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Abstract The intricate balance among cost, output, and quality has substantially hindered the practical application of graphene within the downstream industry chain. Here we present a scalable and green supercritical CO2-assisted mechano-exfoliation (SCME) process that omits the use of organic solvents and oxidants throughout the production lifecycle, including exfoliation, separation, and purification. The SCME process achieves graphene powder space-time yields exceeding 40 kg/(m³·day) at laboratory (0.06–0.2 kg) and pilot scales ( > 4 kg), with resultant free-standing films showing conductivities up to 5.26 × 10⁵ S/m. Further kinetic investigations propose general guidelines for grinding-assisted exfoliation: (1) the macroscopic optimizing ability of mechanotechnics for mass transfer frequency and stress distribution and (2) the microscopic multiplication ability of exfoliation medium for shear-delamination. The comprehensive techno-economic analysis also underscores the economic viability of the SCME process for large-scale production.

Sepsis progression is significantly associated with the disruption of gut eubiosis. However, the modulatory mechanisms of gut microbiota operating during sepsis are still unclear. Herein, we investigated how gut commensals impact sepsis development in a pre-clinical model. Cecal ligation and puncture (CLP) surgery was used to establish polymicrobial sepsis in mice. Mice depleted of gut microbiota by an antibiotic cocktail (ABX) exhibited a significantly higher level of mortality than controls. As determined by metabolomics analysis, ABX treatment has depleted many metabolites, and subsequent supplementation with l-rhamnose (rhamnose, Rha), a bacterial carbohydrate metabolite, exerted profound immunomodulatory properties with a significant enhancement in macrophage phagocytosis, which in turn improved organ damage and mortality. Mechanistically, rhamnose binds directly to and activates the solute carrier family 12 (potassium-chloride symporter), member 4 (SLC12A4) in macrophages and promotes phagocytosis by activating the small G-proteins, Ras-related C3 botulinum toxin substrate1 (Rac1) and cell division control protein 42 homolog (Cdc42). Interestingly, rhamnose has enhanced the phagocytosis capacity of macrophages from sepsis patients. In conclusion, by identifying SLC12A4 as the host interacting protein, we disclosed that the gut commensal metabolite rhamnose is a functional molecular that could promote the phagocytosis capacity of macrophages and protect the host against sepsis.

Under the background of “dual carbon” strategy, the integration of renewable energy adds volatility to the grid. Relying solely on generation-side resources for regulation is inadequate, necessitating a flexible demand response from diverse demandside resources. This paper employs a physical connection and information exchange between the distribution network and microgrids to leverage the advantages of centralized-distributed optimization. This establishes a coordinated demand response model between the distribution network and microgrids, gradually establishing a new type of distribution network that integrates interconnected grids and microgrids. This also necessitates the analysis of the response characteristics of various load resources within microgrids and the categorization and modeling of loads based on their response speeds. Additionally, a method for evaluating the multi-time scale schedulable capacity of microgrids is proposed. Finally, a coordinated demand response model between the distribution network and microgrids based on the schedulable capacity assessment is established. This model is validated through case studies, demonstrating its effectiveness. The coordinated demand response between distribution networks and microgrids enables them to operate in a collaborative and economically safe manner.

The objective of this work is to investigate the chemical and nutritional value of milk thistle meal (MTM) in order to improve it and to provide theoretical support for its application in dairy cattle production. MTM was assessed in comparison with seven conventional protein feed sources, namely, soybean meal (SBM), cottonseed meal (CS), canola meal (CN), palm kernel meal (PK), rice bran meal (RB), corn germ meal (CG), and sesame meal (SS). The chemical composition of these feedstuffs was assessed using wet chemical analysis, the Cornell Net Carbohydrate and Protein System was used to evaluate the carbohydrate and protein fractions, and the in situ nylon bag technique and the modified three-step in vitro method were used to assess the rumen degradation and intestinal digestibility. Additionally, Fourier transform infrared technology was used to determine the feedstuff protein spectral molecular structure and its amino acid profile was also assessed. The result showed that MTM acid detergent fiber, lignin, unavailable nitrogen, and non-degradable carbohydrate content were higher than those of the other feedstuffs. It had a 17% and 36% rumen effective degradation rate of neutral detergent fiber and dry matter, respectively, and had the lowest small intestinal rumen undegradable protein digestibility rate. It was low in leucine, histidine, arginine, and proline, but high in methionine. The total area of amide I and amide II in the protein secondary structure was similar to that of CN and CS, and the amide I and II ratio was not different from that of RB. To sum up, MTM has a poor carbohydrate composition and is high in fiber but, in comparison to most other protein feeds, has a higher crude protein rumen effective degradation rate, similar to that of SBM, and it is a good source of methionine, a limiting amino acid. Hence, its nutritional value can be further improved for application in dairy feeding through processes such as microbial or enzymatic fermentation.

In order to improve the efficiency of urban drinking water safety monitoring and early warning management, a pollution risk early warning model of urban drinking water supply chain is proposed. Firstly, the current situation of urban drinking water supply is analyzed and the causes of pollution are analyzed. Then, the autoregressive model is used to predict the time series of multiple water quality indicators by constantly introducing new monitoring data modes for the residual vector group, the outlier scores of each vector group are obtained by using the isolated forest algorithm to judge whether the water quality is abnormal or not, and the fuzzy comprehensive evaluation method is used to evaluate the level of the abnormal situation and carry out the corresponding level early warning. The experimental results show that the area under the receiver operating characteristic curve can reach 0.919 when using the prediction residual vector group of turbidity and conductivity to detect the numerical changes of water quality parameters in the drinking water supply chain, accurately predict the abnormal data, make early warning, and provide the guarantee for the survival of urban residents and urban development. HIGHLIGHTS Current situation of urban drinking water supply.; The causes of pollution.; AR model (autoregressive model).; Isolated forest algorithm.; Accurately predict the abnormal data;

Summary: Obesity and type 2 diabetes (T2D) remain major global healthcare challenges, and developing therapeutics necessitates using nonhuman primate models. Here, we present a transcriptomic and proteomic atlas of all the major organs of cynomolgus monkeys with spontaneous obesity or T2D in comparison to healthy controls. Molecular changes occur predominantly in the adipose tissues of individuals with obesity, while extensive expression perturbations among T2D individuals are observed in many tissues such as the liver and kidney. Immune-response-related pathways are upregulated in obesity and T2D, whereas metabolism and mitochondrial pathways are downregulated. Moreover, we highlight some potential therapeutic targets, including SLC2A1 and PCSK1 in obesity as well as SLC30A8 and SLC2A2 in T2D. Our study provides a resource for exploring the complex molecular mechanism of obesity and T2D and developing therapies for these diseases, with limitations including lack of hypothalamus, isolated islets of Langerhans, longitudinal data, and body fat percentage.

Micro-energy networks are the smallest element of integrated energy systems, and tapping into the integrated demand response potential of micro-energy networks is conducive to improving energy use efficiency and promoting the development of new energy sources on a large scale. This paper proposes a day-ahead integrated demand response strategy for micro-energy grid that takes into account the dispatchable loads. Considering the gradient use of thermal energy, a typical micro-energy grid structure including electricity, gas, medium-grade heat, low-grade heat, and cold energy is constructed, a comprehensive energy equipment model is established, and the refined scheduling models of the dispatchable loads are given. On this basis, with the operating economy of the micro-energy grid as the optimization objective, the integrated demand response strategies of tariff-type and incentive-type are proposed. Through case study analysis, it is verified that the proposed strategy can optimize the energy consumption structure of the micro-energy grid under the guidance of time-of-use tariffs, reducing the operating costs. The proposed strategy fully exploits the demand response potential of the micro-energy grid through the dispatchable loads and the multi-energy complementarity of electricity, heat, and cold, realizes the comprehensive coordination and optimization of source-network-load-storage, provides a larger peak-regulating capacity, and exhibits practical applicability in engineering.

Atmospheric heavy metal pollution presents a severe threat to public health and environmental stability. Transition metal catalysts have emerged as a potent solution for the selective capture and removal of these pollutants. This review provides a comprehensive summary of current advancements in the field, emphasizing the efficiency and specificity of nanostructured transition metals, including manganese, iron, cobalt, nickel, copper, and zinc. Looking forward, we delve into the prospective trajectory of catalyst development, underscoring the need for materials with enhanced stability, regenerability, and environmental compatibility. We project that advancements in computational materials science, nanotechnology, and green chemistry will be pivotal in discovering innovative catalysts that are economically and environmentally sustainable. The integration of smart technologies for real-time monitoring and adaptive control is anticipated to revolutionize heavy metal remediation, ensuring efficient and responsive pollution abatement strategies in the face of evolving industrial scenarios and regulatory landscapes.

Abstract The traditional method for analyzing the content of instant tea has disadvantages such as complicated operation and being time-consuming. In this study, a method for the rapid determination of instant tea components by near-infrared (NIR) spectroscopy was established and optimized. The NIR spectra of 118 instant tea samples were used to evaluate the modeling and prediction performance of a combination of binary particle swarm optimization (BPSO) with support vector regression (SVR), BPSO with partial least squares (PLS), and SVR and PLS without BPSO. Under optimal conditions, Rp for moisture, caffeine, tea polyphenols, and tea polysaccharides were 0.9678, 0.9757, 0.7569, and 0.8185, respectively. The values of SEP were less than 0.9302, and absolute values of Bias were less than 0.3667. These findings indicate that machine learning can be used to optimize the detection model of instant tea components based on NIR methods to improve prediction accuracy.

5G base stations (BSs), which are the essential parts of the 5G network, are important user-side flexible resources in demand response (DR) for electric power system. However, a 5G BS has little and difference dispatchable potential, how to make massive 5G BSs participate in DR conveniently is an urgent problem to be solved. Clustering is an effective solution. Aiming at the special requirements of big data analysis and dispatching difficulties brought by the massive and ultra-dense distribution of 5G BSs, this paper proposes a double-layer K-means++ clustering method for regional 5G BSs considering the main characteristics of 5G BSs. The proposed clustering method considers both the geographical location and power consumption characteristics of the regional 5G BSs to partition the 5G BSs into appropriate number of clusters. Besides, the comprehensive charging and discharging dispatchable potential evaluation indices are established based on the clustering results to evaluate the dispatchable potential of each cluster. The effectiveness of the proposed method and the dispatchable potential evaluation indices are verified through a case of 5000 5G BSs in Jiaxing, China. The simulation findings reveal that the performance of the proposed clustering method is better than classical clustering algorithm and the dispatchable potential of the 5G BSs clusters can be accurately analyzed by the proposed comprehensive indices, which lay the foundation for friendly and fine interaction between 5G BSs and the power grid.

Abstract Simultaneous localisation and mapping (SLAM) are the basis for many robotic applications. As the front end of SLAM, visual odometry is mainly used to estimate camera pose. In dynamic scenes, classical methods are deteriorated by dynamic objects and cannot achieve satisfactory results. In order to improve the robustness of visual odometry in dynamic scenes, this paper proposed a dynamic region detection method based on RGB‐D images. Firstly, all feature points on the RGB image are classified as dynamic and static using a triangle constraint and the epipolar geometric constraint successively. Meanwhile, the depth image is clustered using the K‐Means method. The classified feature points are mapped to the clustered depth image, and a dynamic or static label is assigned to each cluster according to the number of dynamic feature points. Subsequently, a dynamic region mask for the RGB image is generated based on the dynamic clusters in the depth image, and the feature points covered by the mask are all removed. The remaining static feature points are applied to estimate the camera pose. Finally, some experimental results are provided to demonstrate the feasibility and performance.

Speech recognition (SR) systems based on deep neural networks are increasingly widespread in smart devices. However, they are vulnerable to human-imperceptible adversarial attacks, which cause the SR to generate incorrect or targeted adversarial commands. Meanwhile, audio adversarial attacks are particularly susceptible to various factors, e.g., ambient noise, after applying them to a real-world attack. To circumvent this issue, we develop a universal adversarial perturbation (UAP) generation method to construct robust real-world UAP by integrating ambient noise into the generation process. The proposed UAP can work well in the case of input-agnostic and independent sources. We validate the effectiveness of our method on two different SRs in different real-world scenarios and parameters, the results demonstrate that our method yields state-of-the-art performance, i.e. given any audio waveform, the word error rate can be up to 80%. Extensive experiments investigate the impact of different parameters (e.g, signal-to-noise ratio, distance, and attack angle) on the attack success rate.

Liver health has long been linked to the homeostasis of gut microbiota. Although some studies have shown that alterations in the species and function of gut microbiota contribute to the initiation and development of acute liver injury (ALI), studies investigating the effects of ALI on gut microbial dynamic composition changes are still limited. To observe whether liver damage can alter the composition of gut microbiota dynamically, we established three chemical models (e.g., acetaminophen, carbon tetrachloride, lipopolysaccharide) of ALI. Using these models, multiple time points of liver injury and intestinal microbiome were analyzed through plasma biochemical analysis and 16S rRNA gene sequencing. We assessed α-diversity, Unifrac principal coordinates analysis (PCoA), and linear discriminant analysis effect size (LEfSe) in the injury and control groups. The composition of the gut microbiota underwent dramatic shifts with liver injury and recovery in each model. Additionally, specific microbial abundance was significantly correlated with the level of plasma alanine transaminase and aspartate transaminase. These data provide new evidence that liver dysfunction and restoration is dynamically linked with the changes in the intestinal microbiome.

Tea residue is a by-product of tea processing and contains ∼ 60 % insoluble dietary fiber. We investigated the physicochemical properties and structure of the insoluble dietary fiber of tea (T-IDF), and its defecation function was evaluated. The physical and chemical indexes of the T-IDF, including its water holding, oil holding, swelling, cation exchange, and cholesterol exchange capacities, were measured, while its structure was analyzed by a range of analytical techniques. Furthermore, the related indexes of the animal defecation function were determined, and the in vitro detection of fermented short chain fatty acid was conducted. We found that T-IDF exhibits excellent physical and chemical properties. Moreover, the consumption of T-IDF significantly promoted defecation in slow transit intestinal dyskinesia mice and enhanced the production of short chain fatty acids. Overall, we demonstrated a good correlation between the physicochemical properties and the structure/function of T-IDF.

Copy number variants in the human genome (CNVs) are associated with neurodevelopmental and psychiatric disorders such as schizophrenia and autism. Here the authors investigate how the large deletion CNV on chromosome 22q11.2 alters chromatin organization.

Abstract Shanxi aged vinegar (SAV) is a well-known vinegar produced by traditional solid-state fermentation and has been used in China for thousands of years. However, how microorganisms and their metabolites change along with fermentation is unclear. Here, 454 high-throughput sequencing and denaturing gradient gel electrophoresis were used to investigate the composition of microbial community. Metabolites were further analyzed by gas chromatography–mass spectrometry and high–performance liquid chromatography. Results showed that the composition of bacterial community changed dramatically at different stages of fermentation. The bacterial genera (relative abundance > 0.1%) decreased from 17 in daqu (starter used in starch saccharification) to 2 at the 12th day of alcohol fernemtation (AF). 15 bacterial genera at the 1st day of acetic acid fermentation (AAF) decreased to 4 genera, involving Acetobacter (50.9%), Lactobacillus (47.9%), Komagataeibacter (formerly Gluconacetobacter, 0.7%) and Propionibacterium (0.1%) at the 7th day of AAF. The structure of fungal community was more homogeneous. Saccharomyces and Saccharomycopsis were predominant in AF and AAF. A total of 87 kinds of nonvolatile metabolites were detected. Canonical correspondence analysis showed a significant correlation between the microbiota succession and the formation of metabolites during the fermentation of SAV. This study provides detailed information for the fermentation mechanism of traditional SAV.