Search

The development of high-quality flexible surface-enhanced Raman spectroscopy (SERS) substrates is crucial for developing rapid SERS analysis in situ. Silver nanowire membranes as novel flexible substrates could benefit from the high collection efficiency of analytes by wrapping complex surfaces or wiping the surfaces of samples. However, their low SERS performance impedes further applications of silver nanowire membranes in analyte detection. Herein, we report an ultra-high-sensitivity silver nanowire membrane synthesized by a simple and time-saving cyclic voltammetry (CV) method. After CV treatment, a part of the silver nanowires on the silver nanowire membrane turned into small nanoparticles and nanorods. This nanostructure’s reconstitution increased the analytical enhancement factor of silver nanowire membranes by 14.4 times. Scanning and transmission electron microscopy, UV-vis spectroscopy, X-ray diffraction, and X-ray photoelectron spectroscopy were employed to investigate the transformation in the membrane nanostructure. The CV-treated substrates exhibited high surface-enhanced Raman activity and good temporal stability. The limits of detection (LODs) for p-aminothiophenol, crystal violet, tetramethylthiuram disulfide, sodium perchlorate, malachite green, fluoranthene, and potassium nitrate are 3.7 × 10−12 M, 5.1 × 10−11 M, 5.4 × 10−11 M, 6.3 × 10−9 M, 0.00693 ng, 0.0810 ng, and 0.0273 ng on this substrate, respectively. Additionally, the developed substrate is feasible for the detection of crystal violet in real samples. These results certify that CV-treated substrates possess broad application prospects in on-site SERS analysis.

The exploration of microbial genetic resources for the production of fFen-flavor Baijiu has not only enriched the microbial library for baijiu production but has also laid the foundation for process improvement and strain optimization in baijiu brewing. In this study, a total of 177 fungal isolates were screened, including Saccharomyces cerevisiae, non-Saccharomyces cerevisiae, molds, and some pathogenic bacteria. Among them, Saccharomyces cerevisiae was the most abundant with 119 isolates, playing a major role in the fermentation of baijiu production. A total of 148 bacterial isolates were obtained from the fermentation mash samples, showing greater diversity compared to fungi. Bacillus species were the most abundant, with 94 isolates. Bacillus licheniformis, in particular, can produce a rich enzymatic system and flavor precursors, making it an important contributor to the sensory quality of baijiu. Lactic acid bacteria were the second most abundant, with 16 isolates. Additionally, five pathogenic fungal species were identified, including Candida pelliculosa, Candida lusitaniae, Fusarium oxysporum, Fusarium solani and Talaromyces marneffei. Six pathogenic bacterial species were also isolated, namely Staphylococcus aureus, Staphylococcus epidermidis, Staphylococcus xylosus, Moraxella osloensis, Actinomyces meyeri and Stenotrophomonas maltophilia. Finally, two strains of high acetate ethyl ester-producing yeast and lactate-degrading bacteria with good tolerance to temperature, pH, and ethanol concentration were identified as Saccharomyces cerevisiae and Bacillus licheniformis, respectively.

Ectomycorrhizal fungi employ different strategies for mycelial growth and host colonization under varying nutrient conditions. However, key genes associated with mycorrhizal interaction should be influenced solely by the inoculation treatment and not by nutrient variations. To utilize subtle nutrient differences and rapidly screen for key genes related to the interaction between Suillus luteus and Pinus massoniana, we performed an inoculation experiment using culture bottles containing high- and low-nutrient media. Interestingly, S. luteus LS88 promoted the growth of P. massoniana seedlings without mature ectomycorrhiza, and the impact of LS88 inoculation on P. massoniana roots was greater than that of nutrient changes. In this study, the resequenced genome of the LS88 strain was utilized for transcriptome analysis of the strain. The analysis indicated that a unique gene encoding glutathione S-transferase (GST) in LS88 is likely involved in colonizing P. massoniana roots. In this study, the GST gene expression was independent of nutrient levels. It was probably induced by P. massoniana and could be used as a marker for S. luteus colonization degree.

The natural environment in the high-altitude regions of Northwest China is extremely harsh, characterized by numerous salt lakes. The high concentrations of chloride salts, sulfates, and alkali metal ions in these areas can induce alkali–silica reactions (ASRs) in concrete. These reactions generate harmful gel within the concrete, causing expansion and cracking, which significantly impacts the durability of concrete structures. This study investigates the evolution of the mechanical properties in high-performance concrete (HPC) under long-term ASR by incorporating different admixtures and varying the equivalent alkali content. A three-dimensional random aggregate mesoscopic model was used to simulate static compression tests under various operational conditions. Non-destructive testing methods were utilized to determine the expansion rate, internal, and surface damage variables of the concrete. The experimental results indicate that the 10-year expansion rate differs from the 1-year rate by approximately 1%, and under long-term ASR mitigation measures, the internal damage in the HPC is minimal, though the surface damage is more severe. As the equivalent alkali content increases, the compressive strength of the concrete cubes decreases, initially rising before falling by 5–15% over time. The HPC with only air-entraining agent added exhibited better mechanical performance than the HPC with both air-entraining and corrosion inhibitors added, with the poorest performance observed in the HPC with only a corrosion inhibitor. A relationship was established between the surface and internal damage variables, with the surface damage initially increasing rapidly before stabilizing as the internal damage rose. Numerical simulations effectively describe the damage behavior of HPC under static uniaxial compression. Comparisons with actual failure morphologies revealed that, in the cube compression tests, crack propagation directly penetrated both coarse and fine aggregates rather than circumventing them. The simulations closely matched the experimental outcomes, demonstrating their accuracy in modeling experiments. This study discusses the compressive mechanical properties of concrete under prolonged ASR through a combination of experimental and simulation approaches. It also delves into the impact of surface damage on the overall mechanical performance and failure modes of concrete. The findings provide experimental and simulation support for the concrete structures in regions with high alkali contents.

In regions characterized by the challenging combination of brine corrosion in the salt lakes and river sand with alkali silica reaction (ASR) activity in areas of the Northwest, high-performance concrete (HPC) formulated with high-volume composite mineral admixtures as ASR suppression measures has been preferred for civil engineering structures in the region. This study investigates the splitting tensile strength, corrosion products, microscopic structure characteristics, and mesoscopic mechanical mechanisms of splitting failure of such HPC under 10-year corrosion from salt lake brine. The relationship between mechanical properties and corrosion damage, as well as the characteristics of internal crack propagation paths and failure mechanisms of HPC under splitting load, are explored. The findings reveal that as the alkali content within HPC rises, corrosion damage intensifies, resulting in a reduction in splitting tensile strength. Moreover, a linear association between mechanical properties and corrosion damage is observed. Microscopic structural analysis and numerical simulation of the splitting failure process of HPC elucidate that while the substantial presence of mineral admixtures effectively suppresses the ASR risk associated with alkali-reactive aggregates in concrete, uneven ASR gel products persist. These discontinuous micro-fine interface cracks induced by the gel products and the cracks induced by the gel products around the selective alkali-active aggregate particles distributed in the local area are the initiation sources of mortar cracks in HPC splitting failure. In terms of the overall failure state observed during the concrete splitting process, mortar cracks manifest two distinct extension paths: along the coarse aggregate interface and directly through the aggregates themselves. Notably, a greater proportion of coarse aggregates are directly penetrated by mortar cracks, as opposed to the number of interface failures bypassing coarse aggregates. More importantly, the above work establishes a theoretical reference in three dimensions: macroscopic, mesoscopic, and microscopic, for studying concrete corrosion damage in complex environments such as salt lake brine corrosion and ASR inhibition.

The fermentation process of Chinese Baijiu’s fermented grains involves the intricate succession and metabolism of microbial communities, collectively shaping the Baijiu’s quality. Understanding the composition and succession of these living microbial communities within fermented grains is crucial for comprehending fermentation and flavor formation mechanisms. However, conducting high-throughput analysis of living microbial communities within the complex microbial system of fermented grains poses significant challenges. Thus, this study addressed this challenge by devising a high-throughput analysis framework using light-flavor Baijiu as a model. This framework combined propidium monoazide (PMA) pretreatment technology with amplicon sequencing techniques. Optimal PMA treatment parameters, including a concentration of 50 μM and incubation in darkness for 5 min followed by an exposure incubation period of 5 min, were identified. Utilizing this protocol, viable microorganism biomass ranging from 8.71 × 106 to 1.47 × 108 copies/μL was successfully detected in fermented grain samples. Subsequent amplicon sequencing analysis revealed distinct microbial community structures between untreated and PMA-treated groups, with notable differences in relative abundance compositions, particularly in dominant species such as Lactobacillus, Bacillus, Pediococcus, Saccharomycopsis, Issatchenkia and Pichia, as identified by LEfSe analysis. The results of this study confirmed the efficacy of PMA-amplicon sequencing technology for analyzing living microbial communities in fermented grains and furnished a methodological framework for investigating living microbial communities in diverse traditional fermented foods. This technical framework holds considerable significance for advancing our understanding of the fermentation mechanisms intrinsic to traditional fermented foods.

This research presents a new, eco-friendly, and swift method combining solid-phase extraction and hydrophobic deep eutectic solvents (DES) with high-performance liquid chromatography (SPE-DES-HPLC) for extracting and quantifying catechin and epicatechin in Shanxi aged vinegar (SAV). The parameters, such as the elution solvent type, the XAD-2 macroporous resin dosage, the DES ratio, the DES volume, the adsorption time, and the desorption time, were optimized via a one-way experiment. A central composite design using the Box–Behnken methodology was employed to investigate the effects of various factors, including 17 experimental runs and the construction of three-dimensional response surface plots to identify the optimal conditions. The results show that the optimal conditions were an HDES (tetraethylammonium chloride and octanoic acid) ratio of 1:3, an XAD-2 macroporous resin dosage of 188 mg, and an adsorption time of 11 min. Under these optimal conditions, the coefficients of determination of the method were greater than or equal to 0.9917, the precision was less than 5%, and the recoveries ranged from 98.8% to 118.8%. The environmentally friendly nature of the analytical process and sample preparation was assessed via the Analytical Eco-Scale and AGREE, demonstrating that this method is a practical and eco-friendly alternative to conventional determination techniques. In summary, this innovative approach offers a solid foundation for the assessment of flavanol compounds present in SAV samples.

This study addresses the issue of poor construction labor productivity, exploring the process and methodology for increasing construction workers’ participation in labor productivity improvement, as well as a situational awareness (SA) development approach for construction workers based on lean construction (LC). A framework for enhancing labor productivity through SA has been developed. A case study approach is utilized to describe the LC improvement process, which includes enhancing construction workers’ SA for waste minimization and process optimization and engaging them in LC with improved value stream mapping (VSM). The labor productivity measurement approach, combined with VSM, is used to evaluate the labor productivity improvement with labor productivity improvement rate, resource efficiency, and flow efficiency. The results demonstrate that the labor productivity of the case project has increased by 24.07%, with 11.54% contributing resource efficiency and 88.46% contributing flow efficiency, indicating a greater increase in flow efficiency. This study fills a research gap in terms of the combination of construction workers’ SA enhancement and the involvement of LC practices to boost labor productivity. Furthermore, this study proposes an SA development approach based on LC at three levels: waste perception, VSM implementation, and labor productivity improvement assessment. This provides new perspectives for leveraging construction workers’ SA to increase labor productivity. The improved VSM approach and labor productivity measurements provide an operationalization of workers’ participation in continuous improvement. This research is of great practical relevance for augmenting the competencies of construction laborers and inspiring them to engage in labor productivity enhancement.

Suillus bovinus is a wild edible ectomycorrhizal fungus with important economic and ecological value, which often forms an ectomycorrhiza with pine trees. We know little about the mechanisms associated with the metabolism and symbiosis of S. bovinus and its effects on the nutritional value. In this study, the whole-genome sequencing of S. bovinus was performed using Illumina, HiFi, and Hi-C technologies, and the sequencing data were subjected to genome assembly, gene prediction, and functional annotation to obtain a high-quality chromosome-level genome of S. bovinus. The final assembly of the S. bovinus genome includes 12 chromosomes, with a total length of 43.03 Mb, a GC content of 46.58%, and a contig N50 size of 3.78 Mb. A total of 11,199 coding protein sequences were predicted from genome annotation. The S. bovinus genome contains a large number of small secreted proteins (SSPs) and genes that encode enzymes related to carbohydrates, as well as genes related to terpenoids, auxin, and lipochitooligosaccharides. These genes may contribute to symbiotic processes. The whole-genome sequencing and genetic information provide a theoretical basis for a deeper understanding of the mechanism of the mycorrhizal symbiosis of S. bovinus and can serve as a reference for comparative genomics of ectomycorrhizal fungi.

High-precision spatial-temporal wind power prediction technology is of great significance for ensuring the safe and stable operation of power grids. The development of artificial intelligence technology provides a new scheme for modeling with strong spatial-temporal correlation. In addition, the existing prediction models are mostly ‘black box’ models, lacking interpretability, which may lead to a lack of trust in the model by power grid dispatchers. Therefore, improving the model to obtain interpretability has become an important challenge. In this paper, an interpretable short-term wind power prediction model based on ensemble deep graph neural network is designed. Firstly, the graph network model (GNN) with an attention mechanism is applied to the aggregate and the spatial-temporal features of wind power data are extracted, and the interpretable ability is obtained. Then, the long short-term memory (LSTM) method is used to process the extracted features and establish a wind power prediction model. Finally, the random sampling algorithm is used to optimize the hyperparameters to improve the learning rate and performance of the model. Through multiple comparative experiments and a case analysis, the results show that the proposed model has a higher prediction accuracy than other traditional models and obtains reasonable interpretability in time and space dimensions.

Internal combustion engines, during their operation, subject the piston to high-temperature and high-pressure conditions, requiring it to endure intense, continuous reciprocating motion. This strenuous process leads to significant wear and tear. Among the engine’s crucial components, the piston ring plays a pivotal role but is particularly susceptible to wear. Therefore, extensive research has been devoted to investigating the wear of piston rings, a critical sealing component within internal combustion engines. To address the high cost of existing coating methods, which hinders widespread application, we propose a bionic design approach inspired by groove structures observed on earthworm bodies, aimed at enhancing the wear resistance of piston rings. Bionic piston rings featuring optimally designed groove structures inspired by the earthworm’s anatomy were designed. These rings exhibited varying groove depths (1 mm, 2 mm, and 3 mm), groove widths (0.1 mm, 0.3 mm, and 0.5 mm), and groove spacings (0.1 mm, 0.2 mm, and 0.3 mm). We conducted thermal–structural coupling analyses on both standard piston rings and these bionic counterparts. The results revealed that the maximum stress was concentrated at the first piston ring, precisely at the opposing region of the end gap. Thus, the initial piston ring endured the primary frictional losses. Moreover, a comparison of stress levels between bionic rings and the standard ring revealed that the bionic groove structure substantially reduced stress and minimized stress concentration, thus enhancing wear resistance. Groove width had the most notable influence on wear performance, followed by groove depth and groove spacing. Optimal wear resistance was achieved when the groove depth was 3 mm, groove width was 0.1 mm, and groove spacing was 0.1 mm. Subsequently, we constructed a piston ring friction test bench to validate the wear resistance of the most effective piston ring. The results indicated that the wear resistance of the bionic piston ring exceeded that of the standard piston ring by up to 19.627%. Therefore, incorporating a bionic groove structure within the piston ring can effectively reduce surface friction and enhance wear resistance. This, in turn, can enhance the operational lifespan of internal combustion engines under favorable working conditions.

The flavonoids from Perilla leaves were extracted using flash extraction assisted by ultrasonic extraction with ethanol. Subsequently, macroporous resin was employed for the isolation and purification of these flavonoids, followed by an investigation into their antioxidant activity. The process conditions for the extraction of flavonoids from Perilla leaves were designed and optimized using a one-way experiment combined with a response surface methodology. The optimal extraction conditions were determined as follows: the liquid–solid ratio was 20:1, ethanol volume fraction of 60%, ultrasound temperature of 60 °C, ultrasound time of 10 min and flash evaporation time of 60 s. The optimal extraction rate of flavonoids is 9.8 mg/g. In terms of separation and purification, a high-performance macroporous resin (HPD450 resin) with high purification efficiency was selected through static analysis and adsorption experiments. The optimal enrichment conditions were as follows: loading concentration of 0.06 mg/mL, optimal loading concentration of 20 mL, elution concentration of 70% and 76 mL, providing a reference for the further development and utilization of Perilla leaf flavonoids.

Adenosine, an immunosuppressive metabolite, is produced by adenosine triphosphate (ATP) released from dying or stressed cells and is found at high levels in the tumor microenvironment of most solid tumors. It mediates pro-tumor activities by inducing tumor cell proliferation, migration or invasion, tumor tissue angiogenesis, and chemoresistance. In addition, adenosine plays an important role in regulating anti-tumor immune responses and facilitating tumor immune escape. Adenosine receptors are broadly expressed by tumor-infiltrated immune cells, including suppressive tumor-associated macrophages and CD4+ regulatory T cells, as well as effector CD4+ T cells and CD8+ cytotoxic T lymphocytes. Therefore, adenosine is indispensable in down-regulating anti-tumor immune responses in the tumor microenvironment and contributes to tumor progression. This review describes the current progress on the role of adenosine/adenosine receptor pathway in regulating the tumor-infiltrating immune cells that contribute to tumor immune evasion and aims to provide insights into adenosine-targeted tumor immunotherapy.

This study aimed to explore key physicochemical characteristics and evolutionary patterns of microbial community structure during the fermentation of aged vinegar. The correlation between microorganisms and physicochemical characteristics during fermentation was examined. The results revealed significant differences in genera at different stages of fermentation. The dominant bacteria in R1 were Bacillus, Lactobacillus, Aspergillus, and Issatchenkia. During the R2 fermentation stage, Lactobacillus, Acetobacter, and Saccharomyces exhibited an upward trend and finally became the dominant bacteria. Aspergillus was the main bacterial genus at the end of overall fermentation. The correlation analysis showed that the bacterial genera significantly positively and negatively correlated with reducing sugars and amino acid nitrogen were the same in Cuqu. Similarly, the bacterial genera significantly positively and negatively correlated with pH and saccharification power were the same. pH, reducing sugar, and saccharification ability were mainly positively correlated with bacterial genera during fermentation. Further, studies found that the overall correlation between fungal communities and physicochemical characteristics was weaker than the correlation with bacteria during fermentation.

In order to investigate the antioxidant activity of Elaeagnus umbellata polysaccharides, the physicochemical characteristics of purified Elaeagnus umbellata polysaccharides (EUP, consisting of two fractions, EUP1 and EUP2) were investigated using UV spectrophotometry, high-performance liquid chromatography (HPLC), high-performance gel permeation chromatography (HPGPC), and Fourier transform infrared spectroscopy (FT-IR). This revealed that EUP1 and EUP2 were acidic polysaccharides with an average molecular weight (MW) of 63 and 38 kDa, respectively. EUP1 mainly consisted of L-rhamnose and D-galactose in a molar ratio of 2.05:1, and EUP2 consisted of D-mannose, L-rhamnose, D-galactose, and D-arabinose in a molar ratio of 2.06:1:2.78:1. Furthermore, EUP exhibited considerable antioxidant potential for scavenging hydroxyl, superoxide anion, DPPH, and ABTS radicals. Therefore, EUP can be developed as a potential antioxidant for the functional food or pharmaceutical field.

This research presents a novel, eco-friendly, vortex-assisted liquid–liquid microextraction (VALLME) approach, integrating hydrophobic deep eutectic solvents (DESs) with HPLC for the identification and quantification of nine specific flavonoids in Shanxi aged vinegar (SAV). The parameters of DES-VALLME, including the ratio of trioctylmethylammonium chloride to 1,4-butanediol (1:6), DES volume (150 μL), vortex duration (5 min), the concentration of NaCl (0.40 g), and centrifugation time (10 min), were optimized to achieve the maximum extraction efficiency of target substances. Under these optimal conditions, quantitative analyses performed via HPLC demonstrated a broad linear range of 0.20–50.00 μg/mL and correlation coefficients (r2) greater than 0.9944 for all nine calibration curves. The limits of detection (LOD) and limits of quantitation (LOQ) were 0.09–0.18 μg/mL and 0.30–0.60 μg/mL, respectively, ensuring high sensitivity. The relative standard deviations for intra-day and inter-day variability were within the acceptable range, 2.34–3.77% and 3.04–4.96%, respectively, demonstrating the method’s reliability. The recovery rates ranged from 85.97% to 108.11%, underscoring the method’s precision. This technique exhibited a significant enrichment effect (enrichment factor: 43 to 296) on SAV flavonoids. Notably, the eco-friendliness of this procedure was evaluated using the Analytical Eco-Scale, Green Analytical Procedure Index, and Analytical Greenness Metric. The results suggested that this technique is a viable green alternative to traditional flavonoid determination methods in SAV. In summary, this novel method provides a theoretical basis for assessing flavonoid content in SAV samples and tracing SAV products. This contribution has significant implications for enhancing analytical techniques in food chemistry and environmental science and the sustainable development of the food industry.

Food is an indispensable part of destination tourism resources and attractions, playing a vital role in the marketing and promotion of the destination. Food can also be viewed as an important brand that the destination can develop. Yangzhou has been listed as a UNESCO Creative City of Gastronomy since 2019, and the government aims to enhance the food brand for the city. This study attempts to assess the impact of a destination’s food brand equity on tourist satisfaction and travel intentions, and to evaluate the potential of developing food tourism. Questionnaires were conducted with 481 tourists, followed by semi-structured interviews with 29 tourists. A structural equation modelling analysis addressed the positive relationships among destination food brand equity, tourist satisfaction and travel intentions. A qualitative analysis contributes to further clarifying the relationships of the model. Implications for theory, research and practice are discussed.

By analyzing the support of load-carrying rolling elements when the rolling elements fall into the fault position, the dynamics model of a rolling bearing variable stiffness system with local faults is proposed, considering the retention factor of the contact deformation. Then, this paper researches the change of effective contact stiffness, contact deformation, contact force, and the total effective stiffness of the rolling elements. The results show that the contact stiffness of the rolling elements abruptly decreases when the rolling elements fall into the fault position. The contact deformation and contact force of the load-carrying rolling elements in the load zone increase, rebalancing the external radial load while causing a sudden reduction in the total effective stiffness, resulting in the vibration of the system. When different rolling elements fall into the outer ring fault position, the change in total effective stiffness and the system response are equal in magnitude. Additionally, there is a significant outer race fault characteristic frequency accompanied by frequency multiplication in the fault characteristic spectrums. When different rolling elements fall into the inner race fault position, the total effective stiffness is modulated by the inner race rotation and varies dramatically, resulting in the amplitude of the system time domain vibration response also being modulated by the inner race rotation and varying dramatically. Additionally, there is a significant inner race rotational frequency accompanied by frequency multiplication, an inner race fault characteristic frequency accompanied by frequency multiplication, and a side frequency in the fault characteristic spectrums. The research can provide some reference for the effective diagnosis of the rolling bearing fault.

Active air spring suspensions can improve the vehicle ride comfort and meanwhile realize the vehicle height regulation, and therefore, they have been widely used and studied. However, to achieve better ride comfort and a satisfactory vehicle body height adjustment, the active air suspension controller becomes an indispensable and significant part of the system. Since the nonlinear suspension system possesses uncertainties, it is difficult to take into account both ride comfort and height regulation. This study innovatively proposes an adaptive control algorithm to specifically address the problem of vehicle height regulation and ride comfort for nonlinear active air suspension systems with uncertainties. The accurate tracking to reference vehicle body height curves is realized, and the ride comfort is also improved. Through simulations with two scenarios, it is illustrated that the active air suspension controller owns better control effectiveness than the PID controller. Compared with the PID controller, the designed controller can track the reference vehicle body height curves faster and more accurately. The result also verifies the priority of the designed controller.

Accurate prediction of wind power is of great significance to the stable operation of the power system and the vigorous development of the wind power industry. In order to further improve the accuracy of ultra-short-term wind power forecasting, an ultra-short-term wind power forecasting method based on the CGAN-CNN-LSTM algorithm is proposed. Firstly, the conditional generative adversarial network (CGAN) is used to fill in the missing segments of the data set. Then, the convolutional neural network (CNN) is used to extract the eigenvalues of the data, combined with the long short-term memory network (LSTM) to jointly construct a feature extraction module, and add an attention mechanism after the LSTM to assign weights to features, accelerate model convergence, and construct an ultra-short-term wind power forecasting model combined with the CGAN-CNN-LSTM. Finally, the position and function of each sensor in the Sole du Moulin Vieux wind farm in France is introduced. Then, using the sensor observation data of the wind farm as a test set, the CGAN-CNN-LSTM model was compared with the CNN-LSTM, LSTM, and SVM to verify the feasibility. At the same time, in order to prove the universality of this model and the ability of the CGAN, the model of the CNN-LSTM combined with the linear interpolation method is used for a controlled experiment with a data set of a wind farm in China. The final test results prove that the CGAN-CNN-LSTM model is not only more accurate in prediction results, but also applicable to a wide range of regions and has good value for the development of wind power.

A theoretical model for describing the heat transfer characteristics of a turbine-based combined cycle (TBCC) engine cabin was established in Matlab/Simulink to quickly predict the thermal protection performance for engine accessories. The model’s effectiveness was verified by comparing the numerical results with the experimental data. The effects of different heat insulation layer thicknesses and fuel temperatures on the thermal protection performance are discussed; based on these effects, the heat insulation layer of 5 mm and fuel of 353 K were chosen to design the thermal protection cases. Nineteen different thermal protection cases were proposed and evaluated by using the model. Two representative accessories were chosen for the evaluation of the thermal protection performance of these cases. For accessory 1 with an internal heat source of 1000 W and internal fuel access, the thermal protection effect of adding a heat insulation layer and ventilation was the best, which decreased the accessory temperature by 43 K. For accessory 2 without an internal heat source, the thermal protection effect of adding a heat insulation layer to the casing and fuel cooling was the most ideal, which decreased the accessory temperature by 190 K. In addition, a comprehensive assessment was made to compare the performances of thermal protection cases.

In this paper, a method for rationally allocating energy storage capacity in a high-permeability distribution network is proposed. By constructing a bi-level programming model, the optimal capacity of energy storage connected to the distribution network is allocated by considering the operating cost, load fluctuation, and battery charging and discharging strategy. By constructing four scenarios with energy storage in the distribution network with a photovoltaic permeability of 29%, it was found that the bi-level decision-making model proposed in this paper saves 2346.66 yuan and 2055.05 yuan, respectively, in daily operation cost compared to the scenario without energy storage and the scenario with single-layer energy storage. After accessing IEEE-33 nodes for simulation verification, it was found that the bi-level decision-making model proposed in this paper has a good inhibition effect on voltage fluctuation and load fluctuation after energy storage configuration. In addition, this paper analyzes the energy storage that can be accessed by photovoltaic distribution networks with different permeability and finds that when photovoltaic permeability reaches 45% and corresponding energy storage is configured, the economic and energy storage benefits of the system are the best.

With the increasing integration of wind and photovoltaic power, the security and stability of the power system operations are greatly influenced by the intermittency and fluctuation of these renewable sources of energy generation. The accurate and reliable short-term forecasting of renewable energy generation can effectively reduce the impacts of uncertainty on the power system. In this paper, we propose an adaptive, data-driven stacking ensemble learning framework for the short-term output power forecasting of renewable energy. Five base-models are adaptively selected via the determination coefficient (R2) indices from twelve candidate models. Then, cross-validation is used to increase the data diversity, and Bayesian optimization is used to tune hyperparameters. Finally, base modes with different weights determined by minimizing the cross-validation error are ensembled using a linear model. Four datasets in different seasons from wind farms and photovoltaic power stations are used to verify the proposed model. The results illustrate that the proposed stacking ensemble learning model for renewable energy power forecasting can adapt to dynamic changes in data and has better prediction precision and a stronger generalization performance compared to the benchmark models.

In recent years, Shanghai has entered a stage of microscale land subsidence, but the uneven subsidence is still significant, with long-term impacts on the operational safety of subways and other infrastructures. On the basis of 154 high-resolution Terra Synthetic Aperture Radar-X (TerraSAR-X) images captured from 2013 to 2020 and the time-series persistent scatterer-interferometric SAR (PS-InSAR) method, the land subsidence along the subways in Shanghai was acquired, and the levelling data of 56 benchmarks were used to validate the measurements derived by PS-InSAR. The results indicated that the two data sets agreed well, with a correlation coefficient of 0.9 and maximum D-value of 4.0 mm derived from six pairs of comparative data sequences. The proportion of PS points showing deformation rates between −3.0 mm/a and 3.0 mm/a reached 99.4%. These results indicated that the land subsidence trend along the subway was relatively stable overall, while significant deformation was distributed mainly along the suburban subways, especially the lines that were newly open to traffic, such as Line 5 and the Pujiang line (PJ Line); along these lines, the proportions of PS points with deformation rates exceeding ±3 mm/a were 7.2% and 7.6%, respectively, and the proportions were much smaller in the other lines. The maximum cumulative deformation (MCD) along the subways was located between Jiangchuan Road Station and Xidu Station of Line 5 with a value of −66.4 mm, while the second and third MCDs were −48.2 mm along Line 16 and −44.5 mm along PJ Line, respectively. Engineering constructions, such as human-induced ground loads, foundation pit constructions, and road constructions, were the main factors affecting local land subsidence. The analysis results also showed that land subsidence was relatively significant during the period before the subways were open to traffic due to subway construction, while land subsidence clearly slowed after the subway lines were open to traffic. This deceleration in land subsidence was closely related to the rise in the groundwater level.

Radiotherapy (RT) is one of the most effective cancer treatments. However, successful radiation protection for normal tissue is a clinical challenge. Our previous study observed that MitoQ, a mitochondria-targeted antioxidant, was adsorbed to the inner mitochondrial membrane and remained the cationic moiety in the intermembrane space. The positive charges in MitoQ restrained the activity of respiratory chain complexes and decreased proton production. Therefore, a pseudo-mitochondrial membrane potential (PMMP) was developed via maintenance of exogenous positive charges. This study identified that PMMP constructed by MitoQ could effectively inhibit mitochondrial respiration within normal cells, disrupt energy metabolism, and activate adenosine 5′-monophosphate (AMP)-activated protein kinase (AMPK) signaling to induce autophagy. As such, it could not lead to starvation-induced autophagy among tumor cells due to the different energy phenotypes between normal and tumor cells (normal cells depend on mitochondrial respiration for energy supply, while tumor cells rely on aerobic glycolysis). Therefore, we successfully protected the normal cells from radiation-induced damage without affecting the tumor-killing efficacy of radiation by utilizing selective autophagy. MitoQ-constructed PMMP provides a new therapeutic strategy for specific radiation protection.

To study the variation rules of nonlinear stiffness of double-row angular contact ball bearings (DR-ACBB), this paper proposed a general mathematic model for DR-ACBB under three different configurations based on the improved quasi-static model of ball bearings, an explicit expression stiffness matrix of DR-ACBB is analytically derived, and a double-layer nested iterative algorithm based on the Newton–Raphson method is designed to realize the efficient solution of the proposed model. Then, the effects of the preload, speeds, and loads on the nonlinear stiffness variations of DR-ACBB under different arrangements are comparatively analyzed. The results show that DR-ACBB under the DB and DF configurations have the same variation rule in axial and radial stiffness; that is, a nonlinear soft-spring stiffness characteristic (i.e., the stiffness decreases with the external load) within the low-speed range and light load condition, and a nonlinear hard spring stiffness characteristic (i.e., the stiffness increases with the external load) within the high-speed range or heavy load condition.

The incomplete enforcement of environmental regulations in China is a serious issue in environmental protection affairs, and this paper attempts to provide a new explanation for its prevalence from the perspective of strategic interaction. Under Chinese decentralization, environmental regulations are seen by local governments as a tool to compete for scarce resources, which leads to strategic interactions between regions. Therefore, under the theoretical framework of regional policy spillovers, this paper examines the strategic interaction behavior of local governments in environmental regulation with a spatial econometric approach research methodology based on panel data of 29 Chinese provinces (autonomous regions and municipalities directly under the central government) from 2015 to 2019, taking spatial interdependence and the strategic interaction relationship of local governments as the entry point. The study finds that the intensity of environmental regulation in a region is not only related to the characteristics of the region, but also related to the intensity of environmental regulation in competing provinces, and there is a significant strategic interaction of environmental regulation behavior between regions, which is manifested as complementary spatial strategies. If the neighboring provinces invest more in environmental regulation, the region will also strengthen its level of environmental regulation accordingly, showing the contagiousness of non-complete enforcement of environmental regulation. At the same time, the complementary strategic interaction behavior of environmental regulation between regions has weakened since 2017, which highlights the role of green environmental performance assessment. Based on this, this paper proposes to provide a policy reference to avoid the environmental regulation enforcement dilemma.

Land cover change (LCC) is increasingly affecting global climate change, energy cycle, carbon cycle, and water cycle, with far-reaching consequences to human well-being. Web service-based online change detection applications have bloomed over the past decade for monitoring land cover change. Currently, massive processing services and data services have been published and used over the internet. However, few studies consider both service integration and resource sharing in land cover domain, making end-users rarely able to acquire the LCC information timely. The behavior interaction between services is also growing more complex due to the increasing use of web service composition technology, making it challenging for static web services to provide collaboration and matching between diverse web services. To address the above challenges, a Dynamic Service Computing Model (DSCM) was proposed for monitoring LCC. Three dynamic computation strategies were proposed according to different users’ requirements of change detection. WMS-LCC was first developed by extending the existing WMS for ready-use LCC data access. Spatial relation-based LCC data integration was then proposed for extracting LCC information based on multi-temporal land cover data. Processing service encapsulation and service composition methods were also developed for chaining various land cover services to a complex service chain. Finally, a prototype system was implemented to evaluate the validity and feasibility of the proposed DSCM. Two walk-through examples were performed with GlobeLand30 datasets and muti-temporal Landsat imagery, respectively. The experimental results indicate that the proposed DSCM approach was more effective and applicable to a wider range of issues in land cover change detection.

With the rapid development of web service technology, automatic land cover web service composition has become one of the key challenges in solving complex geoprocessing tasks of land cover. Service composition requires the creation of service chains based on semantic information about the services and all the constraints that should be respected. Artificial intelligence (AI) planning algorithms have recently significantly progressed in solving web service composition problems. However, the current approaches lack effective constraints to guarantee the accuracy of automatic land cover service composition. To address this challenge, the paper proposes a domain constraints-driven automatic service composition approach for online land cover geoprocessing. First, a land cover service ontology was built to semantically describe land cover tasks, data, and services, which assist in constructing domain constraints. Then, a constraint-aware GraphPlan algorithm was proposed, which constructs a service planning graph and searches services based on the domain constraints for generating optimal web service composition solutions. In this paper, the above method was integrated into a web prototype system and a case study for the online change detection automatic geoprocessing was implemented to test the accuracy of the method. The experimental results show that with this method, a land cover service chain can generate automatically by user desire objective and domain constraints, and the service chain execution result is more accurate.

The study aimed to assess the gait adjustment techniques of limbs on different slopes and investigate the relationship between forelimb and hindlimb kinetics and the center of mass (COM) during the uphill movement of a specific Boer goat using a pressure-sensitive walkway (PSW). During the uphill and downhill movements at a comfortable walking speed, we measured the ground reaction force (GRF) of the forelimbs and hindlimbs on the slope, the change in the included angle of the propulsive force direction of the forelimbs and hindlimbs, and the impulse relationship between GRF and propulsive force. According to the study, since the forelimbs of the goat were nearer the COM, they were primarily adjusted during the movement on the slope. By lowering the initial included angle of the propulsive force and the angle variation range, the forelimbs and hindlimbs could walk steadily. The forelimbs and hindlimbs exhibited completely different adjustment strategies during uphill and downhill movements. In particular, the forelimbs performed braking and the hindlimbs performed driving. In addition, we discovered that the goat altered its adjustment strategy when climbing the steep slope. All findings of this study indicate the need to understand the gait adjustment mode of the Boer goat during movement on the slope to thoroughly comprehend the driving strategy of quadrupeds with the ability to walk on specialized terrains.

In this study, lactic acid bacteria (LAB) strains were isolated from fermented grains of traditional Chinese Baijiu, and their probiotic properties were characterized. Eleven out of 29 LAB strains showed good tolerance to the gastrointestinal tract and bile salts. The surface characteristics (auto-aggregation, co-aggregation, hydrophobicity), safety (hemolytic and antibiotic sensitivity), antibacterial activity against three foodborne pathogens, and antioxidant and hypoglycemic properties of the 11 LAB strains were investigated. Principal component analysis (PCA) was used to comprehensively evaluate LAB strains and their probiotic properties. It was found that Weissella cibaria (OP288150), Pediococcus acidilactici (OP288151), Pediococcus pentosaceus (OP288154), Pediococcus pentosaceus (OP288156) and Levilactobacillus brevis (OP288158) showed high probiotic properties, with potential for commercial development. The results also demonstrated that fermented grains of Chinese Baijiu can be used as a source of high-quality probiotics.

Diabetes is a type of metabolic disease associated with changes in the intestinal flora. In this study, the regulatory effect of millet bran on intestinal microbiota in a model of type 2 diabetes (T2DM) was investigated in an effort to develop new approaches to prevent and treat diabetes and its complications in patients. The effect of purified millet bran polysaccharide (MBP) with three different intragastric doses (400 mg/kg, 200 mg/kg, and 100 mg/kg) combined with a high-fat diet was determined in a streptozotocin (STZ)-induced model of T2DM. By analyzing the changes in indicators, weight, fasting blood sugar, and other bio-physiological parameters, the changes in gut microbiota were analyzed via high-throughput sequencing to establish the effect of MBP on the intestinal flora. The results showed that MBP alleviated symptoms of high-fat diet-induced T2DM. A high dosage of MBP enhanced the hypoglycemic effects compared with low and medium dosages. During gavage, the fasting blood glucose (FBG) levels of rats in the MBP group were significantly reduced (p < 0.05). The glucose tolerance of rats in the MBP group was significantly improved (p < 0.05). In diabetic mice, MBP significantly increased the activities of CAT, SOD, and GSH-Px. The inflammatory symptoms of liver cells and islet cells in the MBP group were alleviated, and the anti-inflammatory effect was partially correlated with the dose of MBP. After 4 weeks of treatment with MBP, the indices of blood lipid in the MBP group were significantly improved compared with those of the DM group (p < 0.05). Treatment with MBP (400 mg/kg) increases the levels of beneficial bacteria and decreases harmful bacteria in the intestinal tract of rats, thus altering the intestinal microbial community and antidiabetic effect on mice with T2DM by modulating gut microbiota. The findings suggest that MBP is a potential pharmaceutical supplement for preventing and treating diabetes.

Hyperspectral unmixing decomposes the observed mixed spectra into a collection of constituent pure material signatures and the associated fractional abundances. Because of the universal modeling ability of neural networks, deep learning (DL) techniques are gaining prominence in solving hyperspectral analysis tasks. The autoencoder (AE) network has been extensively investigated in linear blind source unmixing. However, the linear mixing model (LMM) may fail to provide good unmixing performance when the nonlinear mixing effects are nonnegligible in complex scenarios. Considering the limitations of LMM, we propose an unsupervised nonlinear spectral unmixing method, based on autoencoder architecture. Firstly, a deep neural network is employed as the encoder to extract the low-dimension feature of the mixed pixel. Then, the generalized bilinear model (GBM) is used to design the decoder, which has a linear mixing part and a nonlinear mixing one. The coefficient of the bilinear mixing part can be adjusted by a set of learnable parameters, which makes the method perform well on both nonlinear and linear data. Finally, some regular terms are imposed on the loss function and an alternating update strategy is utilized to train the network. Experimental results on synthetic and real datasets verify the effectiveness of the proposed model and show very competitive performance compared with several existing algorithms.

Liver fibrosis is a wound-healing response caused by the abnormal accumulation of extracellular matrix, which is produced by activated hepatic stellate cells (HSCs). Most studies have focused on the activated HSCs themselves in liver fibrosis, and whether hepatocytes can modulate the process of fibrosis is still unclear. Sma mothers against decapentaplegic homologue 4 (Smad4) is a key intracellular transcription mediator of transforming growth factor-β (TGF-β) during the development and progression of liver fibrosis. However, the role of hepatocyte Smad4 in the development of fibrosis is poorly elucidated. Here, to explore the functional role of hepatocyte Smad4 and the molecular mechanism in liver fibrosis, a CCl4-induced liver fibrosis model was established in mice with hepatocyte-specific Smad4 deletion (Smad4Δhep). We found that hepatocyte-specific Smad4 deficiency reduced liver inflammation and fibrosis, alleviated epithelial-mesenchymal transition, and inhibited hepatocyte proliferation and migration. Molecularly, Smad4 deletion in hepatocytes suppressed the expression of inhibitor of differentiation 1 (ID1) and the secretion of connective tissue growth factor (CTGF) of hepatocytes, which subsequently activated the p38 and p65 signaling pathways of HSCs in an epidermal growth factor receptor-dependent manner. Taken together, our results clearly demonstrate that the Smad4 expression in hepatocytes plays an important role in promoting liver fibrosis and could therefore be a promising target for future anti-fibrotic therapy.

The lack of labeled samples severely restricts the classification performance of deep learning on hyperspectral image classification. To solve this problem, Generative Adversarial Networks (GAN) are usually used for data augmentation. However, GAN have several problems with this task, such as the poor quality of the generated samples and an unstable training process. Thereby, knowing how to construct a GAN to generate high-quality hyperspectral training samples is meaningful for the small-sample classification task of hyperspectral data. In this paper, an Auxiliary Classifier based Wasserstein GAN with Gradient Penalty (AC-WGAN-GP) was proposed. The framework includes AC-WGAN-GP, an online generation mechanism, and a sample selection algorithm. The proposed method has the following distinctive advantages. First, the input of the generator is guided by prior knowledge and a separate classifier is introduced to the architecture of AC-WGAN-GP to produce reliable labels. Second, an online generation mechanism ensures the diversity of generated samples. Third, generated samples that are similar to real data are selected. Experiments on three public hyperspectral datasets show that the generated samples follow the same distribution as the real samples and have enough diversity, which effectively expands the training set. Compared to other competitive methods, the proposed framework achieved better classification accuracy with a small number of labeled samples.

During a steering manoeuvre in a ground vehicle, both yaw motion and roll motion can occur simultaneously, and their dynamics can be coupled, as the roll motion is generalized directly from the tires’ lateral force under steering. Hence, it is of significance to analyze them as an integrated plant in the vehicle steering stability control problem. Furthermore, the actuator saturation of yaw control cannot be neglected, as vehicles often steer at a high velocity or on low-friction roads. In this paper, an integrated steering dynamics model is established considering the coupling between the roll motion and lateral motion, then a novel nonlinear adaptive controller is proposed to stabilize the steering motion considering the actuator saturation of yaw motion control. Simulation results indicate that the designed integrated controller is effective in improving the performance of both the yaw rate tracking error and ride comfort taking into account vehicle parameter uncertainties and actuator saturation; the steering stability of ground vehicles can consequently be guaranteed.

Underground coal gasification (UCG) may change the energy consumption structure from coal-dominated to gas-dominated in the years to come. Before that, three important problems need to be solved, including failure of gasification due to large amounts of water pouring into the gasifier, environmental pollution caused by gas migration to the surface, and low calorific value caused by poor control of the degree of gasification. In this study, a geological model is first established using the computer modeling group (CMG), a commercial software package for reservoir simulation. Then, the inflow of coal seam water into the gasifier during the controlled retracting injection point (CRIP) gasification process is simulated based on the geological model, and the maximum instantaneous water inflow is simulated too. Meanwhile, the migration of gasified gas is also simulated, and the migration discipline of different gases is shown. Finally, the pressure distributions in two stages are presented, pointing out the dynamic pressure characteristics during the UCG process. The results show that (a) the cavity width, production pressure, and gasifier pressure are negatively correlated with the maximum instantaneous water inflow, while the initial formation pressure, injection pressure, coal seam floor aquifer energy, and temperature are positively correlated; (b) CO2 is mainly concentrated near the production well and largely does not migrate upward, O2 migrates upward slowly, while CH4, CO and H2 migrate relatively quickly. When the injection–production pressure difference is 2 MPa, it takes 33.5 years, 40 years, and 44.6 years for CH4, CO, and H2 to migrate from a depth of 1000 m to 200 m, respectively. When the pressure difference increases to 4 MPa, the gas migration rate increases about two-fold. The aquifer (3 MPa) above a coal outcrop can slow down the upward migration rate of gas by 0.03 m/day; (c) the pressure near the production well changes more significantly than the pressure near the injection well. The overall gasifier pressure rises with gasifier width increases, and the pressure distribution always presents an asymmetric unimodal distribution during the receding process of the gas injection point. The simulation work can provide a theoretical basis for the operation parameters design and monitoring of the well deployment, ensuring the safety and reliability of on-site gasification.

Microrobots can operate in tiny areas that traditional bulk robots cannot reach. The combination of acoustic actuation with microrobots extensively expands the application areas of microrobots due to their desirable miniaturization, flexibility, and biocompatibility features. Herein, an overview of the research and development of acoustics-actuated microrobots is provided. We first introduce the currently established manufacturing methods (3D printing and photolithography). Then, according to their different working principles, we divide acoustics-actuated microrobots into three categories including bubble propulsion, sharp-edge propulsion, and in-situ microrotor. Next, we summarize their established applications from targeted drug delivery to microfluidics operation to microsurgery. Finally, we illustrate current challenges and future perspectives to guide research in this field. This work not only gives a comprehensive overview of the latest technology of acoustics-actuated microrobots, but also provides an in-depth understanding of acoustic actuation for inspiring the next generation of advanced robotic devices.

Identifying influential nodes in complex networks has attracted the attention of many researchers in recent years. However, due to the high time complexity, methods based on global attributes have become unsuitable for large-scale complex networks. In addition, compared with methods considering only a single attribute, considering multiple attributes can enhance the performance of the method used. Therefore, this paper proposes a new multiple local attributes-weighted centrality (LWC) based on information entropy, combining degree and clustering coefficient; both one-step and two-step neighborhood information are considered for evaluating the influence of nodes and identifying influential nodes in complex networks. Firstly, the influence of a node in a complex network is divided into direct influence and indirect influence. The degree and clustering coefficient are selected as direct influence measures. Secondly, based on the two direct influence measures, we define two indirect influence measures: two-hop degree and two-hop clustering coefficient. Then, the information entropy is used to weight the above four influence measures, and the LWC of each node is obtained by calculating the weighted sum of these measures. Finally, all the nodes are ranked based on the value of the LWC, and the influential nodes can be identified. The proposed LWC method is applied to identify influential nodes in four real-world networks and is compared with five well-known methods. The experimental results demonstrate the good performance of the proposed method on discrimination capability and accuracy.

Porcine epidemic diarrhea virus (PEDV), an enteric coronavirus, causes neonatal pig acute gastrointestinal infection with a characterization of severe diarrhea, vomiting, high morbidity, and high mortality, resulting in tremendous damages to the swine industry. Neither specific antiviral drugs nor effective vaccines are available, posing a high priority to screen antiviral drugs. The aim of this study is to investigate anti-PEDV effects of carbazole alkaloid derivatives. Eighteen carbazole derivatives (No.1 to No.18) were synthesized, and No.5, No.7, and No.18 were identified to markedly reduce the replication of enhanced green fluorescent protein (EGFP) inserted-PEDV, and the mRNA level of PEDV N. Flow cytometry assay, coupled with CCK8 assay, confirmed No.7 and No.18 carbazole derivatives displayed high inhibition effects with low cell toxicity. Furthermore, time course analysis indicated No.7 and No.18 carbazole derivatives exerted inhibition at the early stage of the viral life cycle. Collectively, the analysis underlines the benefit of carbazole derivatives as potential inhibitors of PEDV, and provides candidates for the development of novel therapeutic agents.

Pneumatic soft grippers have been widely studied. However, the structures and material properties of existing pneumatic soft grippers limit their load capacity and manipulation range. In this article, inspired by sea lampreys, we present a pneumatic novel combined soft gripper to achieve a high load capacity and a large grasping range. This soft gripper consists of a cylindrical soft actuator and a detachable sucker. Three internal air chambers of the cylindrical soft actuator are inflated, which enables them to hold objects. Under vacuum pressure, the cylindrical soft actuator and the detachable sucker can both adsorb objects. A finite element model was constructed to simulate three inflation chambers for predicting the grasping range of the cylindrical soft actuator. The validity of the finite element model was established by an experiment. The mechanism of holding force and adsorption force were analyzed. Several groups of experiments were conducted to determine adsorption range, holding force, and adsorption force. In addition, practical applications further indicated that the novel combined soft gripper has a high load capacity (10.85 kg) at a low pressure (16 kPa) and a large grasping range (minimum diameter of the object: d = 6 mm), being able to lift a variety of objects with different weights, material properties, and shapes.

Due to huge demand in engineering, vibration control technology and related studies have always been at the frontiers of research. Although traditional vibration control methods are stable and reliable, they have obvious shortcomings. Through evolution and natural selection, certain body-parts of animals in the natural world have been cleverly constructed and well designed. This provides a steady stream of inspiration for the design of vibration control equipment. The prime objective of this review is to highlight recent advances in the bionic design of vibration control devices. Current bionic vibration control devices were classified, and their bionic principles were briefly described. One kind was the bionic device based on the brain structure of the woodpecker, which is mostly used to reduce vibration at high frequencies. Another kind of bionic device was based on animal leg structure and showed outstanding performance in low frequency vibration reduction. Finally, we briefly listed the problems that need to be solved in current bionic vibration control technology and gave recommendations for future research direction.

Chronic liver disease mediated by the activation of hepatic stellate cells (HSCs) leads to liver fibrosis. The signal adaptor MyD88 of Toll-like receptor (TLR) signaling is involved during the progression of liver fibrosis. However, the specific role of MyD88 in myeloid cells in liver fibrosis has not been thoroughly investigated. In this study, we used a carbon tetrachloride (CCl4)-induced mouse fibrosis model in which MyD88 was selectively depleted in myeloid cells. MyD88 deficiency in myeloid cells attenuated liver fibrosis in mice and decreased inflammatory cell infiltration. Furthermore, deficiency of MyD88 in macrophages inhibits the secretion of CXC motif chemokine 2 (CXCL2), which restrains the activation of HSCs characterized by NLR Family Pyrin Domain Containing 3 (NLRP3) inflammasome activation. Moreover, targeting CXCL2 by CXCR2 inhibitors attenuated the activation of HSCs and reduced liver fibrosis. Thus, MyD88 may represent a potential candidate target for the prevention and treatment of liver fibrosis.

Colorectal cancer (CRC) is a malignant tumor in the digestive system whose incidence and mortality is high-ranking among tumors worldwide. The initiation and progression of CRC is a complex process involving genetic alterations in cancer cells and multiple factors from the surrounding tumor cell microenvironment. As accumulating evidence has shown, tumor-associated macrophages (TAMs)—as abundant and active infiltrated inflammatory cells in the tumor microenvironment (TME)—play a crucial role in CRC. This review focuses on the different mechanisms of TAM in CRC, including switching of phenotypical subtypes; promoting tumor proliferation, invasion, and migration; facilitating angiogenesis; mediating immunosuppression; regulating metabolism; and interacting with the microbiota. Although controversy remains in clinical evidence regarding the role of TAMs in CRC, clarifying their significance in therapy and the prognosis of CRC may shed new light on the optimization of TAM-centered approaches in clinical care.

Cell can integrate the caspase family and mammalian target of rapamycin (mTOR) signaling in response to cellular stress triggered by environment. It is necessary here to elucidate the direct response and interaction mechanism between the two signaling pathways in regulating cell survival and determining cell fate under cellular stress. Members of the caspase family are crucial regulators of inflammation, endoplasmic reticulum stress response and apoptosis. mTOR signaling is known to mediate cell growth, nutrition and metabolism. For instance, over-nutrition can cause the hyperactivation of mTOR signaling, which is associated with diabetes. Nutrition deprivation can inhibit mTOR signaling via SH3 domain-binding protein 4. It is striking that Ras GTPase-activating protein 1 is found to mediate cell survival in a caspase-dependent manner against increasing cellular stress, which describes a new model of apoptosis. The components of mTOR signaling-raptor can be cleaved by caspases to control cell growth. In addition, mTOR is identified to coordinate the defense process of the immune system by suppressing the vitality of caspase-1 or regulating other interferon regulatory factors. The present review discusses the roles of the caspase family or mTOR pathway against cellular stress and generalizes their interplay mechanism in cell fate determination.

Ultrasound-assisted extraction (UAE) was applied to extract rutin (RU), nicotiflorin (NI), narcissoside (NA), kaempferol (KA), isorhamnetin (IS), quercetin (QU), and total flavonoids of Flos Sophorae Immaturus (TFFSI) from Flos Sophorae Immaturus (FSI). Through single factor test and response surface methodology (RSM), the optimal extraction conditions were concluded as follows: ethanol concentration 70%, time 30 min, temperature 61 °C, and liquid/solid ratio 15.30 mL/g, respectively. The actual extraction rates of RU, NI, NA, KA, IS, QU, and TFFSI were 14.6101%, 2.9310%, 7.1987%, 0.1041%, 0.4920%, 2.7998%, and 26.4260%, respectively. The experimental results demonstrated that the extraction method with accuracy and efficiency could be used for the comprehensive evaluation quality control of extracts from FSI. The antioxidant activities of hydroalcoholic extraction from FSI on 1,1-diphenyl-2-picrylhydrazyl (DPPH), 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS•+), superoxide anion (•O2−) free radicals, and ferric reducing/antioxidant power (FRAP) were assessed. The results showed that the antioxidation activities of extracts on DPPH, ABTS•+, and •O2− free radicals were reached 89.29%, 97.86%, and 56.61%, and 81.4% in FRAP at 1.0 mg/mL, respectively. The antioxidant capacity of FSI extract was positively correlated with the amount of total flavonoids.

The mammalian or mechanistic target of rapamycin (mTOR) pathway plays a crucial role in regulation of cell survival, metabolism, growth and protein synthesis in response to upstream signals in both normal physiological and pathological conditions, especially in cancer. Aberrant mTOR signaling resulting from genetic alterations from different levels of the signal cascade is commonly observed in various types of cancers. Upon hyperactivation, mTOR signaling promotes cell proliferation and metabolism that contribute to tumor initiation and progression. In addition, mTOR also negatively regulates autophagy via different ways. We discuss mTOR signaling and its key upstream and downstream factors, the specific genetic changes in the mTOR pathway and the inhibitors of mTOR applied as therapeutic strategies in eight solid tumors. Although monotherapy and combination therapy with mTOR inhibitors have been extensively applied in preclinical and clinical trials in various cancer types, innovative therapies with better efficacy and less drug resistance are still in great need, and new biomarkers and deep sequencing technologies will facilitate these mTOR targeting drugs benefit the cancer patients in personalized therapy.

The hard-shelled mussel (Mytilus coruscus) has been used as Chinese traditional medicine for thousands of years; however, to date the ingredients responsible for the various beneficial health outcomes attributed to Mytilus coruscus are still unclear. An α-d-Glucan, called MP-A, was isolated from Mytilus coruscus, and observed to exert anti-inflammatory activity in THP-1 human macrophage cells. Specifically, we showed that MP-A treatment inhibited the production of inflammatory markers, including TNF-α, NO, and PGE2, inducible NOS (iNOS), and cyclooxygenase-2 (COX-2), in LPS-activated THP-1 cells. It was also shown to enhance phagocytosis in the analyzed cells, but to severely inhibit the phosphorylation of mitogen-activated protein kinases (MAPKs) and the nuclear translocation of NF-κB P65. Finally, MP-A was found to exhibit a high binding affinity for the cell surface receptor TLR4, but a low affinity for TLR2 and dectin-1, via surface plasmon resonance (SPR) analysis. The study indicates that MP-A suppresses LPS-induced TNF-α, NO and PEG2 production via TLR4/NF-κB/MAPK pathway inhibition, and suggests that MP-A may be a promising therapeutic candidate for diseases associated with TNF-α, NO, and/or PEG2 overproduction.