investigate the kinetics of total gas and CH4 production through 48-h in vitro ruminal fermentation, while the second experiment selected incubation time of 12 and 48 h to represent the early and late stage of in vitro ruminal incubation, respectively, and then investigated changes in biodegradation, fermentation end products, and selected representative microbial populations. The bc15 mutant straw had lower contents of cellulose, neutral detergent fiber (NDF) and acid detergent fiber (ADF), and higher contents of water-soluble carbohydrates, neutral detergent solubles (NDS) and monosaccharides. The bc15 mutant straw exhibited a distinct kinetics of 48-h total gas and CH4 production with faster increases in early incubation when compared with WT straw. The bc15 mutant straw had higher DM degradation, NDF degradation and total volatile fatty acid concentration at 12 h of incubation, and lower NDF degradation and CH4 production at 48 h of incubation, together with lower acetate to propionate ratio and ADF degradation and higher butyrate molar percentage and NDS degradation at both incubation times. Furthermore, the bc15 mutant straw resulted in greater 16S gene copies of F. succinogenes, with lower 18S gene copies of fungi at both incubation times. These results indicated that the BC15 gene mutation decreased fibrosis of cell wall of rice straw, enhanced degradation at the early stage of rumen fermentation, and shifts fermentation pattern from acetate to propionate and butyrate production, leading to the decreased volume and fractional rate of CH4 production. However, BC15 gene mutation may enhance hardenability of cell wall structure of rice straw, which is more resistant for microbial colonization with decreased fiber degradation. Thus, this study modified rice straw by manipulating a cell wall biosynthesis gene and provides a potential strategy to alter degradation and CH4 production during in vitro ruminal fermentation process.
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Longitudinal data analysis has been very common in various fields. It is important in longitudinal studies to choose appropriate numbers of subjects and repeated measurements and allocation of time points as well. Therefore, existing studies proposed many criteria to select the optimal designs. However, most of them focused on the precision of the mean estimation based on some specific models and certain structures of the covariance matrix. In this paper, we focus on both the mean and the marginal covariance matrix. Based on the mean-covariance models, it is shown that the trick of symmetrization can generate better designs under a Bayesian D-optimality criterion over a given prior parameter space. Then, we propose a novel criterion to select the optimal designs. The goal of the proposed criterion is to make the estimates of both the mean vector and the covariance matrix more accurate, and the total cost is as low as possible. Further, we develop an algorithm to solve the corresponding optimization problem. Based on the algorithm, the criterion is illustrated by an application to a real dataset and some simulation studies. We show the superiority of the symmetric optimal design and the symmetrized optimal design in terms of the relative efficiency and parameter estimation. Moreover, we also demonstrate that the proposed criterion is more effective than the previous criteria, and it is suitable for both maximum likelihood estimation and restricted maximum likelihood estimation procedures.