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Cross-species research has advanced human understanding of brain regions, with cross-species comparisons using magnetic resonance imaging technology becoming increasingly common. Currently, cross-species research on human language regions has primarily focused on traditional brain areas such as the Broca region. While some studies have indicated that human language function also involves other language regions, the corresponding relationships between these brain regions in humans and macaques remain unclear. This study calculated the strength of the connections between the high-level language processing regions in human and macaque brains, identified homologous target areas based on the structural connections of white-matter fiber bundles, and compared the connectivity profiles of both species. The results of the experiment demonstrated that macaques possess brain regions which exhibit connectivity patterns resembling those found in human high-level language processing regions. This discovery suggests that while the function of a human brain region is specialized, it still maintains a structural connectivity similar to that seen in macaques.

Mixed fermentation with Saccharomyces cerevisiae and Pichia kudriavzevii has been shown to enhance wine aroma, yet the underlying mechanisms remain unclear. Monoculture of S. cerevisiae, monoculture of P. kudriavzevii, and mixed culture of S. cerevisiae and P. kudriavzevii were conducted, and the study analyzed and compared the biomass, flavor profile, and transcriptome responses of the three groups. Both yeast species exhibited growth inhibition in mixed culture, especially P. kudriavzevii. Significant differences were observed in three organic acids and the foremost 20 volatile compounds. Mixed fermentation enhanced esters (e.g., ethyl butyrate, isoamyl acetate) and volatile acids (e.g., hexanoic acid), but decreased isobutanol, phenylethyl alcohol, and quinic acid. Transcriptomic analysis revealed 294 and 332 differentially expressed genes (DEGs) in S. cerevisiae and P. kudriavzevii, respectively. The Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation results indicated that DEGs in mixed fermentation were concentrated in carbohydrate metabolism and amino acid metabolism. Our integrated analysis suggested that genes such as TDH2, TDH3, and ENO2 were pivotal for ester biosynthesis. Moreover, ADH1, ADH2, HPA3, ALD6, and ARO8 were associated with quinic acid synthesis. Furthermore, ILV2, ILV5, ALD6, and others were central to the production of isobutanol and phenylethyl alcohol.

With the advancement of service robot technology, the demand for higher boundary precision in indoor semantic segmentation has increased. Traditional methods of extracting Euclidean features using point cloud and voxel data often neglect geodesic information, reducing boundary accuracy for adjacent objects and consuming significant computational resources. This study proposes a novel network, the Euclidean–geodesic network (EGNet), which uses point cloud–voxel–mesh data to characterize detail, contour, and geodesic features, respectively. The EGNet performs feature fusion through Euclidean and geodesic branches. In the Euclidean branch, the features extracted from point cloud data compensate for the detail features lost by voxel data. In the geodesic branch, geodesic features from mesh data are extracted using inter-domain fusion and aggregation modules. These geodesic features are then combined with contextual features from the Euclidean branch, and the simplified trajectory map of the grid is used for up-sampling to produce the final semantic segmentation results. The Scannet and Matterport datasets were used to demonstrate the effectiveness of the EGNet through visual comparisons with other models. The results demonstrate the effectiveness of integrating Euclidean and geodesic features for improved semantic segmentation. This approach can inspire further research combining these feature types for enhanced segmentation accuracy.

In recent decades, China has collected extensive strong-motion data from the Loess Plateau, which is valuable for understanding the dynamic characteristics of loess sites and the effects of site conditions on seismic motions. The horizontal-to-vertical Fourier spectrum ratio (HVSR) and the horizontal-to-vertical velocity response spectrum ratio (VRSR) are widely used to study site dynamics. This study analyzes strong-motion data from the Loess Plateau to identify key features of the HVSR and VRSR curves. The results show that these spectral ratio curves effectively capture the dynamic behaviors of loess sites, minimizing the influences of earthquake magnitude and propagation path. While the spectral ratio peaks are less affected by magnitude, epicentral distance, and focal depth, they are significantly influenced by site conditions. Conversely, the dominant periods estimated from these curves are strongly influenced by magnitude, epicentral distance, and focal depth. For sites located on the Loess Plateau, the average amplification factor is approximately 3, with a mean predominant period of 0.4 s. These results provide valuable insights into the dynamic characteristics of loess sites and have practical implications for seismic design in the region.

In order to obtain more reasonable failure modes and more precise surrounding rock pressures of deep-buried underground rectangular chambers, the failure mode of a “wedge-shaped collapse body + arc rotator + logarithmic spiral rotator” deep-buried chamber is constructed based on the analysis of existing failure mode. The upper bound solution of the surrounding rock pressure is derived based on limit analysis. The validity of the proposed failure model and the reliability of the limit analysis approach are demonstrated through numerical simulations, theoretical verification, and comparisons with engineering practices. The influence of various parameters on surrounding rock pressure is analyzed. The results show that each parameter has different influence on the surrounding rock pressure. The surrounding rock pressure q and e increases linearly with the increase in rock gravity γ and chamber size, and decreases with the increase in cohesion c and internal friction angle φ. The surrounding rock pressure q decreases with the increase in lateral pressure coefficient K, while surrounding rock pressure e increases with the increase in lateral pressure coefficient K.

Predicting soil–water dynamics in Moistube irrigation (ΜΤΙ) favours understanding ΜΤΙ functioning mechanisms and technical parameter design. This study proposed a physically based infiltration (PH) model extending the Green–Ampt (GA) model to a two-dimensional polar coordinate system. We treated Moistube as a clay and considered the infiltration from internal Moistube to surrounding soils. The performances of the PH model, together with a semi-physical–empirical (PH–EM) model and a numerical simulation (NUM) model, were evaluated based on regulated working pressure head (WPH) experiments. A HYDRUS 2D model was used based on experimental design to reproduce the soil–water dynamics by assigning Moistube and soil two sets of hydraulic parameters. WPH increase or decrease treatments were applied to Moistube. The Moistube discharge rate, infiltration volume, and wetting front (WF) advance were analyzed and predicted by three models. The results showed that cumulative infiltration, Moistube discharge, and effective saturation around Moistube were enhanced or abated under WPH increase or decrease, with WF accelerating or decelerating. The modelled effective saturation varied between 0.45 and 0.70, providing suitable moist conditions for crops. Percentage of bias (PBIAS) and mean absolute percentage relative error (MAPRE) were employed to evaluate model performances. Three models well-predicted infiltration characteristics and WF advance but differed in accuracy. The PH model overestimated and underestimated the Moistube discharge rate in early and later phases. The prediction accuracy in WF varied across infiltration phases and WPH modes. The PH–EM model yielded accurate results due to its empirical attribute. The NUM model produced novel phenomena of infiltration characteristics at WPH adjustment points, i.e., the discharge rate exponentially decreased over time after the WPH increased but presented restraining followed by rebounding trends after the WPH decreased. The NUM model strongly depended on the selection of the Moistube hydraulic parameters. Extending the GA model to a two-dimensional polar coordinate system by treating Moistube as a clay was practicable in modelling soil water dynamics, thereby contributing to designing and optimizing MTI technical indexes.

Lager yeast, which is mainly used for lager beer brewing, withstands significant oxidative stress during brewing process, leading to its rapid aging in serial beer brewing. Mitophagy plays a critical role in the antioxidant stress response of yeast. However, the relationship between mitophagy and the antioxidant capacity of yeast is still unclear. Previous studies indicated that ATG gene family in mitophagy significantly affects the antioxidant capacity of yeast cells in beer brewing. Herein, the expression of ATG8, ATG11, ATG32, DNM1, and MMM1 genes was regulated. The results showed that the overexpression of ATG8 and ATG11 significantly reduced the intracellular ROS contents to 52.05% and 22.57% of the initial state, respectively, and helped to maintain a high mitochondrial vitality during serial fermentation. Disruption of ATG8, ATG11 and ATG32 resulted in significant decrease in cell vitality when exposed to H2O2 stimulation. Meanwhile, the disruptions of these genes were detrimental to the balance of intracellular ROS. Excess DNM1 activity could affect the cellular energy balance and ATP depletion under prolonged stress conditions. The repression of MMM1 led to lower ATP levels during serial beer fermentation. The ATG8, ATG11, and ATG32 genes might be potential targets for regulating the antioxidant capacity of yeast. The current work provides new insights into improving the antioxidant capacity of yeast through mitophagy regulation.

Previous evaluation models for cultural relic buildings in relation to fire risk fail to consider the necessity for effective firefighting and rescue capabilities in complex forest environments. This paper incorporates variables, including those pertaining to forest fires and climatic conditions, into the assessment index system. The hierarchical analysis method and the local punishment-incentive variable weighting method are employed to introduce a compensation coefficient score. A model for the evaluation of firefighting and rescue capability in a continuous area of cultural relic buildings in conjunction with the surrounding forest environment has been developed. The firefighting and rescue capability of the Yuelu Mountain scenic spot was evaluated at 73.91 (level III) using the fixed weight method and 69.52 (level IV) using the variable weight method. The variable weight method proved to be a more accurate approach for evaluating the status and importance of dynamic targets, thus enabling a more precise evaluation of the comprehensive evaluation area. The evaluation results inform the formulation of targeted improvement measures for enhancing the firefighting and rescue capabilities of cultural relic buildings.

Mixed Reality (MR) technology possesses profound and extensive potential across a multitude of domains, including, but not limited to industry, healthcare, and education. However, prolonged use of MR devices to watch stereoscopic content may lead to visual fatigue. Since visual fatigue involves multiple brain regions, our study aims to explore the topological characteristics of brain networks derived from electroencephalogram (EEG) data. Because the Phase-Locked Value (PLV) is capable of effectively measuring the phase synchronization relationship between brain regions, it was calculated between all pairs of channels in both comfort and fatigue states. Subsequently, a sparse brain network was constructed based on PLV by applying an appropriate threshold. The node properties (betweenness centrality, clustering coefficient, node efficiency) and edge properties (characteristic path length) were calculated based on the corresponding brain network within specific frequency bands for both comfort and fatigue states. In analyzing the PLV of brain connectivity in comfort and fatigue states, a notable enhancement in brain connectivity is observed within the alpha, theta, and delta frequency bands during fatigue status. By analyzing the node and edge properties of brain networks, it is evident that the mean values of these properties in the fatigue state were higher than those in the comfort state. By analyzing the node and edge properties at a local level, the average difference in betweenness centrality, clustering coefficients, and nodal efficiency across the three EEG frequency bands was computed to find significant brain regions. The main findings are as follows: Betweenness centrality primarily differs in frontal and parietal regions, with minor involvement in temporal and central regions. The clustering Coefficient mainly varies in the frontal region, with slight differences being seen in the temporal and occipital regions. Nodal efficiency primarily varies in the frontal, temporal, and central regions, with minor differences being seen in the parietal and occipital regions. Edge property analysis indicates that there is a higher occurrence of long-distance connections among brain regions during the fatigue state, which reflects a loss of synaptic transmission efficiency on a global level. Our study plays a crucial role in understanding the neural mechanisms underlying visual fatigue, potentially providing insights that could be applied to high-demand cognitive fields where prolonged use of MR devices leads to visual fatigue.

Shale oil resources are abundant in the second member of the Kongdian Formation, Cangdong Sag, Bohai Bay Basin, China. However, the shale oil here has high viscosity and poor fluidity, resulting in low recovery and huge difficulty in development, gathering, and transporting. This study assembled a catanionic surfactant (PSG) through electrostatic interactions between cetyltrimethylammonium bromide (CTAB) and α-olefin sulfonate (AOS) in an aqueous phase, which can be used as an effective emulsifying and viscosity-reducing agents for shale oils of Dagang oilfield. The interfacial activity and emulsification performance of PSG can be optimized by changing the molar ratio of CTAB to AOS. Notably, the PSG assembled at the molar ratio of 6:4 shows the best performance, with ultra-high surface activity and excellent salt resistance. At an oil/water ratio of 1:1 and 50 °C, an aqueous solution of 0.2% PSG can emulsify five types of shale oil, making it form shale oil-in-water (O/W) emulsion with a viscosity of less than 35 mPa·s, thereby reducing the viscosity of shale oil and improving its flowability. Importantly, shale oil and water can be separated by simple sedimentation without adding demulsifiers. This study has important guiding significance for the efficient development and transportation of shale oil.

To reduce the hygroscopicity of betaine (BET), three co-crystals were synthesized: betaine-3,5-dihydroxybenzoic acid (BET-3,5-DHB), betaine-2,4-dihydroxybenzoic acid (BET-2,4-DHB), and betaine-3-hydroxybenzoic acid (BET-3-HDB). BET, commonly present in natural products, is used to treat homocysteine methyltransferase and has additional benefits such as hepatoprotective and neuroprotective properties. However, BET is extremely hygroscopic. Three cocrystals are acquired to address the problem. Comprehensive characterization, such as X-ray diffraction analysis, spectral analysis and thermal analysis, were conducted for co-crystals and BET. Based on the evaluation results for dynamic vapor sorption, the three co-crystals, especially BET-3-HDB, significantly reduce BET’s hygroscopicity. At 90% RH, the weight changes of BET-2,4-DHB (0.36%), BET-3,5-DHB (1.79%) and BET-3-HDB (0.32%) are 306.9, 61.7, and 345.3 times greater than those of the raw material BET, respectively. Hygroscopicity results from BET’s ability to easily create hydrogen bonding interactions with water molecules due to its carboxylate ions. Nevertheless, carboxylate ions establish hydrogen bonds with CCFs in co-crystals, lowering BET’s hygroscopicity and minimizing the likelihood of contact with water molecules. The contributions of contacts in the co-crystals are shown by Hirschfeld surface analysis as follows: H-H > O-H > H-O. Coulomb forces are dominant in the co-crystals by 3D energy frameworks.

Due to the impact of global warming, drought has caused serious damage to China’s ecological environment and social status. This article selects Laibin City in the Guangxi Zhuang Autonomous Region as the research area, utilizing multispectral remote sensing data as the data source and Landsat series image data for relevant preprocessing. It calculates the monthly normalized vegetation index (NDVI) and surface temperature (LST) data for Laibin City. Based on the ecological environment and surface coverage conditions of the research area, the ratio vegetation index (RVI), normalized vegetation moisture index (NDWI), temperature vegetation drought index (TVDI), and conditional vegetation temperature drought index (VTCI) were selected to calculate and invert the drought monitoring results of Laibin City. The drought monitoring results were obtained and overlaid with the vegetation area map to generate the vegetation drought monitoring results of Laibin City. Based on the climate, geography, and ecological characteristics of the monitored area in Laibin City, a specific analysis will be conducted to develop an appropriate TVDI index drought level, and generate vegetation drought level result maps for Laibin City in 2021, 2022, and 2023. Then, a detailed analysis of the vegetation drought situation in Laibin City is conducted according to time and space. Among them, in the past three years, the vegetation areas in Laibin City have experienced drought seasons mostly in summer and autumn. The interannual drought is mainly mild drought, and the proportion of areas with mild drought shows a relatively stable trend. In conclusion, TVDI proves to be a valuable tool for monitoring vegetation drought in Laibin City, offering insights for efficient water resource management strategies.

Substantial advancements have been achieved in hyperspectral image (HSI) classification through contemporary deep learning techniques. Nevertheless, the incorporation of an excessive number of irrelevant tokens in large-scale remote sensing data results in inefficient long-range modeling. To overcome this hurdle, this study introduces the Group-Sensitive Selective Perception Transformer (GSAT) framework, which builds upon the Vision Transformer (ViT) to enhance HSI classification outcomes. The innovation of the GSAT architecture is primarily evident in several key aspects. Firstly, the GSAT incorporates a Group-Sensitive Pixel Group Mapping (PGM) module, which organizes pixels into distinct groups. This allows the global self-attention mechanism to function within these groupings, effectively capturing local interdependencies within spectral channels. This grouping tactic not only boosts the model’s spatial awareness but also lessens computational complexity, enhancing overall efficiency. Secondly, the GSAT addresses the detrimental effects of superfluous tokens on model efficacy by introducing the Sensitivity Selection Framework (SSF) module. This module selectively identifies the most pertinent tokens for classification purposes, thereby minimizing distractions from extraneous information and bolstering the model’s representational strength. Furthermore, the SSF refines local representation through multi-scale feature selection, enabling the model to more effectively encapsulate feature data across various scales. Additionally, the GSAT architecture adeptly represents both global and local features of HSI data by merging global self-attention with local feature extraction. This integration strategy not only elevates classification precision but also enhances the model’s versatility in navigating complex scenes, particularly in urban mapping scenarios where it significantly outclasses previous deep learning methods. The advent of the GSAT architecture not only rectifies the inefficiencies of traditional deep learning approaches in processing extensive remote sensing imagery but also markededly enhances the performance of HSI classification tasks through the deployment of group-sensitive and selective perception mechanisms. It presents a novel viewpoint within the domain of hyperspectral image classification and is poised to propel further advancements in the field. Empirical testing on six standard HSI datasets confirms the superior performance of the proposed GSAT method in HSI classification, especially within urban mapping contexts, where it exceeds the capabilities of prior deep learning techniques. In essence, the GSAT architecture markedly refines HSI classification by pioneering group-sensitive pixel group mapping and selective perception mechanisms, heralding a significant breakthrough in hyperspectral image processing.

The diversity of the microbial community structure plays a crucial role in the flavor and nutritional value of Shanxi aged vinegar in fermentation. Illumina Miseq high-throughput sequencing identified thirteen bacterial genera, with Lactobacillales (44.89%) and Acetobacter (21.04%) being the predominant species. Meanwhile, the fermentation characteristics of selected lactic acid bacteria strains isolated from Shanxi aged vinegar were studied in different media. The results showed that the biomass, and physical and chemical indices, as well as flavor compounds of the four strains of lactic acid bacteria in the simulated vinegar fermented grains medium were superior to those in barley and pea medium and sorghum juice medium. The bacterial interaction was conducted to investigate the effects on growth, the physicochemical indices, and flavor substances in order to determine the optimal combination. Furthermore, the interaction between pure cultures and co-cultures of lactic acid bacteria in a simulated vinegar culture medium was investigated, with a focus on the impact of this interaction on strain growth, fermentation characteristics, and flavor compound production. Compared with the pure culture, when strains L7 and L729 were co-inoculated, the reducing sugar content was 0.31 ± 0.01 g/100 g, total acid content was 3.02 ± 0.06 g/100 g, acetoin content was 2.41 ± 0.07 g/100 g, and total organic acid content was 3.77 ± 0.17 g/100 g. In terms of flavor compounds, the combined culture system exhibited higher levels of esters, aldehydes, and acids compared to pure cultures or other co-culture systems. This study revealed the fermentation characteristics of selected lactic acid strains in Shanxi aged vinegar under different conditions and their interaction in simulated vinegar fermentation media, which could provide theoretical support for the safety and health evaluation of aged vinegar.

Drought stress is a primary abiotic stress that causes significant losses to forestry and agricultural production. Therefore, exploring drought-responsive genes and their regulatory mechanism is crucial for plant molecular breeding for forestry and agriculture production safety. Small auxin-up RNA (SAUR) proteins are essential in plant growth and development but show functional diversity in stress response. In this study, the transcriptome sequencing data of Ammopiptanthus nanus seedlings revealed that the expression of AnSAUR50 was continuously downregulated under drought stress. Hence, the AnSAUR50 gene was cloned and functionally analyzed in drought response. The results showed that the coding sequence of AnSAUR50 was 315 bp in length and encoded 104 amino acids. The AnSAUR50 protein showed high conservation, possessed a SAUR-specific domain, and localized in the nucleus and cell membrane. The heterologous expression of the AnSAUR50 gene enhanced the drought sensitivity of the transgenic Arabidopsis with a lower survival rate, biomass, and higher malondialdehyde content and relative electrolyte leakage. Moreover, transgenic plants showed shorter root lengths and bigger stomatal apertures, resulting in facilitating water loss under drought stress. The study indicates that AnSAUR50 negatively regulates drought tolerance by inhibiting root growth and stomatal closure, which provides insights into the underlying function and regulatory mechanism of SAURs in plant stress response.

The real-time monitoring and evaluation of water quality provides a scientific basis for water resource management and promotes regional sustainable development. This study established a database using Landsat-8 satellite data and water quality data from the Ningxia Yellow River basin in China, spanning 2021 to 2023, and this paper proposes a custom residual convolutional neural network model with a hybrid attention mechanism, referred to as PCWA-ResCNN. The accuracy of the model in predicting turbidity, permanganate, ammonia nitrogen, and dissolved oxygen concentration was more than 95%. Compared to convolutional neural networks and long short-term memory models, this model performed better in predicting water quality parameters with significantly improved prediction performance. In terms of spatial distribution, the pollution degree in the middle reaches of the basin is relatively serious. However, the overall water quality is good, being mainly Class I and Class II water quality. The hybrid model established in this paper can better capture the complex nonlinear relationship between the observed values and the surface water reflectance, showing strong robustness. This model can be used for the water quality monitoring of complex inland rivers and lakes, and it can also provide effective support for relevant government departments to formulate scientific and reasonable water quality management policies.

The pore fracture structure of deep coal reservoirs is crucial for evaluating the potential of deep coalbed methane resources, conducting exploration and development, and controlling coal mine gas disasters. Mercury intrusion porosimetry, the liquid nitrogen method, and the low-temperature carbon dioxide adsorption method were used to study the full pore size structure and pore fractal characteristics of different coal grades in deep coal and comprehensively characterize the pore structure of kilometer-level coal mining. The sponge, Frenkel–Halsey–Hill (FHH), and density function models were applied to comprehensively analyze the pore complexity of coal, and the influence of metamorphic degree on pore size structure was evaluated. The distribution relationship of pore volume in different stages of coal samples was macropore→mesopore→micropore, and macropores had the best connectivity. Micropores and mesopores had the largest specific surface area, and the development of micropores and microcracks controlled the deep gas adsorption performance. The micropore volume and specific surface area both revealed a nonlinear decreasing trend with the increase in volatile matter, and coal metamorphism promoted the development of micropores. The pore volume and specific surface area of mesopores and macropores decreased first and then increased in a “U” shape with increasing volatile matter. In contrast, the fractal dimension D1 revealed an inverted U shape with increasing volatile matter, followed by a decrease. The D2 value decreased nonlinearly with increasing volatile matter, whereas the D3 value increased nonlinearly with increasing volatile matter. The degree of metamorphism increased, and the microporous structure became more regular.

Wearable sensor-based human activity recognition (HAR) methods hold considerable promise for upper-level control in exoskeleton systems. However, such methods tend to overlook the critical role of data quality and still encounter challenges in cross-subject adaptation. To address this, we propose an active learning framework that integrates the relation network architecture with data sampling techniques. Initially, target data are used to fine tune two auxiliary classifiers of the pre-trained model, thereby establishing subject-specific classification boundaries. Subsequently, we assess the significance of the target data based on classifier discrepancy and partition the data into sample and template sets. Finally, the sampled data and a category clustering algorithm are employed to tune model parameters and optimize template data distribution, respectively. This approach facilitates the adaptation of the model to the target subject, enhancing both accuracy and generalizability. To evaluate the effectiveness of the proposed adaptation framework, we conducted evaluation experiments on a public dataset and a self-constructed electromyography (EMG) dataset. Experimental results demonstrate that our method outperforms the compared methods across all three statistical metrics. Furthermore, ablation experiments highlight the necessity of data screening. Our work underscores the practical feasibility of implementing user-independent HAR methods in exoskeleton control systems.

Melatonin plays a critical role in regulating embryo attachment in ruminants. While numerous studies have investigated its effects on early embryo development in vitro, the precise mechanisms by which melatonin influences the receptivity of endometrial epithelial cells in dairy cows remain unclear. The prerequisite for embryo implantation is the specific physiological condition of the endometrium that allows the embryo to implant, also known as endometrial receptivity. In addition to this, endometrial cells undergo processes such as proliferation, differentiation, and renewal, which makes the embryo more easily implanted. In this study, bovine endometrial epithelial cells were cultured and treated with melatonin, Silent Information Regulator 1 (SIRT1) inhibitor (EX527), and protein kinase B (AKT) phosphorylation inhibitor (periposine). RT-qPCR, Western blot, and immunofluorescence analysis were performed to investigate the effects of melatonin on the expression of target gene (SIRT1); cell proliferative genes, phosphatidylinositol-4,5-bisphosphate 3-Kinase (PI3K), AKT, cyclinD1, cyclinE1; and receptive genes (Leukemia Inhibitory Factor (LIF), Vascular Endothelial Growth Factor (VEGF), Homeobox Structure Gene 10 (HOXA10)). Additionally, microRNA (miRNA) mimics and inhibitors were used to transfect the cells to study the regulatory relationship between miRNA and receptive genes. Results indicated that melatonin activates the PI3K/AKT signaling pathway, upregulates cyclinD1 and cyclinE1, and promotes the proliferation of bovine endometrial epithelial cells. Melatonin also upregulated the expression of VEGF and HOXA10 and downregulated the expression of bta-miR-497 and bta-miR-27a-3p through SIRT1/PI3K/AKT signaling pathway. Further, bta-miR-497 and bta-miR-27a-3p were found to negatively regulate VEGF and HOXA10, respectively. Therefore, melatonin regulates the expression of VEGF and HOXA10 through the SIRT1/PI3K/AKT pathway and promotes the establishment of receptivity in bovine endometrial epithelial cells.

Straw returning enhances soil fertility and increases corn yield, but the impact on soil fertility varies with different incorporation methods. To explore the optimal straw-returning method, this study, based on a long-term field experiment, investigated the following different corn-straw-returning methods: deep plowing and straw returning (B), rotary tillage and straw returning (RT), crushing and mixing straw returning (TM), pulverized cover straw returning (C), high-stubble-retention straw returning (LHS), strip cover (S), and flat no-tillage without straw returning (CK). High-throughput sequencing technology was employed to analyze the soil bacterial community composition and structural changes under different straw-returning methods. The study further explored the relationships between the soil bacterial community and nutrient content. The results indicated that different straw-returning methods altered the composition and structure of the soil bacterial community. The TM treatment significantly increased the richness and diversity of the soil bacterial communities. Shredding and covering (C and TM) effectively improved the soil nutrient content and bacterial community structure. In the C treatment, the abundance of Blastococcus, Nocardioides, and Microvirga increased the most, by 241.02%, 77.79%, and 355.08%, respectively, compared with CK. In the TM treatment, Pseudarthrobacter showed the highest abundance, increasing by 343.30%. The genes involved in soil carbon hydrolysis (pulA), nitrification (hao), organic nitrogen degradation and synthesis (gudB), and the nitrogen limitation response (glnR) significantly decreased by 56.21%, 78.75%, 66.46%, and 67.40%, respectively, in the C treatment. The genes involved in soil carbon hydrolysis (IMA), carbon fixation (pccB-A), methane metabolism (moxF), nitrate reduction in soil (nirD), organic nitrogen degradation and synthesis (gdh, ureAB, ureE), and phosphate absorption (glpT) significantly increased by 93.37%, 92.68%, 95.00%, 23.42%, 35.40%, 114.21%, 59.14%, and 75.86%, respectively, in the C treatment. The nitrate reduction gene (nrfA) significantly increased by 80.27% in the TM treatment. Therefore, we concluded that straw primarily stimulates the activity of bacterial communities and regulates the bacterial community by changing the relative abundance of the soil microorganisms and functional genes, thereby improving the soil nutrient content. This study considered pulverized cover straw returning and crushing and mixing straw returning to be the most reasonable methods.

Background: As large language models continue to expand in size and diversity, their substantial potential and the relevance of their applications are increasingly being acknowledged. The rapid advancement of these models also holds profound implications for the long-term design of stimulus-responsive materials used in drug delivery. Methods: The large model used Hugging Face’s Transformers package with BigBird, Gemma, and GPT NeoX architectures. Pre-training used the PubChem dataset, and fine-tuning used QM7b. Chemist instruction training was based on Direct Preference Optimization. Drug Likeness, Synthetic Accessibility, and PageRank Scores were used to filter molecules. All computational chemistry simulations were performed using ORCA and Time-Dependent Density-Functional Theory. Results: To optimize large models for extensive dataset processing and comprehensive learning akin to a chemist’s intuition, the integration of deeper chemical insights is imperative. Our study initially compared the performance of BigBird, Gemma, GPT NeoX, and others, specifically focusing on the design of photoresponsive drug delivery molecules. We gathered excitation energy data through computational chemistry tools and further investigated light-driven isomerization reactions as a critical mechanism in drug delivery. Additionally, we explored the effectiveness of incorporating human feedback into reinforcement learning to imbue large models with chemical intuition, enhancing their understanding of relationships involving -N=N- groups in the photoisomerization transitions of photoresponsive molecules. Conclusions: We implemented an efficient design process based on structural knowledge and data, driven by large language model technology, to obtain a candidate dataset of specific photoswitchable molecules. However, the lack of specialized domain datasets remains a challenge for maximizing model performance.

Selenium (Se) is an essential trace element known for its significant role in maintaining human health and mitigating disease progression. Selenium and its compounds exhibit high selective cytotoxicity against tumor cells. However, their anti-cervical cancer (CC) effects and underlying mechanisms have not been fully explored. This study found that sodium selenite (SS) inhibits the viability of HeLa and SiHa cells in a dose- and time-dependent manner. Intraperitoneal injection of 3 and 6 mg/kg SS for 14 days in female nude mice significantly inhibited the growth of HeLa cell xenografts without evident hepatotoxicity or nephrotoxicity. RNA sequencing results indicated that the AMP-activated protein kinase (AMPK), Forkhead box protein O (FOXO), and apoptosis signaling pathways are key regulatory pathways in SS’s anti-CC effects, and SS’s inhibition of HeLa cell proliferation may be related to autophagy and ROS-induced apoptosis. Further research has revealed that SS induces cell autophagy and apoptosis through the AMPK/mTOR/FOXO3a pathway, characterized by the upregulation of p-AMPK/AMPK, FOXO3a, LC3-II, cleaved-caspase3, and cleaved-PARP and the downregulation of p-mTOR/mTOR and p62. Additionally, SS impaired mitochondrial function, including decreased mitochondrial membrane potential, mitochondrial Ca2+ overload, and accumulation of mitochondrial reactive oxygen species (mtROS). Pretreatment with Mitoquinone mesylate (Mito Q) and compound C partially reversed SS-induced apoptosis, autophagy, and proliferation inhibition. Pretreatment with 3-methyladenine (3-MA) enhances SS-induced apoptosis and proliferation inhibition in HeLa cells but reverses these effects in SiHa cells. In summary, SS induces apoptosis, autophagy, and proliferation inhibition in HeLa and SiHa cells through the activation of the AMPK/mTOR/FOXO3a signaling pathway via mtROS. Autophagy activation may be a major risk factor for SS-induced apoptosis in SiHa cells but can protect HeLa cells from SS-induced apoptosis. These findings provide new evidence for understanding the molecular mechanisms underlying SS in potential new drug development for CC.

The rapid development of unmanned aerial vehicle (UAV) technology has contributed to the increasing sophistication of UAV-based object-detection systems, which are now extensively utilized in civilian and military sectors. However, object detection from UAV images has numerous challenges, including significant variations in the object size, changing spatial configurations, and cluttered backgrounds with multiple interfering elements. To address these challenges, we propose SOD-YOLO, an innovative model based on the YOLOv8 model, to detect small objects in UAV images. The model integrates the receptive field convolutional block attention module (RFCBAM) in the backbone network to perform downsampling, improving feature extraction efficiency and mitigating the spatial information sparsity caused by downsampling. Additionally, we developed a novel neck architecture called the balanced spatial and semantic information fusion pyramid network (BSSI-FPN) designed for multi-scale feature fusion. The BSSI-FPN effectively balances spatial and semantic information across feature maps using three primary strategies: fully utilizing large-scale features, increasing the frequency of multi-scale feature fusion, and implementing dynamic upsampling. The experimental results on the VisDrone2019 dataset demonstrate that SOD-YOLO-s improves the mAP50 indicator by 3% compared to YOLOv8s while reducing the number of parameters and computational complexity by 84.2% and 30%, respectively. Compared to YOLOv8l, SOD-YOLO-l improves the mAP50 indicator by 7.7% and reduces the number of parameters by 59.6%. Compared to other existing methods, SODA-YOLO-l achieves the highest detection accuracy, demonstrating the superiority of the proposed method.

Commercial graphite anode has advantages such as low potential platform, high electronic conductivity, and abundant reserves. However, its theoretical capacity is only 372 mA h g−1. High-energy lithium-ion batteries have been a research hotspot. The Si anode has an extremely high specific capacity, but its application is hindered by defects such as large volume changes, poor electronic conductivity, and a small lithium-ion diffusion coefficient. Here, the Si/thermally reduced graphite oxide@carbon (Si/RGtO@C) composite was fabricated by electrostatic self-assembly followed by thermal treatment. The RGtO synergistic carbon coating layer can effectively compensate for the low electronic conductivity and buffer the volume expansion effect of the Si nanoparticles during charge/discharge cycles. The Si/RGtO@C anode demonstrated a significantly increased capacity compared to the RGtO. After 300 cycles, Si/RGtO@C kept a discharged capacity of 367.6 mA h g−1 at a high current density of 1.0 A g−1. The Si/RGtO@C anode shows an application potential for commercial high-energy lithium-ion batteries.

Organic carbon in grassland mainly exists in the soil, and root production and turnover play important roles in carbon input into the soil. However, the effects of climate change on plant root dynamics in desert steppe are unknown. We conducted an experiment in a desert steppe, which included ambient temperature (T0); temperature increased by 2 °C (T1); temperature increased by 4 °C (T2); natural precipitation (P0); precipitation increased by 25% (P1); precipitation increased by 50% (P2); and the interaction between warming and increased precipitation. Plant community aboveground characteristics; root production; and root turnover were measured. We found that the root length production of the T0P2; T1P1; T2P0; and T2P1 treatments were significantly higher than that of the T0P0 treatment, with an increment of 98.70%, 11.72%, 163.03%, and 85.14%, respectively. Three treatments with temperature increased by 2 °C (T1P0; T1P1; and T1P2) and significantly increased root turnover rate compared to the T0P0 treatment, with increases of 62.53%, 42.57%, and 35.55%, respectively. The interaction between warming and increased precipitation significantly affected the root production of the community (p < 0.01), but this interaction was non-additive. Future climate warming will benefit the accumulation of root-derived carbon in desert steppe communities.

In situ electric heating is an important method used to increase production capacity during the extraction of natural gas hydrates. This work numerically evaluated the sensitivity of different heating parameters on gas production behavior with a vertical well depressurization in the Shenhu Sea area hydrate reservoir, the production pressure difference of 4 MPa, and continuous depressurization for 1080 days. The results showed that the in situ electric heating method can effectively enhance production capability by promoting hydrate dissociation and eliminating secondary hydrates. Compared with scenarios without heating, implementing whole wellbore heating (100 W/m) increases cumulative gas production (Vg) by 118.56%. When intermittent heating is applied to the local wellbore (15 m) located in the three-phase layer (with an interval of 30 days) and stops heating in advance at 480 days, there is no significant difference in Vg compared to the whole wellbore heating case, and the cumulative heat input is only 4.76%. We recommend considering intermittent heating of the local wellbore and stopping heating in advance during vertical well depressurization as this approach significantly reduces heating energy consumption while simultaneously improving production capacity.

Monoclonal antibodies (mAbs) used in biomedical research and therapeutic applications are primarily produced by Chinese hamster ovary (CHO) cells via fed-batch culture. The growing need for elevated quantities of biologics mandates the continual optimization of the mAb production process. The development of an effective process control method is indispensable for the production of specified mAbs by CHO cells. In this study, the effects of the pH control strategy on CHO cell fed-batch culture to produce an antibody (EA5) were first investigated in a 3 L bioreactor. The results indicate that controlling the culture pH at 7.2 during the fed-batch stage could produce a higher EA5 titer of 6.1 g/L with a lower Man5 ratio of 2.2% by day 14. Based on this, an optimized CHO cell fed-batch culture was conducted in a 15 L bioreactor to verify its effectiveness and stability. In this case, on day 14, an EA5 titer of 6.5 g/L was achieved with productivity of 0.46 g/L/day, which was 1.07-fold higher compared to that of the culture in the 3 L bioreactor. Furthermore, regarding the product quality, a monomer abundance of 96.0%, a main peak of 55.0%, and a Man5 proportion of 2.4% were maintained in the 15 L bioreactor. In addition, different cell clarification processes were evaluated using the CHO cell culture broth from the 3 L and 15 L bioreactors to further improve productivity and economic performance. Overall, this study provides some directions for process intensification and improving the quality of mAbs produced by CHO cells in the biopharmaceutical industry.

Stepwise depressurization is an important depressurization strategy in the development of natural gas hydrates. This work numerically analyzes the effects of different depressurization gradients and constant pressure durations on gas and water production during stepwise depressurization extraction with a vertical well in the Shenhu Sea area hydrate reservoir of the South China Sea. The results indicate that stepwise depressurization can reduce water production and raise the gas-to-water ratio in the early stages of production while ensuring cumulative gas output. When the vertical well is deployed at the model’s center with a completion length of 70 m and a constant pressure duration of 10 days, a depressurization gradient of 0.5 MPa, stepwise depressurization by 6 MPa, and continuous production for one year is achieved. Compared with direct depressurization, its cumulative gas production is 2.966 × 106 ST m3, which only decreases by 2.94%. However, it maintains a higher gas-to-water ratio in the early stages of production. Considering factors such as engineering operability, cumulative gas output, and gas-to-water ratio, it is recommended to use a small pressure gradient and a medium constant pressure stabilization time for stepwise depressurization Stepwise depressurization can maintain a high gas-to-water ratio while ensuring gas production and reducing water production can alleviate sand production problems and improve economic efficiency. The understanding gained from this work has reference value for the development of similar hydrate reservoirs worldwide.

Improving the production capacity of natural gas hydrates (NGHs) is crucial for their commercial development. Based on the data of the first on-site testing production of NGHs in the Shenhu Sea area, numerical methods were used to analyze the production behavior of radial lateral well (RLW) and horizontal snake well (HSW) with different completion lengths when they deployed at different layers of the Class-1 type hydrate reservoir (with a fixed pressure difference of 6 MPa and continuous production for 360 days). The results indicate that compared with the single vertical well production, RLW and HSW can effectively increase production capacity by enlarging drainage area and the productivity is directly proportional to the total completion length. The RLW and HSW deployed at the three-phase layer (TPL) have optimal mining performance within a 360-day production period. Different to the previous research findings, during a short-term production period of 360 days, regardless of the deployment layer, the overall production capacity of HSW is better than RLW’s. The total gas production of HSW-2 circles well type is about four times that of a single vertical well, reaching 1.554 × 107 ST m3. Moreover, the HSW-1 lateral well type stands out with an average Qg of 3.63 × 104 ST m3/d and a specific production index J of 16.93; it has the highest J-index among all well types, which means the best mining efficiency. It is recommended to choose the HSW-1 circle well type, if the coiled tubing drilling technique is used for on-site testing production of NGHs in the future. The research results provide insights into the potential applications of RLW and HSW in this sea area.

This study investigates the determinants of average Medicare payments, focusing on the impact of secondary diagnoses, demographic factors, and regional disparities. Our model reveals that major complications and comorbidities (MCCs) significantly increase average Medicare payments compared to less severe complications (CCs), with coefficients of 7674.13 and 1542.67, respectively. Higher percentages of the elderly living in poverty correlate with increased payments, highlighting the influence of social determinants on healthcare costs. Demographic analysis shows that counties with higher populations of Black, Hispanic, Asian, or other races receive higher Medicare payments, indicating potential racial disparities in healthcare access and quality. Political affiliation also plays a role, with Democrat-voting counties incurring higher Medicare costs. Surprisingly, larger populations served correlate with decreased payments, possibly due to cost-containment programs like Medicare’s Competitive Bidding. State-level analysis reveals significant regional variations, with states like Oregon and Wyoming showing higher payments, while Mississippi and Georgia have the lowest. Our findings underscore the complexity of Medicare funding and the need for targeted policy interventions to address health disparities and ensure sustainable healthcare delivery.

A novel composite consisting of fluorine-doped carbon and graphene double-coated LiMn0.6Fe0.4PO4 (LMFP) nanorods was synthesized via a facile low-temperature solvothermal method that employs a hybrid glucose and polyvinylidene fluoride as carbon and fluorine sources. As revealed by physicochemical characterization, F-doped carbon coating and graphene form a ‘point-to-surface’ conductive network, facilitating rapid electron transport and mitigating electrochemical polarization. Furthermore, the uniform thickness of the F-doped carbon coating alters the growth of nanoparticles and prevents direct contact between the material and the electrolyte, thereby enhancing structural stability. The strongly electronegative F− can inhibit the structural changes in LMFP during charge/discharge, thus reducing the Jahn–Teller effect of Mn3+. The distinctive architecture of the LMFP/C-F/G cathode material exhibits excellent electrochemical properties, exhibiting an initial discharge capacity of 163.1 mAh g−1 at 0.1 C and a constant Coulombic efficiency of 99.7% over 100 cycles. Notably, the LMFP/C-F/G cathode material achieves an impressive energy density of 607.6 Wh kg−1, surpassing that of commercial counterparts. Moreover, it delivers a reversible capacity of 90.3 mAh g−1 at a high current rate of 5 C. The high-capacity capability and energy density of the prepared materials give them great potential for use in next-generation lithium-ion batteries.

The Global Navigation Satellite System (GNSS) can be utilized for long-distance and high-precision time transmission. With the ongoing development of low Earth orbit (LEO) satellites and the rapidly changing geometric relationships between them, the convergence rate of ambiguity parameters in Precise Point Positioning (PPP) algorithms has increased, enabling fast and reliable time transfer. In this paper, GPS is used as an experimental case, the LEO satellite constellation is designed, and simulated LEO observation data are generated. Then, using the GPS observation data provided by IGS, a LEO-enhanced PPP model is established. The LEO-augmented PPP model is employed to facilitate faster and more reliable high-precision time transfer. The application of the LEO-augmented PPP model to time transfer is examined and discussed through experimental examples. These examples show multiple types of time transfer links, and the experimental outcomes are uniform. GPS + LEO is compared with exclusive GPS time transfer schemes. The clock offset of the time transfer link for the GPS + LEO scheme converges more swiftly, meaning that the time required for the clock offset to reach a stable level is the briefest. In this paper, standard deviation is employed to assess stability, and Allan deviation is utilized to assess frequency stability. The results show that the clock offset stability and frequency stability achieved by the GPS + LEO scheme are superior within the convergence time range. Controlled experiments with different numbers of satellites for LEO enhancement indicate that time transfer performance can be improved by increasing the number of satellites. As a result, augmenting GPS tracking data with LEO observations enhances the time transfer service compared to GPS alone.

Ducted fan motors play a crucial role in promoting various applications of flying electric vehicles. In ducted fan motor systems, motor performance affects the speed of the fan, the flow field of the fan affects the thermal field of the motor, and the thermal field influences the performance of the motor. The coupling model between fan static thrust, motor power, and motor temperature rise is established in this paper. After confirming the external dimensions of the motor, three cooling schemes of the motor casing are designed. The casing forms are as follows: model 1 with smooth surface, model 2 with circular fins, and model 3 with longitudinal fins. The optimization work was carried out on the geometric dimensions of two types of fins for model 2 and model 3, and the static thrust and heat transfer performance of the motors were calculated. This study proposes that the ratio of thrust-to-temperature rise is an indicator for future optimization design of ducted fan motors. Model 3 with longitudinal fins has a higher thrust-to-temperature rise ratio. The thrust temperature rise in model 3 has increased by 24.77% compared to model 1.

The evolved resistance of Bromus japonicus Houtt. to ALS-inhibiting herbicides is well established. Previous studies have primarily focused on target-site resistance; however, non-target-site resistance has not been well characterized. This investigation demonstrated that ALS gene sequencing did not detect any previously known resistance mutations in a mesosulfuron-methyl-resistant (MR) population, and notably, treatment with the P450 monooxygenase (P450) inhibitor malathion markedly heightened susceptibility to mesosulfuron-methyl. Utilizing UPLC-MS/MS analysis confirmed elevated mesosulfuron-methyl metabolism in MR plants. The integration of Isoform Sequencing (Iso-Seq) and RNA Sequencing (RNA-Seq) facilitated the identification of candidate genes associated with non-target sites in a subpopulation with two generations of herbicide selection. Through qRT-PCR analysis, 21 differentially expressed genes were characterized, and among these, 10 genes (comprising three P450s, two glutathione S-transferases, one glycosyltransferase, two ATP-binding cassette transporters, one oxidase, and one hydrolase) exhibited constitutive upregulation in resistant plants. Our findings substantiated that increased herbicide metabolism is a driving force behind mesosulfuron-methyl resistance in this B. japonicus population.

Although existing 3D object-detection methods have achieved promising results on conventional datasets, it is still challenging to detect objects in data collected under adverse weather conditions. Data distortion from LiDAR and cameras in such conditions leads to poor performance of traditional single-sensor detection methods. Multi-modal data-fusion methods struggle with data distortion and low alignment accuracy, making accurate target detection difficult. To address this, we propose a multi-modal object-detection algorithm, Snow-CLOCs, specifically for snowy conditions. In image detection, we improved the YOLOv5 algorithm by integrating the InceptionNeXt network to enhance feature extraction and using the Wise-IoU algorithm to reduce dependency on high-quality data. For LiDAR point-cloud detection, we built upon the SECOND algorithm and employed the DROR filter to remove noise, enhancing detection accuracy. We combined the detection results from the camera and LiDAR into a unified detection set, represented using a sparse tensor, and extracted features through a 2D convolutional neural network to achieve object detection and localization. Snow-CLOCs achieved a detection accuracy of 86.61% for vehicle detection in snowy conditions.

Microbial inoculants derived from plant growth-promoting rhizobacteria (PGPR) offer eco-friendly alternatives to traditional chemical fertilizers, maintaining microbiota balance in agricultural systems. However, limited research has explored the combined effects of microbial inoculants and chemical fertilizers on crop growth and soil properties. In this study, we investigated seven fertilizer combinations, ranging from no fertilizer to various proportions of chemical fertilizers with microbial inoculants, on timothy (Phleum pratense L.) growth, chlorophyll content, soil properties, enzyme activities, and soil microbial communities. A randomized block design was employed to analyze these effects. The results indicate that the combination of 85% chemical fertilizer with microbial inoculants significantly increased timothy yield and chlorophyll content. In addition, a reduction to 55% chemical fertilizer in conjunction with microbial inoculants resulted in comparable yield to that of 100% fertilizer with no inoculants. The microbial inoculants treatments notably elevated soil catalase, urease, acid phosphatase, and invertase activities, along with soil fast-acting nutrient content. The sequencing results show that the abundance of beneficial bacteria increased, while that of fungi decreased in the soil rhizosphere after the application of microbial inoculants. This study underscored the potential of microbial inoculants combined with reductions in chemical fertilizers to enhance soil microbiology, nutrient content, and beneficial microbial abundance while suppressing pathogenic fungi, thereby promoting timothy growth and yield. These findings provide a theoretical basis for the use of microbial inoculants in sustainable agricultural practices, providing valuable insights for optimizing microbial inoculants and chemical fertilizer formulations to mitigate the sustainability challenges posed by conventional fertilizers.

Measurement error is common in many statistical problems and has received considerable attention in various regression contexts. In this study, we consider the random coefficient autoregressive model with measurement error possibly present in covariates. The least squares and weighted least squares methods are used to estimate the model parameters, and the consistency and asymptotic normality of the two kinds of estimators are proved. Furthermore, we propose an empirical likelihood method based on weighted score equations to construct confidence regions for the parameters. The simulation results show that the weighted least squares estimators are superior to the least squares estimators and that the confidence regions have good finite-sample behavior. At last, the model is applied to a real data example.

Herein, a new starch film incorporating laver was developed to address issues related to inadequate water resistance and suboptimal preservation quality in food packaging. The integration of laver into starch film formulations offers a compelling avenue for creating biodegradable, active, and smart food packaging. Scanning electron microscope (SEM) analysis revealed that the starch film with a laver concentration of 70% exhibited a uniformly flat microstructure, as expected. Fourier-transform infrared spectroscopy (FTIR) confirmed the presence of intermolecular interactions and hydrogen bonding between the starch and laver. Viscoelastic tests demonstrated the superior film-forming performance of the starch/laver composite films. Moreover, it was found that the most favorable concentration of incorporated laver was 10%. Specifically, the S7-3 film emerged as a promising candidate for food packaging applications, boasting the highest contact angle (CA) value of 114.98 ± 1.28°, the lowest water solubility (WS) value of 15.38%, and a reduced water vapor transmission rate (WVTR) value of 2.52 g/m2 × h. Additionally, the S3-7 film displayed an extraordinary tensile strength of 32.47 MPa, an elongation at break of 19.04%, and a Young’s modulus of 606.83 MPa. Furthermore, the starch/laver composite films exhibited outstanding UV-blocking capabilities, exceptional pH-responsive behavior, and significant antioxidant activity, underscoring their potential for packaging applications with laver integration.

Phytoremediation is considered an effective strategy for remediation of heavy-metal-contaminated soil in mining areas. However, single-species plants cannot reach the highest potential for uptake of heavy metals due to inhibition of their growth by high concentrations of heavy metals in the soil. Therefore, this study has explored the effects of illite application and two plant species’ co-cropping on soil quality, plant growth, and heavy metal transformation in a soil–plant system. The results reveal that the addition of 1% (mass fraction) of illite significantly enhances soil pH. The co-cropping of Ilex cornuta and Epipremnum aureum is beneficial for improving the organic matter content of the soil. The contents of EDTA-extractable Pb, Zn, and Cu were significantly reduced by 29.8–32.5%, 1.85–5.72%, and 30.0–32.9%, respectively, compared to the control. The co-cropping of Ilex cornuta and Epipremnum aureum promoted enrichment effects of Epipremnum aureum on Pb and Ilex cornuta on Cd (p < 0.05). The co-cropping pattern lowered the biomass of Ilex cornuta and Epipremnum aureum; however, co-cropping of Ilex cornuta and Epipremnum aureum promoted the elimination of Pb, Zn, Cu, and Cd from the soil at 13.0–75.8%, 11.1–38.2%, 8.39–88.4%, and 27.8–72.5%, respectively. It is concluded that illite application combined with co-cropping of Ilex cornuta and Epipremnum aureum is highly effective for the elimination of Pb, Zn, Cu, and Cd from contaminated soil. This study provides a theoretical basis and pathway for the restoration of heavy-metal-contaminated soil in mining with the application of bentonite combined with phytoremediation.

Metamaterials are artificial materials with properties depending mainly on their designed structures instead of their materials. Pentamode metamaterials are one type of metamaterial. They have solid structures with fluid-like properties, which can only withstand compressive stresses, not shear stresses. Two-dimensional pentamode metamaterials are easier to manufacture than three-dimensional models, so they have received wide attention. In this review, the properties, manufacturing, and applications of two-dimensional pentamode metamaterials will be discussed. Their water-like properties are their most important properties, and their velocities and anisotropy can be designed. They can be processed by wire-cut electrical discharge machining, waterjet cutting, and additive manufacturing techniques. They have a broad application prospect in acoustic fields such as acoustic stealth cloaks, acoustic waveguides, flat acoustic focusing lenses, pentamode acoustic meta-surfaces, etc.

Accurately characterizing the mechanical behavior and fracture mechanisms of rock containing holes under dynamic loads is essential for ensuring the stability of underground rock structures. In this study, to enhance the understanding of the fracture processes in rock specimens with cavities subjected to dynamic impacts, experimental and numerical studies focusing on the influence of borehole geometry and strain rate are conducted. The results reveal that the strain rate affects the specimens’ dynamic mechanical strength and peak strain. However, the degree of such influence diminishes as the borehole diameter increases in specimens containing two holes. Fractures that lead to failure are primarily initiated at the axial and radial edges of the holes, the specimen extremities, and around the rock bridges in specimens with dual cavities, indicating significant stress concentration zones within the stress field distribution for specimens with a single hole. Further analysis using displacement field diagrams confirms that shear-induced fractures are the predominant cause of failure across all specimens. These findings provide critical insights for developing borehole pressure relief technology to protect against the risks of deep dynamic impacts.

It is well known that daidzein has various significant medicinal values and health benefits, such as anti-oxidant, anti-inflammatory, anti-cancer, anti-diabetic, cholesterol lowering, neuroprotective, cardioprotective and so on. To our disappointment, poor solubility, low permeability and inferior bioavailability seriously limit its clinical application and market development. To optimize the solubility, permeability and bioavailability of daidzein, the cocrystal of daidzein and piperazine was prepared through a scientific and reasonable design, which was thoroughly characterized by single-crystal X-ray diffraction, powder X-ray diffraction, Fourier transform infrared spectroscopy, differential scanning calorimetry and thermogravimetric analysis. Combining single-crystal X-ray diffraction analysis with theoretical calculation, detailed structural information on the cocrystal was clarified and validated. In addition, a series of evaluations on the pharmacogenetic properties of the cocrystal were investigated. The results indicated that the cocrystal of daidzein and piperazine possessed the favorable stability, increased solubility, improved permeability and optimized bioavailability of daidzein. Compared with the parent drug, the formation of cocrystal, respectively, resulted in 3.9-, 3.1-, 4.9- and 60.8-fold enhancement in the solubility in four different media, 4.8-fold elevation in the permeability and 3.2-fold in the bioavailability of daidzein. Targeting the pharmaceutical defects of daidzein, the surprising elevation in the solubility, permeability and bioavailability of daidzein was realized by a clever cocrystal strategy, which not only devoted assistance to the market development and clinical application of daidzein but also paved a new path to address the drug-forming defects of insoluble drugs.

In practical applications, the temporal completeness of knowledge graphs is of great importance. However, previous studies have mostly focused on static knowledge graphs, generally neglecting the dynamic evolutionary properties of facts. Moreover, the unpredictable and limited availability of temporal knowledge graphs, together with the complex temporal dependency patterns, make current models inadequate for effectively describing facts that experience temporal transitions. To better represent the evolution of things over time, we provide a learning technique that uses quaternion rotation to describe temporal knowledge graphs. This technique describes the evolution of entities as a temporal rotation change in quaternion space. Compared to the Ermitian inner product in complex number space, the Hamiltonian product in quaternion space is better at showing how things might be connected. This leads to a learning process that is both more effective and more articulate. Experimental results demonstrate that our learning method significantly outperforms existing methods in capturing the dynamic evolution of temporal knowledge graphs, with improved accuracy and robustness across a range of benchmark datasets.

Biochar and beneficial microorganisms have been widely used in ecological agriculture. However, the impact of biochar loaded with microbes (BM) on plant growth remains to be understood. In this study, BM was produced by incubating pecan biochar with the bacterial strain N33, and the effects of BM on pecan growth and the microbial community in the rhizosphere were explored. BM application significantly enhanced the biomass and height of pecan plants. Meanwhile, BM treatment improved nutrient uptake in plants and significantly increased the chlorophyll, soluble sugars, and soluble proteins of plants. Furthermore, BM treatment improved the soil texture and environment. Finally, BM application substantially enhanced the diversity of soil fungi and bacteria as well as the relative abundances of the phyla Firmicutes and Chloroflexi, and families Bacillaceae and Paenibacillaceae, as shown by high-throughput sequencing. Together, this study clarified the growth-promotive effects of BM on pecan plants and suggested an alternative to synthetic fertilizers in their production.

Porcine reproductive and respiratory syndrome virus (PRRSV) is one of the most important pathogens in the pig industry. Marc-145 cells are widely used for PRRSV isolation, vaccine production, and investigations into virus biological characteristics. Despite their significance in PRRSV research, Marc-145 cells struggle to isolate specific strains of the North American virus genotype (PRRSV-2). The involvement of viral GP2a, GP2b, and GP3 in this phenomenon has been noted. However, the vital amino acids have not yet been identified. In this study, we increased the number of blind passages and successfully isolated two strains that were previously difficult to isolate with Marc-145 cells. Both strains carried an amino acid substitution in GP2a, specifically phenylalanine to leucine at the 98th amino acid position. Through a phylogenetic and epidemiologic analysis of 32 strains, those that were not amenable to isolation widely exhibited this mutation. Then, by using the PRRSV reverse genetics system, IFA, and Western blotting, we identified the mutation that could affect the tropism of PRRSV-2 for Marc-145 cells. Furthermore, an animal experiment was conducted. Through comparisons of clinical signs, mortality rates, and viral load in the organs and sera, we found that mutation did not affect the pathogenicity of PRRSV-2. In conclusion, our study firmly establishes the 98th amino acid in GP2a as a key determinant of PRRSV-2 tropism for Marc-145 cells.

The inevitable noise generated in the acquisition and transmission process of MRIs seriously affects the reliability and accuracy of medical research and diagnosis. The denoising effect for Rician noise, whose distribution is related to MR image signal, is not good enough. Furthermore, the brain has a complex texture structure and a small density difference between different parts, which leads to higher quality requirements for brain MR images. To upgrade the reliability and accuracy of brain MRIs application and analysis, we designed a new and dedicated denoising algorithm (named VST–MCAATE), based on their inherent characteristics. Comparative experiments were performed on the same simulated and real brain MR datasets. The peak signal-to-noise ratio (PSNR), and mean structural similarity index measure (MSSIM) were used as objective image quality evaluation. The one-way ANOVA was used to compare the effects of denoising between different approaches. p < 0.01 was considered statistically significant. The experimental results show that the PSNR and MSSIM values of VST–MCAATE are significantly higher than state-of-the-art methods (p < 0.01), and also that residual images have no anatomical structure. The proposed denoising method has advantages in improving the quality of brain MRIs, while effectively removing the noise with a wide range of unknown noise levels without damaging texture details, and has potential clinical promise.

In recent years, deep convolutional neural networks with multi-scale features have been widely used in image super-resolution reconstruction (ISR), and the quality of the generated images has been significantly improved compared with traditional methods. However, in current image super-resolution network algorithms, these methods need to be further explored in terms of the effective fusion of multi-scale features and cross-domain application of attention mechanisms. To address these issues, we propose a novel multi-scale cross-attention fusion network (MCFN), which optimizes the feature extraction and fusion process in structural design and modular innovation. In order to make better use of the attention mechanism, we propose a Pyramid Multi-scale Module (PMM) to extract multi-scale information by cascading. This PMM is introduced in MCFN and is mainly constructed by multiple multi-scale cross-attention modules (MTMs). To fuse the feature information of PMMs efficiently in both channel and spatial dimensions, we propose the cross-attention fusion module (CFM). In addition, an improved integrated attention enhancement module (IAEM) is inserted at the network’s end to enhance the correlation of high-frequency feature information between layers. Experimental results show that the algorithm significantly improves the reconstructed images’ edge information and texture details, and the benchmark dataset’s performance evaluation shows comparable performance to current state-of-the-art techniques.

Enhancing the production capacity of natural gas hydrates (NGHs) is critical for its commercial development. Complex structure wells may efficiently increase drainage areas while enhancing exploitation efficiency. Based on the field data of China’s first offshore NGH test production, the numerical method was used to analyze the production performance of different complex structure well types by continuous depressurization production for 360 days under the preconditions of fixed effective completion length of 300 m and a pressure difference of 6 MPa. Results indicated that the complex structure well types deployed at the three-phase layer demonstrated superior production performance within 240 days of production; the DLW2 and HW2 well types stood out, with an average gas production rate Qg reaching 43,333 m3/d and a specific production index J of 24.1. After 360 days of production, benefiting from multi-layer combined production, the Cluster vertical well deployed at the multi-layer had the best production performance, with an average Qg of 34,444 m3/d and a J-index of 19.1. The research results provided insights into the complex structure well-type selection strategy for NGH depressurization in this sea area.

During the production process of refined betel nuts in China, a large amount of processing by-product, betel nut waste seeds, is generated. Betel nut waste seeds are rich in bioactive elements, but they have not been effectively utilized yet. In this study, an ultrasonic-assisted deep eutectic solvent method (DES) was used to selectively extract α-glucosidase inhibitors from waste seeds. Compared with traditional extraction solvents such as water and ethanol, the extraction efficiency of specific DESs is higher, and the content of alkaloids in the extracts is lower. However, it should be noted that some pure DESs exhibit inhibitory activity towards α-glucosidase. DESs, based on choline chloride/urea, were selected due to the high extraction efficiency of α-glucosidase inhibitors and their low alkaloid content as well as low inhibitory activity. The optimal extraction conditions were determined using single-factor experiments as follows: 30% (v/v) water content, a choline chloride/urea ratio of 5:3, a solid–liquid ratio of 1:10, extraction temperature of 40 °C, and a duration of 30 min. Through recovery experiments, it was found that the DES can be reused four times under these conditions, maintaining an inhibition rate comparable to alcohol extraction methods. The IC50 value of the extract was measured at 0.0066 mg/mL, superior to acarbose. In summary, this research has successfully developed an efficient and selective method for extracting α-glucosidase inhibitors from betel nut waste seeds, thereby presenting a promising avenue for future applications.

Surface defects on the permanent magnetic ferrite magnet rotor are the primary cause for the decline in performance and safety hazards in permanent magnet motors. Machine-vision methods offer the possibility to identify defects automatically. In response to the challenges in the permanent magnetic ferrite magnet rotor, this study proposes an improved You Only Look Once (YOLO) algorithm named SAB-YOLOv5. Utilizing a line-scan camera, images capturing the complete surface of a general object are obtained, and a dataset containing surface defects is constructed. Simultaneously, an improved YOLOv5-based surface defect algorithm is introduced. Firstly, the algorithm enhances the capability to extract features at different scales by incorporating the Atrous Spatial Pyramid Pooling (ASPP) structure. Then, the fusion of features is improved by combining the tensor concatenation operation of the feature-melting network with the Bidirectional Feature Pyramid Network (BiFPN) structure. Finally, the introduction of the spatial pyramid dilated (SPD) convolutional structure into the backbone network and output end enhances the detection performance for minute defects on the target surface. In the study, the SAB-YOlOv5 algorithm shows an obvious increase from 84.2% to 98.3% in the mean average precision (mAP) compared to that of the original YOLOv5 algorithm. The results demonstrate that the data acquisition method and detection algorithm designed in this paper effectively enhance the efficiency of defect detection permanent magnetic ferrite magnet rotors.