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The component analysis of raw meal is critical to the quality of cement. In recent years, near-infrared (NIR) has been emerged as an innovative and efficient analytical method to determine the oxide content of cement raw meal. This study aims to utilize NIR spectroscopy combined with machine learning and chemometrics to improve the prediction of oxide content in cement raw meal. The Savitzky-Golay convolution smoothing method is applied to eliminate noise interference for the analysis of calcium carbonate (CaCO3), silicon dioxide (SiO2), aluminum oxide (Al2O3), and ferric oxide (Fe2O3) in cement raw materials. Different wavelength selection techniques are used to perform a comprehensive analysis of the model, comparing the performance of several wavelength selection techniques. The back-propagation neural network regression model based on particle swarm optimization algorithm was also applied to optimize the extracted and screened feature wavelengths, and the model prediction performance was checked and evaluated using Rp and RMSE. In conclusion, the results indicate that NIR spectroscopy in combination with ML and chemometrics has great potential to effectively improve the prediction performance of oxide content in raw materials and highlight the importance of modeling and wavelength selection techniques. By enabling more accurate and efficient determination of oxide content in raw materials, NIR spectroscopy coupled with meta-modeling has the potential to revolutionize quality assurance practices in cement manufacturing.

The role of acoustic cavitation in various surface cleaning disciplines is important. However, the physical mechanisms underlying acoustic cavitation-induced surface cleansing are poorly understood. This is due to the combination of microscopic and ultrashort timescales associated with the dynamics of acoustic cavitation bubbles. Here, we have precisely controlled single-bubble cavitation in both space and time. Ultrasonic excitation leads to the cavitation of generated single bubbles. A synchronous ultrafast photomicrographic system simultaneously records the dynamics of single acoustic cavitation bubbles (SACBs) and the cleaning process of the nearby surface in liquids with varying viscosities. Finally, we analysed the correlation between bubble dynamics and surface cleaning situations. The differences in the typical dynamic characteristics of the bubbles during collapse in liquids with varying viscosities reveal two main mechanisms underlying surface cleaning by acoustic cavitation, which are respective the Laplace pressure during the bubble’s movement and liquid jets during bubble collapse. Our study provides a better physical understanding of the ultrasonic cleaning process based on acoustic cavitation, and will help to optimize and facilitate the applications of surface cleaning, especially for the cleaning of substrates with tightly attached dirt.

Acetone is a biomarker found in the expired air of patients suffering from diabetes. Therefore, early and accurate detection of its concentration in the breath of such patients is extremely important. We prepared Tin(IV) oxide (SnO2) nanospheres via hydrothermal treatment and then decorated them with bimetallic PtAu nanoparticles (NPs) employing the approach of in situ reduction. The topology, elemental composition, as well as crystal structure of the prepared materials were studied via field emission scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy, and X-ray diffraction. The findings revealed that bimetallic PtAu-decorated SnO2 nanospheres (PtAu/SnO2) were effectively synthesized as well as PtAu NPs evenly deposited onto the surface of the SnO2 nanospheres. Pure SnO2 nanospheres and PtAu/SnO2 sensors were prepared, and their acetone gas sensitivity was explored. The findings demonstrated that in comparison to pristine SnO2 nanosphere sensors, the sensors based on PtAu/SnO2 displayed superior sensitivity to acetone of 0.166–100 ppm at 300 °C, providing a low theoretical limit of detection equal to 158 ppm. Moreover, the PtAu/SnO2 sensors showed excellent gas response (Ra/Rg = 492.3 to 100 ppm), along with fast response and recovery (14 s/13 s to 10 ppm), good linearity of correlation, excellent repeatability, long-term stability, and satisfactory selectivity at 300 °C. This improved gas sensitivity was because of the electron sensitization of the Pt NPs, the chemical sensitization of the Au NPs, as well as the synergistic effects of bimetallic PtAu. The PtAu/SnO2 sensors have considerable potential for the early diagnosis and screening of diabetes.

As a safe and effective method for systemic transdermal drug delivery (TDD), sonophoresis has drawn much attention from researchers. Despite numerous studies confirming cavitation as the main reason for sonophoresis, the effect skin has on cavitation bubble dynamics remains elusive due to the difficulty of experimental challenges. For a start, we reveal how single cavitation bubble (SCB) dynamics are affected by skin properties, including elasticity, hydrophilicity and texture. We use polydimethylsiloxane (PDMS) to simulate human skin and record the temporary evolution of SCBs with synchronous ultrafast photography. The influences of skin properties on SCBs are concluded: 1) SCBs collapse later near walls with better elasticities and generate microjets with higher speed; 2) SCBs collapse later near hydrophilic walls with slower microjets; and 3) the existence of a texture structure on walls also delays the time of bubble collapse near them and slows the velocities of microjets (v) during collapses.

Babao Dan capsule (BBDC) is a state secrecy traditional Chinese medicine patent prescription (TCMPP) that is widely used for hepatobiliary and urinary diseases. However, its quality uniformity evaluation (QUE) is lacking. It is necessary to develop a novel and multi–dimensional QUE strategy for BBDC based on chemical properties and bioactivities. An ultrahigh performance liquid chromatography–triple quadrupole–mass spectrometry (UHPLC–QQQ–MS/MS) was developed to quantify 14 compounds, and a QUE strategy was to assess intra–batch and inter–batches consistency. Furthermore, the anti-inflammatory activities were measured in vitro by RAW264.7 cell and NF-κB-RE-luc HEK293 cell. The results indicated that 14 compounds from 16 samples (10 batches) were accurately quantified. And the intra–batch consistency values were less than or equal to 20.1%, whereas the largest inter–batches consistency value was 167.5% of glycocholic acid. 10 batches were divided into three categories to distinction inter–batches differences. BBDC had anti-inflammatory activities including inhibition of LPS–induced NO production and TNF α–activated luciferase counts. The NO inhibition difference of 10 batches was 10.87 μM and accounted for 49.8%. Briefly, the anti-inflammatory activities of 10 batches were different, but the dynamic trends within the same batch were similar, which was supported by luciferase counts. And the correlation was confirmed that the main compounds related to anti-inflammatory in BBDC were bile acids and saponins. Hence, a QUE strategy for BBDC was proposed to improve quality and ensure efficacy. This strategy can be utilized as a reference for consistency evaluations of TCMPPs.

Moldy corn produces aflatoxin and gibberellin, which can have adverse effects on human health if consumed. Mold is a significant factor that affects the safe storage of corn. If not detected and controlled in a timely manner, it will result in substantial food losses. Understanding the infection patterns of mold on corn kernels and the changing characteristics of the internal structure of corn kernels after infection is crucial for guiding innovation and optimizing detection methods for moldy corn. This knowledge also helps maintain corn storage and ensure food safety. This study was based on X-ray tomography technology to non-destructively detect changes in the structural characteristics of moldy corn kernels. It used image processing technology and model reconstruction algorithms to obtain the 3D model of the embryo, pores and cracks, endosperm and seed coat, and kernels of moldy corn kernels; qualitative analysis of the characteristic changes of two-dimensional slice grayscale images and 3D models of moldy corn kernels; and quantitative analysis of changes in the volume parameters of corn kernels, embryos, endosperm, and seed coats as a whole. It explored the detection method of moldy corn kernels based on a combination of X-ray tomography technology and deep learning algorithms. The analysis concluded that mold infection in maize begins in the embryo and gradually spreads and that mold damage to the tissue structure of maize kernels is irregular in nature. The overall volume parameter changes of corn kernels, embryos, endosperm, and seed coats in the four stages of 0 d, 5 d, 10 d, and 15 d showed a trend of first increasing and then decreasing. The ResNet50 model was enhanced for detecting mold on maize kernels, achieving an accuracy of over 93% in identifying mold features in sliced images of maize kernels. This advancement enabled the non-destructive detection and classification of the degree of mold in maize kernel samples. This article studies the characterization of the characteristic changes of moldy corn kernels and the detection of mildew, which will provide certain help for optimizing the monitoring of corn kernel mildew and the development of rapid detection equipment.

To study the phenomenon of large deformation and the failure of roadways under dynamic pressure, this paper takes the 10607 gas drainage roadway as the research object and presents the results of a theoretical derivation of equations for the boundary of the plastic zone of a dynamic pressure roadway with and without supporting force. The impacts of the mining influence coefficient and supporting force on the plastic zone boundary were explored for various lateral pressure coefficients. As the supporting force was increased, it was observed that there was a slight reduction in the radius of the plastic zone of the dynamic pressure roadway, while its scope and shape barely changed. Based on the failure mechanism of the dynamic pressure roadway, active and passive support technology was proposed, and the support effect was analyzed using FLAC3D 5.0 numerical simulation software for the case of the 10607 gas drainage roadway.

In the R22 (chlorodifuoromethane) steam-cracking process, which is used to produce a TFE (tetrafluoroethylene) monomer, distillation is employed to separate the high-purity TFE monomer from the cracked gas generated during this procedure. Traditionally, this distillation process is carried out using five towers. In this study, the traditional five-tower distillation method was transformed into a four-tower distillation method through the Aspen Plus simulation software, and this process was simulated and optimized. Meanwhile, a double-effect distillation process was designed for the transformed four-tower distillation process. The transformed distillation process not only meets the requirements of 99.999% purity for the TFE monomer and 99.99% purity for R22 recycling, but it also reduces the footprint by eliminating one distillation tower and saves 112.9002 kW of tower load, thus reducing the operating costs. This research provides valuable guidance for practical production.

Insulators, pivotal to the integrity of railway catenaries, demand impeccable functioning to prevent system failures. Their consistent assessment is vital for railway safety. Current insulator evaluations in subways predominantly involve human intervention, a method fraught with inefficiencies, inaccuracies, and oversights, exacerbated by the complex backdrop of subway tunnels and minuscule defect dimensions. This study introduces an enhanced algorithm, anchored in the YOLOv5 framework, to refine insulator defect identification. Challenges in defect detection include limited, imbalanced data samples and adaptability. Addressing this, an accurate catenary model mirrors the subway line’s architecture, facilitating the creation of synthetic instances of both intact and impaired insulators. An atomization technique augments the dataset volume, fortifying the algorithm’s resilience in reduced visibility conditions, such as fog. Tackling sample equilibrium, the study introduces an equilibrium loss function, assigning disparate weights to various sample categories during training, thereby sharpening the algorithm’s focus on positive instances, particularly those that are challenging to discern, and rectifying the disproportion in sample categories. Incorporating lightweight structures like GhostNet and the Efficient Channel Attention Network (ECA-Net) channel attention scheme not only diminishes the network’s computational demands, thereby elevating the detection capabilities, but also minimizes superfluous data processing, enhancing the accuracy in identifying smaller targets. Empirical analyses indicate substantial model optimization: a size reduction to 60 pp of its original (from 15 MB to 9 MB), a near 1.4 pp increase in mean average precision (mAP) to 96.57%, and a tripling of the detection speed (from 30 to 90 FPS). Real-world image assessments further reveal a mAP improvement of approximately 2.5 pp (reaching 98.43%), confirming the model’s suitability for real-time applications.

Low-rank coal accounts for over half of China’s proven coal reserves. The possibility of coal and gas outbursts in low-rank coal is higher, especially in the m·agmatic thermal evolution area. The complexity of coal’s molecular structure is one of the reasons for problems during the process of mining operations. Different analysis techniques, including XRD, FTIR and Raman spectroscopy, were used to obtain the molecular characteristics of magmatic thermal evolution coal samples and normal coal samples, so that a comparative study could be carried out to investigate the influence of the magmatic thermal evolution effect on the molecular structure of low-rank coal. The ranges of the aromatic interlayer spacing (d002), average stacking heights (Lc) and stacking layer number (Nave) of the thermally evolved coal samples are 3.41–3.51 Å, 22.76–27.02 Å, and 6.68–7.70, respectively. The ranges of the full width at half maximum ratio (FD1/FG) and the peak integral intensity ratio (ID1/IG) are 2.16–2.19 and 1.55–1.84, respectively. Compared with the normal coal samples, those affected by magmatic thermal evolution have smaller d002, ID1/IG, and FD1/FG values, but larger Lc values. The results indicate that the thermally evolved coal samples have more ordered structures and more developed microcrystalline structure sizes than normal coal samples.

Graphene quantum dots (GQDs), as 0D graphene nanomaterials, have aroused increasing interest in chemiresistive gas sensors owing to their remarkable physicochemical properties and tunable electronic structures. Research on GQDs has been booming over the past decades, and a number of excellent review articles have been provided on various other sensing principles of GQDs, such as fluorescence-based ion-sensing, bio-sensing, bio-imaging, and electrochemical, photoelectrochemical, and electrochemiluminescence sensing, and therapeutic, energy and catalysis applications. However, so far, there is no single review article on the application of GQDs in the field of chemiresistive gas sensing. This is our primary inspiration for writing this review, with a focus on the chemiresistive gas sensors reported using GQD-based composites. In this review, the various synthesized strategies of GQDs and its composites, gas sensing enhancement mechanisms, and the resulting sensing characteristics are presented. Finally, the current challenges and future prospects of GQDs in the abovementioned application filed have been discussed for the more rational design of advanced GQDs-based gas-sensing materials and innovative gas sensors with novel functionalities.

There will be great damage in the process of harvesting, transporting, and storing after grain matures. The injury rate is as high as 8% to 12%. After damage, the germination rate of the grain becomes lower, the quality decreases, and it is easily infected with pests and molds. This study of the grain-crushing characteristics is of great significance to ensure grain quality, and an accurate crushing model is a prerequisite for effectively simulating crushing characteristics. This paper studies the shattering characteristics of wheat grains. Two-dimensional slices of wheat grain were obtained using X-ray tomography technology. Then, an accurate three-dimensional outer contour model of the wheat particle was constructed using image filtering and segmentation algorithms. The particle filling process was conducted using EDEM 2018 software to establish a wheat particle simulation model based on the Hertz–Mindlin with a Bonding contact model. Using the DOE experimental design method, single-factor experiments, Plackett–Burman experiments, steepest-climb experiments, and Box–Behnken were designed to study the fragmentation characteristics of wheat particles combined with parameter calibration and physical experiments. The test results show that the normal stiffness per unit area is 7.392 × 1010 N/m3, critical normal stress is 5.293 × 106 Pa, critical tangential stress is 5.001 × 106 Pa, and the relative error about 3%, which verifies the reliability of the simulation parameters in the discrete-element crushing model of wheat grain. This study focuses on two essential aspects: 1. establishing an accurate wheat-grain contour model; and 2. calibrating the bonding parameters of the discrete-element simulation model of wheat grain. The wheat grain discrete-element crushing model and the calibration of its bonding parameters are constructed to provide a foundation for the study of wheat-grain crushing characteristics. It is of great significance to study the situation of wheat grains and where cracks are produced. In this paper, an accurate model of the wheat-grain contour is established, and the bonding parameters of the discrete-element simulation model of the wheat grain are calibrated. The calibration of the model of the discrete elements of wheat-grain fragmentation and its bonding parameters will provide a basis for studying the crushing characteristics of wheat grain. Understanding the condition of wheat grains and the causes of cracks carries significant academic significance.

DNA methylation is an important epigenetic modification that has been shown to be associated with responses to non-biological stressors. However, there is currently no research on DNA methylation in response to environmental signals in shrimp. In this study, we conducted a comprehensive comparative analysis of DNA methylation profiles and differentially expressed genes between two strains of Litopenaeus vannamei with significantly different cold tolerance through whole genome bisulfite sequencing (WGBS) and transcriptome sequencing. Between Lv-C and Lv-T (constant temperature of 28 °C and low temperatures of 18 °C and 10 °C) under cytosine-guanine (CG) environments, 39,100 differentially methylated regions (DMRs) were identified, corresponding to 9302 DMR-related genes (DMRGs). The DMRs were mainly located in the gene body (exons and introns). Gene Ontology (GO) analysis showed that these DMRGs were significantly enriched in cell parts, catalytic activity, and metabolic processes. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed significant enrichment of these DMRGs in pathways such as proteasome (ko03050), oxidative phosphorylation (ko00190), mTOR signaling pathway (ko04150), fatty acid metabolism (ko01212), and fatty acid degradation (ko00071). The comprehensive results suggested that L. vannamei mainly regulates gene expression in response to low temperatures through hypermethylation or demethylation of some genes involved in thermogenesis, glycolysis, the autophagy pathway, the peroxisome, and drug metabolism pathways. These results provide important clues for studying DNA methylation patterns and identifying cold tolerance genes in shrimp.

One of the significant factors contributing to high energy consumption is the unutilized waste heat from flue gas in industrial boilers. Thermoelectric generator (TEG) technology can directly convert thermal energy into electrical energy, and has been gradually applied in the field of waste heat recovery due to its simple and reliable structure, environmental protection, and other advantages. In this paper, a thermoelectric generator simulation system of boiler flue gas waste heat is proposed. The experimental platform is designed by simulating the flue gas waste heat temperature condition of boiler, and the structure of cold end module and hot end module is optimized. During the experiment, the fixed temperature difference was set at 120 °C (hot end:150 °C~cold end: 30 °C). An analysis is conducted on the volt-ampere characteristics and output power of the TEG module. The output characteristics of the TEG system are analyzed under the conditions of variable load, constant load, different pump speed, different heat dissipation modes, and series and parallel connection method. The results show that the experimental platform can instantaneously and accurately test the output parameters of the TEG system, and ensure the intended design requirements. When the ratio of the load resistance to the internal resistance of the TEG module is approximately 1–1.15, the output power of the system reaches its maximum. In order to optimize the output power of the TEG system, a power prediction-based adaptive variable step size maximum power point tracking (MPPT) algorithm is introduced. Additionally, a corresponding mathematical model is formulated. Simulations demonstrate that the time of the improved algorithm to reach the stable maximum power point is 1.54 s faster than that of the traditional algorithm. The improved MPPT algorithm satisfies the criteria for speed and accuracy, diminishes superfluous energy waste, and enhances the overall system efficiency. The research results have certain guiding significance for the design and application of subsequent TEG system.

Pseudomonas aeruginosa, a pathogenic Gram-negative bacterium for which currently antibiotic resistance is posing a significant problem and for which no vaccines are available, protects itself by the formation of a biofilm. The Pel polysaccharide, a cationic polymer composed of cis-linked galactosamine (GalN), N-acetyl galactosamine (GalNAc), glucosamine (GlcN) and N-acetyl glucosamine (GlcNAc) monosaccharides, is an important constituent of the biofilm. Well-defined Pel oligosaccharides will be valuable tools to probe the biosynthesis machinery of this polysaccharide and may serve as diagnostic tools or be used as components of glycoconjugate vaccines. We here, report on the development of synthetic chemistry to access well-defined Pel-oligosaccharides. The chemistry hinges on the use of di-tert-butylsilylidene protected GalN and GlcN building blocks, which allow for completely cis-selective glycosylation reactions. We show the applicability of the chemistry by the assembly of a matrix of 3 × 6 Pel heptasaccharides, which has been generated from a single set of suitably protected Pel heptasaccharides, in which a single glucosamine residue is incorporated and positioned at different places along the Pel oligo-galactosamine chain.

Prenylated flavonol glycosides in Epimedium plants, as key medicinal components, are known to have great pharmaceutical activities for human health. Among the main prenylated flavonol glycosides, the modification mechanism of different sugar moieties is still not well understood. In the current study, a novel prenylated flavonol rhamnoside xylosyltransferase gene (EpF3R2″XylT) was cloned from E. pubescens, and the enzymatic activity of its decoding proteins was examined in vitro with different prenylated flavonol rhamnoside substrates and different 3-O-monosaccharide moieties. Furthermore, the functional and structural domains of EpF3R2″XylT were analyzed by bioinformatic approaches and 3-D protein structure remodeling. In summary, EpF3R2″XylT was shown to cluster with GGT (glycosyltransferase that glycosylates sugar moieties of glycosides) through phylogenetic analysis. In enzymatic analysis, EpF3R2″XylT was proven to transfer xylose moiety from UDP-xylose to prenylated flavonol rhamnoside at the 2″-OH position of rhamnose. The analysis of enzymatic kinetics showed that EpF3R2″XylT had the highest substrate affinity toward icariin with the lowest Km value of 75.96 ± 11.91 mM. Transient expression of EpF3R2″XylT in tobacco leaf showed functional production of EpF3R2″XylT proteins in planta. EpF3R2″XylT was preferably expressed in the leaves of E. pubescens, which is consistent with the accumulation levels of major prenylflavonol 3-O-triglycoside. The discovery of EpF3R2″XylT will provide an economical and efficient alternative way to produce prenylated flavonol trisaccharides through the biosynthetic approach.

Recently, great advances had been made by using scanning probe microscopy (SPM) to quantify the relative permittivity of thin film materials on a nanometer scale. The imaging techniques of permittivity for thin film materials with SPM, especially for photoelectric materials, have not been fully researched until now. Here, we presented a method to image permittivity of thin film materials by using a scanning capacitance microscope (SCM). This method combined the quantitative measurement by using SCM with the capacitance gradient–distance fitting curve to obtain the two-dimensional (2D) permittivity image at room temperature under atmospheric conditions. For the demonstration, a 2D permittivity image of film of molybdenum oxide (MoO3), a kind of photoelectric material, was acquired. From the image, it could be found that the average values of permittivity of MoO3 film and of MoO3 film-doped NaCl were about 8.0 and 9.5, respectively. The experimental results were quantitatively consistent with other experimental results of the same material. The reported technique here could provide a novel method for imaging the relative permittivity with nanometer resolution and be helpful for the study of photoelectric materials.

To solve the bottleneck problem of low deep coal seam permeability and difficult coalbed methane (CBM) mining. Combining hydraulic splitting technology and directional drilling technology, a directional hydraulic splitting enhancement method of deep CBM mining was proposed. The selection equation for the directional hydraulic splitting of deep coalbed was constructed. The numerical simulation reveals the variation in coal fractures around different split angles. The split angle under the maximum coal damage effect was obtained. It was found that the combined effect of the double crack damage disturbance region led to reciprocal stress fluctuations during crack development and, eventually, the formation of a zigzag fracture. The larger the splitting angle, the larger the fissure development length and the larger the coal-damaged area. A double crack takes 25% less time to complete propagation than a single crack. When the splitting angle is 90°, the disturbed area occupies 2/3 of the area around the borehole, and the overall fracturing effect is the best. In the application process, the new directional hydraulic splitting technology can increase CBM mining by 5.08%, greatly improve CBM mining efficiency, and reduce the coal mining risk, which is of great significance to the project.

Human milk oligosaccharides (HMOs) exhibit various biological activities for infants, such as serving as prebiotics, blocking pathogens, and aiding in brain development. HMOs are a complex mixture of hetero-oligosaccharides that are generally highly branched, containing multiple structural isomers and no intrinsic chromophores, presenting a challenge to both their resolution and quantitative detection. While liquid chromatography-mass spectrometry (LC-MS) has become the primary strategy for analysis of various compounds, the very polar and chromophore-free properties of native glycans hinder their separation in LC and ionization in MS. Various labeling approaches have been developed to achieve separation of glycans with higher resolution and greater sensitivity of detection. Here, we compared five commonly used labeling techniques [by 2-aminobenzamide, 2-aminopyridine, 2-aminobenzoic acid (2-AA), 2,6-diaminopyridine, and 1-phenyl-3-methyl-5-pyrazolone] for analyzing HMOs specifically under hydrophilic-interaction chromatography-mass spectrometry (HILIC-MS) conditions. The 2-AA labeling showed the most consistent deprotonated molecular ions, the enhanced sensitivity with the least structural selectivity, and the sequencing-informative tandem MS fragmentation spectra for the widest range of HMOs; therefore, this labeling technique was selected for further optimization under the porous graphitized carbon chromatography-mass spectrometry (PGC-MS) conditions. The combination strategy of 2-AA labeling and PGC-MS techniques provided online decontamination (removal of excess 2-AA, salts, and lactose) and resolute detection of many HMOs, enabling us to characterize the profiles of complicated HMO mixtures comprehensively in a simple protocol.

Insulin-like androgenic gland factor (IAG) plays an important role in sex manipulation in decapods. Understanding the molecular regulation mechanism of IAG in Procambarus clarkii (PcIAG) is important for realizing its sex control. In this study, the promoter and gene structure of PcIAG, mRNA, and miRNA expression profiles after interfering with two siRNAs synthesized according to the two short repeats in the 5′ untranslated regions (5′UTR) of PcIAG were analyzed, and miRNAs of exosomes were investigated to explore the role of repeated sequences with tandem two short repeats located in the 5′UTR of PcIAG isolated from the androgenic gland (AG) in the regulation of IAG expression. The results showed that the repeated sequences of 5′UTR only occurred completely in the cDNA from AG, and the function of the two repeats was different in regulating the expression of PcIAG, in which the Wnt signaling pathway may be involved. Furthermore, we found that six miRNAs including miR-133, miR-193, miR-34, miR-1, miR-100, and let-7 might be involved in the regulation of the expression of PcIAG, wherein miR-133 might directly be related with the repeated sequences of 5′UTR.

Acute hepatopancreatic necrosis disease (AHPND), caused by a unique strain of Vibrio parahaemolyticus (Vp (AHPND)), has become the world’s most severe debilitating disease in cultured shrimp. Thus far, the pathogenesis of AHPND remains largely unknow. Herein, in Litopenaeus vannamei, we found that a Vp (AHPND) infection significantly increased the expression of lipid droplets (LDs) protein LvPerilipin, as well as promoted the formation of LDs. In addition, the knockdown of LvPerilipin increased the shrimp survival rate in response to the Vp (AHPND) infection, and inhibited the proliferation of Vp (AHPND). Furthermore, we demonstrated that LvPerilipin depletion could increase the production of reactive oxygen species (ROS), which may be responsible for the decreased Vp (AHPND) proliferation. Taken together, our current data for the first time reveal that the shrimp lipid droplets protein Perilipin is involved in the pathogenesis of Vp (AHPND) via promoting LDs accumulation and decreasing ROS production.

Structural reliability analysis has an inherent contradiction between efficiency and accuracy. The metamodel can significantly reduce the computational cost of reliability analysis by a simpler approximation. Therefore, it is crucial to build a metamodel, which achieves the minimum simulations and accurate estimation for reliability analysis. Aiming at this, an effective adaptive metamodel based on the combination of radial basis function (RBF) model and Monte Carlo simulation (MCS) is proposed. Different shape parameters are first used to generate the weighted prediction variance, and the search for new training samples is guided by the active learning function that achieves a tradeoff of (1) being close enough to limit state function (LSF) to have a high reliability sensitivity; (2) keeping enough distance between the existing samples to avoid a clustering problem; and (3) being in the sensitive region to ensure the effectiveness of the information obtained. The performance of the proposed method for a nonlinear, non-convex, and high dimensional reliability analysis is validated by three numerical cases. The results indicate the high efficiency and accuracy of the proposed method.

Growth Regulatory Factors (GRF) are plant-specific transcription factors that play critical roles in plant growth and development as well as plant tolerance against stress. In this study, a total of 16 GRF genes were identified from the genomes of Medicago truncatula and Medicago sativa. Multiple sequence alignment analysis showed that all these members contain conserved QLQ and WRC domains. Phylogenetic analysis suggested that these GRF proteins could be classified into five clusters. The GRF genes showed similar exon–intron organizations and similar architectures in their conserved motifs. Many stress-related cis-acting elements were found in their promoter region, and most of them were related to drought and defense response. In addition, analyses on microarray and transcriptome data indicated that these GRF genes exhibited distinct expression patterns in various tissues or in response to drought and salt treatments. In particular, qPCR results showed that the expression levels of gene pairs MtGRF2–MsGRF2 and MtGRF6–MsGRF6 were significantly increased under NaCl and mannitol treatments, indicating that they are most likely involved in salt and drought stress tolerance. Collectively, our study is valuable for further investigation on the function of GRF genes in Medicago and for the exploration of GRF genes in the molecular breeding of highly resistant M. sativa.

Nuclear factor YB (NF-YB) are plant-specific transcription factors that play a critical regulatory role in plant growth and development as well as in plant resistance against various stresses. In this study, a total of 49 NF-YB genes were identified from the genomes of Medicago truncatula and Medicago sativa. Multiple sequence alignment analysis showed that all of these NF-YB members contain DNA binding domain, NF-YA interaction domain and NF-YC interaction domain. Phylogenetic analysis suggested that these NF-YB proteins could be classified into five distinct clusters. We also analyzed the exon–intron organizations and conserved motifs of these NF-YB genes and their deduced proteins. We also found many stress-related cis-acting elements in their promoter region. In addition, analyses on genechip for M. truncatula and transcriptome data for M. sativa indicated that these NF-YB genes exhibited a distinct expression pattern in various tissues; many of these could be induced by drought and/or salt treatments. In particular, RT-qPCR analysis revealed that the expression levels of gene pairs MsNF-YB27/MtNF-YB15 and MsNF-YB28/MtNF-YB16 were significantly up-regulated under NaCl and mannitol treatments, indicating that they are most likely involved in salt and drought stress response. Taken together, our study on NF-YB family genes in Medicago is valuable for their functional characterization, as well as for the application of NF-YB genes in genetic breeding for high-yield and high-resistance alfalfa.

In this work, SnO2 nanoflowers were prepared by a simple one-step hydrothermal process. The morphology and structure of SnO2 nanoflowers were characterized by SEM, TEM, Raman spectroscopy, and XRD, which demonstrated the good crystallinity of the SnO2 tetrahedron structure of the as-synthesized materials. In addition, the sensing properties of SnO2 nanoflowers were studied in detail. It was found that the SnO2 nanoflower-based gas sensor exhibits excellent gas response (9.2 to 120 ppm), fast response and recovery (2/15 s to 6 ppm), good linearity of correlation between response (S) vs. concentration (C) (lgS = 0.505 lgC − 0.147, R2 = 0.9863), superb repeatability, and selectivity at 300 °C. The outstanding performance can also be attributed to the high specific surface area ratio and size of SnO2 nanoflowers close to the thickness of the electron depletion layer that can provide abundant active sites, promote the rate of interaction, and make it easier for gas molecules to diffuse into the interior of the material. Therefore, SnO2 nanoflowers can be an ideal sensing material for real-time monitoring of low-concentration HCHO.

Aquaporins (AQP) are distributed ubiquitously in plants, and they play important roles in multiple aspects of plant growth and development, as well as in plant resistance to various environmental stresses. In this study, 43 MsAQP genes were identified in the forage crop Medicago sativa. All the MsAQP proteins were clustered into four subfamilies based on sequence similarity and phylogenetic relationship, including 17 TIPs, 14 NIPs, 9 PIPs and 3 SIPs. Analyses of gene structure and conserved domains indicated that the majority of the deduced MsAQP proteins contained the signature transmembrane domains and the NPA motifs. Analyses on cis-acting elements in the promoter region of MsAQP genes revealed the presence of multiple and diverse stress-responsive and hormone-responsive cis-acting elements. In addition, by analyzing the available and comprehensive gene expression data of M. truncatula, we screened ten representative MtAQP genes that were responsive to NaCl or drought stress. By analyzing the sequence similarity and phylogenetic relationship, we finally identified the corresponding ten salt- or drought-responsive AQP genes in M. sativa, including three MsTIPs, three MsPIPs and four MsNIPs. The qPCRs showed that the relative expression levels of these ten selected MsAQP genes responded differently to NaCl or drought treatment in M. sativa. Gene expression patterns showed that most MsAQP genes were preferentially expressed in roots or in leaves, which may reflect their tissue-specific functions associated with development. Our results lay an important foundation for the future characterization of the functions of MsAQP genes, and provide candidate genes for stress resistance improvement through genetic breeding in M. sativa.

In order to enrich the current prestack stochastic inversion theory, we propose a prestack stochastic inversion method based on adaptive particle swarm optimization combined with Markov chain Monte Carlo (MCMC). The MCMC could provide a stochastic optimization approach, and, with the APSO, have a better performance in global optimization methods. This method uses logging data to define a preprocessed model space. It also uses Bayesian statistics and Markov chains with a state transition matrix to update and evolve each generation population in the data domain, then adaptive particle swarm optimization is used to find the global optimal value in the finite model space. The method overcomes the problem of over-fitting deterministic inversion and improves the efficiency of stochastic inversion. Meanwhile, the fusion of multiple sources of information can reduce the non-uniqueness of solutions and improve the inversion accuracy. We derive the APSO algorithm in detail, give the specific workflow of prestack stochastic inversion, and verify the validity of the inversion theory through the inversion test of two-dimensional prestack data in real areas.

Cytochrome P450 monooxygenases (P450s) catalyze a great number of biochemical reactions and play vital roles in plant growth, development and secondary metabolism. As yet, the genome-scale investigation on P450s is still lacking in the model legume Medicago truncatula. In particular, whether and how many MtP450s are involved in drought and salt stresses for Medicago growth, development and yield remain unclear. In this study, a total of 346 MtP450 genes were identified and classified into 10 clans containing 48 families. Among them, sixty-one MtP450 genes pairs are tandem duplication events and 10 MtP450 genes are segmental duplication events. MtP450 genes within one family exhibit high conservation and specificity in intron–exon structure. Meanwhile, many Mt450 genes displayed tissue-specific expression pattern in various tissues. Specifically, the expression pattern of 204 Mt450 genes under drought/NaCl treatments were analyzed by using the weighted correlation network analysis (WGCNA). Among them, eight genes (CYP72A59v1, CYP74B4, CYP71AU56, CYP81E9, CYP71A31, CYP704G6, CYP76Y14, and CYP78A126), and six genes (CYP83D3, CYP76F70, CYP72A66, CYP76E1, CYP74C12, and CYP94A52) were found to be hub genes under drought/NaCl treatments, respectively. The expression levels of these selected hub genes could be induced, respectively, by drought/NaCl treatments, as validated by qPCR analyses, and most of these genes are involved in the secondary metabolism and fatty acid pathways. The genome-wide identification and co-expression analyses of M. truncatulaP450 superfamily genes established a gene atlas for a deep and systematic investigation of P450 genes in M. truncatula, and the selected drought-/salt-responsive genes could be utilized for further functional characterization and molecular breeding for resistance in legume crops.

Nitroaromatic compounds (NACs) are ubiquitous in the environment due to their extensive industrial applications. The recalcitrance of NACs causes their arduous degradation, subsequently bringing about potential threats to human health and environmental safety. The problem of how to effectively predict the toxicity of NACs has drawn public concern over time. Quantitative structure–activity relationship (QSAR) is introduced as a cost-effective tool to quantitatively predict the toxicity of toxicants. Both OECD (Organization for Economic Co-operation and Development) and REACH (Registration, Evaluation and Authorization of Chemicals) legislation have promoted the use of QSAR as it can significantly reduce living animal testing. Although numerous QSAR studies have been conducted to evaluate the toxicity of NACs, systematic reviews related to the QSAR modeling of NACs toxicity are less reported. The purpose of this review is to provide a thorough summary of recent QSAR studies on the toxic effects of NACs according to the corresponding classes of toxic response endpoints.

The reburning process in a furnace, a key way to reduce NOx emissions, is a heterogeneous reaction during coal combustion, in which the heterogeneous adsorption is dominant. Zhundong coal with a high content of alkali metal can enhance the reburning process. In this paper, the influence of sodium and a defect on NO heterogeneous adsorption was studied by the density functional theory, and the thermodynamic characteristic was also analyzed. The results indicate that the binding energy for NO adsorption on the pristine graphene surface (graphene-NO), Na-decorated pristine graphene surface (graphene-Na-NO), defect graphene surface (gsv-NO) and Na-decorated defect graphene (gsv-Na-NO) is −5.86, −137.12, −48.94 and −74.85 kJ/mol, respectively, and that the heterogeneous adsorption is an exothermic reaction. Furthermore, except for covalent bonds of C and N, C and O for gsv-NO, other interactions are a closed-shell one, based on the analysis of AIM, ELF and IGM. The area of electron localization for NO is graphene-Na-NO > gsv-Na-NO > gsv-NO > graphene-NO. The dispersion interaction is the main interaction force between NO and the pristine graphene surface. The δg index for the atom pairs about N–C and O–C on the pristine graphene surface is also the smallest. The density of spikes at graphene-Na-NO is bigger than that at gsv-Na-NO. Moreover, the thermodynamics characteristic showed that the reaction equilibrium constant of graphene-NO is less than those on the other surfaces under the same temperature. Thus, NO on the pristine graphene surface is the most difficult to adsorb, but the presence of sodium and a defect structure can promote its adsorption.

Core-shell nanofibers have great potential for bio-medical applications such as wound healing dressings where multiple drugs and growth factors are expected to be delivered at different healing phases. Compared to monoaxial nanofibers, core-shell nanofibers can control the drug release profile easier, providing sustainable and effective drugs and growth factors for wound healing. However, it is challenging to produce core-shell structured nanofibers with a high production rate at low energy consumption. Co-axial centrifugal spinning is an alternative method to address the above limitations to produce core-shell nanofibers effectively. In this study, a co-axial centrifugal spinning device was designed and assembled to produce core-shell nanofibers for controlling the release rate of ibuprofen and hEGF in inflammation and proliferation phases during the wound healing process. Core-shell structured nanofibers were confirmed by TEM. This work demonstrated that the co-axial centrifugal spinning is a high productivity process that can produce materials with a 3D environment mimicking natural tissue scaffold, and the specific drug can be loaded into different layers to control the drug release rate to improve the drug efficiency and promote wound healing.

The calcineurin B-like protein (CBL) and CBL-interacting protein kinase (CIPK) play important roles in plant signal transduction and response to abiotic stress. Plants of Medicago genus contain many important forages, and their growth is often affected by a variety of abiotic stresses. However, studies on the CBL and CIPK family member and their function are rare in Medicago. In this study, a total of 23 CBL and 58 CIPK genes were identified from the genome of Medicago sativa as an important forage crop, and Medicaog truncatula as the model plant. Phylogenetic analysis suggested that these CBL and CIPK genes could be classified into five and seven groups, respectively. Moreover, these genes/proteins showed diverse exon-intron organizations, architectures of conserved protein motifs. Many stress-related cis-acting elements were found in their promoter region. In addition, transcriptional analyses showed that these CBL and CIPK genes exhibited distinct expression patterns in various tissues, and in response to drought, salt, and abscisic acid treatments. In particular, the expression levels of MtCIPK2 (MsCIPK3), MtCIPK17 (MsCIPK11), and MtCIPK18 (MsCIPK12) were significantly increased under PEG, NaCl, and ABA treatments. Collectively, our study suggested that CBL and CIPK genes play crucial roles in response to various abiotic stresses in Medicago.

Fibrosis is characterized by excessive production of disorganized collagen- and fibronectin-rich extracellular matrices (ECMs) and is driven by the persistence of myofibroblasts within tissues. A key protein contributing to myofibroblast differentiation is extra domain A fibronectin (EDA-FN). We sought to target and interfere with interactions between EDA-FN and its integrin receptors to effectively inhibit profibrotic activity and myofibroblast formation. Molecular docking was used to assist in the design of a blocking polypeptide (antifibrotic 38-amino-acid polypeptide, AF38Pep) for specific inhibition of EDA-FN associations with the fibroblast-expressed integrins α4β1 and α4β7. Blocking peptides were designed and evaluated in silico before synthesis, confirmation of binding specificity, and evaluation in vitro. We identified the high-affinity EDA-FN C-C′ loop binding cleft within integrins α4β1 and α4β7. The polypeptide with the highest predicted binding affinity, AF38Pep, was synthesized and could achieve specific binding to myofibroblast fibronectin-rich ECM and EDA-FN C-C′ loop peptides. AF38Pep demonstrated potent myofibroblast inhibitory activity at 10 µg/mL and was not cytotoxic. Treatment with AF38Pep prevented integrin α4β1-mediated focal adhesion kinase (FAK) activation and early signaling through extracellular-signal-regulated kinases 1 and 2 (ERK1/2), attenuated the expression of pro-matrix metalloproteinase 9 (MMP9) and pro-MMP2, and inhibited collagen synthesis and deposition. Immunocytochemistry staining revealed an inhibition of α-smooth muscle actin (α-SMA) incorporation into actin stress fibers and attenuated cell contraction. Increases in the expression of mRNA associated with fibrosis and downstream from integrin signaling were inhibited by treatment with AF38Pep. Our study suggested that AF38Pep could successfully interfere with EDA-FN C-C′ loop-specific integrin interactions and could act as an effective inhibitor of fibroblast of myofibroblast differentiation.

Spermatogenesis is a complex biological process regulated by well-coordinated gene regulation, including MicroRNAs (miRNAs). miRNAs are endogenous non-coding ribonucleic acids (ncRNAs) that mainly regulate the gene expression at post-transcriptional levels. Several studies have reported miRNAs expression in bull sperm and the process of spermatogenic arrest in cattle and yak. However, studies for the identification of differential miRNA expression and its mechanisms during the developmental stages of testis still remain uncertain. In the current study, we comprehensively analyzed the expression of miRNA in bovine testes at neonatal (3 days after birth, n = 3) and mature (13 months, n = 3) stages by RNA-seq. Moreover, the role of bta-miR-146b was also investigated in regulating the proliferation and apoptosis of bovine male germline stem cells (mGSCs) followed by a series of experiments. A total of 652 miRNAs (566 known and 86 novel miRNAs) were identified, whereas 223 miRNAs were differentially expressed between the two stages. Moreover, an elevated expression level of bta-miR-146b was found in bovine testis among nine tissues, and the functional studies indicated that the overexpression of bta-miR-146b inhibited the proliferation of bovine mGSCs and promoted apoptosis. Conversely, regulation of bta-miR-146b inhibitor promoted bovine mGSCs proliferation. This study provides a basis for understanding the regulation roles of miRNAs in bovine testis development and spermatogenesis.

Aggregation-caused quenching (ACQ) effect, known as the main cause to restrain solid-state luminescence of carbon quantum dots (CQDs), hinders further application of CQDs in white light-emitting diodes (WLED). Here, a complex of CQDs and phthalimide crystals (CQDs/PC) was prepared through a one-step solvothermal method. CQDs/PC prevented CQDs from touching directly by embedding the CQDs in phthalimide crystal matrix in situ, which effectively reduced the ACQ effect. Furthermore, CQDs/PC exhibited multi-peak fluorescence spectra that span the green, yellow and orange spectral regions. Finally, a WLED fabricated based on CQDs/PC achieved a color-rendering index of 82 and a correlated color temperature of 5430 K. This work provides a quick and effective strategy to apply CQDs to WLED.

To greatly improve the production quality and efficiency of carbon quantum dots (CQDs), and provide a new approach for the large-scale production of high-quality CQDs, green carbon quantum dots (g-CQDs) with high product yield (PY) and high fluorescent quantum yield (QY) were synthesized by an efficient one-step solvothermal method with 2,7-dihydroxynaphthalene as the carbon source and ethylenediamine as the nitrogen dopant in this study. The PY and QY of g-CQDs were optimised by adjusting reaction parameters such as an amount of added ethylenediamine, reaction temperature, and reaction duration. The results showed that the maximum PY and QY values of g-CQDs were achieved, which were 70.90% and 62.98%, respectively when the amount of added ethylenediamine, reaction temperature, and reaction duration were 4 mL, 180 °C, and 12 h, respectively. With the optimised QY value of g-CQDs, white light emitting diodes (white LEDs) were prepared by combining g-CQDs and blue chip. The colour rendering index of white LEDs reached 87, and the correlated colour temperature was 2520 K, which belongs to the warm white light area and is suitable for indoor lighting. These results indicate that g-CQDs have potential and wide application prospects in the field of white LEDs.