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A silicon nanoparticle–graphite nanosheet composite was prepared via a facile ball milling process for use as the anode for high-rate lithium-ion batteries. The size effect of Si nanoparticles on the structure and on the lithium-ion battery performance of the composite is evaluated. SEM and TEM analyses show a structural alteration of the composites from Si nanoparticle-surrounded graphite nanosheets to Si nanoparticle-embedded graphite nanosheets by decreasing the size of Si nanoparticles from 250 nm to 40 nm. The composites with finer Si nanoparticles provide an effective nanostructure containing encapsulated Si and free space. This structure facilitates the indirect exposure of Si to electrolyte and Si expansion during cycling, which leads to a stable solid–electrolyte interphase and elevated conductivity. An enhanced rate capability was obtained for the 40 nm Si nanoparticle–graphite nanosheet composite, delivering a specific capacity of 276 mAh g−1 at a current density of 1 C after 1000 cycles and a rate capacity of 205 mAh g−1 at 8 C.

Extremely low field emergence rates for canola are primarily attributed to soil compaction from field traffic during and after planting. This study aimed to determine the critical compaction level for canola emergence across different soil types. A laboratory experiment was conducted using sandy loam, silt clay, and clay soils, compacted to five levels (zero to four) using Proctor hammer drops after sowing canola (Brassica napus L.). The lab results were validated through two years of field experiments in sandy loam, applying four compaction levels (zero to three) using a tractor. Soil properties (bulk density and surface resistance) and canola growth parameters (plant emergence rate, count, height, and above-ground biomass) were measured. Zero compaction resulted in lower bulk density and surface resistance across all soil types. Laboratory results showed maximum emergence rates of 95% for sandy loam, 100% for silt clay, and 60% for clay, while field emergence rates were 63% and 87.59% in the first and second years, respectively, both at zero compaction. Recommendations include light or no compaction for sandy loam, and zero compaction for silt clay, while clay soil did not achieve the 80% emergence target at any compaction level. These results can assist agricultural producers in optimizing their seeding equipment setup and managing field traffic for canola production.

To solve the problem that traditional seed recognition methods are not completely suitable for needle-shaped seeds, such as Bidens L., in agricultural production, this paper proposes a model construction idea that combines the advantages of deep residual models in extracting high-level abstract features with multiscale feature extraction fusion, taking into account the depth and width of the network. Based on this, a multiscale feature fusion deep residual network (MSFF-ResNet) is proposed, and image segmentation is performed before classification. The image segmentation is performed by a popular semantic segmentation method, U2Net, which accurately separates seeds from the background. The multiscale feature fusion network is a deep residual model based on a residual network of 34 layers (ResNet34), and it contains a multiscale feature fusion module and an attention mechanism. The multiscale feature fusion module is designed to extract features of different scales of needle-shaped seeds, while the attention mechanism is used to improve the ability to select features of our model so that the model can pay more attention to the key features. The results show that the average accuracy and average F1-score of the multiscale feature fusion deep residual network on the test set are 93.81% and 94.44%, respectively, and the numbers of floating-point operations per second (FLOPs) and parameters are 5.95 G and 6.15 M, respectively. Compared to other deep residual networks, the multiscale feature fusion deep residual network achieves the highest classification accuracy. Therefore, the network proposed in this paper can classify needle-shaped seeds efficiently and provide a reference for seed recognition in agriculture.

Volatile Organic Compounds (VOCs) are not only essential precursors for the formation of ozone and PM2.5, but also hazardous to human health and responsible for unpleasant odors. The pharmaceutical industry has become an important industrial source of VOCs due to China’s large emissions and complex emission chains. In total, 245 VOCs samples were collected and analyzed from 11 typical pharmaceutical companies in Zibo City of the North China Plain, in order to investigate the VOCs emission characteristics and odor impacts. The emission factor for the pharmaceutical industry was 7.97 ± 8.21 g/kg pharmaceuticals, while the main emission links were chimney emissions, equipment sealing leakage, and so on. Finally, considering both purifying efficiency and economic benefits, the multistage absorption (AB) method is most effective for VOCs concentrations below 100 mg/m3, while UV photo-oxygenation combined with adsorption (UVA) is more suitable for concentrations below 300 mg/m3. The Regenerative Thermal Oxidizer (RTO), Catalytic Oxidizer (CO), and Condensation + Adsorption (CA) technologies demonstrated greater stability and efficiency, particularly in the treatment of complex organic pollutants, highlighting their advantages in both VOCs and odor removal at higher concentrations.

When classifying breeds of dogs, the accuracy of classification significantly affects breed identification and dog research. Using images to classify dog breeds can improve classification efficiency; however, it is increasingly challenging due to the diversities and similarities among dog breeds. Traditional image classification methods primarily rely on extracting simple geometric features, while current convolutional neural networks (CNNs) are capable of learning high-level semantic features. However, the diversity of dog breeds continues to pose a challenge to classification accuracy. To address this, we developed a model that integrates multiple CNNs with a machine learning method, significantly improving the accuracy of dog images classification. We used the Stanford Dog Dataset, combined image features from four CNN models, filtered the features using principal component analysis (PCA) and gray wolf optimization algorithm (GWO), and then classified the features with support vector machine (SVM). The classification accuracy rate reached 95.24% for 120 breeds and 99.34% for 76 selected breeds, respectively, demonstrating a significant improvement over existing methods using the same Stanford Dog Dataset. It is expected that our proposed method will further serve as a fundamental framework for the accurate classification of a wider range of species.

Objectives: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by joint damage and commonly linked to symptoms such as inflammation, swelling, and pain. Traditional Chinese Medicine offers complementary and integrative approaches in the management of rheumatoid arthritis, potentially providing additional options that may help address treatment challenges and enhance overall patient care. This paper explores the mechanism of action of berberine from the perspective of cellular exosomes by mediating exosomal contents and thus treating RA. Methods: With the help of flow cytometry and confocal laser scanning microscope, it was determined that berberine promotes apoptosis in RA-FLS cells, and then lipid metabolomics technology was applied to screen and characterize the exosomes of RA-FLS cells to identify lipid core biomarkers closely related to RA, which were then projected into various databases for comprehensive analysis. Results: The data analysis showed that berberine could call back 11 lipid core biomarkers closely associated with RA, and interactive visualization of the database revealed that these markers were mainly focused on lipid metabolism aspects such as fatty acid elongation, degradation, and biosynthesis, as well as the biosynthesis of unsaturated fatty acids or PPARA activation of gene expression, PPARα‘s role in lipid metabolism regulation, glycerophospholipid metabolism, mitochondrial fatty acid oxidation disorders, and organelle biogenesis and maintenance. Conclusions: Berberine exerts its therapeutic effect on RA by mediating exosomal contents and thus regulating multiple lipid-related biological pathways, affecting the PPARγ-NF-κB complex binding rate, CREB and EGR-1 expression, cellular phagocytosis, and other aspects needed to inhibit proliferation and inflammatory responses in RA-FLS. This study offers a research foundation for exploring the mechanism of action of berberine in the treatment of RA.

The genetic regulation of goose meat quality traits remains relatively unexplored, and the underlying mechanisms are yet to be elucidated. This study aims to employ single nucleotide polymorphism (SNP) genotyping in conjunction with genome-wide association studies (GWAS) to investigate critical candidate regions and genes associated with the pH trait of meat in Sichuan white geese. A cohort of 203 healthy male Sichuan white geese was randomly selected and slaughtered at 70 days of age. Measurements were taken of meat pH, growth parameters, body dimensions, and post-slaughter traits. High-throughput sequencing on the Illumina HiSeq X Ten platform facilitated gene resequencing and SNP evaluation, and GWAS was employed to detect key genes within quantitative trait loci (QTL) intervals. The sequencing of 203 individuals yielded a total of 2601.19 Gb of genomic data, with an average sequencing depth of 10.89×. Through GWAS analysis, a total of 30 SNPs associated with pH were identified. These SNPs were identified on multiple chromosomes, including on chromosome 17 (chr: 23.57–23.68 Mb) and chromosome 13 (chr13: 31.52–31.61 Mb). By annotating these associated SNPs, nine candidate genes (including C19L2, AMFR, POL, RERGL, ZN484, GMDS, WAC) associated with the pH of goose meat were identified. The matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF) genotyping of 10 SNPs centered on these nine candidate genes was confirmed. GO enrichment analysis revealed that genes within 1 Mb of the associated SNPs are significantly enriched in pathways involved in lymphocyte activation, in response to hydrogen peroxide, Salmonella infection, and other metabolic processes. This study explores the gene regulatory pathways influencing pH traits in goose meat and provides molecular markers for enhancing meat quality. These findings are expected to facilitate the advancement of molecular breeding programs in geese.

In the rapid process of urbanization, revealing the patterns and driving forces behind the evolution of blue–green spaces holds significant value for optimizing urban blue–green environments. This study systematically investigates the spatial–temporal evolution characteristics and driving forces of blue–green space in the Chengdu–Chongqing Economic Circle from 1990 to 2020, utilizing GIS technology, landscape pattern analysis, and geographic detectors. The research findings indicate the following: (1) The area of blue–green space in the study area exhibits a general trend of initial growth followed by decline, with significant changes occurring between 2010 and 2020. (2) The fragmentation degree of blue–green space is gradually increasing, while connectivity among landscapes is decreasing; however, there has been an increase in landscape distribution uniformity. More than 90% of blue–green spaces expanded mainly through adjacency patterns. (3) In examining driving forces, it was found that temperature, topographic relief, elevation, population density, and construction intensity are the primary driving factors. Notably, the influence of natural factors has diminished over time while human social factors have significantly intensified. This study offers solutions for optimizing the configuration of blue–green spaces within the Chengdu–Chongqing Economic Circle. It also serves as a reference case for promoting high-quality urbanization in developing countries undergoing rapid urbanization.

The deflection control of the main girder in suspension bridges, as flexible structures, is critically important during their operation. To predict the vertical deflection of existing suspension bridge girders under the combined effects of stochastic traffic loads and environmental temperature, this paper proposes an integrated deflection interval prediction method based on a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), a probability density estimation layer, and bridge monitoring data. A time-series training dataset consisting of environmental temperature, vehicle load, and deflection monitoring data was built based on bridge health monitoring data. The CNN-LSTM combined layer is used to capture both local features and long-term dependencies in the time series. A Gaussian distribution (GD) is adopted as the probability density function, and its parameters are estimated using the maximum likelihood method, which outputs the optimal deflection prediction and probability intervals. Furthermore, this paper proposes a method for identifying abnormal deflections of the main girder in existing suspension bridges and establishes warning thresholds. This study indicates that, for short time scales, the CNN-LSTM-GD model achieves a 47.22% improvement in Root Mean Squared Error (RMSE) and a 12.37% increase in the coefficient of determination (R2) compared to the LSTM model. When compared to the CNN-LSTM model, it shows an improvement of 28.30% in RMSE and 6.55% in R2. For long time scales, the CNN-LSTM-GD model shows a 54.40% improvement in RMSE and a 10.22% increase in R2 compared to the LSTM model. Compared to the CNN-LSTM model, it improves RMSE by 38.43% and R2 by 5.31%. This model is instrumental in more accurately identifying abnormal deflections and determining deflection thresholds, making it applicable to bridge deflection early-warning systems.

Background: Invasion is an important characteristic of the malignancy of glioblastoma (GBM) and a significant prognostic factor. Sempervirine (SPV), a yohimbine-type alkaloid, has been proven to inhibit GBM cells proliferation in previous research and found to have a potential effect in anti-invasion, but its mechanism of anti-invasion is still unknown. Methods: To explore its pharmacodynamics in inhibiting GBM cell invasion in this study, we combined network pharmacology and bioinformatics to comprehensive exploratory analysis of SPV and verified the mechanism in vitro. Results: Firstly, targets of SPV and invasion-related genes were collected from public databases. Moreover, GBM samples were obtained to analyze differentially expressed genes (DEGs) from The Cancer Genome Atlas (TCGA). Then, the relevant targets of SPV inhibiting GBM invasion (SIGI) were obtained through the intersection of the three gene sets. Further, GO and KEGG analysis showed that the targets of SIGI were heavily enriched in the AKT signaling pathway. Subsequently, based on the method of machine learning, a clinical prognostic model of the relevant targets of SIGI was constructed using GBM samples from TCGA and the Gene Expression Omnibus (GEO). A four-genes model (DUSP6, BMP2, MMP2, and MMP13) was successfully constructed, and Vina Scores of MMP2 and MMP13 in molecular docking were higher, which may be the main targets of SIGI. Then, the effect of SIGI was confirmed via functional experiments on invasion, migration, and adhesion assay, and the effect involved changes in the expressions of p-AKT, MMP2 and MMP13. Finally, combined with AKT activator (SC79) and inhibitor (MK2206), we further confirmed that SPV inhibits GBM invasion through AKT phosphorylation. Conclusions: This study provides valuable and an expected point of view into the regulation of AKT phosphorylation and inhibition of GBM invasion by SPV.

In view of professional learning and practical training in traditional electronic information education of application-oriented universities, this paper constructs electronic information–innovation practice teaching reform (EI-IPTR).In this scheme, by an integrating artificial intelligence (AI)-enabled curriculum with a multi-mode integrated platform and open-style module, big data-based comprehensive education resources are optimally configured. We jointly perform the multi-mode construction of innovative practice teaching, professional education stage design, and teaching management improvement, respectively. Subsequently, new practice teaching mechanisms with information technology and its implementation and management methods are established to achieve better teaching effects. It first strengthens learning and intra-group competition to promote students’ innovation in competitions. Then, the AI technique, i.e., attention mechanism-aided long short-term memory (LSTM), is used to model individual students’ abilities. Thus, it accurately evaluates them for teachers to efficiently manage their teaching process in accordance with their aptitude. The teaching reform practice verifies that the AI-enabled big data optimization of teaching reform has a better effect by the above multi-mode innovation. It exhibits an obvious improvement in the quantity and quality of students’ professional knowledge, personal ability, teamwork, and innovative practice. It is also in accordance with the independent completion of practical course teaching in the analysis of big education data. In addition, it realizes high-quality practical teaching by combining multi-mode, multi-level, and open discipline foundations together with efficient, professional skills.

Animal coloration offers a unique opportunity to explore the evolutionary mechanisms underlying phenotypic diversity. Conspicuous coloration caused by pigments plays a crucial role in social signaling across multiple species by conveying information about individual quality, social ranks, or reproductive condition. Nevertheless, most previous studies have focused predominantly on colors produced by the exogenous pigments—carotenoids. Pterins are another prevalent group of conspicuous pigments, which can be produced endogenously and have received comparatively little attention. Whether pterin-based colors represent reliable signals remains elusive. The remarkable red ventrolateral coloration exhibited by males of the Guinan toad-headed lizard (Phrynocephalus guinanensis) in the Mugetan Desert presents an ideal model for investigating pterin-based coloration. Through electron microscopy and metabolomic identification, we discovered three types of pterin pigments within xanthophores. Integrating a series of morphological measurements and behavioral experiments, we found that this red coloration was not correlated with body size, bite force, and testosterone level, nor did females show a preference bias toward it. However, the red intensity predicted male–male competition outcomes, with deeper red males being more likely to emerge as winners. Our results indicated that the pterin-based coloration could convey information about male quality, suggesting its potential role in honest signaling, given the vital importance of pterin metabolism in physiological processes. This study provides a novel case into the understanding of pterin-based colors in animals.

As the popularity of fresh air systems (FAS) in residential buildings increases, exploring the behavioral characteristics of their use can help to provide a comprehensive understanding of the potential for demand flexibility in residential buildings. However, few studies in the past have focused on the personalized usage behavior of FAS. To fill this gap, this study proposes a method based on data mining techniques to reveal the behavioral patterns of FAS usage and the motivations behind them, including motivational patterns, operation duration patterns, and human–machine interaction patterns, for 13 households in Beijing. The simultaneously obtained behavioral patterns, in turn, form the basis of association rules, which can classify FAS usage behavior into two typical residential user profiles containing user behavioral characteristics. This study can not only provide more accurate assumptions and inputs for behavioral stochastic models but also provide data support for the development and optimization of demand response strategies.

China stands out as one of the countries with the largest and fastest-growing elderly populations. Elderly requirements for the quality of high-speed railway stations environment are increasing. The relationship between the built environment of high-speed railway stations and elderly health has become an urgent problem to be solved. The challenge necessitates a systematic approach to age-friendly design. Based on the practical experience of the authors in an age-friendly design of an indoor environment at Nanjing South Railway Station, questionnaire surveys and user interviews were employed to profile four types of typical elderly passengers. Data collection methods were used to create behavior maps. (1) This study identifies the age-friendly needs of elderly passengers, pinpointing the key environment issues. (2) It proposes age-friendly design strategies for various functional areas of Nanjing South Railway Station. (3) It introduces an age-friendly module combination design system, which including basic type I, enhanced type II, and optimized type III, in order to meet the demands for six levels of high-speed railway stations in China. This study promotes the age-friendly transformation of environments at a high-speed railway station, offering theoretical foundations and methodological references for constructing an age-friendly environment from a healthy ageing perspective.

Porous starch has been created through hydrolysis by amyloglucosidase and α-amylase. However, little information is known about the precise evolution of multi-scale structures of starch during digestion. In this study, rice starch and potato starch, containing different crystalline structures, were hydrolyzed by amyloglucosidase and α-amylase for 20 and 60 min, respectively, and their resulting structural changes were examined. The digestion process caused significant degradation of the molecular structures of rice and potato starches. In addition, the alterations in the ordered structures varied between the two starches. Rice starch exhibited porous structures, thicker crystalline lamellae as determined by small-angle X-ray scattering, and enhanced thermostability after digestion using differential scanning calorimetry. For rice starch, the extent of crystalline structures was analyzed with an X-ray diffractometer; it was found to first increase after 20 min of digestion and then decrease after 60 min of digestion. In contrast, potato starch did not display porous structures but exhibited thicker crystalline lamellae and a reduction in ordered structures after digestion. These findings suggest that it is possible to intentionally modulate the multi-scale structures of starch by controlling the digestion time, thereby providing valuable insights for the manipulation of starch functionalities.

Astrocytes are one of the key glial types of the central nervous system (CNS), accounting for over 20% of total glial cells in the brain. Extensive evidence has established their indispensable functions in the maintenance of CNS homeostasis, as well as their broad involvement in neurological conditions. In particular, astrocytes can participate in various neuroinflammatory processes, e.g., releasing a repertoire of cytokines and chemokines or specific neurotrophic factors, which result in both beneficial and detrimental effects. It has become increasingly clear that such astrocyte-mediated neuroinflammation, together with its complex crosstalk with other glial cells or immune cells, designates neuronal survival and the functional integrity of neurocircuits, thus critically contributing to disease onset and progression. In this review, we focus on the current knowledge of the neuroinflammatory responses of astrocytes, summarizing their common features in neurological conditions. Moreover, we highlight several vital questions for future research that promise novel insights into diagnostic or therapeutic strategies against those debilitating CNS diseases.

Pandan (Pandanus amaryllifolius Roxb.) has been used in the production of bakery goods either as a functional ingredient or a natural flavoring that, when roasted, exerts a fragrant rice-like aroma and an attractive green color. This study elucidated the typical aroma compounds from pandan leaves and explored the influence of thermal treatments on their aroma profiles using GC-O-MS, E-nose, and GC-IMS analyses. The effects of formulation and baking conditions on the qualities of pandan-flavored sponge cake were comprehensively evaluated through a holistic approach covering several aspects including cake batter gravity, color, texture, and sensory characteristics. The baking treatment introduced more types of pleasant aromas (9 aromas vs. 17 aromas) and increased the odor intensities of the original volatile compounds, especially for the roasted and steamed rice-like odors. The increased amount of pandan flavoring reshaped the color of the cake crumb (especially for the L* and a* spaces) and significantly decreased the hardness (3.87 N to 1.01 N), gumminess (3.81 N to 0.67 N), and chewiness (13.22 mJ to 4.56 mJ) of the sponge cake. The perceived intensities of bitterness and sweetness can be adjusted by modulating the levels of 2-phenylethanol, 2-methyl-1-butanol, hexyl alcohol, and decanal, along with the total alcohols and aldehydes, due to their significant correlations revealed by correlation heatmap analyses.

As high-value chitosan derivatives, chitooligosaccharides (COSs) with biodegradable, biocompatible, nontoxic, antimicrobial, and antioxidant activities have been widely applied in food-related fields. Chitosanases can hydrolyze chitosan to produce COSs. Herein, a chitosanase (LeCho1) from Lentinula edodes was successfully expressed in Escherichia coli and was then purified and characterized. LeCho1 had a low sequence identity with other chitosanases reported from the GH75 family. The recombinant protein showed a molecular mass of 27 kDa on SDS-PAGE. LeCho1 preferentially hydrolyzed chitosan with a high degree of deacetylation (DDA) and exhibited maximal activity (71.88 U/mg) towards 95% DDA chitosan at pH 3.0 and 50 °C. It possessed good stability at pH 2.0–6.0 and temperatures below 45 °C. Its hydrolytic activity was remarkably enhanced by the metal ion Mn2+ at 1 mM, while it was totally inhibited by 1 mM Fe3+ or 10 mM EDTA. Its Km and Vmax values were 0.04 μM and 76.81 μmol·min−1·mg−1, respectively, indicating good substrate affinity. LeCho1 degraded chitosan into COSs with degrees of polymerization (DPs) of 2–5, while it had no action on COSs with DPs of less than 5, revealing its endo-chitosanase activity. This study proved that chitosanase LeCho1 is a promising candidate in the industrial preparation of COSs due to its excellent properties.

As a traditional Chinese medicine (TCM), Allii Macrostemonis Bulbus (AMB) is a key herb for the treatment of thoracic paralytic cardiac pain, but its quality evaluation method has not yet been fully clarified. In this study, chromatographic fingerprints of AMB were developed using solid-phase extraction–high-performance liquid chromatography–evaporative light scattering detection (SPE-HPLC-ELSD) to evaluate the quality of AMB from various origins and processing methods. This was achieved by employing chemical pattern recognition techniques and verifying the feasibility and applicability of the quality evaluation of AMB through the quantitative analysis of multi-components via a single-marker (QAMS) method. Through the analysis of the fingerprints of 18 batches of AMB, 30 common peaks were screened, and 6 components (adenosine, syringin, macrostemonoside T, macrostemonoside A, macrostemonoside U, and macrostemonoside V) were identified. Moreover, three differential markers (macrostemonoside A, macrostemonoside T, and macrostemonoside U) were screened out using chemometrics techniques, including principal component analysis (PCA), hierarchical cluster analysis (HCA), and orthogonal partial least squares discriminant analysis (OPLS-DA). Subsequently, a QAMS method was established for macrostemonoside T and macrostemonoside U using macrostemonoside A as an internal reference. The results demonstrate the method’s accuracy, reproducibility, and stability, rendering it suitable for the quality evaluation of AMB. This study provides a theoretical basis for drug quality control and the discovery of quality markers for AMB.

Gastrodin, a highly potent compound found in the traditional Chinese medicine Gastrodia elata Blume, exhibits significant antihypertensive properties. However, its role and the mechanism behind its protective effects on hypertensive cardiac conditions are not well understood. This study aims to investigate the cardiac protective effects and underlying mechanisms of gastrodin in angiotensin II (Ang II)-induced hypertensive models, both in vivo and in vitro. Treatment with gastrodin significantly decreased blood pressure and the heart weight/tibial length (HW/TL) ratio and attenuated cardiac dysfunction and pathological damage in Ang II-infused C57BL/6 mice. RNA sequencing analysis (RNA-seq) revealed 697 up-regulated and 714 down-regulated transcripts, along with 1105 signaling pathways, in Ang II-infused C57BL/6 mice following gastrodin treatment, compared to Ang II-induced hypertensive mice. Furthermore, the analyses of the top 30 Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway indicated significant enrichment in apoptosis and the peroxiredoxin 2 (PRDX2)/p53 pathway. Consistently, gastrodin treatment significantly reduced myocardial apoptosis in both the cardiac tissues of Ang II-induced hypertensive mice and Ang II-stimulated H9c2 cells. Additionally, gastrodin treatment significantly decreased the protein levels of PRDX2, p53, cleaved caspase-3, cleaved caspase-9, and Bax/Bcl-2 ratio in the cardiac tissues of Ang II-infused mice and H9c2 cells stimulated with Ang II. In conclusion, gastrodin treatment can mitigate hypertension-induced myocardial apoptosis in hypertensive mice by inhibiting the PRDX2/p53 pathway.

Dryopteris fragrans (L.) Schott has anti-inflammatory and antioxidant properties, and terpenoids are important components of its active constituents. The methyl-D-erythritol 4-phosphate (MEP) pathway is one of the major pathways for the synthesis of terpene precursors in plants, and 1-deoxy-D-xylulose-5-phosphate synthase (DXS) is the first rate-limiting enzyme in this pathway. DXS has been shown to be associated with increased stress tolerance in plants. In this experiment, two DXS genes were extracted from the D. fragrans transcriptome and named DfDXS1 and DfDXS2. Based on phylogenetic tree and conserved motif analyses, DXS was shown to be highly conserved evolutionarily and its localization to chloroplasts was determined by subcellular localization. Prokaryotic expression results showed that the number and growth status of recombinant colonies were better than the control under 400 mM NaCl salt stress and 800 mM mannitol-simulated drought stress. In addition, the DfDXS1 and DfDXS2 transgenic tobacco plants showed improved resistance to drought and salt stress. DfDXS1 and DfDXS2 responded strongly to methyl jasmonate (MeJA) and PEG-mimicked drought stress following exogenous hormone and abiotic stress treatments of D. fragrans. The transcriptional active sites were investigated by dual luciferase and GUS staining assays, and the results showed that the STRE element (AGGGG), the ABRE element (ACGTGGC), and the MYC element (CATTTG) were the important transcriptional active sites in the promoters of the two DXS genes, which were closely associated with hormone response and abiotic stress. These results suggest that the DfDXS gene of D. fragrans plays an important role in hormone signaling and response to stress. This study provides a reference for analyzing the molecular mechanisms of stress tolerance in D. fragrans.

Obtaining discriminative joint features is crucial for skeleton-based human action recognition. Current models mainly focus on the research of skeleton topology encoding. However, their predefined topology is the same and fixed for all action samples, making it challenging to obtain discriminative joint features. Although some studies have considered the complex non-natural connection relationships between joints, the existing methods cannot fully capture this complexity by using high-order adjacency matrices or adding trainable parameters and instead increase the computation parameters. Therefore, this study constructs a novel adaptive channel-enhanced graph convolution (ACE-GCN) model for human action recognition. The model generates similar and affinity attention maps by encoding channel attention in the input features. These maps are complementarily applied to the input feature map and graph topology, which can realize the refinement of joint features and construct an adaptive and non-shared channel-based adjacency matrix. This method of constructing the adjacency matrix improves the model’s capacity to capture intricate non-natural connections between joints, prevents the accumulation of unnecessary information, and minimizes the number of computational parameters. In addition, integrating the Edgeconv module into a multi-branch aggregation improves the model’s ability to aggregate different scale and temporal features. Ultimately, comprehensive experiments were carried out on NTU-RGB+D 60 and NTU-RGB+D 120, which are two substantial datasets. On the NTU RGB+D 60 dataset, the accuracy of human action recognition was 92% (X-Sub) and 96.3% (X-View). The model achieved an accuracy of 96.6% on the NW-UCLA dataset. The experimental results confirm that the ACE-GCN exhibits superior recognition accuracy and lower computing complexity compared to current methodologies.

The application of laparoscopic ovum pick-up (LOPU) and in vitro production of embryos (IVEP) technologies has opened up a new path for purebred breeding and breed improvement in goats. However, due to the complexity of the procedures and multiple influencing factors, these technologies have not been widely adopted in goat production. This study explores factors affecting the efficiency of LOPU in goats by comparing the use of controlled internal drug release (CIDR) for estrus synchronization, conventional FSH versus long-acting recombinant ovine FSH (R-FSH) for superovulation, and the timing of LOPU at 48 h, 60 h, and 72 h of follicular development. The metrics evaluated included the recovery rate of cumulus–oocyte complexes (COCs), the average number of ovarian follicles, the average number of COCs, and the average number of available COCs. The results demonstrated that the efficiency of LOPU was significantly higher with two doses of R-FSH compared to the conventional FSH superovulation protocol and the control group (p < 0.05), with two doses of R-FSH providing a higher LOPU efficiency than one dose (p < 0.05). Using CIDR for estrus synchronization showed no significant difference in LOPU efficiency compared to the non-CIDR group. Similarly, the efficiency of LOPU showed no significant difference between the hormone treatments at 48 h, 60 h, and 72 h. By exploring and optimizing the factors influencing LOPU, we ultimately established a LOPU technology system for goats that meets the production needs of small ruminants.

The digital economy is a prevailing trend in global development, yet traditional firms still face challenges in digital transformation. Under institutional pressure, firms might imitate the digital strategy of their peers to mitigate these issues; there is still a lack of empirical research to support this. Therefore, this study, drawing on new institutional theory, focuses on investigating whether and how the institutional environment influences companies in embracing digital transformation in the digital economy era. We employ generalized least squares (GLS) regression models on a sample of 2862 non-IT listed firms in China from 2012 to 2020. In addition, we conduct a series of robustness checks. The results show that firms’ mimetic isomorphism of digital transformation is related to the institutional environment. Specifically, both industrial digitalization and regional digitalization promote digital mimetic isomorphism independently; their interaction is positively related to the digital mimetic isomorphism of successful firms but negatively related to similar firms. The results provide empirical evidence for non-IT firms to converge upwards in digital transformation and achieve high-quality development.

With the advancement of social life, the aging of building walls has become an unavoidable phenomenon. Due to the limited efficiency of manually detecting cracks, it is especially necessary to explore intelligent detection techniques. Currently, deep learning has garnered growing attention in crack detection, leading to the development of numerous feature learning methods. Although the technology in this area has been progressing, it still faces problems such as insufficient feature extraction and instability of prediction results. To address the shortcomings in the current research, this paper proposes a new Adaptive Attention-Enhanced Yolo. The method employs a Swin Transformer-based Cross-Stage Partial Bottleneck with a three-convolution structure, introduces an adaptive sensory field module in the neck network, and processes the features through a multi-head attention structure during the prediction process. The introduction of these modules greatly improves the performance of the model, thus effectively improving the precision of crack detection.

Related party transactions (RPTs) can serve as channels for the spread of credit risk events among blockchain firms. However, current credit risk-assessment models typically only consider a firm’s individual characteristics, overlooking the impact of related parties in the blockchain. We suggest incorporating RPT network analysis to improve credit risk evaluation. Our approach begins by representing an RPT network using a weighted adjacency matrix. We then apply DANE, a deep network embedding algorithm, to generate condensed vector representations of the firms within the network. These representations are subsequently used as inputs for credit risk-evaluation models to predict the default distance. Following this, we employ SHAP (Shapley Additive Explanations) to analyze how the network information contributes to the prediction. Lastly, this study demonstrates the enhancing effect of using DANE-based integrated features in credit risk assessment.

Invasive alien plants (IAPs) pose a significant threat to island biodiversity and severely impact ecosystems. Understanding the species–area relationship and environmental determinants of growth forms for IAP species on subtropical islands is crucial for establishing an IAP’s early warning mechanism, enhancing island ecological management, and protecting the ecosystems of Fujian and other subtropical islands. The study identified significant species–area relationships for IAPs and different life-form plants (trees, shrubs, and herbs), with slopes of 0.27, 0.16, 0.15, and 0.24, respectively. The small island effect does not apply to all species. Isolation has little effect on species richness, and the IAPs on Fujian islands do not conform to the isolation effect in island biogeography. Landscape factors are the main determinants of IAPs and different life-form species richness, with area, shape index, and perimeter–area ratio being the three primary landscape factors. These environmental factors are closely related to habitat heterogeneity. Besides landscape factors, different life forms respond differently to environmental factors. Climate drives the species richness distribution of shrubs and herbs, while trees are mainly influenced by human activities. Overall, landscape, human disturbance, and climate jointly drive the distribution of IAPs, with landscape factors being the most significant.

RGB-D image mapping is an important tool in applications such as robotics, 3D reconstruction, autonomous navigation, and augmented reality (AR). Efficient and reliable mapping methods can improve the accuracy, real-time performance, and flexibility of sensors in various fields. However, the currently widely used Truncated Signed Distance Field (TSDF) still suffers from the problem of inefficient memory management, making it difficult to directly use it for large-scale 3D reconstruction. In order to address this problem, this paper proposes a highly efficient and accurate TSDF voxel fusion method, RGBTSDF. First, based on the sparse characteristics of the volume, an improved grid octree is used to manage the whole scene, and a hard coding method is proposed for indexing. Second, during the depth map fusion process, the depth map is interpolated to achieve a more accurate voxel fusion effect. Finally, a mesh extraction method with texture constraints is proposed to overcome the effects of noise and holes and improve the smoothness and refinement of the extracted surface. We comprehensively evaluate RGBTSDF and similar methods through experiments on public datasets and the datasets collected by commercial scanning devices. Experimental results show that RGBTSDF requires less memory and can achieve real-time performance experience using only the CPU. It also improves fusion accuracy and achieves finer grid details.

In the non-deterministic, large-scale navigation environment under the Mars exploration mission, there is a large space for action and many environmental states. Traditional reinforcement learning algorithms that can only obtain rewards at target points and obstacles will encounter the problems of reward sparsity and dimension explosion, making the training speed too slow or even impossible. This work proposes a deep layered learning algorithm based on the goal-driven layered deep Q-network (GDH-DQN), which is more suitable for mobile robots to explore, navigate, and avoid obstacles without a map. The algorithm model is designed in two layers. The lower layer provides behavioral strategies to achieve short-term goals, and the upper layer provides selection strategies for multiple short-term goals. Use known position nodes as short-term goals to guide the mobile robot forward and achieve long-term obstacle avoidance goals. Hierarchical execution not only simplifies tasks but also effectively solves the problems of reward sparsity and dimensionality explosion. In addition, each layer of the algorithm integrates a Hindsight Experience Replay mechanism to improve performance, make full use of the goal-driven function of the node, and effectively avoid the possibility of misleading the agent by complex processes and reward function design blind spots. The agent adjusts the number of model layers according to the number of short-term goals, further improving the efficiency and adaptability of the algorithm. Experimental results show that, compared with the hierarchical DQN method, the navigation success rate of the GDH-DQN algorithm is significantly improved, and it is more suitable for unknown scenarios such as Mars exploration.

In recent years, enterprises have increasingly recognized the pivotal role of external users in driving product innovation. Open innovation platforms (OIPs), which facilitate interactions between companies and external innovators, have emerged as critical conduits in this regard. However, OIP managers face the challenge of motivating innovators to sustain their contributions. While some OIPs have implemented material incentives, the impact of such rewards on users’ ongoing innovation efforts remains uncertain. This study utilized a large-scale dataset from an OIP to examine how performance-contingent material rewards influence the subsequent behaviors of online innovators. Employing a quasi-experimental design involving propensity score matching (PSM) and difference-in-differences (DID) analysis, we found that receiving performance-contingent material rewards led to a decrease in the quantity of subsequent ideas generated by innovators. However, these rewarded innovators produced ideas of higher quality. Interestingly, the novelty of ideas submitted by innovators declined following their receiving of rewards. Moreover, newly enlisted innovators exhibited a more positive response to these incentives. Our findings provide valuable insights for platform managers seeking to optimize incentive mechanisms. We suggest adopting diversified incentive approaches and refining incentive strategies to effectively motivate continuous innovation among users on OIPs.

This study employed nano-indentation technology, molecular dynamics simulation, and experimental investigation to examine the stress relaxation behaviour of a polycrystalline γ-TiAl alloy. The simulation enabled the generation of a load-time curve, the visualisation of internal defect evolution, and the mapping of stress distribution across each grain during the stress relaxation stage. The findings indicate that the load remains stable following an initial decline, thereby elucidating the underlying mechanism of load change during stress relaxation. Furthermore, a nano-indentation test was conducted on the alloy, providing insight into the load variation and stress relaxation behaviour under different loading conditions. By comparing the simulation and experimental results, this study aims to guide the theoretical research and practical application of γ-TiAl alloys.

As a widely used support, TiO2 has often been combined with Pd to form highly sensitive gas-chromic materials. Herein, we prepared a series of Pd/TiO2 catalysts with different Pd content (from 0.1 to 5 wt.%) by the impregnation method for their utilization in fast room-temperature CO detection. The detection was simply based on visible color change when the Pd/TiO2 was exposed to CO. The sample with 1 wt.% Pd/TiO2 presented an excellent CO gasochromic character, associated with a maximum chromatic aberration value of 90 before and after CO exposure. Systematic catalyst characterizations of XPS, FT-IR, CO-TPD, and N2 adsorption–desorption and density functional theory calculations for the CO adsorption and charge transfer over the Pd and PdO surfaces were further carried out. It was found that the interaction between CO and the Pd surface was strong, associated with a large adsorption energy of −1.99 eV and charge transfer of 0.196 e. The color change was caused by a reduction in Pd2+ to metallic Pd0 over the Pd/TiO2 surface after CO exposure.

Immunotherapy combats tumors by enhancing the body’s immune surveillance and clearance of tumor cells. Various nucleic acid drugs can be used in immunotherapy, such as DNA expressing cytokines, mRNA tumor vaccines, small interfering RNAs (siRNA) knocking down immunosuppressive molecules, and oligonucleotides that can be used as immune adjuvants. Nucleic acid drugs, which are prone to nuclease degradation in the circulation and find it difficult to enter the target cells, typically necessitate developing appropriate vectors for effective in vivo delivery. Biomimetic drug delivery systems, derived from viruses, bacteria, and cells, can protect the cargos from degradation and clearance, and deliver them to the target cells to ensure safety. Moreover, they can activate the immune system through their endogenous activities and active components, thereby improving the efficacy of antitumor immunotherapeutic nucleic acid drugs. In this review, biomimetic nucleic acid delivery systems for relieving a tumor immunosuppressive microenvironment are introduced. Their immune activation mechanisms, including upregulating the proinflammatory cytokines, serving as tumor vaccines, inhibiting immune checkpoints, and modulating intratumoral immune cells, are elaborated. The advantages and disadvantages, as well as possible directions for their clinical translation, are summarized at last.

The impact of climate change on agricultural quality development under the constraint of China’s “Double Carbon” target has been widely discussed by policy practitioners and academic theorists. This paper attempts to deconstruct the logic of how climate change affects agricultural total factor productivity (TFP) in three dimensions—the structure of agricultural input factors, the change in the cropping system, and the stability of crop supply. This paper also reveals the mechanism through which biased technological progress increases agricultural TFP by weakening the magnitude of climate change and empirically tests it by using China’s provincial-level data from 2000 to 2021. This study showed that average annual temperature and annual precipitation had significant negative effects on agricultural TFP, that the number of sunshine hours had a significant positive effect on agricultural TFP, and that obvious regional differences existed in the effect of climate change on agricultural TFP. Further mechanism tests revealed that biased technological progress positively moderated the effect of climate change on agricultural TFP. Based on these findings, the appropriate countermeasures for improving climate early warning mechanisms, promoting the progress of appropriate technology, and fostering new agricultural management bodies.

As a famous probiotic, Lacticaseibacillus rhamnosus HN001 is widely added to probiotic products. Different L. rhamnosus strains have different probiotic effects, and the active HN001 strain is the key to exerting probiotic effects, so it is of great practical significance for realising the detection of L. rhamnosus HN001 at the strain level in probiotic products. In this study, strain-specific primer pairs and probes were designed. A combined treatment of sodium deoxycholate (SD) and propidium monoazide (PMA) inhibited the amplification of dead bacterial DNA, establishing a SD-PMA-ddPCR system and conditions for detecting live L. rhamnosus HN001 in probiotic powders. Specificity was confirmed using type strains and commercial strains. Sensitivity tests with spiked samples showed a detection limit of 10⁵ CFU/g and a linear quantification range of 1.42 × 10⁵–1.42 × 10⁹ CFU/g. Actual sample testing demonstrated the method’s efficiency in quantifying HN001 in compound probiotic products. This method offers a reliable tool for the rapid and precise quantification of viable L. rhamnosus HN001, crucial for the quality monitoring of probiotic products.

Astroviruses are single-stranded, positive-sense RNA viruses capable of infecting humans as well as a wide range of mammalian and avian species, with a length of approximately 6.6–7.7 kb. In this study, 139 goat fecal samples collected from the Guangxi province were used for the RT-PCR detection, and two of these were positive for goat astrovirus, with a positivity rate of 1.44% (2/139). The complete genome sequence of an astrovirus strain and the partial genome sequence of a strain astrovirus, named GX WZ 2023 and GX HC 2023, were amplified and sequenced, and their sequence lengths were 6284 nt and 6213 nt, respectively. Among them, the capsid protein of goat astrovirus GX HC 2023 showed the highest amino acid identity of 95.9% with ovine astrovirus GX, which belonged to the MAstV-2 genotype. However, the closest relative of the GX WZ 2023 strain was found to be the caprine astrovirus Sichuan, with a nucleotide sequence identity of 76.8%. The ORF1ab nonstructural protein of this strain showed the highest amino acid identities of 89.2 and 95.8% with the ovine astrovirus S5.1 and caprine astrovirus G5.1 strains, respectively. However, its ORF2 capsid protein has 68.4% amino acid identity with the bovine astrovirus (BAstV) 16 2021 CHN strain and only 21.9–64% amino acid identity with all available strains of goat astrovirus. The GX WZ 2023 strain was recombined with the Chinese (BAstV 16 2021 CHN) and Japanese bovine strains (BAstV JPN 2015) in the ORF2 region. Therefore, the goat astrovirus GX WZ 2023 is proposed as a new member of the family goat astroviridae based on the species classification criteria of the International Committee on Taxonomy of Viruses. These findings enhance our understanding of the prevalence and genetic evolution of goat astrovirus and provide a scientific basis for future studies of these viruses in other animals.

Green ammonia has become an increasingly popular fuel in recent years because of its combustion process without carbon oxide release. Adding ammonia to methane fuel for co-combustion has become one of the important research topics in the current combustion field. In the present study, the CH4/NH3/Air counterflow diffusion flame was taken as the research object, and Chemkin-2019 R3 software was used to explore and analyze the flame extinction limit and chemical kinetics characteristics under different ammonia mixing ratios, initial pressures, and air preheating temperatures. It was obtained that the flame extinction stretch rate was decreased by increasing the NH3 mole fraction in the CH4/NH3 mixed fuel. The increase in pressure or air preheating temperature would accelerate the chemical reaction rate of each component in the combustion process, increase the flame extinction limit, and counteract the “stretching effect” of the flame, thus restraining the flame extinguishing phenomenon. The results of a path analysis show that the formation and consumption of OH had an important influence on flame extinction in the chain reaction. The net reaction rate of OH increases with increasing the initial pressure or air preheating temperature, which leads to an increase in flame intensity, combustion stability, and the extinction limit. Furthermore, the function curve between the reaction influences the RIF factor and the stretch rate of the first-to-ten reactions, affected by the heat release of flame combustion, was drawn and quantitatively analyzed. Eventually, a sensitivity analysis of the flame under different working conditions was completed, which found that promoting the forward reaction R39 H + O2O + OH also promotes the positive combustion as a whole when the flame was near extinction. The sensitivity coefficient of R39 in the CH4/NH3/Air flame increases with the growing initial pressure. The increasing air preheating temperature was capable of switching the reaction of R248 NH2 + OHNH + H2O in the CH4/NH3/Air flame from an inhibiting reaction to a promoting reaction, while decreasing the sensitivity coefficient of inhibiting the forward reaction R10 O + CH3H + CH2O, R88 OH + HO2O2 + H2O, and R271 H + NO + MHNO + M. Thus, the inhibition effect of flame extinction was weakened, and the positive progress of combustion was promoted.

A deep eutectic solvent (DES) with the ability to change from hydrophilic to hydrophobic was designed and synthesized and applied to the determination of organophosphorus (OPP) pesticides in honeysuckle dew samples. Choline chloride, phenol, and tetrahydrofuran (THF) were used as the hydrogen bond acceptor, hydrogen bond donor, and demulsifier, respectively. Eight OPP pesticides were extracted by DES coupled with ultrasonic-assisted extraction (UA) and then chromatographed by GC-MS. DES used as an extract solvent has the advantages of high extraction efficiency, low cost, and environmental protection. Furthermore, DES is compatible with GC-MS. The single factor experiment design and Box–Behnken design (BBD) were applied to the optimization of experimental factors, including the type and composition of extraction solvent, type of demulsifier solvent, the volume of DES and THF, pH of sample solution, and ultrasonic time. Under the optimum experimental conditions, the high degree of linearity from 0.1 to 20.0 ng mL−1 (R2 ≥ 0.9989), the limits of detection from 0.014 to 0.051 ng mL−1 (S/N = 3), and the recoveries of analytes from 81.4 to 104.4% with relative standard deviation below 8.6%. In addition, the adsorption mechanism of OPPs on DES was explored by adsorption kinetic studies. These results have demonstrated that the present method has offered an effective, accurate, and sensitive methodology for OPP pesticides in honeysuckle dew samples, and this method provides a reference for the detection of pesticide residues in traditional Chinese medicine.

Diabetes is a widespread chronic disease that occurs mainly in the elderly population. Due to the difference in pathophysiology between elderly and young patients, the current clinical practice to treat elderly patients with anti-diabetes medications still faces some challenges and dilemmas, such as the urgent need for early diagnosis and prevention, and an imbalance between restricted dietary intake and the risk of undernutrition. Traditional Chinese medicine (TCM) offers various treatment regimens that are actively utilized in the field of diabetes management. Through multiple targets and multiple pathways, TCM formulas, medicinal herbs, and active natural products enhance the efficacy of diabetes prevention and diabetes control measures, simplify complex medication management, and improve common symptoms and common diabetic complications in elderly people. Historically, natural products have played a key role in material composition analysis of TCM and mechanism interpretation to enable drug discovery. However, there have been few conclusions on this topic. This review summarizes the development of TCM for the prevention and management of diabetes in elderly people, existing evidence-based clinical practices, and prospects for future development.

Obesity is a complex medical condition caused by a positive imbalance between calorie intake and calorie consumption. Brown adipose tissue (BAT), along with the newly discovered “brown-like” adipocytes (called beige cells), functions as a promising therapeutic tool to ameliorate obesity and metabolic disorders by burning out extra nutrients in the form of heat. Many studies in animal models and humans have proved the feasibility of this concept. In this review, we aim to summarize the endeavors over the last decade to achieve a higher number/activity of these heat-generating adipocytes. In particular, pharmacological compounds, especially agonists to the β3 adrenergic receptor (β3-AR), are reviewed in terms of their feasibility and efficacy in elevating BAT function and improving metabolic parameters in human subjects. Alternatively, allograft transplantation of BAT and the transplantation of functional brown or beige adipocytes from mesenchymal stromal cells or human induced pluripotent stem cells (hiPSCs) make it possible to increase the number of these beneficial adipocytes in patients. However, practical and ethical issues still need to be considered before the therapy can eventually be applied in the clinical setting. This review provides insights and guidance on brown- and beige-cell-based strategies for the management of obesity and its associated metabolic comorbidities.

The poly (vinylidene fluoride–trifluoroethylene–chlorofluoroethylene) P(VDF-TrFE-CFE) terpolymer has been identified as a promising candidate for the effective conversion of low-frequency mechanical vibrations into electricity. In this study, we provide a comprehensive and systematic investigation of the solvent-dependent mechanical, microstructural, electrical, frictional properties and triboelectric output performance of a relaxor ferroelectric P(VDF-TrFE-CFE) terpolymer. The P(VDF-TrFE-CFE) terpolymer films obtained from high dipole moment solvents have a longer rod-shaped grain than those from low dipole moment solvents. The crystallinity, Young’s modulus and dielectric constant of P(VDF-TrFE-CFE) terpolymer become larger as the dipole moment of solvents increases, while the remnant polarization remains almost the same. The P(VDF-TrFE-CFE) terpolymer film obtained from the highest dipole moment solvent generates almost 1.55 times larger triboelectric charge than that obtained from the lowest moment. We attributed this large difference to the greatly enhanced lateral friction of terpolymer film obtained from high dipole moment solvents.

Plant proteins have gained significant attention over animal proteins due to their low carbon footprint, balanced nutrition, and high sustainability. These attributes make plant protein nanocarriers promising for applications in drug delivery, nutraceuticals, functional foods, and other areas. Zein, a major by-product of corn starch processing, is inexpensive and widely available. Its unique self-assembly characteristics have led to its extensive use in various food and drug systems. Zein’s functional tunability allows for excellent performance in loading and transporting bioactive substances. Lutein offers numerous bioactive functions, such as antioxidant and vision protection, but suffers from poor chemical stability and low bioavailability. Nano-embedding technology can construct various zein-loaded lutein nanodelivery systems to address these issues. This review provides an overview of recent advances in the construction of zein-loaded lutein nanosystems. It discusses the fundamental properties of these systems; systematically introduces preparation techniques, structural characterization, and functional properties; and analyzes and predicts the target-controlled release and bioaccessibility of zein-loaded lutein nanosystems. The interactions and synergistic effects between Zein and lutein in the nanocomplexes are examined to elucidate the formation mechanism and conformational relationship of zein–lutein nanoparticles. The physical and chemical properties of Zein are closely related to the molecular structure. Zein and its modified products can encapsulate and protect lutein through various methods, creating more stable and efficient zein-loaded lutein nanosystems. Additionally, embedding lutein in Zein and its derivatives enhances lutein’s digestive stability, solubility, antioxidant properties, and overall bioavailability.

There is little robust quantitative evidence on how community gardens impact on physical activity and other health-related behaviours. This natural experiment study aimed to examine the effects of a community garden within a Chinese neighbourhood on health-related behaviours during the COVID-19 epidemic. The study design was a repeat cross-sectional pre–post study, assessing differences between the non-epidemic period and epidemic period. Camera-based systematic observations were conducted at two locations within a community garden. A total of 180 h of video recordings were collected and coded by two researchers during a “non-epidemic” period when there were no COVID-19 restrictions (120 h) and the “epidemic” when COVID-19 restrictions were implemented (60 h). Observations of these video recordings assessed the total number of users, physical activities (Sedentary, Walking, Vigorous), social interactions (Connect), and people taking notice of the environment (Take Notice), followed by a breakdown of observed gender and age group. Data were analysed using paired t-tests and Wilcoxon signed-rank tests, comparing outcomes during the epidemic period and non-epidemic period. Results showed a significant increase in total number of users during the epidemic, particularly in females, older adults and children. Sedentary and moderate physical activities were significantly higher during the epidemic period. The increase in the total number of users of the community garden during the 2022 epidemic outbreak in Suzhou shows the importance of community gardens as a primary space for outdoor activities. This robust natural experiment study demonstrated that the community garden contributed more to physical activity, such as walking, but less to interactions between people (connecting) or people–place interactions (taking notice of the environment). More natural experiment research on community gardens like this is needed to better understand how the health and well-being benefits of community gardens can be maximized.

Ensuring the harmonization between urbanization and water environment systems is imperative for fostering sustainable regional development in the future. With urban agglomerations and metropolitan areas increasingly dominating urbanization trends in China, it is crucial to explore the interdependent relationship between urbanization and the water environment. Such exploration holds significant implications for water resource management and the formulation of urbanization policies. This study utilizes a comprehensive index system encompassing urbanization and the water environment. It examines the coupled and coordinated spatial and temporal dynamics of these systems within the Chengdu-Chongqing Urban Agglomeration from 2011 to 2019. This analysis employs the Coupled Coordination Degree model alongside the spatial autocorrelation model. The results show that there is still much room for improving the urbanization development level and the water environment quality. During the study period, a nonlinear and nearly U-shaped evolutionary trajectory was observed between the two systems. The results suggest that there is a progression from basic to more advanced coordination between urbanization and water environment at the city cluster scale. Urbanization appears to generally lag behind the water environment in terms of coordination. At the municipal scale, there is a gradient in which some cities show better coordination compared to others. Spatially, the coupling and coordination of this region exhibited dual-core development characteristics centered around Chengdu and Chongqing. The region is in the transition stage towards a core-type networked and decentralized development mode, which has not yet formed an integrated pattern. This offers a theoretical and technical framework for harmonizing water environments and urbanization in similar regions globally.

Elbow computerized tomography (CT) scans have been widely applied for describing elbow morphology. To enhance the objectivity and efficiency of clinical diagnosis, an automatic method to recognize, segment, and reconstruct elbow joint bones is proposed in this study. The method involves three steps: initially, the humerus, ulna, and radius are automatically recognized based on the anatomical features of the elbow joint, and the prompt boxes are generated. Subsequently, elbow MedSAM is obtained through transfer learning, which accurately segments the CT images by integrating the prompt boxes. After that, hole-filling and object reclassification steps are executed to refine the mask. Finally, three-dimensional (3D) reconstruction is conducted seamlessly using the marching cube algorithm. To validate the reliability and accuracy of the method, the images were compared to the masks labeled by senior surgeons. Quantitative evaluation of segmentation results revealed median intersection over union (IoU) values of 0.963, 0.959, and 0.950 for the humerus, ulna, and radius, respectively. Additionally, the reconstructed surface errors were measured at 1.127, 1.523, and 2.062 mm, respectively. Consequently, the automatic elbow reconstruction method demonstrates promising capabilities in clinical diagnosis, preoperative planning, and intraoperative navigation for elbow joint diseases.

The Ebola virus (EBOV) is a lethal pathogen causing hemorrhagic fever syndrome which remains a global health challenge. In the EBOV, two multifunctional proteins, VP35 and VP40, have significant roles in replication, virion assembly, and budding from the cell and have been identified as druggable targets. In this study, we employed in silico methods comprising molecular docking, molecular dynamic simulations, and pharmacological properties to identify prospective drugs for inhibiting VP35 and VP40 proteins from the myxobacterial bioactive natural product repertoire. Cystobactamid 934-2, Cystobactamid 919-1, and Cittilin A bound firmly to VP35. Meanwhile, 2-Hydroxysorangiadenosine, Enhypyrazinone B, and Sorangiadenosine showed strong binding to the matrix protein VP40. Molecular dynamic simulations revealed that, among these compounds, Cystobactamid 919-1 and 2-Hydroxysorangiadenosine had stable interactions with their respective targets. Similarly, molecular mechanics Poisson–Boltzmann surface area (MMPBSA) calculations indicated close-fitting receptor binding with VP35 or VP40. These two compounds also exhibited good pharmacological properties. In conclusion, we identified Cystobactamid 919-1 and 2-Hydroxysorangiadenosine as potential ligands for EBOV that target VP35 and VP40 proteins. These findings signify an essential step in vitro and in vivo to validate their potential for EBOV inhibition.

The mature flesh of Cornus officinalis exhibits a vibrant red color, attributed to its rich anthocyanin content, imparting significant edible and medicinal value. Vibrant colors not only enhance the visual allure of medicinal materials but also maintain a close association with their intrinsic quality. However, the intricate process of pigment formation governing the anthocyanin accumulation in the pericarp of Cornus officinalis remains poorly understood. In this study, we conducted the comprehensive sampling and analysis of pericarp tissues at three distinct developmental stages, employing morphological-structure observation and metabolomic and transcriptomic techniques. Our findings reveal a substantial increase in the anthocyanin accumulation during the transition to the red stage of Cornus officinalis fruit maturation. Metabolomic profiling identified the highest expression levels of Cyanidin-3-O-glucoside and Pelargonidin-3-O-rutinoside during the mature stage, suggesting their association with the red coloration of the fruit. Through RNA sequencing, we identified 25,740 differentially expressed genes (DEGs), including 41 DEGs associated with anthocyanin biosynthesis. The correlation between the DEG expression levels and anthocyanin content was explored, further elucidating the regulatory network. Additionally, we validated the pivotal role of the candidate gene BZ1 in the synthesis of Cyanidin-3-O-glucoside through qRT-PCR, confirming its crucial impact on anthocyanin accumulation. This study provides preliminary insights into anthocyanin accumulation in Cornus officinalis, laying the foundation for the future development of new cultivars with enhanced anthocyanin contents.

Hydrogen generation by photocatalytic water-splitting holds great promise for addressing the serious global energy and environmental crises, and has recently received significant attention from researchers. In this work, a method of assembling GeC/MXY (M = Zr, Hf; X, Y = S, Se) heterojunctions (HJs) by combining GeC and MXY monolayers (MLs) to construct direct Z-scheme photocatalytic systems is proposed. Based on first-principles calculations, we found that all the GeC/MXY HJs are stable van der Waals (vdW) HJs with indirect bandgaps. These HJs possess small bandgaps and exhibit strong light-absorption ability across a wide range. Furthermore, the built-in electric field (BIEF) around the heterointerface can accelerate photoinduced carrier separation. More interestingly, the suitable band edges of GeC/MXY HJs ensure sufficient kinetic potential to spontaneously accomplish water redox reactions under light irradiation. Overall, the strong light-harvesting ability, wide light-absorption range, small bandgaps, large heterointerfacial BIEFs, suitable band alignments, and carrier migration paths render GeC/MXY HJs highly efficient photocatalysts for overall water decomposition.

In diabetes mellitus, disordered glucose and lipid metabolisms precipitate diverse complications, including nonalcoholic fatty liver disease, contributing to a rising global mortality rate. Theaflavins (TFs) can improve disorders of glycolipid metabolism in diabetic patients and reduce various types of damage, including glucotoxicity, lipotoxicity, and other associated secondary adverse effects. TFs exert effects to lower blood glucose and lipids levels, partly by regulating digestive enzyme activities, activation of OATP-MCT pathway and increasing secretion of incretins such as GIP. By the Ca2+-CaMKK ꞵ-AMPK and PI3K-AKT pathway, TFs promote glucose utilization and inhibit endogenous glucose production. Along with the regulation of energy metabolism by AMPK-SIRT1 pathway, TFs enhance fatty acids oxidation and reduce de novo lipogenesis. As such, the administration of TFs holds significant promise for both the prevention and amelioration of diabetes mellitus.

This work investigated the physicochemical properties, structural characteristics, and digestive properties of two non-conventional starches extracted from Galanga: Alpinia officinarum Hance starch (AOS) and Alpinia galanga Willd starch (AGS). The extraction rates of the two starches were 22.10 wt% and 15.73 wt%, which is lower than widely studied ginger (Zingiber officinale, ZOS). But they contained similar amounts of basic constituents. AOS and AGS showed a smooth, elongated shape, while ZOS was an oval sheet shape. AOS and ZOS were C-type starches, and AGS was an A-type starch. AOS showed the highest crystallinity (35.26 ± 1.02%) among the three starches, possessed a higher content of amylose (24.14 ± 0.73%) and a longer amylose average chain length (1419.38 ± 31.28) than AGS. AGS starch exhibits the highest viscosity at all stages, while AOS starch shows the lowest pasting temperature, and ZOS starch, due to its high amylose content, displays lower peak and trough viscosities. Significant differences were also found in the physicochemical properties of the three starches, including the swelling power, solubility, thermal properties, and rheological properties of the three starches. The total content of resistant starch (RS) and slowly digestible starch (SDS) in AOS (81.05%), AGS (81.46%), and ZOS (82.58%) are considered desirable. These findings proved to be valuable references for further research and utilization of ginger family starch.